<?xml version="1.0"?>
<doc>
    <assembly>
        <name>MathNet.Numerics</name>
    </assembly>
    <members>
        <member name="T:MathNet.Numerics.AppSwitches">
            <summary>
            AppContext based switches to disable functionality, controllable through also in the
            host application through AppContext or by configuration with AppContextSwitchOverride.
            https://docs.microsoft.com/en-us/dotnet/framework/configure-apps/file-schema/runtime/appcontextswitchoverrides-element
            </summary>
            <remarks>
            Since AppContext is not supported on .NET Framework 4.0, a local implementation is used there instead,
            which cannot be controlled though configuration or through AppContext.
            </remarks>
        </member>
        <member name="T:MathNet.Numerics.ArrayExtensions">
            <summary>
            Useful extension methods for Arrays.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.ArrayExtensions.Copy(System.Double[],System.Double[])">
            <summary>
            Copies the values from on array to another.
            </summary>
            <param name="source">The source array.</param>
            <param name="dest">The destination array.</param>
        </member>
        <member name="M:MathNet.Numerics.ArrayExtensions.Copy(System.Single[],System.Single[])">
            <summary>
            Copies the values from on array to another.
            </summary>
            <param name="source">The source array.</param>
            <param name="dest">The destination array.</param>
        </member>
        <member name="M:MathNet.Numerics.ArrayExtensions.Copy(System.Numerics.Complex[],System.Numerics.Complex[])">
            <summary>
            Copies the values from on array to another.
            </summary>
            <param name="source">The source array.</param>
            <param name="dest">The destination array.</param>
        </member>
        <member name="M:MathNet.Numerics.ArrayExtensions.Copy(MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[])">
            <summary>
            Copies the values from on array to another.
            </summary>
            <param name="source">The source array.</param>
            <param name="dest">The destination array.</param>
        </member>
        <member name="T:MathNet.Numerics.Combinatorics">
            <summary>
            Enumerative Combinatorics and Counting.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Combinatorics.Variations(System.Int32,System.Int32)">
            <summary>
            Count the number of possible variations without repetition.
            The order matters and each object can be chosen only once.
            </summary>
            <param name="n">Number of elements in the set.</param>
            <param name="k">Number of elements to choose from the set. Each element is chosen at most once.</param>
            <returns>Maximum number of distinct variations.</returns>
        </member>
        <member name="M:MathNet.Numerics.Combinatorics.VariationsWithRepetition(System.Int32,System.Int32)">
            <summary>
            Count the number of possible variations with repetition.
            The order matters and each object can be chosen more than once.
            </summary>
            <param name="n">Number of elements in the set.</param>
            <param name="k">Number of elements to choose from the set. Each element is chosen 0, 1 or multiple times.</param>
            <returns>Maximum number of distinct variations with repetition.</returns>
        </member>
        <member name="M:MathNet.Numerics.Combinatorics.Combinations(System.Int32,System.Int32)">
            <summary>
            Count the number of possible combinations without repetition.
            The order does not matter and each object can be chosen only once.
            </summary>
            <param name="n">Number of elements in the set.</param>
            <param name="k">Number of elements to choose from the set. Each element is chosen at most once.</param>
            <returns>Maximum number of combinations.</returns>
        </member>
        <member name="M:MathNet.Numerics.Combinatorics.CombinationsWithRepetition(System.Int32,System.Int32)">
            <summary>
            Count the number of possible combinations with repetition.
            The order does not matter and an object can be chosen more than once.
            </summary>
            <param name="n">Number of elements in the set.</param>
            <param name="k">Number of elements to choose from the set. Each element is chosen 0, 1 or multiple times.</param>
            <returns>Maximum number of combinations with repetition.</returns>
        </member>
        <member name="M:MathNet.Numerics.Combinatorics.Permutations(System.Int32)">
            <summary>
            Count the number of possible permutations (without repetition).
            </summary>
            <param name="n">Number of (distinguishable) elements in the set.</param>
            <returns>Maximum number of permutations without repetition.</returns>
        </member>
        <member name="M:MathNet.Numerics.Combinatorics.GeneratePermutation(System.Int32,System.Random)">
            <summary>
            Generate a random permutation, without repetition, by generating the index numbers 0 to N-1 and shuffle them randomly.
            Implemented using Fisher-Yates Shuffling.
            </summary>
            <returns>An array of length <c>N</c> that contains (in any order) the integers of the interval <c>[0, N)</c>.</returns>
            <param name="n">Number of (distinguishable) elements in the set.</param>
            <param name="randomSource">The random number generator to use. Optional; the default random source will be used if null.</param>
        </member>
        <member name="M:MathNet.Numerics.Combinatorics.SelectPermutationInplace``1(``0[],System.Random)">
            <summary>
            Select a random permutation, without repetition, from a data array by reordering the provided array in-place.
            Implemented using Fisher-Yates Shuffling. The provided data array will be modified.
            </summary>
            <param name="data">The data array to be reordered. The array will be modified by this routine.</param>
            <param name="randomSource">The random number generator to use. Optional; the default random source will be used if null.</param>
        </member>
        <member name="M:MathNet.Numerics.Combinatorics.SelectPermutation``1(System.Collections.Generic.IEnumerable{``0},System.Random)">
            <summary>
            Select a random permutation from a data sequence by returning the provided data in random order.
            Implemented using Fisher-Yates Shuffling.
            </summary>
            <param name="data">The data elements to be reordered.</param>
            <param name="randomSource">The random number generator to use. Optional; the default random source will be used if null.</param>
        </member>
        <member name="M:MathNet.Numerics.Combinatorics.GenerateCombination(System.Int32,System.Random)">
            <summary>
            Generate a random combination, without repetition, by randomly selecting some of N elements.
            </summary>
            <param name="n">Number of elements in the set.</param>
            <param name="randomSource">The random number generator to use. Optional; the default random source will be used if null.</param>
            <returns>Boolean mask array of length <c>N</c>, for each item true if it is selected.</returns>
        </member>
        <member name="M:MathNet.Numerics.Combinatorics.GenerateCombination(System.Int32,System.Int32,System.Random)">
            <summary>
            Generate a random combination, without repetition, by randomly selecting k of N elements.
            </summary>
            <param name="n">Number of elements in the set.</param>
            <param name="k">Number of elements to choose from the set. Each element is chosen at most once.</param>
            <param name="randomSource">The random number generator to use. Optional; the default random source will be used if null.</param>
            <returns>Boolean mask array of length <c>N</c>, for each item true if it is selected.</returns>
        </member>
        <member name="M:MathNet.Numerics.Combinatorics.SelectCombination``1(System.Collections.Generic.IEnumerable{``0},System.Int32,System.Random)">
            <summary>
            Select a random combination, without repetition, from a data sequence by selecting k elements in original order.
            </summary>
            <param name="data">The data source to choose from.</param>
            <param name="elementsToChoose">Number of elements (k) to choose from the data set. Each element is chosen at most once.</param>
            <param name="randomSource">The random number generator to use. Optional; the default random source will be used if null.</param>
            <returns>The chosen combination, in the original order.</returns>
        </member>
        <member name="M:MathNet.Numerics.Combinatorics.GenerateCombinationWithRepetition(System.Int32,System.Int32,System.Random)">
            <summary>
            Generates a random combination, with repetition, by randomly selecting k of N elements.
            </summary>
            <param name="n">Number of elements in the set.</param>
            <param name="k">Number of elements to choose from the set. Elements can be chosen more than once.</param>
            <param name="randomSource">The random number generator to use. Optional; the default random source will be used if null.</param>
            <returns>Integer mask array of length <c>N</c>, for each item the number of times it was selected.</returns>
        </member>
        <member name="M:MathNet.Numerics.Combinatorics.SelectCombinationWithRepetition``1(System.Collections.Generic.IEnumerable{``0},System.Int32,System.Random)">
            <summary>
            Select a random combination, with repetition, from a data sequence by selecting k elements in original order.
            </summary>
            <param name="data">The data source to choose from.</param>
            <param name="elementsToChoose">Number of elements (k) to choose from the data set. Elements can be chosen more than once.</param>
            <param name="randomSource">The random number generator to use. Optional; the default random source will be used if null.</param>
            <returns>The chosen combination with repetition, in the original order.</returns>
        </member>
        <member name="M:MathNet.Numerics.Combinatorics.GenerateVariation(System.Int32,System.Int32,System.Random)">
            <summary>
            Generate a random variation, without repetition, by randomly selecting k of n elements with order.
            Implemented using partial Fisher-Yates Shuffling.
            </summary>
            <param name="n">Number of elements in the set.</param>
            <param name="k">Number of elements to choose from the set. Each element is chosen at most once.</param>
            <param name="randomSource">The random number generator to use. Optional; the default random source will be used if null.</param>
            <returns>An array of length <c>K</c> that contains the indices of the selections as integers of the interval <c>[0, N)</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Combinatorics.GenerateVariation(System.Numerics.BigInteger,System.Int32,System.Random)">
            <summary>
            Generate a random variation, without repetition, by randomly selecting k of n elements with order. This is an O(k) space-complexity implementation optimized for very large N.<br/>
            The space complexity of Fisher-Yates Shuffling is O(n+k). When N is very large, the algorithm will be unexecutable in limited memory, and a more memory-efficient algorithm is needed.<br/>
            You can explicitly cast N to <see cref="T:System.Numerics.BigInteger"/> if N is out of range of <see cref="T:System.Int32"/> or memory, so that this special implementation is called. However, this implementation is slower than Fisher-Yates Shuffling: don't call it if time is more critical than space.<br/>
            The K of type <see cref="T:System.Numerics.BigInteger"/> seems impossible, because the returned array is of size K and must all be stored in memory.
            </summary>
            <param name="n">Number of elements in the set.</param>
            <param name="k">Number of elements to choose from the set. Each element is chosen at most once.</param>
            <param name="randomSource">The random number generator to use. Optional; the default random source will be used if null.</param>
            <returns>An array of length <c>K</c> that contains the indices of the selections as integers of the interval <c>[0, N)</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Combinatorics.SelectVariation``1(System.Collections.Generic.IEnumerable{``0},System.Int32,System.Random)">
            <summary>
            Select a random variation, without repetition, from a data sequence by randomly selecting k elements in random order.
            Implemented using partial Fisher-Yates Shuffling.
            </summary>
            <param name="data">The data source to choose from.</param>
            <param name="elementsToChoose">Number of elements (k) to choose from the set. Each element is chosen at most once.</param>
            <param name="randomSource">The random number generator to use. Optional; the default random source will be used if null.</param>
            <returns>The chosen variation, in random order.</returns>
        </member>
        <member name="M:MathNet.Numerics.Combinatorics.GenerateVariationWithRepetition(System.Int32,System.Int32,System.Random)">
            <summary>
            Generate a random variation, with repetition, by randomly selecting k of n elements with order.
            </summary>
            <param name="n">Number of elements in the set.</param>
            <param name="k">Number of elements to choose from the set. Elements can be chosen more than once.</param>
            <param name="randomSource">The random number generator to use. Optional; the default random source will be used if null.</param>
            <returns>An array of length <c>K</c> that contains the indices of the selections as integers of the interval <c>[0, N)</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Combinatorics.SelectVariationWithRepetition``1(System.Collections.Generic.IEnumerable{``0},System.Int32,System.Random)">
            <summary>
            Select a random variation, with repetition, from a data sequence by randomly selecting k elements in random order.
            </summary>
            <param name="data">The data source to choose from.</param>
            <param name="elementsToChoose">Number of elements (k) to choose from the data set. Elements can be chosen more than once.</param>
            <param name="randomSource">The random number generator to use. Optional; the default random source will be used if null.</param>
            <returns>The chosen variation with repetition, in random order.</returns>
        </member>
        <member name="T:MathNet.Numerics.Complex32">
            <summary>
            32-bit single precision complex numbers class.
            </summary>
            <remarks>
            <para>
            The class <c>Complex32</c> provides all elementary operations
            on complex numbers. All the operators <c>+</c>, <c>-</c>,
            <c>*</c>, <c>/</c>, <c>==</c>, <c>!=</c> are defined in the
            canonical way. Additional complex trigonometric functions
            are also provided. Note that the <c>Complex32</c> structures
            has two special constant values <see cref="F:MathNet.Numerics.Complex32.NaN"/> and
            <see cref="F:MathNet.Numerics.Complex32.PositiveInfinity"/>.
            </para>
            <para>
            <code>
            Complex32 x = new Complex32(1f,2f);
            Complex32 y = Complex32.FromPolarCoordinates(1f, Math.Pi);
            Complex32 z = (x + y) / (x - y);
            </code>
            </para>
            <para>
            For mathematical details about complex numbers, please
            have a look at the <a href="http://en.wikipedia.org/wiki/Complex_number">
            Wikipedia</a>
            </para>
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.Complex32._real">
            <summary>
            The real component of the complex number.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Complex32._imag">
            <summary>
            The imaginary component of the complex number.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Complex32.#ctor(System.Single,System.Single)">
            <summary>
            Initializes a new instance of the Complex32 structure with the given real
            and imaginary parts.
            </summary>
            <param name="real">The value for the real component.</param>
            <param name="imaginary">The value for the imaginary component.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.FromPolarCoordinates(System.Single,System.Single)">
            <summary>
            Creates a complex number from a point's polar coordinates.
            </summary>
            <returns>A complex number.</returns>
            <param name="magnitude">The magnitude, which is the distance from the origin (the intersection of the x-axis and the y-axis) to the number.</param>
            <param name="phase">The phase, which is the angle from the line to the horizontal axis, measured in radians.</param>
        </member>
        <member name="F:MathNet.Numerics.Complex32.Zero">
            <summary>
            Returns a new <see cref="T:MathNet.Numerics.Complex32" /> instance
            with a real number equal to zero and an imaginary number equal to zero.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Complex32.One">
            <summary>
            Returns a new <see cref="T:MathNet.Numerics.Complex32" /> instance
            with a real number equal to one and an imaginary number equal to zero.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Complex32.ImaginaryOne">
            <summary>
            Returns a new <see cref="T:MathNet.Numerics.Complex32" /> instance
            with a real number equal to zero and an imaginary number equal to one.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Complex32.PositiveInfinity">
            <summary>
            Returns a new <see cref="T:MathNet.Numerics.Complex32" /> instance
            with real and imaginary numbers positive infinite.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Complex32.NaN">
            <summary>
            Returns a new <see cref="T:MathNet.Numerics.Complex32" /> instance
            with real and imaginary numbers not a number.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Complex32.Real">
            <summary>
            Gets the real component of the complex number.
            </summary>
            <value>The real component of the complex number.</value>
        </member>
        <member name="P:MathNet.Numerics.Complex32.Imaginary">
            <summary>
            Gets the real imaginary component of the complex number.
            </summary>
            <value>The real imaginary component of the complex number.</value>
        </member>
        <member name="P:MathNet.Numerics.Complex32.Phase">
            <summary>
            Gets the phase or argument of this <c>Complex32</c>.
            </summary>
            <remarks>
            Phase always returns a value bigger than negative Pi and
            smaller or equal to Pi. If this <c>Complex32</c> is zero, the Complex32
            is assumed to be positive real with an argument of zero.
            </remarks>
            <returns>The phase or argument of this <c>Complex32</c></returns>
        </member>
        <member name="P:MathNet.Numerics.Complex32.Magnitude">
            <summary>
            Gets the magnitude (or absolute value) of a complex number.
            </summary>
            <remarks>Assuming that magnitude of (inf,a) and (a,inf) and (inf,inf) is inf and (NaN,a), (a,NaN) and (NaN,NaN) is NaN</remarks>
            <returns>The magnitude of the current instance.</returns>
        </member>
        <member name="P:MathNet.Numerics.Complex32.MagnitudeSquared">
            <summary>
            Gets the squared magnitude (or squared absolute value) of a complex number.
            </summary>
            <returns>The squared magnitude of the current instance.</returns>
        </member>
        <member name="P:MathNet.Numerics.Complex32.Sign">
            <summary>
            Gets the unity of this complex (same argument, but on the unit circle; exp(I*arg))
            </summary>
            <returns>The unity of this <c>Complex32</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.IsZero">
            <summary>
            Gets a value indicating whether the <c>Complex32</c> is zero.
            </summary>
            <returns><c>true</c> if this instance is zero; otherwise, <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.IsOne">
            <summary>
            Gets a value indicating whether the <c>Complex32</c> is one.
            </summary>
            <returns><c>true</c> if this instance is one; otherwise, <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.IsImaginaryOne">
            <summary>
            Gets a value indicating whether the <c>Complex32</c> is the imaginary unit.
            </summary>
            <returns><c>true</c> if this instance is ImaginaryOne; otherwise, <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.IsNaN">
            <summary>
            Gets a value indicating whether the provided <c>Complex32</c>evaluates
            to a value that is not a number.
            </summary>
            <returns>
            <c>true</c> if this instance is <see cref="F:MathNet.Numerics.Complex32.NaN"/>; otherwise,
            <c>false</c>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.IsInfinity">
            <summary>
            Gets a value indicating whether the provided <c>Complex32</c> evaluates to an
            infinite value.
            </summary>
            <returns>
                <c>true</c> if this instance is infinite; otherwise, <c>false</c>.
            </returns>
            <remarks>
            True if it either evaluates to a complex infinity
            or to a directed infinity.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Complex32.IsReal">
            <summary>
            Gets a value indicating whether the provided <c>Complex32</c> is real.
            </summary>
            <returns><c>true</c> if this instance is a real number; otherwise, <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.IsRealNonNegative">
            <summary>
            Gets a value indicating whether the provided <c>Complex32</c> is real and not negative, that is &gt;= 0.
            </summary>
            <returns>
                <c>true</c> if this instance is real nonnegative number; otherwise, <c>false</c>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Exponential">
            <summary>
            Exponential of this <c>Complex32</c> (exp(x), E^x).
            </summary>
            <returns>
            The exponential of this complex number.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.NaturalLogarithm">
            <summary>
            Natural Logarithm of this <c>Complex32</c> (Base E).
            </summary>
            <returns>The natural logarithm of this complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.CommonLogarithm">
            <summary>
            Common Logarithm of this <c>Complex32</c> (Base 10).
            </summary>
            <returns>The common logarithm of this complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Logarithm(System.Single)">
            <summary>
            Logarithm of this <c>Complex32</c> with custom base.
            </summary>
            <returns>The logarithm of this complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Power(MathNet.Numerics.Complex32)">
            <summary>
            Raise this <c>Complex32</c> to the given value.
            </summary>
            <param name="exponent">
            The exponent.
            </param>
            <returns>
            The complex number raised to the given exponent.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Root(MathNet.Numerics.Complex32)">
            <summary>
            Raise this <c>Complex32</c> to the inverse of the given value.
            </summary>
            <param name="rootExponent">
            The root exponent.
            </param>
            <returns>
            The complex raised to the inverse of the given exponent.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Square">
            <summary>
            The Square (power 2) of this <c>Complex32</c>
            </summary>
            <returns>
            The square of this complex number.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.SquareRoot">
            <summary>
            The Square Root (power 1/2) of this <c>Complex32</c>
            </summary>
            <returns>
            The square root of this complex number.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.SquareRoots">
            <summary>
            Evaluate all square roots of this <c>Complex32</c>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Complex32.CubicRoots">
            <summary>
            Evaluate all cubic roots of this <c>Complex32</c>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Equality(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32)">
            <summary>
            Equality test.
            </summary>
            <param name="complex1">One of complex numbers to compare.</param>
            <param name="complex2">The other complex numbers to compare.</param>
            <returns><c>true</c> if the real and imaginary components of the two complex numbers are equal; <c>false</c> otherwise.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Inequality(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32)">
            <summary>
            Inequality test.
            </summary>
            <param name="complex1">One of complex numbers to compare.</param>
            <param name="complex2">The other complex numbers to compare.</param>
            <returns><c>true</c> if the real or imaginary components of the two complex numbers are not equal; <c>false</c> otherwise.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_UnaryPlus(MathNet.Numerics.Complex32)">
            <summary>
            Unary addition.
            </summary>
            <param name="summand">The complex number to operate on.</param>
            <returns>Returns the same complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_UnaryNegation(MathNet.Numerics.Complex32)">
            <summary>
            Unary minus.
            </summary>
            <param name="subtrahend">The complex number to operate on.</param>
            <returns>The negated value of the <paramref name="subtrahend"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Addition(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32)">
            <summary>Addition operator. Adds two complex numbers together.</summary>
            <returns>The result of the addition.</returns>
            <param name="summand1">One of the complex numbers to add.</param>
            <param name="summand2">The other complex numbers to add.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Subtraction(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32)">
            <summary>Subtraction operator. Subtracts two complex numbers.</summary>
            <returns>The result of the subtraction.</returns>
            <param name="minuend">The complex number to subtract from.</param>
            <param name="subtrahend">The complex number to subtract.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Addition(MathNet.Numerics.Complex32,System.Single)">
            <summary>Addition operator. Adds a complex number and float together.</summary>
            <returns>The result of the addition.</returns>
            <param name="summand1">The complex numbers to add.</param>
            <param name="summand2">The float value to add.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Subtraction(MathNet.Numerics.Complex32,System.Single)">
            <summary>Subtraction operator. Subtracts float value from a complex value.</summary>
            <returns>The result of the subtraction.</returns>
            <param name="minuend">The complex number to subtract from.</param>
            <param name="subtrahend">The float value to subtract.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Addition(System.Single,MathNet.Numerics.Complex32)">
            <summary>Addition operator. Adds a complex number and float together.</summary>
            <returns>The result of the addition.</returns>
            <param name="summand1">The float value to add.</param>
            <param name="summand2">The complex numbers to add.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Subtraction(System.Single,MathNet.Numerics.Complex32)">
            <summary>Subtraction operator. Subtracts complex value from a float value.</summary>
            <returns>The result of the subtraction.</returns>
            <param name="minuend">The float vale to subtract from.</param>
            <param name="subtrahend">The complex value to subtract.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Multiply(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32)">
            <summary>Multiplication operator. Multiplies two complex numbers.</summary>
            <returns>The result of the multiplication.</returns>
            <param name="multiplicand">One of the complex numbers to multiply.</param>
            <param name="multiplier">The other complex number to multiply.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Multiply(System.Single,MathNet.Numerics.Complex32)">
            <summary>Multiplication operator. Multiplies a complex number with a float value.</summary>
            <returns>The result of the multiplication.</returns>
            <param name="multiplicand">The float value to multiply.</param>
            <param name="multiplier">The complex number to multiply.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Multiply(MathNet.Numerics.Complex32,System.Single)">
            <summary>Multiplication operator. Multiplies a complex number with a float value.</summary>
            <returns>The result of the multiplication.</returns>
            <param name="multiplicand">The complex number to multiply.</param>
            <param name="multiplier">The float value to multiply.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Division(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32)">
            <summary>Division operator. Divides a complex number by another.</summary>
            <remarks>Enhanced Smith's algorithm for dividing two complex numbers </remarks>
            <see cref="M:MathNet.Numerics.Complex32.InternalDiv(System.Single,System.Single,System.Single,System.Single,System.Boolean)"/>
            <returns>The result of the division.</returns>
            <param name="dividend">The dividend.</param>
            <param name="divisor">The divisor.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.InternalDiv(System.Single,System.Single,System.Single,System.Single,System.Boolean)">
            <summary>
             Helper method for dividing.
            </summary>
            <param name="a">Re first</param>
            <param name="b">Im first</param>
            <param name="c">Re second</param>
            <param name="d">Im second</param>
            <param name="swapped"></param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Division(System.Single,MathNet.Numerics.Complex32)">
            <summary>Division operator. Divides a float value by a complex number.</summary>
            <remarks>Algorithm based on Smith's algorithm</remarks>
            <see cref="M:MathNet.Numerics.Complex32.InternalDiv(System.Single,System.Single,System.Single,System.Single,System.Boolean)"/>
            <returns>The result of the division.</returns>
            <param name="dividend">The dividend.</param>
            <param name="divisor">The divisor.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Division(MathNet.Numerics.Complex32,System.Single)">
            <summary>Division operator. Divides a complex number by a float value.</summary>
            <returns>The result of the division.</returns>
            <param name="dividend">The dividend.</param>
            <param name="divisor">The divisor.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Conjugate">
            <summary>
            Computes the conjugate of a complex number and returns the result.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Reciprocal">
            <summary>
            Returns the multiplicative inverse of a complex number.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Complex32.ToString">
            <summary>
            Converts the value of the current complex number to its equivalent string representation in Cartesian form.
            </summary>
            <returns>The string representation of the current instance in Cartesian form.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.ToString(System.String)">
            <summary>
            Converts the value of the current complex number to its equivalent string representation
            in Cartesian form by using the specified format for its real and imaginary parts.
            </summary>
            <returns>The string representation of the current instance in Cartesian form.</returns>
            <param name="format">A standard or custom numeric format string.</param>
            <exception cref="T:System.FormatException">
              <paramref name="format" /> is not a valid format string.</exception>
        </member>
        <member name="M:MathNet.Numerics.Complex32.ToString(System.IFormatProvider)">
            <summary>
            Converts the value of the current complex number to its equivalent string representation
            in Cartesian form by using the specified culture-specific formatting information.
            </summary>
            <returns>The string representation of the current instance in Cartesian form, as specified by <paramref name="provider" />.</returns>
            <param name="provider">An object that supplies culture-specific formatting information.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.ToString(System.String,System.IFormatProvider)">
            <summary>Converts the value of the current complex number to its equivalent string representation
            in Cartesian form by using the specified format and culture-specific format information for its real and imaginary parts.</summary>
            <returns>The string representation of the current instance in Cartesian form, as specified by <paramref name="format" /> and <paramref name="provider" />.</returns>
            <param name="format">A standard or custom numeric format string.</param>
            <param name="provider">An object that supplies culture-specific formatting information.</param>
            <exception cref="T:System.FormatException">
              <paramref name="format" /> is not a valid format string.</exception>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Equals(MathNet.Numerics.Complex32)">
            <summary>
            Checks if two complex numbers are equal. Two complex numbers are equal if their
            corresponding real and imaginary components are equal.
            </summary>
            <returns>
            Returns <c>true</c> if the two objects are the same object, or if their corresponding
            real and imaginary components are equal, <c>false</c> otherwise.
            </returns>
            <param name="other">
            The complex number to compare to with.
            </param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.GetHashCode">
            <summary>
            The hash code for the complex number.
            </summary>
            <returns>
            The hash code of the complex number.
            </returns>
            <remarks>
            The hash code is calculated as
            System.Math.Exp(ComplexMath.Absolute(complexNumber)).
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Equals(System.Object)">
            <summary>
            Checks if two complex numbers are equal. Two complex numbers are equal if their
            corresponding real and imaginary components are equal.
            </summary>
            <returns>
            Returns <c>true</c> if the two objects are the same object, or if their corresponding
            real and imaginary components are equal, <c>false</c> otherwise.
            </returns>
            <param name="obj">
            The complex number to compare to with.
            </param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Parse(System.String,System.IFormatProvider)">
            <summary>
            Creates a complex number based on a string. The string can be in the
            following formats (without the quotes): 'n', 'ni', 'n +/- ni',
            'ni +/- n', 'n,n', 'n,ni,' '(n,n)', or '(n,ni)', where n is a float.
            </summary>
            <returns>
            A complex number containing the value specified by the given string.
            </returns>
            <param name="value">
            the string to parse.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific
            formatting information.
            </param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.ParsePart(System.Collections.Generic.LinkedListNode{System.String}@,System.Boolean@,System.IFormatProvider)">
            <summary>
            Parse a part (real or complex) from a complex number.
            </summary>
            <param name="token">Start Token.</param>
            <param name="imaginary">Is set to <c>true</c> if the part identified itself as being imaginary.</param>
            <param name="format">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific
            formatting information.
            </param>
            <returns>Resulting part as float.</returns>
            <exception cref="T:System.FormatException"/>
        </member>
        <member name="M:MathNet.Numerics.Complex32.TryParse(System.String,MathNet.Numerics.Complex32@)">
            <summary>
            Converts the string representation of a complex number to a single-precision complex number equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a complex number to convert.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will contain complex32.Zero.  This parameter is passed uninitialized
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.TryParse(System.String,System.IFormatProvider,MathNet.Numerics.Complex32@)">
            <summary>
            Converts the string representation of a complex number to single-precision complex number equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a complex number to convert.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific formatting information about value.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will contain complex32.Zero.  This parameter is passed uninitialized
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Explicit(System.Decimal)~MathNet.Numerics.Complex32">
            <summary>
            Explicit conversion of a real decimal to a <c>Complex32</c>.
            </summary>
            <param name="value">The decimal value to convert.</param>
            <returns>The result of the conversion.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Explicit(System.Numerics.Complex)~MathNet.Numerics.Complex32">
            <summary>
            Explicit conversion of a <c>Complex</c> to a <c>Complex32</c>.
            </summary>
            <param name="value">The decimal value to convert.</param>
            <returns>The result of the conversion.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Implicit(System.Byte)~MathNet.Numerics.Complex32">
            <summary>
            Implicit conversion of a real byte to a <c>Complex32</c>.
            </summary>
            <param name="value">The byte value to convert.</param>
            <returns>The result of the conversion.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Implicit(System.Int16)~MathNet.Numerics.Complex32">
            <summary>
            Implicit conversion of a real short to a <c>Complex32</c>.
            </summary>
            <param name="value">The short value to convert.</param>
            <returns>The result of the conversion.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Implicit(System.SByte)~MathNet.Numerics.Complex32">
            <summary>
            Implicit conversion of a signed byte to a <c>Complex32</c>.
            </summary>
            <param name="value">The signed byte value to convert.</param>
            <returns>The result of the conversion.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Implicit(System.UInt16)~MathNet.Numerics.Complex32">
            <summary>
            Implicit conversion of a unsigned real short to a <c>Complex32</c>.
            </summary>
            <param name="value">The unsigned short value to convert.</param>
            <returns>The result of the conversion.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Implicit(System.Int32)~MathNet.Numerics.Complex32">
            <summary>
            Implicit conversion of a real int to a <c>Complex32</c>.
            </summary>
            <param name="value">The int value to convert.</param>
            <returns>The result of the conversion.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Implicit(System.Numerics.BigInteger)~MathNet.Numerics.Complex32">
            <summary>
            Implicit conversion of a BigInteger int to a <c>Complex32</c>.
            </summary>
            <param name="value">The BigInteger value to convert.</param>
            <returns>The result of the conversion.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Implicit(System.Int64)~MathNet.Numerics.Complex32">
            <summary>
            Implicit conversion of a real long to a <c>Complex32</c>.
            </summary>
            <param name="value">The long value to convert.</param>
            <returns>The result of the conversion.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Implicit(System.UInt32)~MathNet.Numerics.Complex32">
            <summary>
            Implicit conversion of a real uint to a <c>Complex32</c>.
            </summary>
            <param name="value">The uint value to convert.</param>
            <returns>The result of the conversion.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Implicit(System.UInt64)~MathNet.Numerics.Complex32">
            <summary>
            Implicit conversion of a real ulong to a <c>Complex32</c>.
            </summary>
            <param name="value">The ulong value to convert.</param>
            <returns>The result of the conversion.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Implicit(System.Single)~MathNet.Numerics.Complex32">
            <summary>
            Implicit conversion of a real float to a <c>Complex32</c>.
            </summary>
            <param name="value">The float value to convert.</param>
            <returns>The result of the conversion.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.op_Explicit(System.Double)~MathNet.Numerics.Complex32">
            <summary>
            Implicit conversion of a real double to a <c>Complex32</c>.
            </summary>
            <param name="value">The double value to convert.</param>
            <returns>The result of the conversion.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.ToComplex">
            <summary>
            Converts this <c>Complex32</c> to a <see cref="T:System.Numerics.Complex"/>.
            </summary>
            <returns>A <see cref="T:System.Numerics.Complex"/> with the same values as this <c>Complex32</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Negate(MathNet.Numerics.Complex32)">
            <summary>
            Returns the additive inverse of a specified complex number.
            </summary>
            <returns>The result of the real and imaginary components of the value parameter multiplied by -1.</returns>
            <param name="value">A complex number.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Conjugate(MathNet.Numerics.Complex32)">
            <summary>
            Computes the conjugate of a complex number and returns the result.
            </summary>
            <returns>The conjugate of <paramref name="value" />.</returns>
            <param name="value">A complex number.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Add(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32)">
            <summary>
            Adds two complex numbers and returns the result.
            </summary>
            <returns>The sum of <paramref name="left" /> and <paramref name="right" />.</returns>
            <param name="left">The first complex number to add.</param>
            <param name="right">The second complex number to add.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Subtract(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32)">
            <summary>
            Subtracts one complex number from another and returns the result.
            </summary>
            <returns>The result of subtracting <paramref name="right" /> from <paramref name="left" />.</returns>
            <param name="left">The value to subtract from (the minuend).</param>
            <param name="right">The value to subtract (the subtrahend).</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Multiply(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32)">
            <summary>
            Returns the product of two complex numbers.
            </summary>
            <returns>The product of the <paramref name="left" /> and <paramref name="right" /> parameters.</returns>
            <param name="left">The first complex number to multiply.</param>
            <param name="right">The second complex number to multiply.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Divide(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32)">
            <summary>
            Divides one complex number by another and returns the result.
            </summary>
            <returns>The quotient of the division.</returns>
            <param name="dividend">The complex number to be divided.</param>
            <param name="divisor">The complex number to divide by.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Reciprocal(MathNet.Numerics.Complex32)">
            <summary>
            Returns the multiplicative inverse of a complex number.
            </summary>
            <returns>The reciprocal of <paramref name="value" />.</returns>
            <param name="value">A complex number.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Sqrt(MathNet.Numerics.Complex32)">
            <summary>
            Returns the square root of a specified complex number.
            </summary>
            <returns>The square root of <paramref name="value" />.</returns>
            <param name="value">A complex number.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Abs(MathNet.Numerics.Complex32)">
            <summary>
            Gets the absolute value (or magnitude) of a complex number.
            </summary>
            <returns>The absolute value of <paramref name="value" />.</returns>
            <param name="value">A complex number.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Exp(MathNet.Numerics.Complex32)">
            <summary>
            Returns e raised to the power specified by a complex number.
            </summary>
            <returns>The number e raised to the power <paramref name="value" />.</returns>
            <param name="value">A complex number that specifies a power.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Pow(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32)">
            <summary>
            Returns a specified complex number raised to a power specified by a complex number.
            </summary>
            <returns>The complex number <paramref name="value" /> raised to the power <paramref name="power" />.</returns>
            <param name="value">A complex number to be raised to a power.</param>
            <param name="power">A complex number that specifies a power.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Pow(MathNet.Numerics.Complex32,System.Single)">
            <summary>
            Returns a specified complex number raised to a power specified by a single-precision floating-point number.
            </summary>
            <returns>The complex number <paramref name="value" /> raised to the power <paramref name="power" />.</returns>
            <param name="value">A complex number to be raised to a power.</param>
            <param name="power">A single-precision floating-point number that specifies a power.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Log(MathNet.Numerics.Complex32)">
            <summary>
            Returns the natural (base e) logarithm of a specified complex number.
            </summary>
            <returns>The natural (base e) logarithm of <paramref name="value" />.</returns>
            <param name="value">A complex number.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Log(MathNet.Numerics.Complex32,System.Single)">
            <summary>
            Returns the logarithm of a specified complex number in a specified base.
            </summary>
            <returns>The logarithm of <paramref name="value" /> in base <paramref name="baseValue" />.</returns>
            <param name="value">A complex number.</param>
            <param name="baseValue">The base of the logarithm.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Log10(MathNet.Numerics.Complex32)">
            <summary>
            Returns the base-10 logarithm of a specified complex number.
            </summary>
            <returns>The base-10 logarithm of <paramref name="value" />.</returns>
            <param name="value">A complex number.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Sin(MathNet.Numerics.Complex32)">
            <summary>
            Returns the sine of the specified complex number.
            </summary>
            <returns>The sine of <paramref name="value" />.</returns>
            <param name="value">A complex number.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Cos(MathNet.Numerics.Complex32)">
            <summary>
            Returns the cosine of the specified complex number.
            </summary>
            <returns>The cosine of <paramref name="value" />.</returns>
            <param name="value">A complex number.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Tan(MathNet.Numerics.Complex32)">
            <summary>
            Returns the tangent of the specified complex number.
            </summary>
            <returns>The tangent of <paramref name="value" />.</returns>
            <param name="value">A complex number.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Asin(MathNet.Numerics.Complex32)">
            <summary>
            Returns the angle that is the arc sine of the specified complex number.
            </summary>
            <returns>The angle which is the arc sine of <paramref name="value" />.</returns>
            <param name="value">A complex number.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Acos(MathNet.Numerics.Complex32)">
            <summary>
            Returns the angle that is the arc cosine of the specified complex number.
            </summary>
            <returns>The angle, measured in radians, which is the arc cosine of <paramref name="value" />.</returns>
            <param name="value">A complex number that represents a cosine.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Atan(MathNet.Numerics.Complex32)">
            <summary>
            Returns the angle that is the arc tangent of the specified complex number.
            </summary>
            <returns>The angle that is the arc tangent of <paramref name="value" />.</returns>
            <param name="value">A complex number.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Sinh(MathNet.Numerics.Complex32)">
            <summary>
            Returns the hyperbolic sine of the specified complex number.
            </summary>
            <returns>The hyperbolic sine of <paramref name="value" />.</returns>
            <param name="value">A complex number.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Cosh(MathNet.Numerics.Complex32)">
            <summary>
            Returns the hyperbolic cosine of the specified complex number.
            </summary>
            <returns>The hyperbolic cosine of <paramref name="value" />.</returns>
            <param name="value">A complex number.</param>
        </member>
        <member name="M:MathNet.Numerics.Complex32.Tanh(MathNet.Numerics.Complex32)">
            <summary>
            Returns the hyperbolic tangent of the specified complex number.
            </summary>
            <returns>The hyperbolic tangent of <paramref name="value" />.</returns>
            <param name="value">A complex number.</param>
        </member>
        <member name="T:MathNet.Numerics.ComplexExtensions">
            <summary>
            Extension methods for the Complex type provided by System.Numerics
            </summary>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.MagnitudeSquared(MathNet.Numerics.Complex32)">
            <summary>
            Gets the squared magnitude of the <c>Complex</c> number.
            </summary>
            <param name="complex">The <see cref="T:MathNet.Numerics.Complex32"/> number to perform this operation on.</param>
            <returns>The squared magnitude of the <c>Complex</c> number.</returns>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.MagnitudeSquared(System.Numerics.Complex)">
            <summary>
            Gets the squared magnitude of the <c>Complex</c> number.
            </summary>
            <param name="complex">The <see cref="T:System.Numerics.Complex"/> number to perform this operation on.</param>
            <returns>The squared magnitude of the <c>Complex</c> number.</returns>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.Sign(System.Numerics.Complex)">
            <summary>
            Gets the unity of this complex (same argument, but on the unit circle; exp(I*arg))
            </summary>
            <returns>The unity of this <c>Complex</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.Conjugate(System.Numerics.Complex)">
            <summary>
            Gets the conjugate of the <c>Complex</c> number.
            </summary>
            <param name="complex">The <see cref="T:System.Numerics.Complex"/> number to perform this operation on.</param>
            <remarks>
            The semantic of <i>setting the conjugate</i> is such that
            <code>
            // a, b of type Complex32
            a.Conjugate = b;
            </code>
            is equivalent to
            <code>
            // a, b of type Complex32
            a = b.Conjugate
            </code>
            </remarks>
            <returns>The conjugate of the <see cref="T:System.Numerics.Complex"/> number.</returns>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.Reciprocal(System.Numerics.Complex)">
            <summary>
            Returns the multiplicative inverse of a complex number.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.Exp(System.Numerics.Complex)">
            <summary>
            Exponential of this <c>Complex</c> (exp(x), E^x).
            </summary>
            <param name="complex">The <see cref="T:System.Numerics.Complex"/> number to perform this operation on.</param>
            <returns>
            The exponential of this complex number.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.Ln(System.Numerics.Complex)">
            <summary>
            Natural Logarithm of this <c>Complex</c> (Base E).
            </summary>
            <param name="complex">The <see cref="T:System.Numerics.Complex"/> number to perform this operation on.</param>
            <returns>
            The natural logarithm of this complex number.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.Log10(System.Numerics.Complex)">
            <summary>
            Common Logarithm of this <c>Complex</c> (Base 10).
            </summary>
            <returns>The common logarithm of this complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.Log(System.Numerics.Complex,System.Double)">
            <summary>
            Logarithm of this <c>Complex</c> with custom base.
            </summary>
            <returns>The logarithm of this complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.Power(System.Numerics.Complex,System.Numerics.Complex)">
            <summary>
            Raise this <c>Complex</c> to the given value.
            </summary>
            <param name="complex">The <see cref="T:System.Numerics.Complex"/> number to perform this operation on.</param>
            <param name="exponent">
            The exponent.
            </param>
            <returns>
            The complex number raised to the given exponent.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.Root(System.Numerics.Complex,System.Numerics.Complex)">
            <summary>
            Raise this <c>Complex</c> to the inverse of the given value.
            </summary>
            <param name="complex">The <see cref="T:System.Numerics.Complex"/> number to perform this operation on.</param>
            <param name="rootExponent">
            The root exponent.
            </param>
            <returns>
            The complex raised to the inverse of the given exponent.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.Square(System.Numerics.Complex)">
            <summary>
            The Square (power 2) of this <c>Complex</c>
            </summary>
            <param name="complex">The <see cref="T:System.Numerics.Complex"/> number to perform this operation on.</param>
            <returns>
            The square of this complex number.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.SquareRoot(System.Numerics.Complex)">
            <summary>
            The Square Root (power 1/2) of this <c>Complex</c>
            </summary>
            <param name="complex">The <see cref="T:System.Numerics.Complex"/> number to perform this operation on.</param>
            <returns>
            The square root of this complex number.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.SquareRoots(System.Numerics.Complex)">
            <summary>
            Evaluate all square roots of this <c>Complex</c>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.CubicRoots(System.Numerics.Complex)">
            <summary>
            Evaluate all cubic roots of this <c>Complex</c>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.IsZero(System.Numerics.Complex)">
            <summary>
            Gets a value indicating whether the <c>Complex32</c> is zero.
            </summary>
            <param name="complex">The <see cref="T:System.Numerics.Complex"/> number to perform this operation on.</param>
            <returns><c>true</c> if this instance is zero; otherwise, <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.IsOne(System.Numerics.Complex)">
            <summary>
            Gets a value indicating whether the <c>Complex32</c> is one.
            </summary>
            <param name="complex">The <see cref="T:System.Numerics.Complex"/> number to perform this operation on.</param>
            <returns><c>true</c> if this instance is one; otherwise, <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.IsImaginaryOne(System.Numerics.Complex)">
            <summary>
            Gets a value indicating whether the <c>Complex32</c> is the imaginary unit.
            </summary>
            <returns><c>true</c> if this instance is ImaginaryOne; otherwise, <c>false</c>.</returns>
            <param name="complex">The <see cref="T:System.Numerics.Complex"/> number to perform this operation on.</param>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.IsNaN(System.Numerics.Complex)">
            <summary>
            Gets a value indicating whether the provided <c>Complex32</c>evaluates
            to a value that is not a number.
            </summary>
            <param name="complex">The <see cref="T:System.Numerics.Complex"/> number to perform this operation on.</param>
            <returns>
            <c>true</c> if this instance is <c>NaN</c>; otherwise,
            <c>false</c>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.IsInfinity(System.Numerics.Complex)">
            <summary>
            Gets a value indicating whether the provided <c>Complex32</c> evaluates to an
            infinite value.
            </summary>
            <param name="complex">The <see cref="T:System.Numerics.Complex"/> number to perform this operation on.</param>
            <returns>
                <c>true</c> if this instance is infinite; otherwise, <c>false</c>.
            </returns>
            <remarks>
            True if it either evaluates to a complex infinity
            or to a directed infinity.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.IsReal(System.Numerics.Complex)">
            <summary>
            Gets a value indicating whether the provided <c>Complex32</c> is real.
            </summary>
            <param name="complex">The <see cref="T:System.Numerics.Complex"/> number to perform this operation on.</param>
            <returns><c>true</c> if this instance is a real number; otherwise, <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.IsRealNonNegative(System.Numerics.Complex)">
            <summary>
            Gets a value indicating whether the provided <c>Complex32</c> is real and not negative, that is &gt;= 0.
            </summary>
            <param name="complex">The <see cref="T:System.Numerics.Complex"/> number to perform this operation on.</param>
            <returns>
                <c>true</c> if this instance is real nonnegative number; otherwise, <c>false</c>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.Norm(System.Numerics.Complex)">
            <summary>
            Returns a Norm of a value of this type, which is appropriate for measuring how
            close this value is to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.Norm(MathNet.Numerics.Complex32)">
            <summary>
            Returns a Norm of a value of this type, which is appropriate for measuring how
            close this value is to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.NormOfDifference(System.Numerics.Complex,System.Numerics.Complex)">
            <summary>
            Returns a Norm of the difference of two values of this type, which is
            appropriate for measuring how close together these two values are.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.NormOfDifference(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32)">
            <summary>
            Returns a Norm of the difference of two values of this type, which is
            appropriate for measuring how close together these two values are.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.ToComplex(System.String)">
            <summary>
            Creates a complex number based on a string. The string can be in the
            following formats (without the quotes): 'n', 'ni', 'n +/- ni',
            'ni +/- n', 'n,n', 'n,ni,' '(n,n)', or '(n,ni)', where n is a double.
            </summary>
            <returns>
            A complex number containing the value specified by the given string.
            </returns>
            <param name="value">
            The string to parse.
            </param>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.ToComplex(System.String,System.IFormatProvider)">
            <summary>
            Creates a complex number based on a string. The string can be in the
            following formats (without the quotes): 'n', 'ni', 'n +/- ni',
            'ni +/- n', 'n,n', 'n,ni,' '(n,n)', or '(n,ni)', where n is a double.
            </summary>
            <returns>
            A complex number containing the value specified by the given string.
            </returns>
            <param name="value">
            the string to parse.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific
            formatting information.
            </param>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.ParsePart(System.Collections.Generic.LinkedListNode{System.String}@,System.Boolean@,System.IFormatProvider)">
            <summary>
            Parse a part (real or complex) from a complex number.
            </summary>
            <param name="token">Start Token.</param>
            <param name="imaginary">Is set to <c>true</c> if the part identified itself as being imaginary.</param>
            <param name="format">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific
            formatting information.
            </param>
            <returns>Resulting part as double.</returns>
            <exception cref="T:System.FormatException"/>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.TryToComplex(System.String,System.Numerics.Complex@)">
            <summary>
            Converts the string representation of a complex number to a double-precision complex number equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a complex number to convert.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will contain Complex.Zero.  This parameter is passed uninitialized.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.TryToComplex(System.String,System.IFormatProvider,System.Numerics.Complex@)">
            <summary>
            Converts the string representation of a complex number to double-precision complex number equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a complex number to convert.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific formatting information about value.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will contain complex32.Zero.  This parameter is passed uninitialized
            </returns>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.ToComplex32(System.String)">
            <summary>
            Creates a <c>Complex32</c> number based on a string. The string can be in the
            following formats (without the quotes): 'n', 'ni', 'n +/- ni',
            'ni +/- n', 'n,n', 'n,ni,' '(n,n)', or '(n,ni)', where n is a double.
            </summary>
            <returns>
            A complex number containing the value specified by the given string.
            </returns>
            <param name="value">
            the string to parse.
            </param>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.ToComplex32(System.String,System.IFormatProvider)">
            <summary>
            Creates a <c>Complex32</c> number based on a string. The string can be in the
            following formats (without the quotes): 'n', 'ni', 'n +/- ni',
            'ni +/- n', 'n,n', 'n,ni,' '(n,n)', or '(n,ni)', where n is a double.
            </summary>
            <returns>
            A complex number containing the value specified by the given string.
            </returns>
            <param name="value">
            the string to parse.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific
            formatting information.
            </param>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.TryToComplex32(System.String,MathNet.Numerics.Complex32@)">
            <summary>
            Converts the string representation of a complex number to a single-precision complex number equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a complex number to convert.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will contain complex32.Zero.  This parameter is passed uninitialized.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.ComplexExtensions.TryToComplex32(System.String,System.IFormatProvider,MathNet.Numerics.Complex32@)">
            <summary>
            Converts the string representation of a complex number to single-precision complex number equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a complex number to convert.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific formatting information about value.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will contain Complex.Zero.  This parameter is passed uninitialized.
            </returns>
        </member>
        <member name="T:MathNet.Numerics.Constants">
            <summary>
            A collection of frequently used mathematical constants.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.E">
            <summary>The number e</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Log2E">
            <summary>The number log[2](e)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Log10E">
            <summary>The number log[10](e)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Ln2">
            <summary>The number log[e](2)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Ln10">
            <summary>The number log[e](10)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.LnPi">
            <summary>The number log[e](pi)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Ln2PiOver2">
            <summary>The number log[e](2*pi)/2</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.InvE">
            <summary>The number 1/e</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.SqrtE">
            <summary>The number sqrt(e)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Sqrt2">
            <summary>The number sqrt(2)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Sqrt3">
            <summary>The number sqrt(3)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Sqrt1Over2">
            <summary>The number sqrt(1/2) = 1/sqrt(2) = sqrt(2)/2</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.HalfSqrt3">
            <summary>The number sqrt(3)/2</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Pi">
            <summary>The number pi</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Pi2">
            <summary>The number pi*2</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.PiOver2">
            <summary>The number pi/2</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Pi3Over2">
            <summary>The number pi*3/2</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.PiOver4">
            <summary>The number pi/4</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.SqrtPi">
            <summary>The number sqrt(pi)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Sqrt2Pi">
            <summary>The number sqrt(2pi)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.SqrtPiOver2">
            <summary>The number sqrt(pi/2)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Sqrt2PiE">
            <summary>The number sqrt(2*pi*e)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.LogSqrt2Pi">
            <summary>The number log(sqrt(2*pi))</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.LogSqrt2PiE">
            <summary>The number log(sqrt(2*pi*e))</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.LogTwoSqrtEOverPi">
            <summary>The number log(2 * sqrt(e / pi))</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.InvPi">
            <summary>The number 1/pi</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.TwoInvPi">
            <summary>The number 2/pi</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.InvSqrtPi">
            <summary>The number 1/sqrt(pi)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.InvSqrt2Pi">
            <summary>The number 1/sqrt(2pi)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.TwoInvSqrtPi">
            <summary>The number 2/sqrt(pi)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.TwoSqrtEOverPi">
            <summary>The number 2 * sqrt(e / pi)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Degree">
            <summary>The number (pi)/180 - factor to convert from Degree (deg) to Radians (rad).</summary>
            <seealso cref="M:MathNet.Numerics.Trig.DegreeToRadian(System.Double)"/>
            <seealso cref="M:MathNet.Numerics.Trig.RadianToDegree(System.Double)"/>
        </member>
        <member name="F:MathNet.Numerics.Constants.Grad">
            <summary>The number (pi)/200 - factor to convert from NewGrad (grad) to Radians (rad).</summary>
            <seealso cref="M:MathNet.Numerics.Trig.GradToRadian(System.Double)"/>
            <seealso cref="M:MathNet.Numerics.Trig.RadianToGrad(System.Double)"/>
        </member>
        <member name="F:MathNet.Numerics.Constants.PowerDecibel">
            <summary>The number ln(10)/20 - factor to convert from Power Decibel (dB) to Neper (Np). Use this version when the Decibel represent a power gain but the compared values are not powers (e.g. amplitude, current, voltage).</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.NeutralDecibel">
            <summary>The number ln(10)/10 - factor to convert from Neutral Decibel (dB) to Neper (Np). Use this version when either both or neither of the Decibel and the compared values represent powers.</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Catalan">
            <summary>The Catalan constant</summary>
            <remarks>Sum(k=0 -> inf){ (-1)^k/(2*k + 1)2 }</remarks>
        </member>
        <member name="F:MathNet.Numerics.Constants.EulerMascheroni">
            <summary>The Euler-Mascheroni constant</summary>
            <remarks>lim(n -> inf){ Sum(k=1 -> n) { 1/k - log(n) } }</remarks>
        </member>
        <member name="F:MathNet.Numerics.Constants.GoldenRatio">
            <summary>The number (1+sqrt(5))/2, also known as the golden ratio</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Glaisher">
            <summary>The Glaisher constant</summary>
            <remarks>e^(1/12 - Zeta(-1))</remarks>
        </member>
        <member name="F:MathNet.Numerics.Constants.Khinchin">
            <summary>The Khinchin constant</summary>
            <remarks>prod(k=1 -> inf){1+1/(k*(k+2))^log(k,2)}</remarks>
        </member>
        <member name="F:MathNet.Numerics.Constants.SizeOfDouble">
            <summary>
            The size of a double in bytes.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.SizeOfInt">
            <summary>
            The size of an int in bytes.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.SizeOfFloat">
            <summary>
            The size of a float in bytes.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.SizeOfComplex">
            <summary>
            The size of a Complex in bytes.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.SizeOfComplex32">
            <summary>
            The size of a Complex in bytes.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.SpeedOfLight">
            <summary>Speed of Light in Vacuum: c_0 = 2.99792458e8 [m s^-1] (defined, exact; 2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.MagneticPermeability">
            <summary>Magnetic Permeability in Vacuum: mu_0 = 4*Pi * 10^-7 [N A^-2 = kg m A^-2 s^-2] (defined, exact; 2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.ElectricPermittivity">
            <summary>Electric Permittivity in Vacuum: epsilon_0 = 1/(mu_0*c_0^2) [F m^-1 = A^2 s^4 kg^-1 m^-3] (defined, exact; 2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.CharacteristicImpedanceVacuum">
            <summary>Characteristic Impedance of Vacuum: Z_0 = mu_0*c_0 [Ohm = m^2 kg s^-3 A^-2] (defined, exact; 2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.GravitationalConstant">
            <summary>Newtonian Constant of Gravitation: G = 6.67429e-11 [m^3 kg^-1 s^-2] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.PlancksConstant">
            <summary>Planck's constant: h = 6.62606896e-34 [J s = m^2 kg s^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.DiracsConstant">
            <summary>Reduced Planck's constant: h_bar = h / (2*Pi) [J s = m^2 kg s^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.PlancksMass">
            <summary>Planck mass: m_p = (h_bar*c_0/G)^(1/2) [kg] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.PlancksTemperature">
            <summary>Planck temperature: T_p = (h_bar*c_0^5/G)^(1/2)/k [K] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.PlancksLength">
            <summary>Planck length: l_p = h_bar/(m_p*c_0) [m] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.PlancksTime">
            <summary>Planck time: t_p = l_p/c_0 [s] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.ElementaryCharge">
            <summary>Elementary Electron Charge: e = 1.602176487e-19 [C = A s] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.MagneticFluxQuantum">
            <summary>Magnetic Flux Quantum: theta_0 = h/(2*e) [Wb = m^2 kg s^-2 A^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.ConductanceQuantum">
            <summary>Conductance Quantum: G_0 = 2*e^2/h [S = m^-2 kg^-1 s^3 A^2] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.JosephsonConstant">
            <summary>Josephson Constant: K_J = 2*e/h [Hz V^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.VonKlitzingConstant">
            <summary>Von Klitzing Constant: R_K = h/e^2 [Ohm = m^2 kg s^-3 A^-2] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.BohrMagneton">
            <summary>Bohr Magneton: mu_B = e*h_bar/2*m_e [J T^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.NuclearMagneton">
            <summary>Nuclear Magneton: mu_N = e*h_bar/2*m_p [J T^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.FineStructureConstant">
            <summary>Fine Structure Constant: alpha = e^2/4*Pi*e_0*h_bar*c_0 [1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.RydbergConstant">
            <summary>Rydberg Constant: R_infty = alpha^2*m_e*c_0/2*h [m^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.BohrRadius">
            <summary>Bor Radius: a_0 = alpha/4*Pi*R_infty [m] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.HartreeEnergy">
            <summary>Hartree Energy: E_h = 2*R_infty*h*c_0 [J] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.QuantumOfCirculation">
            <summary>Quantum of Circulation: h/2*m_e [m^2 s^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.FermiCouplingConstant">
            <summary>Fermi Coupling Constant: G_F/(h_bar*c_0)^3 [GeV^-2] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.WeakMixingAngle">
            <summary>Weak Mixin Angle: sin^2(theta_W) [1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.ElectronMass">
            <summary>Electron Mass: [kg] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.ElectronMassEnergyEquivalent">
            <summary>Electron Mass Energy Equivalent: [J] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.ElectronMolarMass">
            <summary>Electron Molar Mass: [kg mol^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.ComptonWavelength">
            <summary>Electron Compton Wavelength: [m] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.ClassicalElectronRadius">
            <summary>Classical Electron Radius: [m] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.ThomsonCrossSection">
            <summary>Thomson Cross Section: [m^2] (2002 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.ElectronMagneticMoment">
            <summary>Electron Magnetic Moment: [J T^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.ElectronGFactor">
            <summary>Electon G-Factor: [1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.MuonMass">
            <summary>Muon Mass: [kg] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.MuonMassEnegryEquivalent">
            <summary>Muon Mass Energy Equivalent: [J] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.MuonMolarMass">
            <summary>Muon Molar Mass: [kg mol^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.MuonComptonWavelength">
            <summary>Muon Compton Wavelength: [m] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.MuonMagneticMoment">
            <summary>Muon Magnetic Moment: [J T^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.MuonGFactor">
            <summary>Muon G-Factor: [1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.TauMass">
            <summary>Tau Mass: [kg] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.TauMassEnergyEquivalent">
            <summary>Tau Mass Energy Equivalent: [J] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.TauMolarMass">
            <summary>Tau Molar Mass: [kg mol^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.TauComptonWavelength">
            <summary>Tau Compton Wavelength: [m] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.ProtonMass">
            <summary>Proton Mass: [kg] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.ProtonMassEnergyEquivalent">
            <summary>Proton Mass Energy Equivalent: [J] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.ProtonMolarMass">
            <summary>Proton Molar Mass: [kg mol^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.ProtonComptonWavelength">
            <summary>Proton Compton Wavelength: [m] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.ProtonMagneticMoment">
            <summary>Proton Magnetic Moment: [J T^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.ProtonGFactor">
            <summary>Proton G-Factor: [1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.ShieldedProtonMagneticMoment">
            <summary>Proton Shielded Magnetic Moment: [J T^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.ProtonGyromagneticRatio">
            <summary>Proton Gyro-Magnetic Ratio: [s^-1 T^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.ShieldedProtonGyromagneticRatio">
            <summary>Proton Shielded Gyro-Magnetic Ratio: [s^-1 T^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.NeutronMass">
            <summary>Neutron Mass: [kg] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.NeutronMassEnegryEquivalent">
            <summary>Neutron Mass Energy Equivalent: [J] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.NeutronMolarMass">
            <summary>Neutron Molar Mass: [kg mol^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.NeutronComptonWavelength">
            <summary>Neuron Compton Wavelength: [m] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.NeutronMagneticMoment">
            <summary>Neutron Magnetic Moment: [J T^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.NeutronGFactor">
            <summary>Neutron G-Factor: [1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.NeutronGyromagneticRatio">
            <summary>Neutron Gyro-Magnetic Ratio: [s^-1 T^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.DeuteronMass">
            <summary>Deuteron Mass: [kg] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.DeuteronMassEnegryEquivalent">
            <summary>Deuteron Mass Energy Equivalent: [J] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.DeuteronMolarMass">
            <summary>Deuteron Molar Mass: [kg mol^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.DeuteronMagneticMoment">
            <summary>Deuteron Magnetic Moment: [J T^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.HelionMass">
            <summary>Helion Mass: [kg] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.HelionMassEnegryEquivalent">
            <summary>Helion Mass Energy Equivalent: [J] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.HelionMolarMass">
            <summary>Helion Molar Mass: [kg mol^-1] (2007 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Avogadro">
            <summary>Avogadro constant: [mol^-1] (2010 CODATA)</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Yotta">
            <summary>The SI prefix factor corresponding to 1 000 000 000 000 000 000 000 000</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Zetta">
            <summary>The SI prefix factor corresponding to 1 000 000 000 000 000 000 000</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Exa">
            <summary>The SI prefix factor corresponding to 1 000 000 000 000 000 000</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Peta">
            <summary>The SI prefix factor corresponding to 1 000 000 000 000 000</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Tera">
            <summary>The SI prefix factor corresponding to 1 000 000 000 000</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Giga">
            <summary>The SI prefix factor corresponding to 1 000 000 000</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Mega">
            <summary>The SI prefix factor corresponding to 1 000 000</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Kilo">
            <summary>The SI prefix factor corresponding to 1 000</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Hecto">
            <summary>The SI prefix factor corresponding to 100</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Deca">
            <summary>The SI prefix factor corresponding to 10</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Deci">
            <summary>The SI prefix factor corresponding to 0.1</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Centi">
            <summary>The SI prefix factor corresponding to 0.01</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Milli">
            <summary>The SI prefix factor corresponding to 0.001</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Micro">
            <summary>The SI prefix factor corresponding to 0.000 001</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Nano">
            <summary>The SI prefix factor corresponding to 0.000 000 001</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Pico">
            <summary>The SI prefix factor corresponding to 0.000 000 000 001</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Femto">
            <summary>The SI prefix factor corresponding to 0.000 000 000 000 001</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Atto">
            <summary>The SI prefix factor corresponding to 0.000 000 000 000 000 001</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Zepto">
            <summary>The SI prefix factor corresponding to 0.000 000 000 000 000 000 001</summary>
        </member>
        <member name="F:MathNet.Numerics.Constants.Yocto">
            <summary>The SI prefix factor corresponding to 0.000 000 000 000 000 000 000 001</summary>
        </member>
        <member name="T:MathNet.Numerics.Control">
            <summary>
            Sets parameters for the library.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Control.UseDefaultProviders">
            <summary>
            Use a specific provider if configured, e.g. using
            environment variables, or fall back to the best providers.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Control.UseBestProviders">
            <summary>
            Use the best provider available.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Control.UseNativeMKL">
            <summary>
            Use the Intel MKL native provider for linear algebra.
            Throws if it is not available or failed to initialize, in which case the previous provider is still active.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Control.TryUseNativeMKL">
            <summary>
            Try to use the Intel MKL native provider for linear algebra.
            </summary>
            <returns>
            True if the provider was found and initialized successfully.
            False if it failed and the previous provider is still active.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Control.UseNativeCUDA">
            <summary>
            Use the Nvidia CUDA native provider for linear algebra.
            Throws if it is not available or failed to initialize, in which case the previous provider is still active.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Control.TryUseNativeCUDA">
            <summary>
            Try to use the Nvidia CUDA native provider for linear algebra.
            </summary>
            <returns>
            True if the provider was found and initialized successfully.
            False if it failed and the previous provider is still active.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Control.UseNativeOpenBLAS">
            <summary>
            Use the OpenBLAS native provider for linear algebra.
            Throws if it is not available or failed to initialize, in which case the previous provider is still active.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Control.TryUseNativeOpenBLAS">
            <summary>
            Try to use the OpenBLAS native provider for linear algebra.
            </summary>
            <returns>
            True if the provider was found and initialized successfully.
            False if it failed and the previous provider is still active.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Control.TryUseNative">
            <summary>
            Try to use any available native provider in an undefined order.
            </summary>
            <returns>
            True if one of the native providers was found and successfully initialized.
            False if it failed and the previous provider is still active.
            </returns>
        </member>
        <member name="P:MathNet.Numerics.Control.CheckDistributionParameters">
            <summary>
            Gets or sets a value indicating whether the distribution classes check validate each parameter.
            For the multivariate distributions this could involve an expensive matrix factorization.
            The default setting of this property is <c>true</c>.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Control.ThreadSafeRandomNumberGenerators">
            <summary>
            Gets or sets a value indicating whether to use thread safe random number generators (RNG).
            Thread safe RNG about two and half time slower than non-thread safe RNG.
            </summary>
            <value>
                <c>true</c> to use thread safe random number generators ; otherwise, <c>false</c>.
            </value>
        </member>
        <member name="P:MathNet.Numerics.Control.NativeProviderPath">
            <summary>
            Optional path to try to load native provider binaries from.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Control.MaxDegreeOfParallelism">
            <summary>
            Gets or sets a value indicating how many parallel worker threads shall be used
            when parallelization is applicable.
            </summary>
            <remarks>Default to the number of processor cores, must be between 1 and 1024 (inclusive).</remarks>
        </member>
        <member name="P:MathNet.Numerics.Control.TaskScheduler">
            <summary>
            Gets or sets the TaskScheduler used to schedule the worker tasks.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Control.ParallelizeOrder">
            <summary>
            Gets or sets the order of the matrix when linear algebra provider
            must calculate multiply in parallel threads.
            </summary>
            <value>The order. Default 64, must be at least 3.</value>
        </member>
        <member name="P:MathNet.Numerics.Control.ParallelizeElements">
            <summary>
            Gets or sets the number of elements a vector or matrix
            must contain before we multiply threads.
            </summary>
            <value>Number of elements. Default 300, must be at least 3.</value>
        </member>
        <member name="T:MathNet.Numerics.Differentiate">
            <summary>
            Numerical Derivative.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Differentiate.Points(System.Int32,System.Int32)">
            <summary>
            Initialized a NumericalDerivative with the given points and center.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Differentiate.Order(System.Int32)">
            <summary>
            Initialized a NumericalDerivative with the default points and center for the given order.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Differentiate.Derivative(System.Func{System.Double,System.Double},System.Double,System.Int32)">
            <summary>
            Evaluates the derivative of a scalar univariate function.
            </summary>
            <param name="f">Univariate function handle.</param>
            <param name="x">Point at which to evaluate the derivative.</param>
            <param name="order">Derivative order.</param>
        </member>
        <member name="M:MathNet.Numerics.Differentiate.DerivativeFunc(System.Func{System.Double,System.Double},System.Int32)">
            <summary>
            Creates a function handle for the derivative of a scalar univariate function.
            </summary>
            <param name="f">Univariate function handle.</param>
            <param name="order">Derivative order.</param>
        </member>
        <member name="M:MathNet.Numerics.Differentiate.FirstDerivative(System.Func{System.Double,System.Double},System.Double)">
            <summary>
            Evaluates the first derivative of a scalar univariate function.
            </summary>
            <param name="f">Univariate function handle.</param>
            <param name="x">Point at which to evaluate the derivative.</param>
        </member>
        <member name="M:MathNet.Numerics.Differentiate.FirstDerivativeFunc(System.Func{System.Double,System.Double})">
            <summary>
            Creates a function handle for the first derivative of a scalar univariate function.
            </summary>
            <param name="f">Univariate function handle.</param>
        </member>
        <member name="M:MathNet.Numerics.Differentiate.SecondDerivative(System.Func{System.Double,System.Double},System.Double)">
            <summary>
            Evaluates the second derivative of a scalar univariate function.
            </summary>
            <param name="f">Univariate function handle.</param>
            <param name="x">Point at which to evaluate the derivative.</param>
        </member>
        <member name="M:MathNet.Numerics.Differentiate.SecondDerivativeFunc(System.Func{System.Double,System.Double})">
            <summary>
            Creates a function handle for the second derivative of a scalar univariate function.
            </summary>
            <param name="f">Univariate function handle.</param>
        </member>
        <member name="M:MathNet.Numerics.Differentiate.PartialDerivative(System.Func{System.Double[],System.Double},System.Double[],System.Int32,System.Int32)">
            <summary>
            Evaluates the partial derivative of a multivariate function.
            </summary>
            <param name="f">Multivariate function handle.</param>
            <param name="x">Vector at which to evaluate the derivative.</param>
            <param name="parameterIndex">Index of independent variable for partial derivative.</param>
            <param name="order">Derivative order.</param>
        </member>
        <member name="M:MathNet.Numerics.Differentiate.PartialDerivativeFunc(System.Func{System.Double[],System.Double},System.Int32,System.Int32)">
            <summary>
            Creates a function handle for the partial derivative of a multivariate function.
            </summary>
            <param name="f">Multivariate function handle.</param>
            <param name="parameterIndex">Index of independent variable for partial derivative.</param>
            <param name="order">Derivative order.</param>
        </member>
        <member name="M:MathNet.Numerics.Differentiate.FirstPartialDerivative(System.Func{System.Double[],System.Double},System.Double[],System.Int32)">
            <summary>
            Evaluates the first partial derivative of a multivariate function.
            </summary>
            <param name="f">Multivariate function handle.</param>
            <param name="x">Vector at which to evaluate the derivative.</param>
            <param name="parameterIndex">Index of independent variable for partial derivative.</param>
        </member>
        <member name="M:MathNet.Numerics.Differentiate.FirstPartialDerivativeFunc(System.Func{System.Double[],System.Double},System.Int32)">
            <summary>
            Creates a function handle for the first partial derivative of a multivariate function.
            </summary>
            <param name="f">Multivariate function handle.</param>
            <param name="parameterIndex">Index of independent variable for partial derivative.</param>
        </member>
        <member name="M:MathNet.Numerics.Differentiate.PartialDerivative2(System.Func{System.Double,System.Double,System.Double},System.Double,System.Double,System.Int32,System.Int32)">
            <summary>
            Evaluates the partial derivative of a bivariate function.
            </summary>
            <param name="f">Bivariate function handle.</param>
            <param name="x">First argument at which to evaluate the derivative.</param>
            <param name="y">Second argument at which to evaluate the derivative.</param>
            <param name="parameterIndex">Index of independent variable for partial derivative.</param>
            <param name="order">Derivative order.</param>
        </member>
        <member name="M:MathNet.Numerics.Differentiate.PartialDerivative2Func(System.Func{System.Double,System.Double,System.Double},System.Int32,System.Int32)">
            <summary>
            Creates a function handle for the partial derivative of a bivariate function.
            </summary>
            <param name="f">Bivariate function handle.</param>
            <param name="parameterIndex">Index of independent variable for partial derivative.</param>
            <param name="order">Derivative order.</param>
        </member>
        <member name="M:MathNet.Numerics.Differentiate.FirstPartialDerivative2(System.Func{System.Double,System.Double,System.Double},System.Double,System.Double,System.Int32)">
            <summary>
            Evaluates the first partial derivative of a bivariate function.
            </summary>
            <param name="f">Bivariate function handle.</param>
            <param name="x">First argument at which to evaluate the derivative.</param>
            <param name="y">Second argument at which to evaluate the derivative.</param>
            <param name="parameterIndex">Index of independent variable for partial derivative.</param>
        </member>
        <member name="M:MathNet.Numerics.Differentiate.FirstPartialDerivative2Func(System.Func{System.Double,System.Double,System.Double},System.Int32)">
            <summary>
            Creates a function handle for the first partial derivative of a bivariate function.
            </summary>
            <param name="f">Bivariate function handle.</param>
            <param name="parameterIndex">Index of independent variable for partial derivative.</param>
        </member>
        <member name="T:MathNet.Numerics.Differentiation.FiniteDifferenceCoefficients">
            <summary>
            Class to calculate finite difference coefficients using Taylor series expansion method.
            <remarks>
            <para>
            For n points, coefficients are calculated up to the maximum derivative order possible (n-1).
            The current function value position specifies the "center" for surrounding coefficients.
            Selecting the first, middle or last positions represent forward, backwards and central difference methods.
            </para>
            </remarks>
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Differentiation.FiniteDifferenceCoefficients.Points">
            <summary>
            Number of points for finite difference coefficients. Changing this value recalculates the coefficients table.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.FiniteDifferenceCoefficients.#ctor(System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Differentiation.FiniteDifferenceCoefficients"/> class.
            </summary>
            <param name="points">Number of finite difference coefficients.</param>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.FiniteDifferenceCoefficients.GetCoefficients(System.Int32,System.Int32)">
            <summary>
            Gets the finite difference coefficients for a specified center and order.
            </summary>
            <param name="center">Current function position with respect to coefficients. Must be within point range.</param>
            <param name="order">Order of finite difference coefficients.</param>
            <returns>Vector of finite difference coefficients.</returns>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.FiniteDifferenceCoefficients.GetCoefficientsForAllOrders(System.Int32)">
            <summary>
            Gets the finite difference coefficients for all orders at a specified center.
            </summary>
            <param name="center">Current function position with respect to coefficients. Must be within point range.</param>
            <returns>Rectangular array of coefficients, with columns specifying order.</returns>
        </member>
        <member name="T:MathNet.Numerics.Differentiation.StepType">
            <summary>
            Type of finite different step size.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Differentiation.StepType.Absolute">
            <summary>
            The absolute step size value will be used in numerical derivatives, regardless of order or function parameters.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Differentiation.StepType.RelativeX">
            <summary>
            A base step size value, h, will be scaled according to the function input parameter. A common example is hx = h*(1+abs(x)), however
            this may vary depending on implementation. This definition only guarantees that the only scaling will be relative to the
            function input parameter and not the order of the finite difference derivative.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Differentiation.StepType.Relative">
            <summary>
            A base step size value, eps (typically machine precision), is scaled according to the finite difference coefficient order
            and function input parameter. The initial scaling according to finite different coefficient order can be thought of as producing a
            base step size, h, that is equivalent to <see cref="F:MathNet.Numerics.Differentiation.StepType.RelativeX"/> scaling. This step size is then scaled according to the function
            input parameter. Although implementation may vary, an example of second order accurate scaling may be (eps)^(1/3)*(1+abs(x)).
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Differentiation.NumericalDerivative">
            <summary>
            Class to evaluate the numerical derivative of a function using finite difference approximations.
            Variable point and center methods can be initialized <seealso cref="T:MathNet.Numerics.Differentiation.FiniteDifferenceCoefficients"/>.
            This class can also be used to return function handles (delegates) for a fixed derivative order and variable.
            It is possible to evaluate the derivative and partial derivative of univariate and multivariate functions respectively.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalDerivative.#ctor">
            <summary>
            Initializes a NumericalDerivative class with the default 3 point center difference method.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalDerivative.#ctor(System.Int32,System.Int32)">
            <summary>
            Initialized a NumericalDerivative class.
            </summary>
            <param name="points">Number of points for finite difference derivatives.</param>
            <param name="center">Location of the center with respect to other points. Value ranges from zero to points-1.</param>
        </member>
        <member name="P:MathNet.Numerics.Differentiation.NumericalDerivative.StepSize">
            <summary>
            Sets and gets the finite difference step size. This value is for each function evaluation if relative step size types are used.
            If the base step size used in scaling is desired, see <see cref="P:MathNet.Numerics.Differentiation.NumericalDerivative.Epsilon"/>.
            </summary>
            <remarks>
            Setting then getting the StepSize may return a different value. This is not unusual since a user-defined step size is converted to a
            base-2 representable number to improve finite difference accuracy.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.Differentiation.NumericalDerivative.BaseStepSize">
            <summary>
            Sets and gets the base finite difference step size. This assigned value to this parameter is only used if <see cref="P:MathNet.Numerics.Differentiation.NumericalDerivative.StepType"/> is set to RelativeX.
            However, if the StepType is Relative, it will contain the base step size computed from <see cref="P:MathNet.Numerics.Differentiation.NumericalDerivative.Epsilon"/> based on the finite difference order.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Differentiation.NumericalDerivative.Epsilon">
            <summary>
            Sets and gets the base finite difference step size. This parameter is only used if <see cref="P:MathNet.Numerics.Differentiation.NumericalDerivative.StepType"/> is set to Relative.
            By default this is set to machine epsilon, from which <see cref="P:MathNet.Numerics.Differentiation.NumericalDerivative.BaseStepSize"/> is computed.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Differentiation.NumericalDerivative.Center">
            <summary>
            Sets and gets the location of the center point for the finite difference derivative.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Differentiation.NumericalDerivative.Evaluations">
            <summary>
            Number of times a function is evaluated for numerical derivatives.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Differentiation.NumericalDerivative.StepType">
            <summary>
            Type of step size for computing finite differences. If set to absolute, dx = h.
            If set to relative, dx = (1+abs(x))*h^(2/(order+1)). This provides accurate results when
            h is approximately equal to the square-root of machine accuracy, epsilon.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalDerivative.EvaluateDerivative(System.Double[],System.Int32,System.Double)">
            <summary>
            Evaluates the derivative of equidistant points using the finite difference method.
            </summary>
            <param name="points">Vector of points StepSize apart.</param>
            <param name="order">Derivative order.</param>
            <param name="stepSize">Finite difference step size.</param>
            <returns>Derivative of points of the specified order.</returns>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalDerivative.EvaluateDerivative(System.Func{System.Double,System.Double},System.Double,System.Int32,System.Nullable{System.Double})">
            <summary>
            Evaluates the derivative of a scalar univariate function.
            </summary>
            <remarks>
            Supplying the optional argument currentValue will reduce the number of function evaluations
            required to calculate the finite difference derivative.
            </remarks>
            <param name="f">Function handle.</param>
            <param name="x">Point at which to compute the derivative.</param>
            <param name="order">Derivative order.</param>
            <param name="currentValue">Current function value at center.</param>
            <returns>Function derivative at x of the specified order.</returns>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalDerivative.CreateDerivativeFunctionHandle(System.Func{System.Double,System.Double},System.Int32)">
            <summary>
            Creates a function handle for the derivative of a scalar univariate function.
            </summary>
            <param name="f">Input function handle.</param>
            <param name="order">Derivative order.</param>
            <returns>Function handle that evaluates the derivative of input function at a fixed order.</returns>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalDerivative.EvaluatePartialDerivative(System.Func{System.Double[],System.Double},System.Double[],System.Int32,System.Int32,System.Nullable{System.Double})">
            <summary>
            Evaluates the partial derivative of a multivariate function.
            </summary>
            <param name="f">Multivariate function handle.</param>
            <param name="x">Vector at which to evaluate the derivative.</param>
            <param name="parameterIndex">Index of independent variable for partial derivative.</param>
            <param name="order">Derivative order.</param>
            <param name="currentValue">Current function value at center.</param>
            <returns>Function partial derivative at x of the specified order.</returns>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalDerivative.EvaluatePartialDerivative(System.Func{System.Double[],System.Double}[],System.Double[],System.Int32,System.Int32,System.Nullable{System.Double}[])">
            <summary>
            Evaluates the partial derivatives of a multivariate function array.
            </summary>
            <remarks>
            This function assumes the input vector x is of the correct length for f.
            </remarks>
            <param name="f">Multivariate vector function array handle.</param>
            <param name="x">Vector at which to evaluate the derivatives.</param>
            <param name="parameterIndex">Index of independent variable for partial derivative.</param>
            <param name="order">Derivative order.</param>
            <param name="currentValue">Current function value at center.</param>
            <returns>Vector of functions partial derivatives at x of the specified order.</returns>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalDerivative.CreatePartialDerivativeFunctionHandle(System.Func{System.Double[],System.Double},System.Int32,System.Int32)">
            <summary>
            Creates a function handle for the partial derivative of a multivariate function.
            </summary>
            <param name="f">Input function handle.</param>
            <param name="parameterIndex">Index of the independent variable for partial derivative.</param>
            <param name="order">Derivative order.</param>
            <returns>Function handle that evaluates partial derivative of input function at a fixed order.</returns>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalDerivative.CreatePartialDerivativeFunctionHandle(System.Func{System.Double[],System.Double}[],System.Int32,System.Int32)">
            <summary>
            Creates a function handle for the partial derivative of a vector multivariate function.
            </summary>
            <param name="f">Input function handle.</param>
            <param name="parameterIndex">Index of the independent variable for partial derivative.</param>
            <param name="order">Derivative order.</param>
            <returns>Function handle that evaluates partial derivative of input function at fixed order.</returns>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalDerivative.EvaluateMixedPartialDerivative(System.Func{System.Double[],System.Double},System.Double[],System.Int32[],System.Int32,System.Nullable{System.Double})">
            <summary>
            Evaluates the mixed partial derivative of variable order for multivariate functions.
            </summary>
            <remarks>
            This function recursively uses <see cref="M:MathNet.Numerics.Differentiation.NumericalDerivative.EvaluatePartialDerivative(System.Func{System.Double[],System.Double},System.Double[],System.Int32,System.Int32,System.Nullable{System.Double})"/> to evaluate mixed partial derivative.
            Therefore, it is more efficient to call <see cref="M:MathNet.Numerics.Differentiation.NumericalDerivative.EvaluatePartialDerivative(System.Func{System.Double[],System.Double},System.Double[],System.Int32,System.Int32,System.Nullable{System.Double})"/> for higher order derivatives of
            a single independent variable.
            </remarks>
            <param name="f">Multivariate function handle.</param>
            <param name="x">Points at which to evaluate the derivative.</param>
            <param name="parameterIndex">Vector of indices for the independent variables at descending derivative orders.</param>
            <param name="order">Highest order of differentiation.</param>
            <param name="currentValue">Current function value at center.</param>
            <returns>Function mixed partial derivative at x of the specified order.</returns>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalDerivative.EvaluateMixedPartialDerivative(System.Func{System.Double[],System.Double}[],System.Double[],System.Int32[],System.Int32,System.Nullable{System.Double}[])">
            <summary>
            Evaluates the mixed partial derivative of variable order for multivariate function arrays.
            </summary>
            <remarks>
            This function recursively uses <see cref="M:MathNet.Numerics.Differentiation.NumericalDerivative.EvaluatePartialDerivative(System.Func{System.Double[],System.Double}[],System.Double[],System.Int32,System.Int32,System.Nullable{System.Double}[])"/> to evaluate mixed partial derivative.
            Therefore, it is more efficient to call <see cref="M:MathNet.Numerics.Differentiation.NumericalDerivative.EvaluatePartialDerivative(System.Func{System.Double[],System.Double}[],System.Double[],System.Int32,System.Int32,System.Nullable{System.Double}[])"/> for higher order derivatives of
            a single independent variable.
            </remarks>
            <param name="f">Multivariate function array handle.</param>
            <param name="x">Vector at which to evaluate the derivative.</param>
            <param name="parameterIndex">Vector of indices for the independent variables at descending derivative orders.</param>
            <param name="order">Highest order of differentiation.</param>
            <param name="currentValue">Current function value at center.</param>
            <returns>Function mixed partial derivatives at x of the specified order.</returns>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalDerivative.CreateMixedPartialDerivativeFunctionHandle(System.Func{System.Double[],System.Double},System.Int32[],System.Int32)">
            <summary>
            Creates a function handle for the mixed partial derivative of a multivariate function.
            </summary>
            <param name="f">Input function handle.</param>
            <param name="parameterIndex">Vector of indices for the independent variables at descending derivative orders.</param>
            <param name="order">Highest derivative order.</param>
            <returns>Function handle that evaluates the fixed mixed partial derivative of input function at fixed order.</returns>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalDerivative.CreateMixedPartialDerivativeFunctionHandle(System.Func{System.Double[],System.Double}[],System.Int32[],System.Int32)">
            <summary>
            Creates a function handle for the mixed partial derivative of a multivariate vector function.
            </summary>
            <param name="f">Input vector function handle.</param>
            <param name="parameterIndex">Vector of indices for the independent variables at descending derivative orders.</param>
            <param name="order">Highest derivative order.</param>
            <returns>Function handle that evaluates the fixed mixed partial derivative of input function at fixed order.</returns>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalDerivative.ResetEvaluations">
            <summary>
            Resets the evaluation counter.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Differentiation.NumericalHessian">
            <summary>
            Class for evaluating the Hessian of a smooth continuously differentiable function using finite differences.
            By default, a central 3-point method is used.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Differentiation.NumericalHessian.FunctionEvaluations">
            <summary>
            Number of function evaluations.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalHessian.#ctor">
            <summary>
            Creates a numerical Hessian object with a three point central difference method.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalHessian.#ctor(System.Int32,System.Int32)">
            <summary>
            Creates a numerical Hessian with a specified differentiation scheme.
            </summary>
            <param name="points">Number of points for Hessian evaluation.</param>
            <param name="center">Center point for differentiation.</param>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalHessian.Evaluate(System.Func{System.Double,System.Double},System.Double)">
            <summary>
            Evaluates the Hessian of the scalar univariate function f at points x.
            </summary>
            <param name="f">Scalar univariate function handle.</param>
            <param name="x">Point at which to evaluate Hessian.</param>
            <returns>Hessian tensor.</returns>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalHessian.Evaluate(System.Func{System.Double[],System.Double},System.Double[])">
            <summary>
            Evaluates the Hessian of a multivariate function f at points x.
            </summary>
            <remarks>
            This method of computing the Hessian is only valid for Lipschitz continuous functions.
            The function mirrors the Hessian along the diagonal since d2f/dxdy = d2f/dydx for continuously differentiable functions.
            </remarks>
            <param name="f">Multivariate function handle.></param>
            <param name="x">Points at which to evaluate Hessian.></param>
            <returns>Hessian tensor.</returns>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalHessian.ResetFunctionEvaluations">
            <summary>
            Resets the function evaluation counter for the Hessian.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Differentiation.NumericalJacobian">
            <summary>
            Class for evaluating the Jacobian of a function using finite differences.
            By default, a central 3-point method is used.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Differentiation.NumericalJacobian.FunctionEvaluations">
            <summary>
            Number of function evaluations.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalJacobian.#ctor">
            <summary>
            Creates a numerical Jacobian object with a three point central difference method.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalJacobian.#ctor(System.Int32,System.Int32)">
            <summary>
            Creates a numerical Jacobian with a specified differentiation scheme.
            </summary>
            <param name="points">Number of points for Jacobian evaluation.</param>
            <param name="center">Center point for differentiation.</param>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalJacobian.Evaluate(System.Func{System.Double,System.Double},System.Double)">
            <summary>
            Evaluates the Jacobian of scalar univariate function f at point x.
            </summary>
            <param name="f">Scalar univariate function handle.</param>
            <param name="x">Point at which to evaluate Jacobian.</param>
            <returns>Jacobian vector.</returns>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalJacobian.Evaluate(System.Func{System.Double[],System.Double},System.Double[])">
            <summary>
            Evaluates the Jacobian of a multivariate function f at vector x.
            </summary>
            <remarks>
            This function assumes that the length of vector x consistent with the argument count of f.
            </remarks>
            <param name="f">Multivariate function handle.</param>
            <param name="x">Points at which to evaluate Jacobian.</param>
            <returns>Jacobian vector.</returns>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalJacobian.Evaluate(System.Func{System.Double[],System.Double},System.Double[],System.Double)">
            <summary>
            Evaluates the Jacobian of a multivariate function f at vector x given a current function value.
            </summary>
            <remarks>
            To minimize the number of function evaluations, a user can supply the current value of the function
            to be used in computing the Jacobian. This value must correspond to the "center" location for the
            finite differencing. If a scheme is used where the center value is not evaluated, this will provide no
            added efficiency. This method also assumes that the length of vector x consistent with the argument count of f.
            </remarks>
            <param name="f">Multivariate function handle.</param>
            <param name="x">Points at which to evaluate Jacobian.</param>
            <param name="currentValue">Current function value at finite difference center.</param>
            <returns>Jacobian vector.</returns>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalJacobian.Evaluate(System.Func{System.Double[],System.Double}[],System.Double[])">
            <summary>
            Evaluates the Jacobian of a multivariate function array f at vector x.
            </summary>
            <param name="f">Multivariate function array handle.</param>
            <param name="x">Vector at which to evaluate Jacobian.</param>
            <returns>Jacobian matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalJacobian.Evaluate(System.Func{System.Double[],System.Double}[],System.Double[],System.Double[])">
            <summary>
            Evaluates the Jacobian of a multivariate function array f at vector x given a vector of current function values.
            </summary>
            <remarks>
            To minimize the number of function evaluations, a user can supply a vector of current values of the functions
            to be used in computing the Jacobian. These value must correspond to the "center" location for the
            finite differencing. If a scheme is used where the center value is not evaluated, this will provide no
            added efficiency. This method also assumes that the length of vector x consistent with the argument count of f.
            </remarks>
            <param name="f">Multivariate function array handle.</param>
            <param name="x">Vector at which to evaluate Jacobian.</param>
            <param name="currentValues">Vector of current function values.</param>
            <returns>Jacobian matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.Differentiation.NumericalJacobian.ResetFunctionEvaluations">
            <summary>
            Resets the function evaluation counter for the Jacobian.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.DifferIntegrate.DoubleExponential(System.Func{System.Double,System.Double},System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Evaluates the Riemann-Liouville fractional derivative that uses the double exponential integration.
            </summary>
            <remarks>
            <para>order = 1.0 : normal derivative</para>
            <para>order = 0.5 : semi-derivative</para>
            <para>order = -0.5 : semi-integral</para>
            <para>order = -1.0 : normal integral</para>
            </remarks>
            <param name="f">The analytic smooth function to differintegrate.</param>
            <param name="x">The evaluation point.</param>
            <param name="order">The order of fractional derivative.</param>
            <param name="x0">The reference point of integration.</param>
            <param name="targetAbsoluteError">The expected relative accuracy of the Double-Exponential integration.</param>
            <returns>Approximation of the differintegral of order n at x.</returns>
        </member>
        <member name="M:MathNet.Numerics.DifferIntegrate.GaussLegendre(System.Func{System.Double,System.Double},System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Evaluates the Riemann-Liouville fractional derivative that uses the Gauss-Legendre integration.
            </summary>
            <remarks>
            <para>order = 1.0 : normal derivative</para>
            <para>order = 0.5 : semi-derivative</para>
            <para>order = -0.5 : semi-integral</para>
            <para>order = -1.0 : normal integral</para>
            </remarks>
            <param name="f">The analytic smooth function to differintegrate.</param>
            <param name="x">The evaluation point.</param>
            <param name="order">The order of fractional derivative.</param>
            <param name="x0">The reference point of integration.</param>
            <param name="gaussLegendrePoints">The number of Gauss-Legendre points.</param>
            <returns>Approximation of the differintegral of order n at x.</returns>
        </member>
        <member name="M:MathNet.Numerics.DifferIntegrate.GaussKronrod(System.Func{System.Double,System.Double},System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Evaluates the Riemann-Liouville fractional derivative that uses the Gauss-Kronrod integration.
            </summary>
            <remarks>
            <para>order = 1.0 : normal derivative</para>
            <para>order = 0.5 : semi-derivative</para>
            <para>order = -0.5 : semi-integral</para>
            <para>order = -1.0 : normal integral</para>
            </remarks>
            <param name="f">The analytic smooth function to differintegrate.</param>
            <param name="x">The evaluation point.</param>
            <param name="order">The order of fractional derivative.</param>
            <param name="x0">The reference point of integration.</param>
            <param name="targetRelativeError">The expected relative accuracy of the Gauss-Kronrod integration.</param>
            <param name="gaussKronrodPoints">The number of Gauss-Kronrod points. Pre-computed for 15, 21, 31, 41, 51 and 61 points.</param>
            <returns>Approximation of the differintegral of order n at x.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distance">
            <summary>
            Metrics to measure the distance between two structures.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.SAD``1(MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Sum of Absolute Difference (SAD), i.e. the L1-norm (Manhattan) of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.SAD(System.Double[],System.Double[])">
            <summary>
            Sum of Absolute Difference (SAD), i.e. the L1-norm (Manhattan) of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.SAD(System.Single[],System.Single[])">
            <summary>
            Sum of Absolute Difference (SAD), i.e. the L1-norm (Manhattan) of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.MAE``1(MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Mean-Absolute Error (MAE), i.e. the normalized L1-norm (Manhattan) of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.MAE(System.Double[],System.Double[])">
            <summary>
            Mean-Absolute Error (MAE), i.e. the normalized L1-norm (Manhattan) of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.MAE(System.Single[],System.Single[])">
            <summary>
            Mean-Absolute Error (MAE), i.e. the normalized L1-norm (Manhattan) of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.SSD``1(MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Sum of Squared Difference (SSD), i.e. the squared L2-norm (Euclidean) of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.SSD(System.Double[],System.Double[])">
            <summary>
            Sum of Squared Difference (SSD), i.e. the squared L2-norm (Euclidean) of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.SSD(System.Single[],System.Single[])">
            <summary>
            Sum of Squared Difference (SSD), i.e. the squared L2-norm (Euclidean) of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.MSE``1(MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Mean-Squared Error (MSE), i.e. the normalized squared L2-norm (Euclidean) of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.MSE(System.Double[],System.Double[])">
            <summary>
            Mean-Squared Error (MSE), i.e. the normalized squared L2-norm (Euclidean) of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.MSE(System.Single[],System.Single[])">
            <summary>
            Mean-Squared Error (MSE), i.e. the normalized squared L2-norm (Euclidean) of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.Euclidean``1(MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Euclidean Distance, i.e. the L2-norm of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.Euclidean(System.Double[],System.Double[])">
            <summary>
            Euclidean Distance, i.e. the L2-norm of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.Euclidean(System.Single[],System.Single[])">
            <summary>
            Euclidean Distance, i.e. the L2-norm of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.Manhattan``1(MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Manhattan Distance, i.e. the L1-norm of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.Manhattan(System.Double[],System.Double[])">
            <summary>
            Manhattan Distance, i.e. the L1-norm of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.Manhattan(System.Single[],System.Single[])">
            <summary>
            Manhattan Distance, i.e. the L1-norm of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.Chebyshev``1(MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Chebyshev Distance, i.e. the Infinity-norm of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.Chebyshev(System.Double[],System.Double[])">
            <summary>
            Chebyshev Distance, i.e. the Infinity-norm of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.Chebyshev(System.Single[],System.Single[])">
            <summary>
            Chebyshev Distance, i.e. the Infinity-norm of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.Minkowski``1(System.Double,MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Minkowski Distance, i.e. the generalized p-norm of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.Minkowski(System.Double,System.Double[],System.Double[])">
            <summary>
            Minkowski Distance, i.e. the generalized p-norm of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.Minkowski(System.Double,System.Single[],System.Single[])">
            <summary>
            Minkowski Distance, i.e. the generalized p-norm of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.Canberra(System.Double[],System.Double[])">
            <summary>
            Canberra Distance, a weighted version of the L1-norm of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.Canberra(System.Single[],System.Single[])">
            <summary>
            Canberra Distance, a weighted version of the L1-norm of the difference.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.Cosine(System.Double[],System.Double[])">
            <summary>
            Cosine Distance, representing the angular distance while ignoring the scale.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.Cosine(System.Single[],System.Single[])">
            <summary>
            Cosine Distance, representing the angular distance while ignoring the scale.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.Hamming(System.Double[],System.Double[])">
            <summary>
            Hamming Distance, i.e. the number of positions that have different values in the vectors.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.Hamming(System.Single[],System.Single[])">
            <summary>
            Hamming Distance, i.e. the number of positions that have different values in the vectors.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.Pearson(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Pearson's distance, i.e. 1 - the person correlation coefficient.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distance.Jaccard(System.Double[],System.Double[])">
            <summary>
            Jaccard distance, i.e. 1 - the Jaccard index.
            </summary>
            <exception cref="T:System.ArgumentNullException">Thrown if a or b are null.</exception>
            <exception cref="T:System.ArgumentException">Throw if a and b are of different lengths.</exception>
            <returns>Jaccard distance.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distance.Jaccard(System.Single[],System.Single[])">
            <summary>
            Jaccard distance, i.e. 1 - the Jaccard index.
            </summary>
            <exception cref="T:System.ArgumentNullException">Thrown if a or b are null.</exception>
            <exception cref="T:System.ArgumentException">Throw if a and b are of different lengths.</exception>
            <returns>Jaccard distance.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Bernoulli">
            <summary>
            Discrete Univariate Bernoulli distribution.
            The Bernoulli distribution is a distribution over bits. The parameter
            p specifies the probability that a 1 is generated.
            <a href="http://en.wikipedia.org/wiki/Bernoulli_distribution">Wikipedia - Bernoulli distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Bernoulli.#ctor(System.Double)">
            <summary>
            Initializes a new instance of the Bernoulli class.
            </summary>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If the Bernoulli parameter is not in the range [0,1].</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Bernoulli.#ctor(System.Double,System.Random)">
            <summary>
            Initializes a new instance of the Bernoulli class.
            </summary>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If the Bernoulli parameter is not in the range [0,1].</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Bernoulli.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Bernoulli.IsValidParameterSet(System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Bernoulli.P">
            <summary>
            Gets the probability of generating a one. Range: 0 ≤ p ≤ 1.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Bernoulli.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Bernoulli.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Bernoulli.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Bernoulli.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Bernoulli.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Bernoulli.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Bernoulli.Minimum">
            <summary>
            Gets the smallest element in the domain of the distributions which can be represented by an integer.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Bernoulli.Maximum">
            <summary>
            Gets the largest element in the domain of the distributions which can be represented by an integer.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Bernoulli.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Bernoulli.Modes">
            <summary>
            Gets all modes of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Bernoulli.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Bernoulli.Probability(System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Bernoulli.ProbabilityLn(System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Bernoulli.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Bernoulli.PMF(System.Double,System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Bernoulli.PMFLn(System.Double,System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Bernoulli.CDF(System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Bernoulli.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Bernoulli.SampleUnchecked(System.Random,System.Double)">
            <summary>
            Generates one sample from the Bernoulli distribution.
            </summary>
            <param name="rnd">The random source to use.</param>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
            <returns>A random sample from the Bernoulli distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Bernoulli.Sample">
            <summary>
            Samples a Bernoulli distributed random variable.
            </summary>
            <returns>A sample from the Bernoulli distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Bernoulli.Samples(System.Int32[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Bernoulli.Samples">
            <summary>
            Samples an array of Bernoulli distributed random variables.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Bernoulli.Sample(System.Random,System.Double)">
            <summary>
            Samples a Bernoulli distributed random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
            <returns>A sample from the Bernoulli distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Bernoulli.Samples(System.Random,System.Double)">
            <summary>
            Samples a sequence of Bernoulli distributed random variables.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Bernoulli.Samples(System.Random,System.Int32[],System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Bernoulli.Sample(System.Double)">
            <summary>
            Samples a Bernoulli distributed random variable.
            </summary>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
            <returns>A sample from the Bernoulli distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Bernoulli.Samples(System.Double)">
            <summary>
            Samples a sequence of Bernoulli distributed random variables.
            </summary>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Bernoulli.Samples(System.Int32[],System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Beta">
            <summary>
            Continuous Univariate Beta distribution.
            For details about this distribution, see
            <a href="http://en.wikipedia.org/wiki/Beta_distribution">Wikipedia - Beta distribution</a>.
            </summary>
            <remarks>
            There are a few special cases for the parameterization of the Beta distribution. When both
            shape parameters are positive infinity, the Beta distribution degenerates to a point distribution
            at 0.5. When one of the shape parameters is positive infinity, the distribution degenerates to a point
            distribution at the positive infinity. When both shape parameters are 0.0, the Beta distribution
            degenerates to a Bernoulli distribution with parameter 0.5. When one shape parameter is 0.0, the
            distribution degenerates to a point distribution at the non-zero shape parameter.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.#ctor(System.Double,System.Double)">
            <summary>
            Initializes a new instance of the Beta class.
            </summary>
            <param name="a">The α shape parameter of the Beta distribution. Range: α ≥ 0.</param>
            <param name="b">The β shape parameter of the Beta distribution. Range: β ≥ 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.#ctor(System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the Beta class.
            </summary>
            <param name="a">The α shape parameter of the Beta distribution. Range: α ≥ 0.</param>
            <param name="b">The β shape parameter of the Beta distribution. Range: β ≥ 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>A string representation of the Beta distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.IsValidParameterSet(System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="a">The α shape parameter of the Beta distribution. Range: α ≥ 0.</param>
            <param name="b">The β shape parameter of the Beta distribution. Range: β ≥ 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Beta.A">
            <summary>
            Gets the α shape parameter of the Beta distribution. Range: α ≥ 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Beta.B">
            <summary>
            Gets the β shape parameter of the Beta distribution. Range: β ≥ 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Beta.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Beta.Mean">
            <summary>
            Gets the mean of the Beta distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Beta.Variance">
            <summary>
            Gets the variance of the Beta distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Beta.StdDev">
            <summary>
            Gets the standard deviation of the Beta distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Beta.Entropy">
            <summary>
            Gets the entropy of the Beta distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Beta.Skewness">
            <summary>
            Gets the skewness of the Beta distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Beta.Mode">
            <summary>
            Gets the mode of the Beta distribution; when there are multiple answers, this routine will return 0.5.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Beta.Median">
            <summary>
            Gets the median of the Beta distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Beta.Minimum">
            <summary>
            Gets the minimum of the Beta distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Beta.Maximum">
            <summary>
            Gets the maximum of the Beta distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Beta.PDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Beta.PDFLn(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Beta.CDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.InverseCumulativeDistribution(System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Beta.InvCDF(System.Double,System.Double,System.Double)"/>
            <remarks>WARNING: currently not an explicit implementation, hence slow and unreliable.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.Sample">
            <summary>
            Generates a sample from the Beta distribution.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.Samples">
            <summary>
            Generates a sequence of samples from the Beta distribution.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.SampleUnchecked(System.Random,System.Double,System.Double)">
            <summary>
            Samples Beta distributed random variables by sampling two Gamma variables and normalizing.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="a">The α shape parameter of the Beta distribution. Range: α ≥ 0.</param>
            <param name="b">The β shape parameter of the Beta distribution. Range: β ≥ 0.</param>
            <returns>a random number from the Beta distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.PDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="a">The α shape parameter of the Beta distribution. Range: α ≥ 0.</param>
            <param name="b">The β shape parameter of the Beta distribution. Range: β ≥ 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Beta.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.PDFLn(System.Double,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="a">The α shape parameter of the Beta distribution. Range: α ≥ 0.</param>
            <param name="b">The β shape parameter of the Beta distribution. Range: β ≥ 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Beta.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.CDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="a">The α shape parameter of the Beta distribution. Range: α ≥ 0.</param>
            <param name="b">The β shape parameter of the Beta distribution. Range: β ≥ 0.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Beta.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.InvCDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <param name="a">The α shape parameter of the Beta distribution. Range: α ≥ 0.</param>
            <param name="b">The β shape parameter of the Beta distribution. Range: β ≥ 0.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Beta.InverseCumulativeDistribution(System.Double)"/>
            <remarks>WARNING: currently not an explicit implementation, hence slow and unreliable.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.Sample(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="a">The α shape parameter of the Beta distribution. Range: α ≥ 0.</param>
            <param name="b">The β shape parameter of the Beta distribution. Range: β ≥ 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.Samples(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="a">The α shape parameter of the Beta distribution. Range: α ≥ 0.</param>
            <param name="b">The β shape parameter of the Beta distribution. Range: β ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.Samples(System.Random,System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="a">The α shape parameter of the Beta distribution. Range: α ≥ 0.</param>
            <param name="b">The β shape parameter of the Beta distribution. Range: β ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.Sample(System.Double,System.Double)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="a">The α shape parameter of the Beta distribution. Range: α ≥ 0.</param>
            <param name="b">The β shape parameter of the Beta distribution. Range: β ≥ 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.Samples(System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="a">The α shape parameter of the Beta distribution. Range: α ≥ 0.</param>
            <param name="b">The β shape parameter of the Beta distribution. Range: β ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Beta.Samples(System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="a">The α shape parameter of the Beta distribution. Range: α ≥ 0.</param>
            <param name="b">The β shape parameter of the Beta distribution. Range: β ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.BetaBinomial">
            <summary>
            Discrete Univariate Beta-Binomial distribution.
            The beta-binomial distribution is a family of discrete probability distributions on a finite support of non-negative integers arising
            when the probability of success in each of a fixed or known number of Bernoulli trials is either unknown or random.
            The beta-binomial distribution is the binomial distribution in which the probability of success at each of n trials is not fixed but randomly drawn from a beta distribution.
            It is frequently used in Bayesian statistics, empirical Bayes methods and classical statistics to capture overdispersion in binomial type distributed data.
            <a href="https://en.wikipedia.org/wiki/Beta-binomial_distribution">Wikipedia - Beta-Binomial distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaBinomial.#ctor(System.Int32,System.Double,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.BetaBinomial"/> class.
            </summary>
            <param name="n">The number of Bernoulli trials n - n is a positive integer </param>
            <param name="a">Shape parameter alpha of the Beta distribution. Range: a > 0.</param>
            <param name="b">Shape parameter beta of the Beta distribution. Range: b > 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaBinomial.#ctor(System.Int32,System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.BetaBinomial"/> class.
            </summary>
            <param name="n">The number of Bernoulli trials n - n is a positive integer </param>
            <param name="a">Shape parameter alpha of the Beta distribution. Range: a > 0.</param>
            <param name="b">Shape parameter beta of the Beta distribution. Range: b > 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaBinomial.ToString">
            <summary>
            Returns a <see cref="T:System.String"/> that represents this instance.
            </summary>
            <returns>
            A <see cref="T:System.String"/> that represents this instance.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaBinomial.IsValidParameterSet(System.Int32,System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="n">The number of Bernoulli trials n - n is a positive integer </param>
            <param name="a">Shape parameter alpha of the Beta distribution. Range: a > 0.</param>
            <param name="b">Shape parameter beta of the Beta distribution. Range: b > 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaBinomial.IsValidParameterSet(System.Int32,System.Double,System.Double,System.Int32)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="n">The number of Bernoulli trials n - n is a positive integer </param>
            <param name="a">Shape parameter alpha of the Beta distribution. Range: a > 0.</param>
            <param name="b">Shape parameter beta of the Beta distribution. Range: b > 0.</param>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaBinomial.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaBinomial.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaBinomial.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaBinomial.MathNet#Numerics#Distributions#IUnivariateDistribution#Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaBinomial.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaBinomial.MathNet#Numerics#Distributions#IDiscreteDistribution#Mode">
            <summary>
            Gets the mode of the distribution
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaBinomial.MathNet#Numerics#Distributions#IUnivariateDistribution#Median">
            <summary>
            Gets the median of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaBinomial.Minimum">
            <summary>
            Gets the smallest element in the domain of the distributions which can be represented by an integer.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaBinomial.Maximum">
            <summary>
            Gets the largest element in the domain of the distributions which can be represented by an integer.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaBinomial.Probability(System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaBinomial.ProbabilityLn(System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaBinomial.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/></returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaBinomial.PMF(System.Int32,System.Double,System.Double,System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="n">The number of Bernoulli trials n - n is a positive integer </param>
            <param name="a">Shape parameter alpha of the Beta distribution. Range: a > 0.</param>
            <param name="b">Shape parameter beta of the Beta distribution. Range: b > 0.</param>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaBinomial.PMFLn(System.Int32,System.Double,System.Double,System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="n">The number of Bernoulli trials n - n is a positive integer </param>
            <param name="a">Shape parameter alpha of the Beta distribution. Range: a > 0.</param>
            <param name="b">Shape parameter beta of the Beta distribution. Range: b > 0.</param>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaBinomial.CDF(System.Int32,System.Double,System.Double,System.Int32)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="n">The number of Bernoulli trials n - n is a positive integer </param>
            <param name="a">Shape parameter alpha of the Beta distribution. Range: a > 0.</param>
            <param name="b">Shape parameter beta of the Beta distribution. Range: b > 0.</param>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.BetaBinomial.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaBinomial.SampleUnchecked(System.Random,System.Int32,System.Double,System.Double)">
            <summary>
            Samples BetaBinomial distributed random variables by sampling a Beta distribution then passing to a Binomial distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="a">The α shape parameter of the Beta distribution. Range: α ≥ 0.</param>
            <param name="b">The β shape parameter of the Beta distribution. Range: β ≥ 0.</param>
            <param name="n">The number of trials (n). Range: n ≥ 0.</param>
            <returns>a random number from the BetaBinomial distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaBinomial.Sample">
            <summary>
            Samples a <c>BetaBinomial</c> distributed random variable.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaBinomial.Samples(System.Int32[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaBinomial.Samples">
            <summary>
            Samples an array of <c>BetaBinomial</c> distributed random variables.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaBinomial.Sample(System.Random,System.Int32,System.Double,System.Double)">
            <summary>
            Samples a <c>BetaBinomial</c> distributed random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="a">The α shape parameter of the Beta distribution. Range: α ≥ 0.</param>
            <param name="b">The β shape parameter of the Beta distribution. Range: β ≥ 0.</param>
            <param name="n">The number of trials (n). Range: n ≥ 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaBinomial.Samples(System.Random,System.Int32[],System.Int32,System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="a">The α shape parameter of the Beta distribution. Range: α ≥ 0.</param>
            <param name="b">The β shape parameter of the Beta distribution. Range: β ≥ 0.</param>
            <param name="n">The number of trials (n). Range: n ≥ 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaBinomial.Samples(System.Int32,System.Double,System.Double)">
            <summary>
            Samples an array of <c>BetaBinomial</c> distributed random variables.
            </summary>
            <param name="a">The α shape parameter of the Beta distribution. Range: α ≥ 0.</param>
            <param name="b">The β shape parameter of the Beta distribution. Range: β ≥ 0.</param>
            <param name="n">The number of trials (n). Range: n ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaBinomial.Samples(System.Int32[],System.Int32,System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="a">The α shape parameter of the Beta distribution. Range: α ≥ 0.</param>
            <param name="b">The β shape parameter of the Beta distribution. Range: β ≥ 0.</param>
            <param name="n">The number of trials (n). Range: n ≥ 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.#ctor(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Initializes a new instance of the BetaScaled class.
            </summary>
            <param name="a">The α shape parameter of the BetaScaled distribution. Range: α > 0.</param>
            <param name="b">The β shape parameter of the BetaScaled distribution. Range: β > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.#ctor(System.Double,System.Double,System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the BetaScaled class.
            </summary>
            <param name="a">The α shape parameter of the BetaScaled distribution. Range: α > 0.</param>
            <param name="b">The β shape parameter of the BetaScaled distribution. Range: β > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.PERT(System.Double,System.Double,System.Double,System.Random)">
            <summary>
            Create a Beta PERT distribution, used in risk analysis and other domains where an expert forecast
            is used to construct an underlying beta distribution.
            </summary>
            <param name="min">The minimum value.</param>
            <param name="max">The maximum value.</param>
            <param name="likely">The most likely value (mode).</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
            <returns>The Beta distribution derived from the PERT parameters.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>A string representation of the BetaScaled distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.IsValidParameterSet(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="a">The α shape parameter of the BetaScaled distribution. Range: α > 0.</param>
            <param name="b">The β shape parameter of the BetaScaled distribution. Range: β > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaScaled.A">
            <summary>
            Gets the α shape parameter of the BetaScaled distribution. Range: α > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaScaled.B">
            <summary>
            Gets the β shape parameter of the BetaScaled distribution. Range: β > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaScaled.Location">
            <summary>
            Gets the location (μ) of the BetaScaled distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaScaled.Scale">
            <summary>
            Gets the scale (σ) of the BetaScaled distribution. Range: σ > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaScaled.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaScaled.Mean">
            <summary>
            Gets the mean of the BetaScaled distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaScaled.Variance">
            <summary>
            Gets the variance of the BetaScaled distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaScaled.StdDev">
            <summary>
            Gets the standard deviation of the BetaScaled distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaScaled.Entropy">
            <summary>
            Gets the entropy of the BetaScaled distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaScaled.Skewness">
            <summary>
            Gets the skewness of the BetaScaled distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaScaled.Mode">
            <summary>
            Gets the mode of the BetaScaled distribution; when there are multiple answers, this routine will return 0.5.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaScaled.Median">
            <summary>
            Gets the median of the BetaScaled distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaScaled.Minimum">
            <summary>
            Gets the minimum of the BetaScaled distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.BetaScaled.Maximum">
            <summary>
            Gets the maximum of the BetaScaled distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.BetaScaled.PDF(System.Double,System.Double,System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.BetaScaled.PDFLn(System.Double,System.Double,System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.BetaScaled.CDF(System.Double,System.Double,System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.InverseCumulativeDistribution(System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.BetaScaled.InvCDF(System.Double,System.Double,System.Double,System.Double,System.Double)"/>
            <remarks>WARNING: currently not an explicit implementation, hence slow and unreliable.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.Sample">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.Samples">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.PDF(System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="a">The α shape parameter of the BetaScaled distribution. Range: α > 0.</param>
            <param name="b">The β shape parameter of the BetaScaled distribution. Range: β > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.BetaScaled.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.PDFLn(System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="a">The α shape parameter of the BetaScaled distribution. Range: α > 0.</param>
            <param name="b">The β shape parameter of the BetaScaled distribution. Range: β > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.BetaScaled.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.CDF(System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="a">The α shape parameter of the BetaScaled distribution. Range: α > 0.</param>
            <param name="b">The β shape parameter of the BetaScaled distribution. Range: β > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.BetaScaled.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.InvCDF(System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="a">The α shape parameter of the BetaScaled distribution. Range: α > 0.</param>
            <param name="b">The β shape parameter of the BetaScaled distribution. Range: β > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.BetaScaled.InverseCumulativeDistribution(System.Double)"/>
            <remarks>WARNING: currently not an explicit implementation, hence slow and unreliable.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.Sample(System.Random,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="a">The α shape parameter of the BetaScaled distribution. Range: α > 0.</param>
            <param name="b">The β shape parameter of the BetaScaled distribution. Range: β > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.Samples(System.Random,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="a">The α shape parameter of the BetaScaled distribution. Range: α > 0.</param>
            <param name="b">The β shape parameter of the BetaScaled distribution. Range: β > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.Samples(System.Random,System.Double[],System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="a">The α shape parameter of the BetaScaled distribution. Range: α > 0.</param>
            <param name="b">The β shape parameter of the BetaScaled distribution. Range: β > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.Sample(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="a">The α shape parameter of the BetaScaled distribution. Range: α > 0.</param>
            <param name="b">The β shape parameter of the BetaScaled distribution. Range: β > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.Samples(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="a">The α shape parameter of the BetaScaled distribution. Range: α > 0.</param>
            <param name="b">The β shape parameter of the BetaScaled distribution. Range: β > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.BetaScaled.Samples(System.Double[],System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="a">The α shape parameter of the BetaScaled distribution. Range: α > 0.</param>
            <param name="b">The β shape parameter of the BetaScaled distribution. Range: β > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Binomial">
            <summary>
            Discrete Univariate Binomial distribution.
            For details about this distribution, see
            <a href="http://en.wikipedia.org/wiki/Binomial_distribution">Wikipedia - Binomial distribution</a>.
            </summary>
            <remarks>
            The distribution is parameterized by a probability (between 0.0 and 1.0).
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Binomial.#ctor(System.Double,System.Int32)">
            <summary>
            Initializes a new instance of the Binomial class.
            </summary>
            <param name="p">The success probability (p) in each trial. Range: 0 ≤ p ≤ 1.</param>
            <param name="n">The number of trials (n). Range: n ≥ 0.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="p"/> is not in the interval [0.0,1.0].</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="n"/> is negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Binomial.#ctor(System.Double,System.Int32,System.Random)">
            <summary>
            Initializes a new instance of the Binomial class.
            </summary>
            <param name="p">The success probability (p) in each trial. Range: 0 ≤ p ≤ 1.</param>
            <param name="n">The number of trials (n). Range: n ≥ 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="p"/> is not in the interval [0.0,1.0].</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="n"/> is negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Binomial.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Binomial.IsValidParameterSet(System.Double,System.Int32)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="p">The success probability (p) in each trial. Range: 0 ≤ p ≤ 1.</param>
            <param name="n">The number of trials (n). Range: n ≥ 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Binomial.P">
            <summary>
            Gets the success probability in each trial. Range: 0 ≤ p ≤ 1.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Binomial.N">
            <summary>
            Gets the number of trials. Range: n ≥ 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Binomial.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Binomial.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Binomial.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Binomial.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Binomial.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Binomial.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Binomial.Minimum">
            <summary>
            Gets the smallest element in the domain of the distributions which can be represented by an integer.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Binomial.Maximum">
            <summary>
            Gets the largest element in the domain of the distributions which can be represented by an integer.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Binomial.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Binomial.Modes">
            <summary>
            Gets all modes of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Binomial.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Binomial.Probability(System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Binomial.ProbabilityLn(System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Binomial.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Binomial.PMF(System.Double,System.Int32,System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <param name="p">The success probability (p) in each trial. Range: 0 ≤ p ≤ 1.</param>
            <param name="n">The number of trials (n). Range: n ≥ 0.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Binomial.PMFLn(System.Double,System.Int32,System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <param name="p">The success probability (p) in each trial. Range: 0 ≤ p ≤ 1.</param>
            <param name="n">The number of trials (n). Range: n ≥ 0.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Binomial.CDF(System.Double,System.Int32,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="p">The success probability (p) in each trial. Range: 0 ≤ p ≤ 1.</param>
            <param name="n">The number of trials (n). Range: n ≥ 0.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Binomial.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Binomial.SampleUnchecked(System.Random,System.Double,System.Int32)">
            <summary>
            Generates a sample from the Binomial distribution without doing parameter checking.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="p">The success probability (p) in each trial. Range: 0 ≤ p ≤ 1.</param>
            <param name="n">The number of trials (n). Range: n ≥ 0.</param>
            <returns>The number of successful trials.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Binomial.Sample">
            <summary>
            Samples a Binomially distributed random variable.
            </summary>
            <returns>The number of successes in N trials.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Binomial.Samples(System.Int32[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Binomial.Samples">
            <summary>
            Samples an array of Binomially distributed random variables.
            </summary>
            <returns>a sequence of successes in N trials.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Binomial.Sample(System.Random,System.Double,System.Int32)">
            <summary>
            Samples a binomially distributed random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="p">The success probability (p) in each trial. Range: 0 ≤ p ≤ 1.</param>
            <param name="n">The number of trials (n). Range: n ≥ 0.</param>
            <returns>The number of successes in <paramref name="n"/> trials.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Binomial.Samples(System.Random,System.Double,System.Int32)">
            <summary>
            Samples a sequence of binomially distributed random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="p">The success probability (p) in each trial. Range: 0 ≤ p ≤ 1.</param>
            <param name="n">The number of trials (n). Range: n ≥ 0.</param>
            <returns>a sequence of successes in <paramref name="n"/> trials.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Binomial.Samples(System.Random,System.Int32[],System.Double,System.Int32)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="p">The success probability (p) in each trial. Range: 0 ≤ p ≤ 1.</param>
            <param name="n">The number of trials (n). Range: n ≥ 0.</param>
            <returns>a sequence of successes in <paramref name="n"/> trials.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Binomial.Sample(System.Double,System.Int32)">
            <summary>
            Samples a binomially distributed random variable.
            </summary>
            <param name="p">The success probability (p) in each trial. Range: 0 ≤ p ≤ 1.</param>
            <param name="n">The number of trials (n). Range: n ≥ 0.</param>
            <returns>The number of successes in <paramref name="n"/> trials.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Binomial.Samples(System.Double,System.Int32)">
            <summary>
            Samples a sequence of binomially distributed random variable.
            </summary>
            <param name="p">The success probability (p) in each trial. Range: 0 ≤ p ≤ 1.</param>
            <param name="n">The number of trials (n). Range: n ≥ 0.</param>
            <returns>a sequence of successes in <paramref name="n"/> trials.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Binomial.Samples(System.Int32[],System.Double,System.Int32)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="p">The success probability (p) in each trial. Range: 0 ≤ p ≤ 1.</param>
            <param name="n">The number of trials (n). Range: n ≥ 0.</param>
            <returns>a sequence of successes in <paramref name="n"/> trials.</returns>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Burr.A">
            <summary>
            Gets the scale (a) of the distribution. Range: a > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Burr.C">
            <summary>
            Gets the first shape parameter (c) of the distribution. Range: c > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Burr.K">
            <summary>
            Gets the second shape parameter (k) of the distribution. Range: k > 0.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Burr.#ctor(System.Double,System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the Burr Type XII class.
            </summary>
            <param name="a">The scale parameter a of the Burr distribution. Range: a > 0.</param>
            <param name="c">The first shape parameter c of the Burr distribution. Range: c > 0.</param>
            <param name="k">The second shape parameter k of the Burr distribution. Range: k > 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples. Optional, can be null.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Burr.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Burr.IsValidParameterSet(System.Double,System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="a">The scale parameter a of the Burr distribution. Range: a > 0.</param>
            <param name="c">The first shape parameter c of the Burr distribution. Range: c > 0.</param>
            <param name="k">The second shape parameter k of the Burr distribution. Range: k > 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Burr.RandomSource">
            <summary>
            Gets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Burr.Mean">
            <summary>
            Gets the mean of the Burr distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Burr.Variance">
            <summary>
            Gets the variance of the Burr distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Burr.StdDev">
            <summary>
            Gets the standard deviation of the Burr distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Burr.Mode">
            <summary>
            Gets the mode of the Burr distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Burr.Minimum">
            <summary>
            Gets the minimum of the Burr distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Burr.Maximum">
            <summary>
            Gets the maximum of the Burr distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Burr.Entropy">
            <summary>
            Gets the entropy of the Burr distribution (currently not supported).
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Burr.Skewness">
            <summary>
            Gets the skewness of the Burr distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Burr.Median">
            <summary>
            Gets the median of the Burr distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Burr.Sample">
            <summary>
            Generates a sample from the Burr distribution.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Burr.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Burr.Samples">
            <summary>
            Generates a sequence of samples from the Burr distribution.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Burr.Sample(System.Random,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sample from the Burr distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="a">The scale parameter a of the Burr distribution. Range: a > 0.</param>
            <param name="c">The first shape parameter c of the Burr distribution. Range: c > 0.</param>
            <param name="k">The second shape parameter k of the Burr distribution. Range: k > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Burr.Samples(System.Random,System.Double[],System.Double,System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="a">The scale parameter a of the Burr distribution. Range: a > 0.</param>
            <param name="c">The first shape parameter c of the Burr distribution. Range: c > 0.</param>
            <param name="k">The second shape parameter k of the Burr distribution. Range: k > 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Burr.Samples(System.Random,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the Burr distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="a">The scale parameter a of the Burr distribution. Range: a > 0.</param>
            <param name="c">The first shape parameter c of the Burr distribution. Range: c > 0.</param>
            <param name="k">The second shape parameter k of the Burr distribution. Range: k > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Burr.GetMoment(System.Double)">
            <summary>
            Gets the n-th raw moment of the distribution.
            </summary>
            <param name="n">The order (n) of the moment. Range: n ≥ 1.</param>
            <returns>the n-th moment of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Burr.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Burr.PDF(System.Double,System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Burr.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Burr.PDFLn(System.Double,System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Burr.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Burr.CDF(System.Double,System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Burr.PDF(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="a">The scale parameter a of the Burr distribution. Range: a > 0.</param>
            <param name="c">The first shape parameter c of the Burr distribution. Range: c > 0.</param>
            <param name="k">The second shape parameter k of the Burr distribution. Range: k > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Burr.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Burr.PDFLn(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="a">The scale parameter a of the Burr distribution. Range: a > 0.</param>
            <param name="c">The first shape parameter c of the Burr distribution. Range: c > 0.</param>
            <param name="k">The second shape parameter k of the Burr distribution. Range: k > 0.</param>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Burr.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Burr.CDF(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="a">The scale parameter a of the Burr distribution. Range: a > 0.</param>
            <param name="c">The first shape parameter c of the Burr distribution. Range: c > 0.</param>
            <param name="k">The second shape parameter k of the Burr distribution. Range: k > 0.</param>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Burr.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Categorical">
            <summary>
            Discrete Univariate Categorical distribution.
            For details about this distribution, see
            <a href="http://en.wikipedia.org/wiki/Categorical_distribution">Wikipedia - Categorical distribution</a>. This
            distribution is sometimes called the Discrete distribution.
            </summary>
            <remarks>
            The distribution is parameterized by a vector of ratios: in other words, the parameter
            does not have to be normalized and sum to 1. The reason is that some vectors can't be exactly normalized
            to sum to 1 in floating point representation.
            </remarks>
            <remarks>
            Support: 0..k where k = length(probability mass array)-1
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.#ctor(System.Double[])">
            <summary>
            Initializes a new instance of the Categorical class.
            </summary>
            <param name="probabilityMass">An array of nonnegative ratios: this array does not need to be normalized
            as this is often impossible using floating point arithmetic.</param>
            <exception cref="T:System.ArgumentException">If any of the probabilities are negative or do not sum to one.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.#ctor(System.Double[],System.Random)">
            <summary>
            Initializes a new instance of the Categorical class.
            </summary>
            <param name="probabilityMass">An array of nonnegative ratios: this array does not need to be normalized
            as this is often impossible using floating point arithmetic.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
            <exception cref="T:System.ArgumentException">If any of the probabilities are negative or do not sum to one.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.#ctor(MathNet.Numerics.Statistics.Histogram)">
            <summary>
            Initializes a new instance of the Categorical class from a <paramref name="histogram"/>. The distribution
            will not be automatically updated when the histogram changes. The categorical distribution will have
            one value for each bucket and a probability for that value proportional to the bucket count.
            </summary>
            <param name="histogram">The histogram from which to create the categorical variable.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.IsValidProbabilityMass(System.Double[])">
            <summary>
            Checks whether the parameters of the distribution are valid.
            </summary>
            <param name="p">An array of nonnegative ratios: this array does not need to be normalized as this is often impossible using floating point arithmetic.</param>
            <returns>If any of the probabilities are negative returns <c>false</c>, or if the sum of parameters is 0.0; otherwise <c>true</c></returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.IsValidCumulativeDistribution(System.Double[])">
            <summary>
            Checks whether the parameters of the distribution are valid.
            </summary>
            <param name="cdf">An array of nonnegative ratios: this array does not need to be normalized as this is often impossible using floating point arithmetic.</param>
            <returns>If any of the probabilities are negative returns <c>false</c>, or if the sum of parameters is 0.0; otherwise <c>true</c></returns>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Categorical.P">
            <summary>
            Gets the probability mass vector (non-negative ratios) of the multinomial.
            </summary>
            <remarks>Sometimes the normalized probability vector cannot be represented exactly in a floating point representation.</remarks>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Categorical.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Categorical.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Categorical.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Categorical.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Categorical.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Categorical.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
            <remarks>Throws a <see cref="T:System.NotSupportedException"/>.</remarks>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Categorical.Minimum">
            <summary>
            Gets the smallest element in the domain of the distributions which can be represented by an integer.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Categorical.Maximum">
            <summary>
            Gets the largest element in the domain of the distributions which can be represented by an integer.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Categorical.Mode">
            <summary>
            Gets he mode of the distribution.
            </summary>
            <remarks>Throws a <see cref="T:System.NotSupportedException"/>.</remarks>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Categorical.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.Probability(System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.ProbabilityLn(System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.InverseCumulativeDistribution(System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability.
            </summary>
            <param name="probability">A real number between 0 and 1.</param>
            <returns>An integer between 0 and the size of the categorical (exclusive), that corresponds to the inverse CDF for the given probability.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.PMF(System.Double[],System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <param name="probabilityMass">An array of nonnegative ratios: this array does not need to be normalized
            as this is often impossible using floating point arithmetic.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.PMFLn(System.Double[],System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <param name="probabilityMass">An array of nonnegative ratios: this array does not need to be normalized
            as this is often impossible using floating point arithmetic.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.CDF(System.Double[],System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="probabilityMass">An array of nonnegative ratios: this array does not need to be normalized
            as this is often impossible using floating point arithmetic.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Categorical.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.InvCDF(System.Double[],System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability.
            </summary>
            <param name="probabilityMass">An array of nonnegative ratios: this array does not need to be normalized
            as this is often impossible using floating point arithmetic.</param>
            <param name="probability">A real number between 0 and 1.</param>
            <returns>An integer between 0 and the size of the categorical (exclusive), that corresponds to the inverse CDF for the given probability.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.InvCDFWithCumulativeDistribution(System.Double[],System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability.
            </summary>
            <param name="cdfUnnormalized">An array corresponding to a CDF for a categorical distribution. Not assumed to be normalized.</param>
            <param name="probability">A real number between 0 and 1.</param>
            <returns>An integer between 0 and the size of the categorical (exclusive), that corresponds to the inverse CDF for the given probability.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.ProbabilityMassToCumulativeDistribution(System.Double[])">
            <summary>
            Computes the cumulative distribution function. This method performs no parameter checking.
            If the probability mass was normalized, the resulting cumulative distribution is normalized as well (up to numerical errors).
            </summary>
            <param name="probabilityMass">An array of nonnegative ratios: this array does not need to be normalized
            as this is often impossible using floating point arithmetic.</param>
            <returns>An array representing the unnormalized cumulative distribution function.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.SampleUnchecked(System.Random,System.Double[])">
            <summary>
            Returns one trials from the categorical distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="cdfUnnormalized">The (unnormalized) cumulative distribution of the probability distribution.</param>
            <returns>One sample from the categorical distribution implied by <paramref name="cdfUnnormalized"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.Sample">
            <summary>
            Samples a Binomially distributed random variable.
            </summary>
            <returns>The number of successful trials.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.Samples(System.Int32[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.Samples">
            <summary>
            Samples an array of Bernoulli distributed random variables.
            </summary>
            <returns>a sequence of successful trial counts.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.Sample(System.Random,System.Double[])">
            <summary>
            Samples one categorical distributed random variable; also known as the Discrete distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="probabilityMass">An array of nonnegative ratios. Not assumed to be normalized.</param>
            <returns>One random integer between 0 and the size of the categorical (exclusive).</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.Samples(System.Random,System.Double[])">
            <summary>
            Samples a categorically distributed random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="probabilityMass">An array of nonnegative ratios. Not assumed to be normalized.</param>
            <returns>random integers between 0 and the size of the categorical (exclusive).</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.Samples(System.Random,System.Int32[],System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="probabilityMass">An array of nonnegative ratios. Not assumed to be normalized.</param>
            <returns>random integers between 0 and the size of the categorical (exclusive).</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.Sample(System.Double[])">
            <summary>
            Samples one categorical distributed random variable; also known as the Discrete distribution.
            </summary>
            <param name="probabilityMass">An array of nonnegative ratios. Not assumed to be normalized.</param>
            <returns>One random integer between 0 and the size of the categorical (exclusive).</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.Samples(System.Double[])">
            <summary>
            Samples a categorically distributed random variable.
            </summary>
            <param name="probabilityMass">An array of nonnegative ratios. Not assumed to be normalized.</param>
            <returns>random integers between 0 and the size of the categorical (exclusive).</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.Samples(System.Int32[],System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="probabilityMass">An array of nonnegative ratios. Not assumed to be normalized.</param>
            <returns>random integers between 0 and the size of the categorical (exclusive).</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.SampleWithCumulativeDistribution(System.Random,System.Double[])">
            <summary>
            Samples one categorical distributed random variable; also known as the Discrete distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="cdfUnnormalized">An array of the cumulative distribution. Not assumed to be normalized.</param>
            <returns>One random integer between 0 and the size of the categorical (exclusive).</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.SamplesWithCumulativeDistribution(System.Random,System.Double[])">
            <summary>
            Samples a categorically distributed random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="cdfUnnormalized">An array of the cumulative distribution. Not assumed to be normalized.</param>
            <returns>random integers between 0 and the size of the categorical (exclusive).</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.SamplesWithCumulativeDistribution(System.Random,System.Int32[],System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="cdfUnnormalized">An array of the cumulative distribution. Not assumed to be normalized.</param>
            <returns>random integers between 0 and the size of the categorical (exclusive).</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.SampleWithCumulativeDistribution(System.Double[])">
            <summary>
            Samples one categorical distributed random variable; also known as the Discrete distribution.
            </summary>
            <param name="cdfUnnormalized">An array of the cumulative distribution. Not assumed to be normalized.</param>
            <returns>One random integer between 0 and the size of the categorical (exclusive).</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.SamplesWithCumulativeDistribution(System.Double[])">
            <summary>
            Samples a categorically distributed random variable.
            </summary>
            <param name="cdfUnnormalized">An array of the cumulative distribution. Not assumed to be normalized.</param>
            <returns>random integers between 0 and the size of the categorical (exclusive).</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Categorical.SamplesWithCumulativeDistribution(System.Int32[],System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="cdfUnnormalized">An array of the cumulative distribution. Not assumed to be normalized.</param>
            <returns>random integers between 0 and the size of the categorical (exclusive).</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Cauchy">
            <summary>
            Continuous Univariate Cauchy distribution.
            The Cauchy distribution is a symmetric continuous probability distribution. For details about this distribution, see
            <a href="http://en.wikipedia.org/wiki/Cauchy_distribution">Wikipedia - Cauchy distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Cauchy"/> class with the location parameter set to 0 and the scale parameter set to 1
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.#ctor(System.Double,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Cauchy"/> class.
            </summary>
            <param name="location">The location (x0) of the distribution.</param>
            <param name="scale">The scale (γ) of the distribution. Range: γ > 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.#ctor(System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Cauchy"/> class.
            </summary>
            <param name="location">The location (x0) of the distribution.</param>
            <param name="scale">The scale (γ) of the distribution. Range: γ > 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.IsValidParameterSet(System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="location">The location (x0) of the distribution.</param>
            <param name="scale">The scale (γ) of the distribution. Range: γ > 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Cauchy.Location">
            <summary>
            Gets the location  (x0) of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Cauchy.Scale">
            <summary>
            Gets the scale (γ) of the distribution. Range: γ > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Cauchy.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Cauchy.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Cauchy.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Cauchy.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Cauchy.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Cauchy.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Cauchy.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Cauchy.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Cauchy.Minimum">
            <summary>
            Gets the minimum of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Cauchy.Maximum">
            <summary>
            Gets the maximum of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Cauchy.PDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Cauchy.PDFLn(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Cauchy.CDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.InverseCumulativeDistribution(System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Cauchy.InvCDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.Sample">
            <summary>
            Draws a random sample from the distribution.
            </summary>
            <returns>A random number from this distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.Samples">
            <summary>
            Generates a sequence of samples from the Cauchy distribution.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.PDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="location">The location (x0) of the distribution.</param>
            <param name="scale">The scale (γ) of the distribution. Range: γ > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Cauchy.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.PDFLn(System.Double,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="location">The location (x0) of the distribution.</param>
            <param name="scale">The scale (γ) of the distribution. Range: γ > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Cauchy.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.CDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="location">The location (x0) of the distribution.</param>
            <param name="scale">The scale (γ) of the distribution. Range: γ > 0.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Cauchy.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.InvCDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <param name="location">The location (x0) of the distribution.</param>
            <param name="scale">The scale (γ) of the distribution. Range: γ > 0.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Cauchy.InverseCumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.Sample(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="location">The location (x0) of the distribution.</param>
            <param name="scale">The scale (γ) of the distribution. Range: γ > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.Samples(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="location">The location (x0) of the distribution.</param>
            <param name="scale">The scale (γ) of the distribution. Range: γ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.Samples(System.Random,System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="location">The location (x0) of the distribution.</param>
            <param name="scale">The scale (γ) of the distribution. Range: γ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.Sample(System.Double,System.Double)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="location">The location (x0) of the distribution.</param>
            <param name="scale">The scale (γ) of the distribution. Range: γ > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.Samples(System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="location">The location (x0) of the distribution.</param>
            <param name="scale">The scale (γ) of the distribution. Range: γ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Cauchy.Samples(System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="location">The location (x0) of the distribution.</param>
            <param name="scale">The scale (γ) of the distribution. Range: γ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Chi">
            <summary>
            Continuous Univariate Chi distribution.
            This distribution is a continuous probability distribution. The distribution usually arises when a k-dimensional vector's orthogonal
            components are independent and each follow a standard normal distribution. The length of the vector will
            then have a chi distribution.
            <a href="http://en.wikipedia.org/wiki/Chi_distribution">Wikipedia - Chi distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Chi.#ctor(System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Chi"/> class.
            </summary>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Chi.#ctor(System.Double,System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Chi"/> class.
            </summary>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Chi.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Chi.IsValidParameterSet(System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Chi.DegreesOfFreedom">
            <summary>
            Gets the degrees of freedom (k) of the Chi distribution. Range: k > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Chi.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Chi.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Chi.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Chi.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Chi.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Chi.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Chi.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Chi.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Chi.Minimum">
            <summary>
            Gets the minimum of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Chi.Maximum">
            <summary>
            Gets the maximum of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Chi.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Chi.PDF(System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Chi.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Chi.PDFLn(System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Chi.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Chi.CDF(System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Chi.Sample">
            <summary>
            Generates a sample from the Chi distribution.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Chi.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Chi.Samples">
            <summary>
            Generates a sequence of samples from the Chi distribution.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Chi.SampleUnchecked(System.Random,System.Int32)">
            <summary>
            Samples the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <returns>a random number from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Chi.PDF(System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Chi.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Chi.PDFLn(System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Chi.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Chi.CDF(System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Chi.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Chi.Sample(System.Random,System.Int32)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Chi.Samples(System.Random,System.Int32)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Chi.Samples(System.Random,System.Double[],System.Int32)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Chi.Sample(System.Int32)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Chi.Samples(System.Int32)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Chi.Samples(System.Double[],System.Int32)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.ChiSquared">
            <summary>
            Continuous Univariate Chi-Squared distribution.
            This distribution is a sum of the squares of k independent standard normal random variables.
            <a href="http://en.wikipedia.org/wiki/Chi-square_distribution">Wikipedia - ChiSquare distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.#ctor(System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.ChiSquared"/> class.
            </summary>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.#ctor(System.Double,System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.ChiSquared"/> class.
            </summary>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.IsValidParameterSet(System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ChiSquared.DegreesOfFreedom">
            <summary>
            Gets the degrees of freedom (k) of the Chi-Squared distribution. Range: k > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ChiSquared.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ChiSquared.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ChiSquared.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ChiSquared.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ChiSquared.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ChiSquared.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ChiSquared.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ChiSquared.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ChiSquared.Minimum">
            <summary>
            Gets the minimum of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ChiSquared.Maximum">
            <summary>
            Gets the maximum of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.ChiSquared.PDF(System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.ChiSquared.PDFLn(System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.ChiSquared.CDF(System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.InverseCumulativeDistribution(System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.ChiSquared.InvCDF(System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.Sample">
            <summary>
            Generates a sample from the <c>ChiSquare</c> distribution.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.Samples">
            <summary>
            Generates a sequence of samples from the <c>ChiSquare</c> distribution.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.SampleUnchecked(System.Random,System.Double)">
            <summary>
            Samples the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <returns>a random number from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.PDF(System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.ChiSquared.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.PDFLn(System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.ChiSquared.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.CDF(System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.ChiSquared.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.InvCDF(System.Double,System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.Sample(System.Random,System.Double)">
            <summary>
            Generates a sample from the <c>ChiSquare</c> distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <returns>a sample from the distribution. </returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.Samples(System.Random,System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <returns>a sample from the distribution. </returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.Samples(System.Random,System.Double[],System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <returns>a sample from the distribution. </returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.Sample(System.Double)">
            <summary>
            Generates a sample from the <c>ChiSquare</c> distribution.
            </summary>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <returns>a sample from the distribution. </returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.Samples(System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <returns>a sample from the distribution. </returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ChiSquared.Samples(System.Double[],System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="freedom">The degrees of freedom (k) of the distribution. Range: k > 0.</param>
            <returns>a sample from the distribution. </returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.ContinuousUniform">
            <summary>
            Continuous Univariate Uniform distribution.
            The continuous uniform distribution is a distribution over real numbers. For details about this distribution, see
            <a href="http://en.wikipedia.org/wiki/Uniform_distribution_%28continuous%29">Wikipedia - Continuous uniform distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.#ctor">
            <summary>
            Initializes a new instance of the ContinuousUniform class with lower bound 0 and upper bound 1.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.#ctor(System.Double,System.Double)">
            <summary>
            Initializes a new instance of the ContinuousUniform class with given lower and upper bounds.
            </summary>
            <param name="lower">Lower bound. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound. Range: lower ≤ upper.</param>
            <exception cref="T:System.ArgumentException">If the upper bound is smaller than the lower bound.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.#ctor(System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the ContinuousUniform class with given lower and upper bounds.
            </summary>
            <param name="lower">Lower bound. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound. Range: lower ≤ upper.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
            <exception cref="T:System.ArgumentException">If the upper bound is smaller than the lower bound.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.IsValidParameterSet(System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="lower">Lower bound. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound. Range: lower ≤ upper.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ContinuousUniform.LowerBound">
            <summary>
            Gets the lower bound of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ContinuousUniform.UpperBound">
            <summary>
            Gets the upper bound of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ContinuousUniform.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ContinuousUniform.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ContinuousUniform.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ContinuousUniform.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ContinuousUniform.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
            <value></value>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ContinuousUniform.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ContinuousUniform.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
            <value></value>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ContinuousUniform.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
            <value></value>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ContinuousUniform.Minimum">
            <summary>
            Gets the minimum of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ContinuousUniform.Maximum">
            <summary>
            Gets the maximum of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.ContinuousUniform.PDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.ContinuousUniform.PDFLn(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.ContinuousUniform.CDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.InverseCumulativeDistribution(System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.ContinuousUniform.InvCDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.Sample">
            <summary>
            Generates a sample from the <c>ContinuousUniform</c> distribution.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.Samples">
            <summary>
            Generates a sequence of samples from the <c>ContinuousUniform</c> distribution.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.PDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="lower">Lower bound. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound. Range: lower ≤ upper.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.ContinuousUniform.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.PDFLn(System.Double,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="lower">Lower bound. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound. Range: lower ≤ upper.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.ContinuousUniform.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.CDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="lower">Lower bound. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound. Range: lower ≤ upper.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.ContinuousUniform.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.InvCDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <param name="lower">Lower bound. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound. Range: lower ≤ upper.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.ContinuousUniform.InverseCumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.Sample(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sample from the <c>ContinuousUniform</c> distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="lower">Lower bound. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound. Range: lower ≤ upper.</param>
            <returns>a uniformly distributed sample.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.Samples(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the <c>ContinuousUniform</c> distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="lower">Lower bound. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound. Range: lower ≤ upper.</param>
            <returns>a sequence of uniformly distributed samples.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.Samples(System.Random,System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="lower">Lower bound. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound. Range: lower ≤ upper.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.Sample(System.Double,System.Double)">
            <summary>
            Generates a sample from the <c>ContinuousUniform</c> distribution.
            </summary>
            <param name="lower">Lower bound. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound. Range: lower ≤ upper.</param>
            <returns>a uniformly distributed sample.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.Samples(System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the <c>ContinuousUniform</c> distribution.
            </summary>
            <param name="lower">Lower bound. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound. Range: lower ≤ upper.</param>
            <returns>a sequence of uniformly distributed samples.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ContinuousUniform.Samples(System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="lower">Lower bound. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound. Range: lower ≤ upper.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.ConwayMaxwellPoisson">
            <summary>
            Discrete Univariate Conway-Maxwell-Poisson distribution.
            <para>The Conway-Maxwell-Poisson distribution is a generalization of the Poisson, Geometric and Bernoulli
            distributions. It is parameterized by two real numbers "lambda" and "nu". For
            <list>
                <item>nu = 0 the distribution reverts to a Geometric distribution</item>
                <item>nu = 1 the distribution reverts to the Poisson distribution</item>
                <item>nu -> infinity the distribution converges to a Bernoulli distribution</item>
            </list></para>
            This implementation will cache the value of the normalization constant.
            <a href="http://en.wikipedia.org/wiki/Conway%E2%80%93Maxwell%E2%80%93Poisson_distribution">Wikipedia - ConwayMaxwellPoisson distribution</a>.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Distributions.ConwayMaxwellPoisson._mean">
            <summary>
            The mean of the distribution.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Distributions.ConwayMaxwellPoisson._variance">
            <summary>
             The variance of the distribution.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Distributions.ConwayMaxwellPoisson._z">
            <summary>
            Caches the value of the normalization constant.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Tolerance">
            <summary>
            Since many properties of the distribution can only be computed approximately, the tolerance
            level specifies how much error we accept.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.#ctor(System.Double,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.ConwayMaxwellPoisson"/> class.
            </summary>
            <param name="lambda">The lambda (λ) parameter. Range: λ > 0.</param>
            <param name="nu">The rate of decay (ν) parameter. Range: ν ≥ 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.#ctor(System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.ConwayMaxwellPoisson"/> class.
            </summary>
            <param name="lambda">The lambda (λ) parameter. Range: λ > 0.</param>
            <param name="nu">The rate of decay (ν) parameter. Range: ν ≥ 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.ToString">
            <summary>
            Returns a <see cref="T:System.String"/> that represents this instance.
            </summary>
            <returns>A <see cref="T:System.String"/> that represents this instance.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.IsValidParameterSet(System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="lambda">The lambda (λ) parameter. Range: λ > 0.</param>
            <param name="nu">The rate of decay (ν) parameter. Range: ν ≥ 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Lambda">
            <summary>
            Gets the lambda (λ) parameter. Range: λ > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Nu">
            <summary>
            Gets the rate of decay (ν) parameter. Range: ν ≥ 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Mode">
            <summary>
            Gets the mode of the distribution
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Minimum">
            <summary>
            Gets the smallest element in the domain of the distributions which can be represented by an integer.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Maximum">
            <summary>
            Gets the largest element in the domain of the distributions which can be represented by an integer.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Probability(System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.ProbabilityLn(System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.PMF(System.Double,System.Double,System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <param name="lambda">The lambda (λ) parameter. Range: λ > 0.</param>
            <param name="nu">The rate of decay (ν) parameter. Range: ν ≥ 0.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.PMFLn(System.Double,System.Double,System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <param name="lambda">The lambda (λ) parameter. Range: λ > 0.</param>
            <param name="nu">The rate of decay (ν) parameter. Range: ν ≥ 0.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.CDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="lambda">The lambda (λ) parameter. Range: λ > 0.</param>
            <param name="nu">The rate of decay (ν) parameter. Range: ν ≥ 0.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="P:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Z">
            <summary>
            Gets the normalization constant of the Conway-Maxwell-Poisson distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Normalization(System.Double,System.Double)">
            <summary>
            Computes an approximate normalization constant for the CMP distribution.
            </summary>
            <param name="lambda">The lambda (λ) parameter for the CMP distribution.</param>
            <param name="nu">The rate of decay (ν) parameter for the CMP distribution.</param>
            <returns>
            an approximate normalization constant for the CMP distribution.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.SampleUnchecked(System.Random,System.Double,System.Double,System.Double)">
            <summary>
            Returns one trials from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="lambda">The lambda (λ) parameter. Range: λ > 0.</param>
            <param name="nu">The rate of decay (ν) parameter. Range: ν ≥ 0.</param>
            <param name="z">The z parameter.</param>
            <returns>
            One sample from the distribution implied by <paramref name="lambda"/>, <paramref name="nu"/>, and <paramref name="z"/>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Sample">
            <summary>
            Samples a Conway-Maxwell-Poisson distributed random variable.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Samples(System.Int32[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Samples">
            <summary>
            Samples a sequence of a Conway-Maxwell-Poisson distributed random variables.
            </summary>
            <returns>
            a sequence of samples from a Conway-Maxwell-Poisson distribution.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Sample(System.Random,System.Double,System.Double)">
            <summary>
            Samples a random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="lambda">The lambda (λ) parameter. Range: λ > 0.</param>
            <param name="nu">The rate of decay (ν) parameter. Range: ν ≥ 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Samples(System.Random,System.Double,System.Double)">
            <summary>
            Samples a sequence of this random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="lambda">The lambda (λ) parameter. Range: λ > 0.</param>
            <param name="nu">The rate of decay (ν) parameter. Range: ν ≥ 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Samples(System.Random,System.Int32[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="lambda">The lambda (λ) parameter. Range: λ > 0.</param>
            <param name="nu">The rate of decay (ν) parameter. Range: ν ≥ 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Sample(System.Double,System.Double)">
            <summary>
            Samples a random variable.
            </summary>
            <param name="lambda">The lambda (λ) parameter. Range: λ > 0.</param>
            <param name="nu">The rate of decay (ν) parameter. Range: ν ≥ 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Samples(System.Double,System.Double)">
            <summary>
            Samples a sequence of this random variable.
            </summary>
            <param name="lambda">The lambda (λ) parameter. Range: λ > 0.</param>
            <param name="nu">The rate of decay (ν) parameter. Range: ν ≥ 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.ConwayMaxwellPoisson.Samples(System.Int32[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="lambda">The lambda (λ) parameter. Range: λ > 0.</param>
            <param name="nu">The rate of decay (ν) parameter. Range: ν ≥ 0.</param>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Dirichlet">
            <summary>
            Multivariate Dirichlet distribution. For details about this distribution, see
            <a href="http://en.wikipedia.org/wiki/Dirichlet_distribution">Wikipedia - Dirichlet distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Dirichlet.#ctor(System.Double[])">
            <summary>
            Initializes a new instance of the Dirichlet class. The distribution will
            be initialized with the default <seealso cref="T:System.Random"/> random number generator.
            </summary>
            <param name="alpha">An array with the Dirichlet parameters.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Dirichlet.#ctor(System.Double[],System.Random)">
            <summary>
            Initializes a new instance of the Dirichlet class. The distribution will
            be initialized with the default <seealso cref="T:System.Random"/> random number generator.
            </summary>
            <param name="alpha">An array with the Dirichlet parameters.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Dirichlet.#ctor(System.Double,System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Dirichlet"/> class.
            <seealso cref="T:System.Random"/>random number generator.</summary>
            <param name="alpha">The value of each parameter of the Dirichlet distribution.</param>
            <param name="k">The dimension of the Dirichlet distribution.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Dirichlet.#ctor(System.Double,System.Int32,System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Dirichlet"/> class.
            <seealso cref="T:System.Random"/>random number generator.</summary>
            <param name="alpha">The value of each parameter of the Dirichlet distribution.</param>
            <param name="k">The dimension of the Dirichlet distribution.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Dirichlet.ToString">
            <summary>
            Returns a <see cref="T:System.String"/> that represents this instance.
            </summary>
            <returns>
            A <see cref="T:System.String"/> that represents this instance.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Dirichlet.IsValidParameterSet(System.Double[])">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            No parameter can be less than zero and at least one parameter should be larger than zero.
            </summary>
            <param name="alpha">The parameters of the Dirichlet distribution.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Dirichlet.Alpha">
            <summary>
            Gets or sets the parameters of the Dirichlet distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Dirichlet.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Dirichlet.Dimension">
            <summary>
            Gets the dimension of the Dirichlet distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Dirichlet.AlphaSum">
            <summary>
            Gets the sum of the Dirichlet parameters.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Dirichlet.Mean">
            <summary>
            Gets the mean of the Dirichlet distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Dirichlet.Variance">
            <summary>
            Gets the variance of the Dirichlet distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Dirichlet.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Dirichlet.Density(System.Double[])">
            <summary>
            Computes the density of the distribution.
            </summary>
            <param name="x">The locations at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <remarks>The Dirichlet distribution requires that the sum of the components of x equals 1.
            You can also leave out the last <paramref name="x"/> component, and it will be computed from the others. </remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Dirichlet.DensityLn(System.Double[])">
            <summary>
            Computes the log density of the distribution.
            </summary>
            <param name="x">The locations at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Dirichlet.Sample">
            <summary>
            Samples a Dirichlet distributed random vector.
            </summary>
            <returns>A sample from this distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Dirichlet.Sample(System.Random,System.Double[])">
            <summary>
            Samples a Dirichlet distributed random vector.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="alpha">The Dirichlet distribution parameter.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.DiscreteUniform">
            <summary>
            Discrete Univariate Uniform distribution.
            The discrete uniform distribution is a distribution over integers. The distribution
            is parameterized by a lower and upper bound (both inclusive).
            <a href="http://en.wikipedia.org/wiki/Uniform_distribution_%28discrete%29">Wikipedia - Discrete uniform distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.DiscreteUniform.#ctor(System.Int32,System.Int32)">
            <summary>
            Initializes a new instance of the DiscreteUniform class.
            </summary>
            <param name="lower">Lower bound, inclusive. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound, inclusive. Range: lower ≤ upper.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.DiscreteUniform.#ctor(System.Int32,System.Int32,System.Random)">
            <summary>
            Initializes a new instance of the DiscreteUniform class.
            </summary>
            <param name="lower">Lower bound, inclusive. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound, inclusive. Range: lower ≤ upper.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.DiscreteUniform.ToString">
            <summary>
            Returns a <see cref="T:System.String"/> that represents this instance.
            </summary>
            <returns>
            A <see cref="T:System.String"/> that represents this instance.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.DiscreteUniform.IsValidParameterSet(System.Int32,System.Int32)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="lower">Lower bound, inclusive. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound, inclusive. Range: lower ≤ upper.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.DiscreteUniform.LowerBound">
            <summary>
            Gets the inclusive lower bound of the probability distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.DiscreteUniform.UpperBound">
            <summary>
            Gets the inclusive upper bound of the probability distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.DiscreteUniform.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.DiscreteUniform.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.DiscreteUniform.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.DiscreteUniform.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.DiscreteUniform.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.DiscreteUniform.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.DiscreteUniform.Minimum">
            <summary>
            Gets the smallest element in the domain of the distributions which can be represented by an integer.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.DiscreteUniform.Maximum">
            <summary>
            Gets the largest element in the domain of the distributions which can be represented by an integer.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.DiscreteUniform.Mode">
            <summary>
            Gets the mode of the distribution; since every element in the domain has the same probability this method returns the middle one.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.DiscreteUniform.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.DiscreteUniform.Probability(System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.DiscreteUniform.ProbabilityLn(System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.DiscreteUniform.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.DiscreteUniform.PMF(System.Int32,System.Int32,System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <param name="lower">Lower bound, inclusive. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound, inclusive. Range: lower ≤ upper.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.DiscreteUniform.PMFLn(System.Int32,System.Int32,System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <param name="lower">Lower bound, inclusive. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound, inclusive. Range: lower ≤ upper.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.DiscreteUniform.CDF(System.Int32,System.Int32,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="lower">Lower bound, inclusive. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound, inclusive. Range: lower ≤ upper.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.DiscreteUniform.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.DiscreteUniform.SampleUnchecked(System.Random,System.Int32,System.Int32)">
            <summary>
            Generates one sample from the discrete uniform distribution. This method does not do any parameter checking.
            </summary>
            <param name="rnd">The random source to use.</param>
            <param name="lower">Lower bound, inclusive. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound, inclusive. Range: lower ≤ upper.</param>
            <returns>A random sample from the discrete uniform distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.DiscreteUniform.Sample">
            <summary>
            Draws a random sample from the distribution.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.DiscreteUniform.Samples(System.Int32[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.DiscreteUniform.Samples">
            <summary>
            Samples an array of uniformly distributed random variables.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.DiscreteUniform.Sample(System.Random,System.Int32,System.Int32)">
            <summary>
            Samples a uniformly distributed random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="lower">Lower bound, inclusive. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound, inclusive. Range: lower ≤ upper.</param>
            <returns>A sample from the discrete uniform distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.DiscreteUniform.Samples(System.Random,System.Int32,System.Int32)">
            <summary>
            Samples a sequence of uniformly distributed random variables.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="lower">Lower bound, inclusive. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound, inclusive. Range: lower ≤ upper.</param>
            <returns>a sequence of samples from the discrete uniform distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.DiscreteUniform.Samples(System.Random,System.Int32[],System.Int32,System.Int32)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="lower">Lower bound, inclusive. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound, inclusive. Range: lower ≤ upper.</param>
            <returns>a sequence of samples from the discrete uniform distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.DiscreteUniform.Sample(System.Int32,System.Int32)">
            <summary>
            Samples a uniformly distributed random variable.
            </summary>
            <param name="lower">Lower bound, inclusive. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound, inclusive. Range: lower ≤ upper.</param>
            <returns>A sample from the discrete uniform distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.DiscreteUniform.Samples(System.Int32,System.Int32)">
            <summary>
            Samples a sequence of uniformly distributed random variables.
            </summary>
            <param name="lower">Lower bound, inclusive. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound, inclusive. Range: lower ≤ upper.</param>
            <returns>a sequence of samples from the discrete uniform distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.DiscreteUniform.Samples(System.Int32[],System.Int32,System.Int32)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="lower">Lower bound, inclusive. Range: lower ≤ upper.</param>
            <param name="upper">Upper bound, inclusive. Range: lower ≤ upper.</param>
            <returns>a sequence of samples from the discrete uniform distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Erlang">
            <summary>
            Continuous Univariate Erlang distribution.
            This distribution is a continuous probability distribution with wide applicability primarily due to its
            relation to the exponential and Gamma distributions.
            <a href="http://en.wikipedia.org/wiki/Erlang_distribution">Wikipedia - Erlang distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.#ctor(System.Int32,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Erlang"/> class.
            </summary>
            <param name="shape">The shape (k) of the Erlang distribution. Range: k ≥ 0.</param>
            <param name="rate">The rate or inverse scale (λ) of the Erlang distribution. Range: λ ≥ 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.#ctor(System.Int32,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Erlang"/> class.
            </summary>
            <param name="shape">The shape (k) of the Erlang distribution. Range: k ≥ 0.</param>
            <param name="rate">The rate or inverse scale (λ) of the Erlang distribution. Range: λ ≥ 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.WithShapeScale(System.Int32,System.Double,System.Random)">
            <summary>
            Constructs a Erlang distribution from a shape and scale parameter. The distribution will
            be initialized with the default <seealso cref="T:System.Random"/> random number generator.
            </summary>
            <param name="shape">The shape (k) of the Erlang distribution. Range: k ≥ 0.</param>
            <param name="scale">The scale (μ) of the Erlang distribution. Range: μ ≥ 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples. Optional, can be null.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.WithShapeRate(System.Int32,System.Double,System.Random)">
            <summary>
            Constructs a Erlang distribution from a shape and inverse scale parameter. The distribution will
            be initialized with the default <seealso cref="T:System.Random"/> random number generator.
            </summary>
            <param name="shape">The shape (k) of the Erlang distribution. Range: k ≥ 0.</param>
            <param name="rate">The rate or inverse scale (λ) of the Erlang distribution. Range: λ ≥ 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples. Optional, can be null.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.IsValidParameterSet(System.Int32,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="shape">The shape (k) of the Erlang distribution. Range: k ≥ 0.</param>
            <param name="rate">The rate or inverse scale (λ) of the Erlang distribution. Range: λ ≥ 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Erlang.Shape">
            <summary>
            Gets the shape (k) of the Erlang distribution. Range: k ≥ 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Erlang.Rate">
            <summary>
            Gets the rate or inverse scale (λ) of the Erlang distribution. Range: λ ≥ 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Erlang.Scale">
            <summary>
            Gets the scale of the Erlang distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Erlang.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Erlang.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Erlang.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Erlang.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Erlang.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Erlang.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Erlang.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Erlang.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Erlang.Minimum">
            <summary>
            Gets the minimum value.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Erlang.Maximum">
            <summary>
            Gets the Maximum value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Erlang.PDF(System.Int32,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Erlang.PDFLn(System.Int32,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Erlang.CDF(System.Int32,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.Sample">
            <summary>
            Generates a sample from the Erlang distribution.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.Samples">
            <summary>
            Generates a sequence of samples from the Erlang distribution.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.PDF(System.Int32,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="shape">The shape (k) of the Erlang distribution. Range: k ≥ 0.</param>
            <param name="rate">The rate or inverse scale (λ) of the Erlang distribution. Range: λ ≥ 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Erlang.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.PDFLn(System.Int32,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="shape">The shape (k) of the Erlang distribution. Range: k ≥ 0.</param>
            <param name="rate">The rate or inverse scale (λ) of the Erlang distribution. Range: λ ≥ 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Erlang.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.CDF(System.Int32,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="shape">The shape (k) of the Erlang distribution. Range: k ≥ 0.</param>
            <param name="rate">The rate or inverse scale (λ) of the Erlang distribution. Range: λ ≥ 0.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Erlang.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.Sample(System.Random,System.Int32,System.Double)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="shape">The shape (k) of the Erlang distribution. Range: k ≥ 0.</param>
            <param name="rate">The rate or inverse scale (λ) of the Erlang distribution. Range: λ ≥ 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.Samples(System.Random,System.Int32,System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="shape">The shape (k) of the Erlang distribution. Range: k ≥ 0.</param>
            <param name="rate">The rate or inverse scale (λ) of the Erlang distribution. Range: λ ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.Samples(System.Random,System.Double[],System.Int32,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="shape">The shape (k) of the Erlang distribution. Range: k ≥ 0.</param>
            <param name="rate">The rate or inverse scale (λ) of the Erlang distribution. Range: λ ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.Sample(System.Int32,System.Double)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="shape">The shape (k) of the Erlang distribution. Range: k ≥ 0.</param>
            <param name="rate">The rate or inverse scale (λ) of the Erlang distribution. Range: λ ≥ 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.Samples(System.Int32,System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="shape">The shape (k) of the Erlang distribution. Range: k ≥ 0.</param>
            <param name="rate">The rate or inverse scale (λ) of the Erlang distribution. Range: λ ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Erlang.Samples(System.Double[],System.Int32,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="shape">The shape (k) of the Erlang distribution. Range: k ≥ 0.</param>
            <param name="rate">The rate or inverse scale (λ) of the Erlang distribution. Range: λ ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Exponential">
            <summary>
            Continuous Univariate Exponential distribution.
            The exponential distribution is a distribution over the real numbers parameterized by one non-negative parameter.
            <a href="http://en.wikipedia.org/wiki/Exponential_distribution">Wikipedia - exponential distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.#ctor(System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Exponential"/> class.
            </summary>
            <param name="rate">The rate (λ) parameter of the distribution. Range: λ ≥ 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.#ctor(System.Double,System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Exponential"/> class.
            </summary>
            <param name="rate">The rate (λ) parameter of the distribution. Range: λ ≥ 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.IsValidParameterSet(System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="rate">The rate (λ) parameter of the distribution. Range: λ ≥ 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Exponential.Rate">
            <summary>
            Gets the rate (λ) parameter of the distribution. Range: λ ≥ 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Exponential.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Exponential.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Exponential.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Exponential.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Exponential.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Exponential.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Exponential.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Exponential.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Exponential.Minimum">
            <summary>
            Gets the minimum of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Exponential.Maximum">
            <summary>
            Gets the maximum of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Exponential.PDF(System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Exponential.PDFLn(System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Exponential.CDF(System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.InverseCumulativeDistribution(System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Exponential.InvCDF(System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.Sample">
            <summary>
            Draws a random sample from the distribution.
            </summary>
            <returns>A random number from this distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.Samples">
            <summary>
            Generates a sequence of samples from the Exponential distribution.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.PDF(System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="rate">The rate (λ) parameter of the distribution. Range: λ ≥ 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Exponential.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.PDFLn(System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="rate">The rate (λ) parameter of the distribution. Range: λ ≥ 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Exponential.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.CDF(System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="rate">The rate (λ) parameter of the distribution. Range: λ ≥ 0.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Exponential.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.InvCDF(System.Double,System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <param name="rate">The rate (λ) parameter of the distribution. Range: λ ≥ 0.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Exponential.InverseCumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.Sample(System.Random,System.Double)">
            <summary>
            Draws a random sample from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="rate">The rate (λ) parameter of the distribution. Range: λ ≥ 0.</param>
            <returns>A random number from this distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.Samples(System.Random,System.Double[],System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="rate">The rate (λ) parameter of the distribution. Range: λ ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.Samples(System.Random,System.Double)">
            <summary>
            Generates a sequence of samples from the Exponential distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="rate">The rate (λ) parameter of the distribution. Range: λ ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.Sample(System.Double)">
            <summary>
            Draws a random sample from the distribution.
            </summary>
            <param name="rate">The rate (λ) parameter of the distribution. Range: λ ≥ 0.</param>
            <returns>A random number from this distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.Samples(System.Double[],System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="rate">The rate (λ) parameter of the distribution. Range: λ ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Exponential.Samples(System.Double)">
            <summary>
            Generates a sequence of samples from the Exponential distribution.
            </summary>
            <param name="rate">The rate (λ) parameter of the distribution. Range: λ ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.FisherSnedecor">
            <summary>
            Continuous Univariate F-distribution, also known as Fisher-Snedecor distribution.
            For details about this distribution, see
            <a href="http://en.wikipedia.org/wiki/F-distribution">Wikipedia - FisherSnedecor distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.#ctor(System.Double,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.FisherSnedecor"/> class.
            </summary>
            <param name="d1">The first degree of freedom (d1) of the distribution. Range: d1 > 0.</param>
            <param name="d2">The second degree of freedom (d2) of the distribution. Range: d2 > 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.#ctor(System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.FisherSnedecor"/> class.
            </summary>
            <param name="d1">The first degree of freedom (d1) of the distribution. Range: d1 > 0.</param>
            <param name="d2">The second degree of freedom (d2) of the distribution. Range: d2 > 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.IsValidParameterSet(System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="d1">The first degree of freedom (d1) of the distribution. Range: d1 > 0.</param>
            <param name="d2">The second degree of freedom (d2) of the distribution. Range: d2 > 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.FisherSnedecor.DegreesOfFreedom1">
            <summary>
            Gets the first degree of freedom (d1) of the distribution. Range: d1 > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.FisherSnedecor.DegreesOfFreedom2">
            <summary>
            Gets the second degree of freedom (d2) of the distribution. Range: d2 > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.FisherSnedecor.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.FisherSnedecor.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.FisherSnedecor.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.FisherSnedecor.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.FisherSnedecor.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.FisherSnedecor.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.FisherSnedecor.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.FisherSnedecor.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.FisherSnedecor.Minimum">
            <summary>
            Gets the minimum of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.FisherSnedecor.Maximum">
            <summary>
            Gets the maximum of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.FisherSnedecor.PDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.FisherSnedecor.PDFLn(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.FisherSnedecor.CDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.InverseCumulativeDistribution(System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.FisherSnedecor.InvCDF(System.Double,System.Double,System.Double)"/>
            <remarks>WARNING: currently not an explicit implementation, hence slow and unreliable.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.Sample">
            <summary>
            Generates a sample from the <c>FisherSnedecor</c> distribution.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.Samples">
            <summary>
            Generates a sequence of samples from the <c>FisherSnedecor</c> distribution.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.SampleUnchecked(System.Random,System.Double,System.Double)">
            <summary>
            Generates one sample from the <c>FisherSnedecor</c> distribution without parameter checking.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="d1">The first degree of freedom (d1) of the distribution. Range: d1 > 0.</param>
            <param name="d2">The second degree of freedom (d2) of the distribution. Range: d2 > 0.</param>
            <returns>a <c>FisherSnedecor</c> distributed random number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.PDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="d1">The first degree of freedom (d1) of the distribution. Range: d1 > 0.</param>
            <param name="d2">The second degree of freedom (d2) of the distribution. Range: d2 > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.FisherSnedecor.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.PDFLn(System.Double,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="d1">The first degree of freedom (d1) of the distribution. Range: d1 > 0.</param>
            <param name="d2">The second degree of freedom (d2) of the distribution. Range: d2 > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.FisherSnedecor.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.CDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="d1">The first degree of freedom (d1) of the distribution. Range: d1 > 0.</param>
            <param name="d2">The second degree of freedom (d2) of the distribution. Range: d2 > 0.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.FisherSnedecor.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.InvCDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <param name="d1">The first degree of freedom (d1) of the distribution. Range: d1 > 0.</param>
            <param name="d2">The second degree of freedom (d2) of the distribution. Range: d2 > 0.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.FisherSnedecor.InverseCumulativeDistribution(System.Double)"/>
            <remarks>WARNING: currently not an explicit implementation, hence slow and unreliable.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.Sample(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="d1">The first degree of freedom (d1) of the distribution. Range: d1 > 0.</param>
            <param name="d2">The second degree of freedom (d2) of the distribution. Range: d2 > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.Samples(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="d1">The first degree of freedom (d1) of the distribution. Range: d1 > 0.</param>
            <param name="d2">The second degree of freedom (d2) of the distribution. Range: d2 > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.Samples(System.Random,System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="d1">The first degree of freedom (d1) of the distribution. Range: d1 > 0.</param>
            <param name="d2">The second degree of freedom (d2) of the distribution. Range: d2 > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.Sample(System.Double,System.Double)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="d1">The first degree of freedom (d1) of the distribution. Range: d1 > 0.</param>
            <param name="d2">The second degree of freedom (d2) of the distribution. Range: d2 > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.Samples(System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="d1">The first degree of freedom (d1) of the distribution. Range: d1 > 0.</param>
            <param name="d2">The second degree of freedom (d2) of the distribution. Range: d2 > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.FisherSnedecor.Samples(System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="d1">The first degree of freedom (d1) of the distribution. Range: d1 > 0.</param>
            <param name="d2">The second degree of freedom (d2) of the distribution. Range: d2 > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Gamma">
             <summary>
             Continuous Univariate Gamma distribution.
             For details about this distribution, see
             <a href="http://en.wikipedia.org/wiki/Gamma_distribution">Wikipedia - Gamma distribution</a>.
             </summary>
             <remarks>
             The Gamma distribution is parametrized by a shape and inverse scale parameter. When we want
             to specify a Gamma distribution which is a point distribution we set the shape parameter to be the
             location of the point distribution and the inverse scale as positive infinity. The distribution
             with shape and inverse scale both zero is undefined.
            
             Random number generation for the Gamma distribution is based on the algorithm in:
             "A Simple Method for Generating Gamma Variables" - Marsaglia &amp; Tsang
             ACM Transactions on Mathematical Software, Vol. 26, No. 3, September 2000, Pages 363–372.
             </remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.#ctor(System.Double,System.Double)">
            <summary>
            Initializes a new instance of the Gamma class.
            </summary>
            <param name="shape">The shape (k, α) of the Gamma distribution. Range: α ≥ 0.</param>
            <param name="rate">The rate or inverse scale (β) of the Gamma distribution. Range: β ≥ 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.#ctor(System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the Gamma class.
            </summary>
            <param name="shape">The shape (k, α) of the Gamma distribution. Range: α ≥ 0.</param>
            <param name="rate">The rate or inverse scale (β) of the Gamma distribution. Range: β ≥ 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.WithShapeScale(System.Double,System.Double,System.Random)">
            <summary>
            Constructs a Gamma distribution from a shape and scale parameter. The distribution will
            be initialized with the default <seealso cref="T:System.Random"/> random number generator.
            </summary>
            <param name="shape">The shape (k) of the Gamma distribution. Range: k ≥ 0.</param>
            <param name="scale">The scale (θ) of the Gamma distribution. Range: θ ≥ 0</param>
            <param name="randomSource">The random number generator which is used to draw random samples. Optional, can be null.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.WithShapeRate(System.Double,System.Double,System.Random)">
            <summary>
            Constructs a Gamma distribution from a shape and inverse scale parameter. The distribution will
            be initialized with the default <seealso cref="T:System.Random"/> random number generator.
            </summary>
            <param name="shape">The shape (k, α) of the Gamma distribution. Range: α ≥ 0.</param>
            <param name="rate">The rate or inverse scale (β) of the Gamma distribution. Range: β ≥ 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples. Optional, can be null.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.IsValidParameterSet(System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="shape">The shape (k, α) of the Gamma distribution. Range: α ≥ 0.</param>
            <param name="rate">The rate or inverse scale (β) of the Gamma distribution. Range: β ≥ 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Gamma.Shape">
            <summary>
            Gets or sets the shape (k, α) of the Gamma distribution. Range: α ≥ 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Gamma.Rate">
            <summary>
            Gets or sets the rate or inverse scale (β) of the Gamma distribution. Range: β ≥ 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Gamma.Scale">
            <summary>
            Gets or sets the scale (θ) of the Gamma distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Gamma.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Gamma.Mean">
            <summary>
            Gets the mean of the Gamma distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Gamma.Variance">
            <summary>
            Gets the variance of the Gamma distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Gamma.StdDev">
            <summary>
            Gets the standard deviation of the Gamma distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Gamma.Entropy">
            <summary>
            Gets the entropy of the Gamma distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Gamma.Skewness">
            <summary>
            Gets the skewness of the Gamma distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Gamma.Mode">
            <summary>
            Gets the mode of the Gamma distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Gamma.Median">
            <summary>
            Gets the median of the Gamma distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Gamma.Minimum">
            <summary>
            Gets the minimum of the Gamma distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Gamma.Maximum">
            <summary>
            Gets the maximum of the Gamma distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Gamma.PDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Gamma.PDFLn(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Gamma.CDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.InverseCumulativeDistribution(System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Gamma.InvCDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.Sample">
            <summary>
            Generates a sample from the Gamma distribution.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.Samples">
            <summary>
            Generates a sequence of samples from the Gamma distribution.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.SampleUnchecked(System.Random,System.Double,System.Double)">
            <summary>
            <para>Sampling implementation based on:
            "A Simple Method for Generating Gamma Variables" - Marsaglia &amp; Tsang
            ACM Transactions on Mathematical Software, Vol. 26, No. 3, September 2000, Pages 363–372.</para>
            <para>This method performs no parameter checks.</para>
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="shape">The shape (k, α) of the Gamma distribution. Range: α ≥ 0.</param>
            <param name="rate">The rate or inverse scale (β) of the Gamma distribution. Range: β ≥ 0.</param>
            <returns>A sample from a Gamma distributed random variable.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.PDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="shape">The shape (k, α) of the Gamma distribution. Range: α ≥ 0.</param>
            <param name="rate">The rate or inverse scale (β) of the Gamma distribution. Range: β ≥ 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Gamma.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.PDFLn(System.Double,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="shape">The shape (k, α) of the Gamma distribution. Range: α ≥ 0.</param>
            <param name="rate">The rate or inverse scale (β) of the Gamma distribution. Range: β ≥ 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Gamma.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.CDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="shape">The shape (k, α) of the Gamma distribution. Range: α ≥ 0.</param>
            <param name="rate">The rate or inverse scale (β) of the Gamma distribution. Range: β ≥ 0.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Gamma.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.InvCDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <param name="shape">The shape (k, α) of the Gamma distribution. Range: α ≥ 0.</param>
            <param name="rate">The rate or inverse scale (β) of the Gamma distribution. Range: β ≥ 0.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Gamma.InverseCumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.Sample(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sample from the Gamma distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="shape">The shape (k, α) of the Gamma distribution. Range: α ≥ 0.</param>
            <param name="rate">The rate or inverse scale (β) of the Gamma distribution. Range: β ≥ 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.Samples(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the Gamma distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="shape">The shape (k, α) of the Gamma distribution. Range: α ≥ 0.</param>
            <param name="rate">The rate or inverse scale (β) of the Gamma distribution. Range: β ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.Samples(System.Random,System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="shape">The shape (k, α) of the Gamma distribution. Range: α ≥ 0.</param>
            <param name="rate">The rate or inverse scale (β) of the Gamma distribution. Range: β ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.Sample(System.Double,System.Double)">
            <summary>
            Generates a sample from the Gamma distribution.
            </summary>
            <param name="shape">The shape (k, α) of the Gamma distribution. Range: α ≥ 0.</param>
            <param name="rate">The rate or inverse scale (β) of the Gamma distribution. Range: β ≥ 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.Samples(System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the Gamma distribution.
            </summary>
            <param name="shape">The shape (k, α) of the Gamma distribution. Range: α ≥ 0.</param>
            <param name="rate">The rate or inverse scale (β) of the Gamma distribution. Range: β ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Gamma.Samples(System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="shape">The shape (k, α) of the Gamma distribution. Range: α ≥ 0.</param>
            <param name="rate">The rate or inverse scale (β) of the Gamma distribution. Range: β ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Geometric">
            <summary>
            Discrete Univariate Geometric distribution.
            The Geometric distribution is a distribution over positive integers parameterized by one positive real number.
            This implementation of the Geometric distribution will never generate 0's.
            <a href="http://en.wikipedia.org/wiki/Geometric_distribution">Wikipedia - geometric distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Geometric.#ctor(System.Double)">
            <summary>
            Initializes a new instance of the Geometric class.
            </summary>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Geometric.#ctor(System.Double,System.Random)">
            <summary>
            Initializes a new instance of the Geometric class.
            </summary>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Geometric.ToString">
            <summary>
            Returns a <see cref="T:System.String"/> that represents this instance.
            </summary>
            <returns>A <see cref="T:System.String"/> that represents this instance.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Geometric.IsValidParameterSet(System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Geometric.P">
            <summary>
            Gets the probability of generating a one. Range: 0 ≤ p ≤ 1.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Geometric.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Geometric.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Geometric.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Geometric.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Geometric.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Geometric.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
            <remarks>Throws a not supported exception.</remarks>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Geometric.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Geometric.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Geometric.Minimum">
            <summary>
            Gets the smallest element in the domain of the distributions which can be represented by an integer.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Geometric.Maximum">
            <summary>
            Gets the largest element in the domain of the distributions which can be represented by an integer.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Geometric.Probability(System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Geometric.ProbabilityLn(System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Geometric.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Geometric.PMF(System.Double,System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Geometric.PMFLn(System.Double,System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Geometric.CDF(System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Geometric.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Geometric.SampleUnchecked(System.Random,System.Double)">
            <summary>
            Returns one sample from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
            <returns>One sample from the distribution implied by <paramref name="p"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Geometric.Sample">
            <summary>
            Samples a Geometric distributed random variable.
            </summary>
            <returns>A sample from the Geometric distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Geometric.Samples(System.Int32[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Geometric.Samples">
            <summary>
            Samples an array of Geometric distributed random variables.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Geometric.Sample(System.Random,System.Double)">
            <summary>
            Samples a random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Geometric.Samples(System.Random,System.Double)">
            <summary>
            Samples a sequence of this random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Geometric.Samples(System.Random,System.Int32[],System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Geometric.Sample(System.Double)">
            <summary>
            Samples a random variable.
            </summary>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Geometric.Samples(System.Double)">
            <summary>
            Samples a sequence of this random variable.
            </summary>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Geometric.Samples(System.Int32[],System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="p">The probability (p) of generating one. Range: 0 ≤ p ≤ 1.</param>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Hypergeometric">
            <summary>
            Discrete Univariate Hypergeometric distribution.
            This distribution is a discrete probability distribution that describes the number of successes in a sequence
            of n draws from a finite population without replacement, just as the binomial distribution
            describes the number of successes for draws with replacement
            <a href="http://en.wikipedia.org/wiki/Hypergeometric_distribution">Wikipedia - Hypergeometric distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Hypergeometric.#ctor(System.Int32,System.Int32,System.Int32)">
            <summary>
            Initializes a new instance of the Hypergeometric class.
            </summary>
            <param name="population">The size of the population (N).</param>
            <param name="success">The number successes within the population (K, M).</param>
            <param name="draws">The number of draws without replacement (n).</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Hypergeometric.#ctor(System.Int32,System.Int32,System.Int32,System.Random)">
            <summary>
            Initializes a new instance of the Hypergeometric class.
            </summary>
            <param name="population">The size of the population (N).</param>
            <param name="success">The number successes within the population (K, M).</param>
            <param name="draws">The number of draws without replacement (n).</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Hypergeometric.ToString">
            <summary>
            Returns a <see cref="T:System.String"/> that represents this instance.
            </summary>
            <returns>
            A <see cref="T:System.String"/> that represents this instance.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Hypergeometric.IsValidParameterSet(System.Int32,System.Int32,System.Int32)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="population">The size of the population (N).</param>
            <param name="success">The number successes within the population (K, M).</param>
            <param name="draws">The number of draws without replacement (n).</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Hypergeometric.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Hypergeometric.Population">
            <summary>
            Gets the size of the population (N).
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Hypergeometric.Draws">
            <summary>
            Gets the number of draws without replacement (n).
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Hypergeometric.Success">
            <summary>
            Gets the number successes within the population (K, M).
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Hypergeometric.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Hypergeometric.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Hypergeometric.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Hypergeometric.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Hypergeometric.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Hypergeometric.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Hypergeometric.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Hypergeometric.Minimum">
            <summary>
            Gets the minimum of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Hypergeometric.Maximum">
            <summary>
            Gets the maximum of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Hypergeometric.Probability(System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Hypergeometric.ProbabilityLn(System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Hypergeometric.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Hypergeometric.PMF(System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <param name="population">The size of the population (N).</param>
            <param name="success">The number successes within the population (K, M).</param>
            <param name="draws">The number of draws without replacement (n).</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Hypergeometric.PMFLn(System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <param name="population">The size of the population (N).</param>
            <param name="success">The number successes within the population (K, M).</param>
            <param name="draws">The number of draws without replacement (n).</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Hypergeometric.CDF(System.Int32,System.Int32,System.Int32,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="population">The size of the population (N).</param>
            <param name="success">The number successes within the population (K, M).</param>
            <param name="draws">The number of draws without replacement (n).</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Hypergeometric.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Hypergeometric.SampleUnchecked(System.Random,System.Int32,System.Int32,System.Int32)">
            <summary>
            Generates a sample from the Hypergeometric distribution without doing parameter checking.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="population">The size of the population (N).</param>
            <param name="success">The number successes within the population (K, M).</param>
            <param name="draws">The n parameter of the distribution.</param>
            <returns>a random number from the Hypergeometric distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Hypergeometric.Sample">
            <summary>
            Samples a Hypergeometric distributed random variable.
            </summary>
            <returns>The number of successes in n trials.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Hypergeometric.Samples(System.Int32[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Hypergeometric.Samples">
            <summary>
            Samples an array of Hypergeometric distributed random variables.
            </summary>
            <returns>a sequence of successes in n trials.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Hypergeometric.Sample(System.Random,System.Int32,System.Int32,System.Int32)">
            <summary>
            Samples a random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="population">The size of the population (N).</param>
            <param name="success">The number successes within the population (K, M).</param>
            <param name="draws">The number of draws without replacement (n).</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Hypergeometric.Samples(System.Random,System.Int32,System.Int32,System.Int32)">
            <summary>
            Samples a sequence of this random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="population">The size of the population (N).</param>
            <param name="success">The number successes within the population (K, M).</param>
            <param name="draws">The number of draws without replacement (n).</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Hypergeometric.Samples(System.Random,System.Int32[],System.Int32,System.Int32,System.Int32)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="population">The size of the population (N).</param>
            <param name="success">The number successes within the population (K, M).</param>
            <param name="draws">The number of draws without replacement (n).</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Hypergeometric.Sample(System.Int32,System.Int32,System.Int32)">
            <summary>
            Samples a random variable.
            </summary>
            <param name="population">The size of the population (N).</param>
            <param name="success">The number successes within the population (K, M).</param>
            <param name="draws">The number of draws without replacement (n).</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Hypergeometric.Samples(System.Int32,System.Int32,System.Int32)">
            <summary>
            Samples a sequence of this random variable.
            </summary>
            <param name="population">The size of the population (N).</param>
            <param name="success">The number successes within the population (K, M).</param>
            <param name="draws">The number of draws without replacement (n).</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Hypergeometric.Samples(System.Int32[],System.Int32,System.Int32,System.Int32)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="population">The size of the population (N).</param>
            <param name="success">The number successes within the population (K, M).</param>
            <param name="draws">The number of draws without replacement (n).</param>
        </member>
        <member name="T:MathNet.Numerics.Distributions.IContinuousDistribution">
            <summary>
            Continuous Univariate Probability Distribution.
            </summary>
            <seealso cref="T:MathNet.Numerics.Distributions.IDiscreteDistribution"/>
        </member>
        <member name="P:MathNet.Numerics.Distributions.IContinuousDistribution.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.IContinuousDistribution.Minimum">
            <summary>
            Gets the smallest element in the domain of the distribution which can be represented by a double.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.IContinuousDistribution.Maximum">
            <summary>
            Gets the largest element in the domain of the distribution which can be represented by a double.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.IContinuousDistribution.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.IContinuousDistribution.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.IContinuousDistribution.Sample">
            <summary>
            Draws a random sample from the distribution.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.IContinuousDistribution.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.IContinuousDistribution.Samples">
            <summary>
            Draws a sequence of random samples from the distribution.
            </summary>
            <returns>an infinite sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.IDiscreteDistribution">
            <summary>
            Discrete Univariate Probability Distribution.
            </summary>
            <seealso cref="T:MathNet.Numerics.Distributions.IContinuousDistribution"/>
        </member>
        <member name="P:MathNet.Numerics.Distributions.IDiscreteDistribution.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.IDiscreteDistribution.Minimum">
            <summary>
            Gets the smallest element in the domain of the distribution which can be represented by an integer.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.IDiscreteDistribution.Maximum">
            <summary>
            Gets the largest element in the domain of the distribution which can be represented by an integer.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.IDiscreteDistribution.Probability(System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.IDiscreteDistribution.ProbabilityLn(System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.IDiscreteDistribution.Sample">
            <summary>
            Draws a random sample from the distribution.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.IDiscreteDistribution.Samples(System.Int32[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.IDiscreteDistribution.Samples">
            <summary>
            Draws a sequence of random samples from the distribution.
            </summary>
            <returns>an infinite sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.IDistribution">
            <summary>
            Probability Distribution.
            </summary>
            <seealso cref="T:MathNet.Numerics.Distributions.IContinuousDistribution"/>
            <seealso cref="T:MathNet.Numerics.Distributions.IDiscreteDistribution"/>
        </member>
        <member name="P:MathNet.Numerics.Distributions.IDistribution.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Distributions.InverseGamma">
            <summary>
            Continuous Univariate Inverse Gamma distribution.
            The inverse Gamma distribution is a distribution over the positive real numbers parameterized by
            two positive parameters.
            <a href="http://en.wikipedia.org/wiki/Inverse-gamma_distribution">Wikipedia - InverseGamma distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGamma.#ctor(System.Double,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.InverseGamma"/> class.
            </summary>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="scale">The scale (β) of the distribution. Range: β > 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGamma.#ctor(System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.InverseGamma"/> class.
            </summary>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="scale">The scale (β) of the distribution. Range: β > 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGamma.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGamma.IsValidParameterSet(System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="scale">The scale (β) of the distribution. Range: β > 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGamma.Shape">
            <summary>
            Gets or sets the shape (α) parameter. Range: α > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGamma.Scale">
            <summary>
            Gets or sets The scale (β) parameter. Range: β > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGamma.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGamma.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGamma.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGamma.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGamma.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGamma.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGamma.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGamma.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
            <remarks>Throws <see cref="T:System.NotSupportedException"/>.</remarks>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGamma.Minimum">
            <summary>
            Gets the minimum of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGamma.Maximum">
            <summary>
            Gets the maximum of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGamma.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.InverseGamma.PDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGamma.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.InverseGamma.PDFLn(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGamma.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.InverseGamma.CDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGamma.Sample">
            <summary>
            Draws a random sample from the distribution.
            </summary>
            <returns>A random number from this distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGamma.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGamma.Samples">
            <summary>
            Generates a sequence of samples from the Cauchy distribution.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGamma.PDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="scale">The scale (β) of the distribution. Range: β > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.InverseGamma.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGamma.PDFLn(System.Double,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="scale">The scale (β) of the distribution. Range: β > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.InverseGamma.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGamma.CDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="scale">The scale (β) of the distribution. Range: β > 0.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.InverseGamma.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGamma.Sample(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="scale">The scale (β) of the distribution. Range: β > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGamma.Samples(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="scale">The scale (β) of the distribution. Range: β > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGamma.Samples(System.Random,System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="scale">The scale (β) of the distribution. Range: β > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGamma.Sample(System.Double,System.Double)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="scale">The scale (β) of the distribution. Range: β > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGamma.Samples(System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="scale">The scale (β) of the distribution. Range: β > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGamma.Samples(System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="scale">The scale (β) of the distribution. Range: β > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGaussian.Mu">
            <summary>
            Gets the mean (μ) of the distribution. Range: μ > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGaussian.Lambda">
            <summary>
            Gets the shape (λ) of the distribution. Range: λ > 0.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGaussian.#ctor(System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the InverseGaussian class.
            </summary>
            <param name="mu">The mean (μ) of the distribution. Range: μ > 0.</param>
            <param name="lambda">The shape (λ) of the distribution. Range: λ > 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGaussian.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGaussian.IsValidParameterSet(System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="mu">The mean (μ) of the distribution. Range: μ > 0.</param>
            <param name="lambda">The shape (λ) of the distribution. Range: λ > 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGaussian.RandomSource">
            <summary>
            Gets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGaussian.Mean">
            <summary>
            Gets the mean of the Inverse Gaussian distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGaussian.Variance">
            <summary>
            Gets the variance of the Inverse Gaussian distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGaussian.StdDev">
            <summary>
            Gets the standard deviation of the Inverse Gaussian distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGaussian.Median">
            <summary>
            Gets the median of the Inverse Gaussian distribution.
            No closed form analytical expression exists, so this value is approximated numerically and can throw an exception.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGaussian.Minimum">
            <summary>
            Gets the minimum of the Inverse Gaussian distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGaussian.Maximum">
            <summary>
            Gets the maximum of the Inverse Gaussian distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGaussian.Skewness">
            <summary>
            Gets the skewness of the Inverse Gaussian distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGaussian.Kurtosis">
            <summary>
            Gets the kurtosis of the Inverse Gaussian distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGaussian.Mode">
            <summary>
            Gets the mode of the Inverse Gaussian distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseGaussian.Entropy">
            <summary>
            Gets the entropy of the Inverse Gaussian distribution (currently not supported).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGaussian.Sample">
            <summary>
            Generates a sample from the inverse Gaussian distribution.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGaussian.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGaussian.Samples">
            <summary>
            Generates a sequence of samples from the inverse Gaussian distribution.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGaussian.Sample(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sample from the inverse Gaussian distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="mu">The mean (μ) of the distribution. Range: μ > 0.</param>
            <param name="lambda">The shape (λ) of the distribution. Range: λ > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGaussian.Samples(System.Random,System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="mu">The mean (μ) of the distribution. Range: μ > 0.</param>
            <param name="lambda">The shape (λ) of the distribution. Range: λ > 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGaussian.Samples(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the Burr distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="mu">The mean (μ) of the distribution. Range: μ > 0.</param>
            <param name="lambda">The shape (λ) of the distribution. Range: λ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGaussian.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.InverseGaussian.PDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGaussian.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.InverseGaussian.PDFLn(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGaussian.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.InverseGaussian.CDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGaussian.InvCDF(System.Double)">
            <summary>
            Computes the inverse cumulative distribution (CDF) of the distribution at p, i.e. solving for P(X ≤ x) = p.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative distribution function.</param>
            <returns>the inverse cumulative distribution at location <paramref name="p"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGaussian.PDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="mu">The mean (μ) of the distribution. Range: μ > 0.</param>
            <param name="lambda">The shape (λ) of the distribution. Range: λ > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.InverseGaussian.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGaussian.PDFLn(System.Double,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="mu">The mean (μ) of the distribution. Range: μ > 0.</param>
            <param name="lambda">The shape (λ) of the distribution. Range: λ > 0.</param>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.InverseGaussian.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGaussian.CDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="mu">The mean (μ) of the distribution. Range: μ > 0.</param>
            <param name="lambda">The shape (λ) of the distribution. Range: λ > 0.</param>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.InverseGaussian.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGaussian.InvCDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the inverse cumulative distribution (CDF) of the distribution at p, i.e. solving for P(X ≤ x) = p.
            </summary>
            <param name="mu">The mean (μ) of the distribution. Range: μ > 0.</param>
            <param name="lambda">The shape (λ) of the distribution. Range: λ > 0.</param>
            <param name="p">The location at which to compute the inverse cumulative distribution function.</param>
             <returns>the inverse cumulative distribution at location <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.InverseGaussian.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseGaussian.Estimate(System.Collections.Generic.IEnumerable{System.Double},System.Random)">
            <summary>
            Estimates the Inverse Gaussian parameters from sample data with maximum-likelihood.
            </summary>
            <param name="samples">The samples to estimate the distribution parameters from.</param>
            <param name="randomSource">The random number generator which is used to draw random samples. Optional, can be null.</param>
            <returns>An Inverse Gaussian distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.InverseWishart">
            <summary>
            Multivariate Inverse Wishart distribution. This distribution is
            parameterized by the degrees of freedom nu and the scale matrix S. The inverse Wishart distribution
            is the conjugate prior for the covariance matrix of a multivariate normal distribution.
            <a href="http://en.wikipedia.org/wiki/Inverse-Wishart_distribution">Wikipedia - Inverse-Wishart distribution</a>.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Distributions.InverseWishart._chol">
            <summary>
            Caches the Cholesky factorization of the scale matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseWishart.#ctor(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.InverseWishart"/> class.
            </summary>
            <param name="degreesOfFreedom">The degree of freedom (ν) for the inverse Wishart distribution.</param>
            <param name="scale">The scale matrix (Ψ) for the inverse Wishart distribution.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseWishart.#ctor(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double},System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.InverseWishart"/> class.
            </summary>
            <param name="degreesOfFreedom">The degree of freedom (ν) for the inverse Wishart distribution.</param>
            <param name="scale">The scale matrix (Ψ) for the inverse Wishart distribution.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseWishart.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseWishart.IsValidParameterSet(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="degreesOfFreedom">The degree of freedom (ν) for the inverse Wishart distribution.</param>
            <param name="scale">The scale matrix (Ψ) for the inverse Wishart distribution.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseWishart.DegreesOfFreedom">
            <summary>
            Gets or sets the degree of freedom (ν) for the inverse Wishart distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseWishart.Scale">
            <summary>
            Gets or sets the scale matrix (Ψ) for the inverse Wishart distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseWishart.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseWishart.Mean">
            <summary>
            Gets the mean.
            </summary>
            <value>The mean of the distribution.</value>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseWishart.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
            <value>The mode of the distribution.</value>
            <remarks>A. O'Hagan, and J. J. Forster (2004). Kendall's Advanced Theory of Statistics: Bayesian Inference. 2B (2 ed.). Arnold. ISBN 0-340-80752-0.</remarks>
        </member>
        <member name="P:MathNet.Numerics.Distributions.InverseWishart.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
            <value>The variance  of the distribution.</value>
            <remarks>Kanti V. Mardia, J. T. Kent and J. M. Bibby (1979). Multivariate Analysis.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseWishart.Density(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Evaluates the probability density function for the inverse Wishart distribution.
            </summary>
            <param name="x">The matrix at which to evaluate the density at.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If the argument does not have the same dimensions as the scale matrix.</exception>
            <returns>the density at <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseWishart.Sample">
            <summary>
            Samples an inverse Wishart distributed random variable by sampling
            a Wishart random variable and inverting the matrix.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.InverseWishart.Sample(System.Random,System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Samples an inverse Wishart distributed random variable by sampling
            a Wishart random variable and inverting the matrix.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="degreesOfFreedom">The degree of freedom (ν) for the inverse Wishart distribution.</param>
            <param name="scale">The scale matrix (Ψ) for the inverse Wishart distribution.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.IUnivariateDistribution">
            <summary>
            Univariate Probability Distribution.
            </summary>
            <seealso cref="T:MathNet.Numerics.Distributions.IContinuousDistribution"/>
            <seealso cref="T:MathNet.Numerics.Distributions.IDiscreteDistribution"/>
        </member>
        <member name="P:MathNet.Numerics.Distributions.IUnivariateDistribution.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.IUnivariateDistribution.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.IUnivariateDistribution.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.IUnivariateDistribution.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.IUnivariateDistribution.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.IUnivariateDistribution.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.IUnivariateDistribution.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Laplace">
            <summary>
            Continuous Univariate Laplace distribution.
            The Laplace distribution is a distribution over the real numbers parameterized by a mean and
            scale parameter. The PDF is:
                p(x) = \frac{1}{2 * scale} \exp{- |x - mean| / scale}.
            <a href="http://en.wikipedia.org/wiki/Laplace_distribution">Wikipedia - Laplace distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Laplace.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Laplace"/> class (location = 0, scale = 1).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Laplace.#ctor(System.Double,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Laplace"/> class.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (b) of the distribution. Range: b > 0.</param>
            <exception cref="T:System.ArgumentException">If <paramref name="scale"/> is negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Laplace.#ctor(System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Laplace"/> class.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (b) of the distribution. Range: b > 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
            <exception cref="T:System.ArgumentException">If <paramref name="scale"/> is negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Laplace.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Laplace.IsValidParameterSet(System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (b) of the distribution. Range: b > 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Laplace.Location">
            <summary>
            Gets the location (μ) of the Laplace distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Laplace.Scale">
            <summary>
            Gets the scale (b) of the Laplace distribution. Range: b > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Laplace.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Laplace.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Laplace.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Laplace.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Laplace.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Laplace.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Laplace.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Laplace.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Laplace.Minimum">
            <summary>
            Gets the minimum of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Laplace.Maximum">
            <summary>
            Gets the maximum of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Laplace.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Laplace.PDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Laplace.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Laplace.PDFLn(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Laplace.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Laplace.CDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Laplace.Sample">
            <summary>
            Samples a Laplace distributed random variable.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Laplace.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Laplace.Samples">
            <summary>
            Generates a sample from the Laplace distribution.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Laplace.PDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (b) of the distribution. Range: b > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Laplace.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Laplace.PDFLn(System.Double,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (b) of the distribution. Range: b > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Laplace.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Laplace.CDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (b) of the distribution. Range: b > 0.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Laplace.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Laplace.Sample(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (b) of the distribution. Range: b > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Laplace.Samples(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (b) of the distribution. Range: b > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Laplace.Samples(System.Random,System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (b) of the distribution. Range: b > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Laplace.Sample(System.Double,System.Double)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (b) of the distribution. Range: b > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Laplace.Samples(System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (b) of the distribution. Range: b > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Laplace.Samples(System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (b) of the distribution. Range: b > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Logistic">
            <summary>
            Continuous Univariate Logistic distribution.
            For details about this distribution, see
            <a href="http://en.wikipedia.org/wiki/Logistic_distribution">Wikipedia - Logistic distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.#ctor">
            <summary>
            Initializes a new instance of the Logistic class. This is a logistic distribution with mean 0.0
            and scale 1.0. The distribution will be initialized with the default <seealso cref="T:System.Random"/>
            random number generator.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.#ctor(System.Random)">
            <summary>
            Initializes a new instance of the Logistic class. This is a logistic distribution with mean 0.0
            and scale 1.0. The distribution will be initialized with the default <seealso cref="T:System.Random"/>
            random number generator.
            </summary>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.#ctor(System.Double,System.Double)">
            <summary>
            Initializes a new instance of the Logistic class with a particular mean and scale parameter. The
            distribution will be initialized with the default <seealso cref="T:System.Random"/> random number generator.
            </summary>
            <param name="mean">The mean (μ) of the logistic distribution.</param>
            <param name="scale">The scale (s) of the logistic distribution. Range: s > 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.#ctor(System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the Logistic class with a particular mean and standard deviation. The distribution will
            be initialized with the default <seealso cref="T:System.Random"/> random number generator.
            </summary>
            <param name="mean">The mean (μ) of the logistic distribution.</param>
            <param name="scale">The scale (s) of the logistic distribution. Range: s > 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.WithMeanScale(System.Double,System.Double,System.Random)">
            <summary>
            Constructs a logistic distribution from a mean and scale parameter.
            </summary>
            <param name="mean">The mean (μ) of the logistic distribution.</param>
            <param name="scale">The scale (s) of the logistic distribution. Range: s > 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples. Optional, can be null.</param>
            <returns>a logistic distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.WithMeanStdDev(System.Double,System.Double,System.Random)">
            <summary>
            Constructs a logistic distribution from a mean and standard deviation.
            </summary>
            <param name="mean">The mean (μ) of the logistic distribution.</param>
            <param name="stddev">The standard deviation (σ) of the logistic distribution. Range: σ > 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples. Optional, can be null.</param>
            <returns>a logistic distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.WithMeanVariance(System.Double,System.Double,System.Random)">
            <summary>
            Constructs a logistic distribution from a mean and variance.
            </summary>
            <param name="mean">The mean (μ) of the logistic distribution.</param>
            <param name="var">The variance (σ^2) of the logistic distribution. Range: (σ^2) > 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples. Optional, can be null.</param>
            <returns>A logistic distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.WithMeanPrecision(System.Double,System.Double,System.Random)">
            <summary>
            Constructs a logistic distribution from a mean and precision.
            </summary>
            <param name="mean">The mean (μ) of the logistic distribution.</param>
            <param name="precision">The precision of the logistic distribution.  Range: precision > 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples. Optional, can be null.</param>
            <returns>A logistic distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.IsValidParameterSet(System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="mean">The mean (μ) of the logistic distribution.</param>
            <param name="scale">The scale (s) of the logistic distribution. Range: s > 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Logistic.Scale">
            <summary>
            Gets the scale parameter of the Logistic distribution. Range: s > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Logistic.Mean">
            <summary>
            Gets the mean (μ) of the logistic distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Logistic.StdDev">
            <summary>
            Gets the standard deviation (σ) of the logistic distribution. Range: σ > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Logistic.Variance">
            <summary>
            Gets the variance of the logistic distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Logistic.Precision">
            <summary>
            Gets the precision of the logistic distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Logistic.RandomSource">
            <summary>
            Gets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Logistic.Entropy">
            <summary>
            Gets the entropy of the logistic distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Logistic.Skewness">
            <summary>
            Gets the skewness of the logistic distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Logistic.Mode">
            <summary>
            Gets the mode of the logistic distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Logistic.Median">
            <summary>
            Gets the median of the logistic distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Logistic.Minimum">
            <summary>
            Gets the minimum of the logistic distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Logistic.Maximum">
            <summary>
            Gets the maximum of the logistic distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Logistic.PDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Logistic.PDFLn(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Logistic.CDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.InverseCumulativeDistribution(System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Logistic.InvCDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.Sample">
            <summary>
            Generates a sample from the logistic distribution using the <i>Box-Muller</i> algorithm.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.Samples">
            <summary>
            Generates a sequence of samples from the logistic distribution using the <i>Box-Muller</i> algorithm.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.PDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="mean">The mean (μ) of the logistic distribution.</param>
            <param name="scale">The scale (s) of the logistic distribution. Range: s > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Logistic.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.PDFLn(System.Double,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="mean">The mean (μ) of the logistic distribution.</param>
            <param name="scale">The scale (s) of the logistic distribution. Range: s > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Logistic.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.CDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="mean">The mean (μ) of the logistic distribution.</param>
            <param name="scale">The scale (s) of the logistic distribution. Range: s > 0.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Logistic.CumulativeDistribution(System.Double)"/>
            <remarks>MATLAB: normcdf</remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.InvCDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <param name="mean">The mean (μ) of the logistic distribution.</param>
            <param name="scale">The scale (s) of the logistic distribution. Range: s > 0.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Logistic.InverseCumulativeDistribution(System.Double)"/>
            <remarks>MATLAB: norminv</remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.Sample(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sample from the logistic distribution using the <i>Box-Muller</i> algorithm.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="mean">The mean (μ) of the logistic distribution.</param>
            <param name="scale">The scale (s) of the logistic distribution. Range: s > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.Samples(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the logistic distribution using the <i>Box-Muller</i> algorithm.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="mean">The mean (μ) of the logistic distribution.</param>
            <param name="scale">The scale (s) of the logistic distribution. Range: s > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.Samples(System.Random,System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="mean">The mean (μ) of the logistic distribution.</param>
            <param name="scale">The scale (s) of the logistic distribution. Range: s > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.Sample(System.Double,System.Double)">
            <summary>
            Generates a sample from the logistic distribution using the <i>Box-Muller</i> algorithm.
            </summary>
            <param name="mean">The mean (μ) of the logistic distribution.</param>
            <param name="scale">The scale (s) of the logistic distribution. Range: s > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.Samples(System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the logistic distribution using the <i>Box-Muller</i> algorithm.
            </summary>
            <param name="mean">The mean (μ) of the logistic distribution.</param>
            <param name="scale">The scale (s) of the logistic distribution. Range: s > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Logistic.Samples(System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="mean">The mean (μ) of the logistic distribution.</param>
            <param name="scale">The scale (s) of the logistic distribution. Range: s > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.LogNormal">
            <summary>
            Continuous Univariate Log-Normal distribution.
            For details about this distribution, see
            <a href="http://en.wikipedia.org/wiki/Log-normal_distribution">Wikipedia - Log-Normal distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.#ctor(System.Double,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.LogNormal"/> class.
            The distribution will be initialized with the default <seealso cref="T:System.Random"/>
            random number generator.
            </summary>
            <param name="mu">The log-scale (μ) of the logarithm of the distribution.</param>
            <param name="sigma">The shape (σ) of the logarithm of the distribution. Range: σ ≥ 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.#ctor(System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.LogNormal"/> class.
            The distribution will be initialized with the default <seealso cref="T:System.Random"/>
            random number generator.
            </summary>
            <param name="mu">The log-scale (μ) of the distribution.</param>
            <param name="sigma">The shape (σ) of the distribution. Range: σ ≥ 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.WithMuSigma(System.Double,System.Double,System.Random)">
            <summary>
            Constructs a log-normal distribution with the desired mu and sigma parameters.
            </summary>
            <param name="mu">The log-scale (μ) of the distribution.</param>
            <param name="sigma">The shape (σ) of the distribution. Range: σ ≥ 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples. Optional, can be null.</param>
            <returns>A log-normal distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.WithMeanVariance(System.Double,System.Double,System.Random)">
            <summary>
            Constructs a log-normal distribution with the desired mean and variance.
            </summary>
            <param name="mean">The mean of the log-normal distribution.</param>
            <param name="var">The variance of the log-normal distribution.</param>
            <param name="randomSource">The random number generator which is used to draw random samples. Optional, can be null.</param>
            <returns>A log-normal distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.Estimate(System.Collections.Generic.IEnumerable{System.Double},System.Random)">
            <summary>
            Estimates the log-normal distribution parameters from sample data with maximum-likelihood.
            </summary>
            <param name="samples">The samples to estimate the distribution parameters from.</param>
            <param name="randomSource">The random number generator which is used to draw random samples. Optional, can be null.</param>
            <returns>A log-normal distribution.</returns>
            <remarks>MATLAB: lognfit</remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.IsValidParameterSet(System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="mu">The log-scale (μ) of the distribution.</param>
            <param name="sigma">The shape (σ) of the distribution. Range: σ ≥ 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.LogNormal.Mu">
            <summary>
            Gets the log-scale (μ) (mean of the logarithm) of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.LogNormal.Sigma">
            <summary>
            Gets the shape (σ) (standard deviation of the logarithm) of the distribution. Range: σ ≥ 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.LogNormal.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.LogNormal.Mean">
            <summary>
            Gets the mu of the log-normal distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.LogNormal.Variance">
            <summary>
            Gets the variance of the log-normal distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.LogNormal.StdDev">
            <summary>
            Gets the standard deviation of the log-normal distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.LogNormal.Entropy">
            <summary>
            Gets the entropy of the log-normal distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.LogNormal.Skewness">
            <summary>
            Gets the skewness of the log-normal distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.LogNormal.Mode">
            <summary>
            Gets the mode of the log-normal distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.LogNormal.Median">
            <summary>
            Gets the median of the log-normal distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.LogNormal.Minimum">
            <summary>
            Gets the minimum of the log-normal distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.LogNormal.Maximum">
            <summary>
            Gets the maximum of the log-normal distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.LogNormal.PDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.LogNormal.PDFLn(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.LogNormal.CDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.InverseCumulativeDistribution(System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.LogNormal.InvCDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.Sample">
            <summary>
            Generates a sample from the log-normal distribution using the <i>Box-Muller</i> algorithm.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.Samples">
            <summary>
            Generates a sequence of samples from the log-normal distribution using the <i>Box-Muller</i> algorithm.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.PDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <param name="mu">The log-scale (μ) of the distribution.</param>
            <param name="sigma">The shape (σ) of the distribution. Range: σ ≥ 0.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.LogNormal.Density(System.Double)"/>
            <remarks>MATLAB: lognpdf</remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.PDFLn(System.Double,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <param name="mu">The log-scale (μ) of the distribution.</param>
            <param name="sigma">The shape (σ) of the distribution. Range: σ ≥ 0.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.LogNormal.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.CDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="mu">The log-scale (μ) of the distribution.</param>
            <param name="sigma">The shape (σ) of the distribution. Range: σ ≥ 0.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.LogNormal.CumulativeDistribution(System.Double)"/>
            <remarks>MATLAB: logncdf</remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.InvCDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <param name="mu">The log-scale (μ) of the distribution.</param>
            <param name="sigma">The shape (σ) of the distribution. Range: σ ≥ 0.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.LogNormal.InverseCumulativeDistribution(System.Double)"/>
            <remarks>MATLAB: logninv</remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.Sample(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sample from the log-normal distribution using the <i>Box-Muller</i> algorithm.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="mu">The log-scale (μ) of the distribution.</param>
            <param name="sigma">The shape (σ) of the distribution. Range: σ ≥ 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.Samples(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the log-normal distribution using the <i>Box-Muller</i> algorithm.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="mu">The log-scale (μ) of the distribution.</param>
            <param name="sigma">The shape (σ) of the distribution. Range: σ ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.Samples(System.Random,System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="mu">The log-scale (μ) of the distribution.</param>
            <param name="sigma">The shape (σ) of the distribution. Range: σ ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.Sample(System.Double,System.Double)">
            <summary>
            Generates a sample from the log-normal distribution using the <i>Box-Muller</i> algorithm.
            </summary>
            <param name="mu">The log-scale (μ) of the distribution.</param>
            <param name="sigma">The shape (σ) of the distribution. Range: σ ≥ 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.Samples(System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the log-normal distribution using the <i>Box-Muller</i> algorithm.
            </summary>
            <param name="mu">The log-scale (μ) of the distribution.</param>
            <param name="sigma">The shape (σ) of the distribution. Range: σ ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.LogNormal.Samples(System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="mu">The log-scale (μ) of the distribution.</param>
            <param name="sigma">The shape (σ) of the distribution. Range: σ ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.MatrixNormal">
            <summary>
            Multivariate Matrix-valued Normal distributions. The distribution
            is parameterized by a mean matrix (M), a covariance matrix for the rows (V) and a covariance matrix
            for the columns (K). If the dimension of M is d-by-m then V is d-by-d and K is m-by-m.
            <a href="http://en.wikipedia.org/wiki/Matrix_normal_distribution">Wikipedia - MatrixNormal distribution</a>.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Distributions.MatrixNormal._m">
            <summary>
            The mean of the matrix normal distribution.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Distributions.MatrixNormal._v">
            <summary>
            The covariance matrix for the rows.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Distributions.MatrixNormal._k">
            <summary>
            The covariance matrix for the columns.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.MatrixNormal.#ctor(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.MatrixNormal"/> class.
            </summary>
            <param name="m">The mean of the matrix normal.</param>
            <param name="v">The covariance matrix for the rows.</param>
            <param name="k">The covariance matrix for the columns.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If the dimensions of the mean and two covariance matrices don't match.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.MatrixNormal.#ctor(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double},System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.MatrixNormal"/> class.
            </summary>
            <param name="m">The mean of the matrix normal.</param>
            <param name="v">The covariance matrix for the rows.</param>
            <param name="k">The covariance matrix for the columns.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If the dimensions of the mean and two covariance matrices don't match.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.MatrixNormal.ToString">
            <summary>
            Returns a <see cref="T:System.String"/> that represents this instance.
            </summary>
            <returns>
            A <see cref="T:System.String"/> that represents this instance.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.MatrixNormal.IsValidParameterSet(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="m">The mean of the matrix normal.</param>
            <param name="v">The covariance matrix for the rows.</param>
            <param name="k">The covariance matrix for the columns.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.MatrixNormal.Mean">
            <summary>
            Gets the mean. (M)
            </summary>
            <value>The mean of the distribution.</value>
        </member>
        <member name="P:MathNet.Numerics.Distributions.MatrixNormal.RowCovariance">
            <summary>
            Gets the row covariance. (V)
            </summary>
            <value>The row covariance.</value>
        </member>
        <member name="P:MathNet.Numerics.Distributions.MatrixNormal.ColumnCovariance">
            <summary>
            Gets the column covariance. (K)
            </summary>
            <value>The column covariance.</value>
        </member>
        <member name="P:MathNet.Numerics.Distributions.MatrixNormal.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.MatrixNormal.Density(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Evaluates the probability density function for the matrix normal distribution.
            </summary>
            <param name="x">The matrix at which to evaluate the density at.</param>
            <returns>the density at <paramref name="x"/></returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If the argument does not have the correct dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.MatrixNormal.Sample">
            <summary>
            Samples a matrix normal distributed random variable.
            </summary>
            <returns>A random number from this distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.MatrixNormal.Sample(System.Random,MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Samples a matrix normal distributed random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="m">The mean of the matrix normal.</param>
            <param name="v">The covariance matrix for the rows.</param>
            <param name="k">The covariance matrix for the columns.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If the dimensions of the mean and two covariance matrices don't match.</exception>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.MatrixNormal.SampleVectorNormal(System.Random,MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Samples a vector normal distributed random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="mean">The mean of the vector normal distribution.</param>
            <param name="covariance">The covariance matrix of the vector normal distribution.</param>
            <returns>a sequence of samples from defined distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Multinomial">
            <summary>
            Multivariate Multinomial distribution. For details about this distribution, see
            <a href="http://en.wikipedia.org/wiki/Multinomial_distribution">Wikipedia - Multinomial distribution</a>.
            </summary>
            <remarks>
            The distribution is parameterized by a vector of ratios: in other words, the parameter
            does not have to be normalized and sum to 1. The reason is that some vectors can't be exactly normalized
            to sum to 1 in floating point representation.
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.Distributions.Multinomial._p">
            <summary>
            Stores the normalized multinomial probabilities.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Distributions.Multinomial._trials">
            <summary>
            The number of trials.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Multinomial.#ctor(System.Double[],System.Int32)">
            <summary>
            Initializes a new instance of the Multinomial class.
            </summary>
            <param name="p">An array of nonnegative ratios: this array does not need to be normalized
            as this is often impossible using floating point arithmetic.</param>
            <param name="n">The number of trials.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If any of the probabilities are negative or do not sum to one.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="n"/> is negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Multinomial.#ctor(System.Double[],System.Int32,System.Random)">
            <summary>
            Initializes a new instance of the Multinomial class.
            </summary>
            <param name="p">An array of nonnegative ratios: this array does not need to be normalized
            as this is often impossible using floating point arithmetic.</param>
            <param name="n">The number of trials.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If any of the probabilities are negative or do not sum to one.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="n"/> is negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Multinomial.#ctor(MathNet.Numerics.Statistics.Histogram,System.Int32)">
            <summary>
            Initializes a new instance of the Multinomial class from histogram <paramref name="h"/>. The distribution will
            not be automatically updated when the histogram changes.
            </summary>
            <param name="h">Histogram instance</param>
            <param name="n">The number of trials.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If any of the probabilities are negative or do not sum to one.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="n"/> is negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Multinomial.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Multinomial.IsValidParameterSet(System.Collections.Generic.IEnumerable{System.Double},System.Int32)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="p">An array of nonnegative ratios: this array does not need to be normalized
            as this is often impossible using floating point arithmetic.</param>
            <param name="n">The number of trials.</param>
            <returns>If any of the probabilities are negative returns <c>false</c>,
            if the sum of parameters is 0.0, or if the number of trials is negative; otherwise <c>true</c>.</returns>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Multinomial.P">
            <summary>
            Gets the proportion of ratios.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Multinomial.N">
            <summary>
            Gets the number of trials.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Multinomial.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Multinomial.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Multinomial.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Multinomial.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Multinomial.Probability(System.Int32[])">
            <summary>
            Computes values of the probability mass function.
            </summary>
            <param name="x">Non-negative integers x1, ..., xk</param>
            <returns>The probability mass at location <paramref name="x"/>.</returns>
            <exception cref="T:System.ArgumentNullException">When <paramref name="x"/> is null.</exception>
            <exception cref="T:System.ArgumentException">When length of <paramref name="x"/> is not equal to event probabilities count.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Multinomial.ProbabilityLn(System.Int32[])">
            <summary>
            Computes values of the log probability mass function.
            </summary>
            <param name="x">Non-negative integers x1, ..., xk</param>
            <returns>The log probability mass at location <paramref name="x"/>.</returns>
            <exception cref="T:System.ArgumentNullException">When <paramref name="x"/> is null.</exception>
            <exception cref="T:System.ArgumentException">When length of <paramref name="x"/> is not equal to event probabilities count.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Multinomial.Sample">
            <summary>
            Samples one multinomial distributed random variable.
            </summary>
            <returns>the counts for each of the different possible values.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Multinomial.Samples">
            <summary>
            Samples a sequence multinomially distributed random variables.
            </summary>
            <returns>a sequence of counts for each of the different possible values.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Multinomial.Sample(System.Random,System.Double[],System.Int32)">
            <summary>
            Samples one multinomial distributed random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="p">An array of nonnegative ratios: this array does not need to be normalized
            as this is often impossible using floating point arithmetic.</param>
            <param name="n">The number of trials.</param>
            <returns>the counts for each of the different possible values.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Multinomial.Samples(System.Random,System.Double[],System.Int32)">
            <summary>
            Samples a multinomially distributed random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="p">An array of nonnegative ratios: this array does not need to be normalized
            as this is often impossible using floating point arithmetic.</param>
            <param name="n">The number of variables needed.</param>
            <returns>a sequence of counts for each of the different possible values.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.NegativeBinomial">
            <summary>
            Discrete Univariate Negative Binomial distribution.
            The negative binomial is a distribution over the natural numbers with two parameters r, p. For the special
            case that r is an integer one can interpret the distribution as the number of failures before the r'th success
            when the probability of success is p.
            <a href="http://en.wikipedia.org/wiki/Negative_binomial_distribution">Wikipedia - NegativeBinomial distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NegativeBinomial.#ctor(System.Double,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.NegativeBinomial"/> class.
            </summary>
            <param name="r">The number of successes (r) required to stop the experiment. Range: r ≥ 0.</param>
            <param name="p">The probability (p) of a trial resulting in success. Range: 0 ≤ p ≤ 1.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NegativeBinomial.#ctor(System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.NegativeBinomial"/> class.
            </summary>
            <param name="r">The number of successes (r) required to stop the experiment. Range: r ≥ 0.</param>
            <param name="p">The probability (p) of a trial resulting in success. Range: 0 ≤ p ≤ 1.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NegativeBinomial.ToString">
            <summary>
            Returns a <see cref="T:System.String"/> that represents this instance.
            </summary>
            <returns>
            A <see cref="T:System.String"/> that represents this instance.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NegativeBinomial.IsValidParameterSet(System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="r">The number of successes (r) required to stop the experiment. Range: r ≥ 0.</param>
            <param name="p">The probability (p) of a trial resulting in success. Range: 0 ≤ p ≤ 1.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.NegativeBinomial.R">
            <summary>
            Gets the number of successes. Range: r ≥ 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.NegativeBinomial.P">
            <summary>
            Gets the probability of success. Range: 0 ≤ p ≤ 1.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.NegativeBinomial.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.NegativeBinomial.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.NegativeBinomial.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.NegativeBinomial.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.NegativeBinomial.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.NegativeBinomial.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.NegativeBinomial.Mode">
            <summary>
            Gets the mode of the distribution
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.NegativeBinomial.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.NegativeBinomial.Minimum">
            <summary>
            Gets the smallest element in the domain of the distributions which can be represented by an integer.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.NegativeBinomial.Maximum">
            <summary>
            Gets the largest element in the domain of the distributions which can be represented by an integer.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NegativeBinomial.Probability(System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NegativeBinomial.ProbabilityLn(System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NegativeBinomial.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NegativeBinomial.PMF(System.Double,System.Double,System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <param name="r">The number of successes (r) required to stop the experiment. Range: r ≥ 0.</param>
            <param name="p">The probability (p) of a trial resulting in success. Range: 0 ≤ p ≤ 1.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NegativeBinomial.PMFLn(System.Double,System.Double,System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <param name="r">The number of successes (r) required to stop the experiment. Range: r ≥ 0.</param>
            <param name="p">The probability (p) of a trial resulting in success. Range: 0 ≤ p ≤ 1.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NegativeBinomial.CDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="r">The number of successes (r) required to stop the experiment. Range: r ≥ 0.</param>
            <param name="p">The probability (p) of a trial resulting in success. Range: 0 ≤ p ≤ 1.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.NegativeBinomial.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NegativeBinomial.SampleUnchecked(System.Random,System.Double,System.Double)">
            <summary>
            Samples a negative binomial distributed random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="r">The number of successes (r) required to stop the experiment. Range: r ≥ 0.</param>
            <param name="p">The probability (p) of a trial resulting in success. Range: 0 ≤ p ≤ 1.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NegativeBinomial.Sample">
            <summary>
            Samples a <c>NegativeBinomial</c> distributed random variable.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NegativeBinomial.Samples(System.Int32[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NegativeBinomial.Samples">
            <summary>
            Samples an array of <c>NegativeBinomial</c> distributed random variables.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NegativeBinomial.Sample(System.Random,System.Double,System.Double)">
            <summary>
            Samples a random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="r">The number of successes (r) required to stop the experiment. Range: r ≥ 0.</param>
            <param name="p">The probability (p) of a trial resulting in success. Range: 0 ≤ p ≤ 1.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NegativeBinomial.Samples(System.Random,System.Double,System.Double)">
            <summary>
            Samples a sequence of this random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="r">The number of successes (r) required to stop the experiment. Range: r ≥ 0.</param>
            <param name="p">The probability (p) of a trial resulting in success. Range: 0 ≤ p ≤ 1.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NegativeBinomial.Samples(System.Random,System.Int32[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="r">The number of successes (r) required to stop the experiment. Range: r ≥ 0.</param>
            <param name="p">The probability (p) of a trial resulting in success. Range: 0 ≤ p ≤ 1.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NegativeBinomial.Sample(System.Double,System.Double)">
            <summary>
            Samples a random variable.
            </summary>
            <param name="r">The number of successes (r) required to stop the experiment. Range: r ≥ 0.</param>
            <param name="p">The probability (p) of a trial resulting in success. Range: 0 ≤ p ≤ 1.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NegativeBinomial.Samples(System.Double,System.Double)">
            <summary>
            Samples a sequence of this random variable.
            </summary>
            <param name="r">The number of successes (r) required to stop the experiment. Range: r ≥ 0.</param>
            <param name="p">The probability (p) of a trial resulting in success. Range: 0 ≤ p ≤ 1.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NegativeBinomial.Samples(System.Int32[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="r">The number of successes (r) required to stop the experiment. Range: r ≥ 0.</param>
            <param name="p">The probability (p) of a trial resulting in success. Range: 0 ≤ p ≤ 1.</param>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Normal">
            <summary>
            Continuous Univariate Normal distribution, also known as Gaussian distribution.
            For details about this distribution, see
            <a href="http://en.wikipedia.org/wiki/Normal_distribution">Wikipedia - Normal distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.#ctor">
            <summary>
            Initializes a new instance of the Normal class. This is a normal distribution with mean 0.0
            and standard deviation 1.0. The distribution will
            be initialized with the default <seealso cref="T:System.Random"/> random number generator.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.#ctor(System.Random)">
            <summary>
            Initializes a new instance of the Normal class. This is a normal distribution with mean 0.0
            and standard deviation 1.0. The distribution will
            be initialized with the default <seealso cref="T:System.Random"/> random number generator.
            </summary>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.#ctor(System.Double,System.Double)">
            <summary>
            Initializes a new instance of the Normal class with a particular mean and standard deviation. The distribution will
            be initialized with the default <seealso cref="T:System.Random"/> random number generator.
            </summary>
            <param name="mean">The mean (μ) of the normal distribution.</param>
            <param name="stddev">The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.#ctor(System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the Normal class with a particular mean and standard deviation. The distribution will
            be initialized with the default <seealso cref="T:System.Random"/> random number generator.
            </summary>
            <param name="mean">The mean (μ) of the normal distribution.</param>
            <param name="stddev">The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.WithMeanStdDev(System.Double,System.Double,System.Random)">
            <summary>
            Constructs a normal distribution from a mean and standard deviation.
            </summary>
            <param name="mean">The mean (μ) of the normal distribution.</param>
            <param name="stddev">The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples. Optional, can be null.</param>
            <returns>a normal distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.WithMeanVariance(System.Double,System.Double,System.Random)">
            <summary>
            Constructs a normal distribution from a mean and variance.
            </summary>
            <param name="mean">The mean (μ) of the normal distribution.</param>
            <param name="var">The variance (σ^2) of the normal distribution.</param>
            <param name="randomSource">The random number generator which is used to draw random samples. Optional, can be null.</param>
            <returns>A normal distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.WithMeanPrecision(System.Double,System.Double,System.Random)">
            <summary>
            Constructs a normal distribution from a mean and precision.
            </summary>
            <param name="mean">The mean (μ) of the normal distribution.</param>
            <param name="precision">The precision of the normal distribution.</param>
            <param name="randomSource">The random number generator which is used to draw random samples. Optional, can be null.</param>
            <returns>A normal distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.Estimate(System.Collections.Generic.IEnumerable{System.Double},System.Random)">
            <summary>
            Estimates the normal distribution parameters from sample data with maximum-likelihood.
            </summary>
            <param name="samples">The samples to estimate the distribution parameters from.</param>
            <param name="randomSource">The random number generator which is used to draw random samples. Optional, can be null.</param>
            <returns>A normal distribution.</returns>
            <remarks>MATLAB: normfit</remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.IsValidParameterSet(System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="mean">The mean (μ) of the normal distribution.</param>
            <param name="stddev">The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Normal.Mean">
            <summary>
            Gets the mean (μ) of the normal distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Normal.StdDev">
            <summary>
            Gets the standard deviation (σ) of the normal distribution. Range: σ ≥ 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Normal.Variance">
            <summary>
            Gets the variance of the normal distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Normal.Precision">
            <summary>
            Gets the precision of the normal distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Normal.RandomSource">
            <summary>
            Gets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Normal.Entropy">
            <summary>
            Gets the entropy of the normal distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Normal.Skewness">
            <summary>
            Gets the skewness of the normal distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Normal.Mode">
            <summary>
            Gets the mode of the normal distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Normal.Median">
            <summary>
            Gets the median of the normal distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Normal.Minimum">
            <summary>
            Gets the minimum of the normal distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Normal.Maximum">
            <summary>
            Gets the maximum of the normal distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Normal.PDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Normal.PDFLn(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Normal.CDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.InverseCumulativeDistribution(System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Normal.InvCDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.Sample">
            <summary>
            Generates a sample from the normal distribution using the <i>Box-Muller</i> algorithm.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.Samples">
            <summary>
            Generates a sequence of samples from the normal distribution using the <i>Box-Muller</i> algorithm.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.PDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="mean">The mean (μ) of the normal distribution.</param>
            <param name="stddev">The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Normal.Density(System.Double)"/>
            <remarks>MATLAB: normpdf</remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.PDFLn(System.Double,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="mean">The mean (μ) of the normal distribution.</param>
            <param name="stddev">The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Normal.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.CDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="mean">The mean (μ) of the normal distribution.</param>
            <param name="stddev">The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Normal.CumulativeDistribution(System.Double)"/>
            <remarks>MATLAB: normcdf</remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.InvCDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <param name="mean">The mean (μ) of the normal distribution.</param>
            <param name="stddev">The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Normal.InverseCumulativeDistribution(System.Double)"/>
            <remarks>MATLAB: norminv</remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.Sample(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sample from the normal distribution using the <i>Box-Muller</i> algorithm.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="mean">The mean (μ) of the normal distribution.</param>
            <param name="stddev">The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.Samples(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the normal distribution using the <i>Box-Muller</i> algorithm.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="mean">The mean (μ) of the normal distribution.</param>
            <param name="stddev">The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.Samples(System.Random,System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="mean">The mean (μ) of the normal distribution.</param>
            <param name="stddev">The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.Sample(System.Double,System.Double)">
            <summary>
            Generates a sample from the normal distribution using the <i>Box-Muller</i> algorithm.
            </summary>
            <param name="mean">The mean (μ) of the normal distribution.</param>
            <param name="stddev">The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.Samples(System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the normal distribution using the <i>Box-Muller</i> algorithm.
            </summary>
            <param name="mean">The mean (μ) of the normal distribution.</param>
            <param name="stddev">The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Normal.Samples(System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="mean">The mean (μ) of the normal distribution.</param>
            <param name="stddev">The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.MeanPrecisionPair">
            <summary>
            This structure represents the type over which the <see cref="T:MathNet.Numerics.Distributions.NormalGamma"/> distribution
            is defined.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.MeanPrecisionPair.#ctor(System.Double,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.MeanPrecisionPair"/> struct.
            </summary>
            <param name="m">The mean of the pair.</param>
            <param name="p">The precision of the pair.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.MeanPrecisionPair.Mean">
            <summary>
            Gets or sets the mean of the pair.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.MeanPrecisionPair.Precision">
            <summary>
            Gets or sets the precision of the pair.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Distributions.NormalGamma">
            <summary>
            Multivariate Normal-Gamma Distribution.
            <para>The <see cref="T:MathNet.Numerics.Distributions.NormalGamma"/> distribution is the conjugate prior distribution for the <see cref="T:MathNet.Numerics.Distributions.Normal"/>
            distribution. It specifies a prior over the mean and precision of the <see cref="T:MathNet.Numerics.Distributions.Normal"/> distribution.</para>
            <para>It is parameterized by four numbers: the mean location, the mean scale, the precision shape and the
            precision inverse scale.</para>
            <para>The distribution NG(mu, tau | mloc,mscale,psscale,pinvscale) = Normal(mu | mloc, 1/(mscale*tau)) * Gamma(tau | psscale,pinvscale).</para>
            <para>The following degenerate cases are special: when the precision is known,
            the precision shape will encode the value of the precision while the precision inverse scale is positive
            infinity. When the mean is known, the mean location will encode the value of the mean while the scale
            will be positive infinity. A completely degenerate NormalGamma distribution with known mean and precision is possible as well.</para>
            <a href="http://en.wikipedia.org/wiki/Normal-gamma_distribution">Wikipedia - Normal-Gamma distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NormalGamma.#ctor(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.NormalGamma"/> class.
            </summary>
            <param name="meanLocation">The location of the mean.</param>
            <param name="meanScale">The scale of the mean.</param>
            <param name="precisionShape">The shape of the precision.</param>
            <param name="precisionInverseScale">The inverse scale of the precision.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NormalGamma.#ctor(System.Double,System.Double,System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.NormalGamma"/> class.
            </summary>
            <param name="meanLocation">The location of the mean.</param>
            <param name="meanScale">The scale of the mean.</param>
            <param name="precisionShape">The shape of the precision.</param>
            <param name="precisionInverseScale">The inverse scale of the precision.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NormalGamma.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NormalGamma.IsValidParameterSet(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="meanLocation">The location of the mean.</param>
            <param name="meanScale">The scale of the mean.</param>
            <param name="precShape">The shape of the precision.</param>
            <param name="precInvScale">The inverse scale of the precision.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.NormalGamma.MeanLocation">
            <summary>
            Gets the location of the mean.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.NormalGamma.MeanScale">
            <summary>
            Gets the scale of the mean.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.NormalGamma.PrecisionShape">
            <summary>
            Gets the shape of the precision.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.NormalGamma.PrecisionInverseScale">
            <summary>
            Gets the inverse scale of the precision.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.NormalGamma.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NormalGamma.MeanMarginal">
            <summary>
            Returns the marginal distribution for the mean of the <c>NormalGamma</c> distribution.
            </summary>
            <returns>the marginal distribution for the mean of the <c>NormalGamma</c> distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NormalGamma.PrecisionMarginal">
            <summary>
            Returns the marginal distribution for the precision of the <see cref="T:MathNet.Numerics.Distributions.NormalGamma"/> distribution.
            </summary>
            <returns>The marginal distribution for the precision of the <see cref="T:MathNet.Numerics.Distributions.NormalGamma"/> distribution/</returns>
        </member>
        <member name="P:MathNet.Numerics.Distributions.NormalGamma.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
            <value>The mean of the distribution.</value>
        </member>
        <member name="P:MathNet.Numerics.Distributions.NormalGamma.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
            <value>The mean of the distribution.</value>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NormalGamma.Density(MathNet.Numerics.Distributions.MeanPrecisionPair)">
            <summary>
            Evaluates the probability density function for a NormalGamma distribution.
            </summary>
            <param name="mp">The mean/precision pair of the distribution</param>
            <returns>Density value</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NormalGamma.Density(System.Double,System.Double)">
            <summary>
            Evaluates the probability density function for a NormalGamma distribution.
            </summary>
            <param name="mean">The mean of the distribution</param>
            <param name="prec">The precision of the distribution</param>
            <returns>Density value</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NormalGamma.DensityLn(MathNet.Numerics.Distributions.MeanPrecisionPair)">
            <summary>
            Evaluates the log probability density function for a NormalGamma distribution.
            </summary>
            <param name="mp">The mean/precision pair of the distribution</param>
            <returns>The log of the density value</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NormalGamma.DensityLn(System.Double,System.Double)">
            <summary>
            Evaluates the log probability density function for a NormalGamma distribution.
            </summary>
            <param name="mean">The mean of the distribution</param>
            <param name="prec">The precision of the distribution</param>
            <returns>The log of the density value</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NormalGamma.Sample">
            <summary>
            Generates a sample from the <c>NormalGamma</c> distribution.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NormalGamma.Samples">
            <summary>
            Generates a sequence of samples from the <c>NormalGamma</c> distribution
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NormalGamma.Sample(System.Random,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sample from the <c>NormalGamma</c> distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="meanLocation">The location of the mean.</param>
            <param name="meanScale">The scale of the mean.</param>
            <param name="precisionShape">The shape of the precision.</param>
            <param name="precisionInverseScale">The inverse scale of the precision.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.NormalGamma.Samples(System.Random,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the NormalGamma distribution
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="meanLocation">The location of the mean.</param>
            <param name="meanScale">The scale of the mean.</param>
            <param name="precisionShape">The shape of the precision.</param>
            <param name="precisionInvScale">The inverse scale of the precision.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Pareto">
            <summary>
            Continuous Univariate Pareto distribution.
            The Pareto distribution is a power law probability distribution that coincides with social,
            scientific, geophysical, actuarial, and many other types of observable phenomena.
            For details about this distribution, see
            <a href="http://en.wikipedia.org/wiki/Pareto_distribution">Wikipedia - Pareto distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.#ctor(System.Double,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Pareto"/> class.
            </summary>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <exception cref="T:System.ArgumentException">If <paramref name="scale"/> or <paramref name="shape"/> are negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.#ctor(System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Pareto"/> class.
            </summary>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
            <exception cref="T:System.ArgumentException">If <paramref name="scale"/> or <paramref name="shape"/> are negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.IsValidParameterSet(System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Pareto.Scale">
            <summary>
            Gets the scale (xm) of the distribution. Range: xm > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Pareto.Shape">
            <summary>
            Gets the shape (α) of the distribution. Range: α > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Pareto.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Pareto.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Pareto.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Pareto.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Pareto.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Pareto.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Pareto.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Pareto.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Pareto.Minimum">
            <summary>
            Gets the minimum of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Pareto.Maximum">
            <summary>
            Gets the maximum of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Pareto.PDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Pareto.PDFLn(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Pareto.CDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.InverseCumulativeDistribution(System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Pareto.InvCDF(System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.Sample">
            <summary>
            Draws a random sample from the distribution.
            </summary>
            <returns>A random number from this distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.Samples">
            <summary>
            Generates a sequence of samples from the Pareto distribution.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.PDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Pareto.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.PDFLn(System.Double,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Pareto.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.CDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Pareto.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.InvCDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Pareto.InverseCumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.Sample(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.Samples(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.Samples(System.Random,System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.Sample(System.Double,System.Double)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.Samples(System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Pareto.Samples(System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Poisson">
            <summary>
            Discrete Univariate Poisson distribution.
            </summary>
            <remarks>
            <para>Distribution is described at <a href="http://en.wikipedia.org/wiki/Poisson_distribution"> Wikipedia - Poisson distribution</a>.</para>
            <para>Knuth's method is used to generate Poisson distributed random variables.</para>
            <para>f(x) = exp(-λ)*λ^x/x!;</para>
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.#ctor(System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Poisson"/> class.
            </summary>
            <param name="lambda">The lambda (λ) parameter of the Poisson distribution. Range: λ > 0.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="lambda"/> is equal or less then 0.0.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.#ctor(System.Double,System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Poisson"/> class.
            </summary>
            <param name="lambda">The lambda (λ) parameter of the Poisson distribution. Range: λ > 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="lambda"/> is equal or less then 0.0.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.ToString">
            <summary>
            Returns a <see cref="T:System.String"/> that represents this instance.
            </summary>
            <returns>
            A <see cref="T:System.String"/> that represents this instance.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.IsValidParameterSet(System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="lambda">The lambda (λ) parameter of the Poisson distribution. Range: λ > 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Poisson.Lambda">
            <summary>
            Gets the Poisson distribution parameter λ. Range: λ > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Poisson.RandomSource">
            <summary>
            Gets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Poisson.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Poisson.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Poisson.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Poisson.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
            <remarks>Approximation, see Wikipedia <a href="http://en.wikipedia.org/wiki/Poisson_distribution">Poisson distribution</a></remarks>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Poisson.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Poisson.Minimum">
            <summary>
            Gets the smallest element in the domain of the distributions which can be represented by an integer.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Poisson.Maximum">
            <summary>
            Gets the largest element in the domain of the distributions which can be represented by an integer.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Poisson.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Poisson.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
            <remarks>Approximation, see Wikipedia <a href="http://en.wikipedia.org/wiki/Poisson_distribution">Poisson distribution</a></remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.Probability(System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.ProbabilityLn(System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.PMF(System.Double,System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <param name="lambda">The lambda (λ) parameter of the Poisson distribution. Range: λ > 0.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.PMFLn(System.Double,System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <param name="lambda">The lambda (λ) parameter of the Poisson distribution. Range: λ > 0.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.CDF(System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="lambda">The lambda (λ) parameter of the Poisson distribution. Range: λ > 0.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Poisson.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.SampleUnchecked(System.Random,System.Double)">
            <summary>
            Generates one sample from the Poisson distribution.
            </summary>
            <param name="rnd">The random source to use.</param>
            <param name="lambda">The lambda (λ) parameter of the Poisson distribution. Range: λ > 0.</param>
            <returns>A random sample from the Poisson distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.DoSampleShort(System.Random,System.Double)">
            <summary>
            Generates one sample from the Poisson distribution by Knuth's method.
            </summary>
            <param name="rnd">The random source to use.</param>
            <param name="lambda">The lambda (λ) parameter of the Poisson distribution. Range: λ > 0.</param>
            <returns>A random sample from the Poisson distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.DoSampleLarge(System.Random,System.Double)">
            <summary>
            Generates one sample from the Poisson distribution by "Rejection method PA".
            </summary>
            <param name="rnd">The random source to use.</param>
            <param name="lambda">The lambda (λ) parameter of the Poisson distribution. Range: λ > 0.</param>
            <returns>A random sample from the Poisson distribution.</returns>
            <remarks>"Rejection method PA" from "The Computer Generation of Poisson Random Variables" by A. C. Atkinson,
            Journal of the Royal Statistical Society Series C (Applied Statistics) Vol. 28, No. 1. (1979)
            The article is on pages 29-35. The algorithm given here is on page 32. </remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.Sample">
            <summary>
            Samples a Poisson distributed random variable.
            </summary>
            <returns>A sample from the Poisson distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.Samples(System.Int32[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.Samples">
            <summary>
            Samples an array of Poisson distributed random variables.
            </summary>
            <returns>a sequence of successes in N trials.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.Sample(System.Random,System.Double)">
            <summary>
            Samples a Poisson distributed random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="lambda">The lambda (λ) parameter of the Poisson distribution. Range: λ > 0.</param>
            <returns>A sample from the Poisson distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.Samples(System.Random,System.Double)">
            <summary>
            Samples a sequence of Poisson distributed random variables.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="lambda">The lambda (λ) parameter of the Poisson distribution. Range: λ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.Samples(System.Random,System.Int32[],System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="lambda">The lambda (λ) parameter of the Poisson distribution. Range: λ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.Sample(System.Double)">
            <summary>
            Samples a Poisson distributed random variable.
            </summary>
            <param name="lambda">The lambda (λ) parameter of the Poisson distribution. Range: λ > 0.</param>
            <returns>A sample from the Poisson distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.Samples(System.Double)">
            <summary>
            Samples a sequence of Poisson distributed random variables.
            </summary>
            <param name="lambda">The lambda (λ) parameter of the Poisson distribution. Range: λ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Poisson.Samples(System.Int32[],System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="lambda">The lambda (λ) parameter of the Poisson distribution. Range: λ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Rayleigh">
            <summary>
            Continuous Univariate Rayleigh distribution.
            The Rayleigh distribution (pronounced /ˈreɪli/) is a continuous probability distribution. As an
            example of how it arises, the wind speed will have a Rayleigh distribution if the components of
            the two-dimensional wind velocity vector are uncorrelated and normally distributed with equal variance.
            For details about this distribution, see
            <a href="http://en.wikipedia.org/wiki/Rayleigh_distribution">Wikipedia - Rayleigh distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.#ctor(System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Rayleigh"/> class.
            </summary>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <exception cref="T:System.ArgumentException">If <paramref name="scale"/> is negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.#ctor(System.Double,System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Rayleigh"/> class.
            </summary>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
            <exception cref="T:System.ArgumentException">If <paramref name="scale"/> is negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.IsValidParameterSet(System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Rayleigh.Scale">
            <summary>
            Gets the scale (σ) of the distribution. Range: σ > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Rayleigh.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Rayleigh.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Rayleigh.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Rayleigh.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Rayleigh.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Rayleigh.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Rayleigh.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Rayleigh.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Rayleigh.Minimum">
            <summary>
            Gets the minimum of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Rayleigh.Maximum">
            <summary>
            Gets the maximum of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Rayleigh.PDF(System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Rayleigh.PDFLn(System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Rayleigh.CDF(System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.InverseCumulativeDistribution(System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Rayleigh.InvCDF(System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.Sample">
            <summary>
            Draws a random sample from the distribution.
            </summary>
            <returns>A random number from this distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.Samples">
            <summary>
            Generates a sequence of samples from the Rayleigh distribution.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.PDF(System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Rayleigh.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.PDFLn(System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Rayleigh.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.CDF(System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Rayleigh.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.InvCDF(System.Double,System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Rayleigh.InverseCumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.Sample(System.Random,System.Double)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.Samples(System.Random,System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.Samples(System.Random,System.Double[],System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.Sample(System.Double)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.Samples(System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Rayleigh.Samples(System.Double[],System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.SkewedGeneralizedError">
            <summary>
            Continuous Univariate Skewed Generalized Error Distribution (SGED).
            Implements the univariate Skewed Generalized Error Distribution. For details about this
            distribution, see
            <a href="https://en.wikipedia.org/wiki/Generalized_normal_distribution">
            Wikipedia - Generalized Error Distribution</a>.
            It includes Laplace, Normal and Student-t distributions.
            This is the <see cref="T:MathNet.Numerics.Distributions.SkewedGeneralizedT"/> distribution with q=Inf.
            </summary>
            <remarks><para>This implementation is based on the R package dsgt and corresponding viginette, see
            <a href="">https://cran.r-project.org/web/packages/sgt/vignettes/sgt.pdf</a>. Compared to that
            implementation, the options for mean adjustment and variance adjustment are always true.
            The location (μ) is the mean of the distribution.
            The scale (σ) squared is the variance of the distribution.
            </para>
            <para>The distribution will use the <see cref="T:System.Random"/> by
            default.  Users can get/set the random number generator by using the
            <see cref="P:MathNet.Numerics.Distributions.SkewedGeneralizedError.RandomSource"/> property.</para>
            <para>The statistics classes will check all the incoming parameters
            whether they are in the allowed range.</para></remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedError.#ctor">
            <summary>
            Initializes a new instance of the SkewedGeneralizedError class. This is a generalized error distribution
            with location=0.0, scale=1.0, skew=0.0 and p=2.0 (a standard normal distribution).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedError.#ctor(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Initializes a new instance of the SkewedGeneralizedT class with a particular location, scale, skew
            and kurtosis parameters. Different parameterizations result in different distributions.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">Parameter that controls kurtosis. Range: p > 0</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.SkewedGeneralizedError.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedError.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedError.IsValidParameterSet(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">Parameter that controls kurtosis. Range: p > 0</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.SkewedGeneralizedError.Location">
            <summary>
            Gets the location (μ) of the Skewed Generalized t-distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.SkewedGeneralizedError.Scale">
            <summary>
            Gets the scale (σ) of the Skewed Generalized t-distribution. Range: σ > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.SkewedGeneralizedError.Skew">
            <summary>
            Gets the skew (λ) of the Skewed Generalized t-distribution. Range: 1 > λ > -1.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.SkewedGeneralizedError.P">
            <summary>
            Gets the parameter that controls the kurtosis of the distribution. Range: p > 0.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedError.Sample(System.Random,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sample from the Skew Generalized Error distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">Parameter that controls kurtosis. Range: p > 0</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedError.Samples(System.Random,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the Skew Generalized Error distribution using inverse transform.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">Parameter that controls kurtosis. Range: p > 0</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedError.Samples(System.Random,System.Double[],System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Fills an array with samples from the Skew Generalized Error distribution using inverse transform.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">Parameter that controls kurtosis. Range: p > 0</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedError.Sample(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sample from the Skew Generalized Error distribution.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">Parameter that controls kurtosis. Range: p > 0</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedError.Samples(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the Skew Generalized Error distribution using inverse transform.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">Parameter that controls kurtosis. Range: p > 0</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedError.Samples(System.Double[],System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Fills an array with samples from the Skew Generalized Error distribution using inverse transform.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">Parameter that controls kurtosis. Range: p > 0</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.SkewedGeneralizedT">
            <summary>
            Continuous Univariate Skewed Generalized T-distribution.
            Implements the univariate Skewed Generalized t-distribution. For details about this
            distribution, see
            <a href="https://en.wikipedia.org/wiki/Skewed_generalized_t_distribution">
            Wikipedia - Skewed generalized t-distribution</a>.
            The skewed generalized t-distribution contains many different distributions within it
            as special cases based on the parameterization chosen.
            </summary>
            <remarks><para>This implementation is based on the R package dsgt and corresponding viginette, see
            <a href="">https://cran.r-project.org/web/packages/sgt/vignettes/sgt.pdf</a>. Compared to that
            implementation, the options for mean adjustment and variance adjustment are always true.
            The location (μ) is the mean of the distribution.
            The scale (σ) squared is the variance of the distribution.
            </para>
            <para>The distribution will use the <see cref="T:System.Random"/> by
            default.  Users can get/set the random number generator by using the
            <see cref="P:MathNet.Numerics.Distributions.SkewedGeneralizedT.RandomSource"/> property.</para>
            <para>The statistics classes will check all the incoming parameters
            whether they are in the allowed range.</para></remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.#ctor">
            <summary>
            Initializes a new instance of the SkewedGeneralizedT class. This is a skewed generalized t-distribution
            with location=0.0, scale=1.0, skew=0.0, p=2.0 and q=Inf (a standard normal distribution).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.#ctor(System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Initializes a new instance of the SkewedGeneralizedT class with a particular location, scale, skew
            and kurtosis parameters. Different parameterizations result in different distributions.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
            <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.FindSpecializedDistribution(System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Given a parameter set, returns the distribution that matches this parameterization.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
            <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
            <returns>Null if no known distribution matches the parameterization, else the distribution.</returns>
        </member>
        <member name="P:MathNet.Numerics.Distributions.SkewedGeneralizedT.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.IsValidParameterSet(System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
            <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.SkewedGeneralizedT.Location">
            <summary>
            Gets the location (μ) of the Skewed Generalized t-distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.SkewedGeneralizedT.Scale">
            <summary>
            Gets the scale (σ) of the Skewed Generalized t-distribution. Range: σ > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.SkewedGeneralizedT.Skew">
            <summary>
            Gets the skew (λ) of the Skewed Generalized t-distribution. Range: 1 > λ > -1.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.SkewedGeneralizedT.P">
            <summary>
            Gets the first parameter that controls the kurtosis of the distribution. Range: p > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.SkewedGeneralizedT.Q">
            <summary>
            Gets the second parameter that controls the kurtosis of the distribution. Range: q > 0.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.PDF(System.Double,System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
            <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.PDFLn(System.Double,System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
            <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.CDF(System.Double,System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
            <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.InvCDF(System.Double,System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="pr">The location at which to compute the inverse cumulative density.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
            <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.InverseCumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.InverseCumulativeDistribution(System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.InvCDF(System.Double,System.Double,System.Double,System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.Sample(System.Random,System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sample from the Skew Generalized t-distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
            <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.Samples(System.Random,System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the Skew Generalized t-distribution using inverse transform.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
            <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.Samples(System.Random,System.Double[],System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Fills an array with samples from the Skew Generalized t-distribution using inverse transform.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
            <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.Sample(System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sample from the Skew Generalized t-distribution.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
            <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.Samples(System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the Skew Generalized t-distribution using inverse transform.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
            <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.SkewedGeneralizedT.Samples(System.Double[],System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Fills an array with samples from the Skew Generalized t-distribution using inverse transform.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="skew">The skew, 1 > λ > -1</param>
            <param name="p">First parameter that controls kurtosis. Range: p > 0</param>
            <param name="q">Second parameter that controls kurtosis. Range: q > 0</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Stable">
            <summary>
            Continuous Univariate Stable distribution.
            A random variable is said to be stable (or to have a stable distribution) if it has
            the property that a linear combination of two independent copies of the variable has
            the same distribution, up to location and scale parameters.
            For details about this distribution, see
            <a href="http://en.wikipedia.org/wiki/Stable_distribution">Wikipedia - Stable distribution</a>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Stable.#ctor(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Stable"/> class.
            </summary>
            <param name="alpha">The stability (α) of the distribution. Range: 2 ≥ α > 0.</param>
            <param name="beta">The skewness (β) of the distribution. Range: 1 ≥ β ≥ -1.</param>
            <param name="scale">The scale (c) of the distribution. Range: c > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Stable.#ctor(System.Double,System.Double,System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Stable"/> class.
            </summary>
            <param name="alpha">The stability (α) of the distribution. Range: 2 ≥ α > 0.</param>
            <param name="beta">The skewness (β) of the distribution. Range: 1 ≥ β ≥ -1.</param>
            <param name="scale">The scale (c) of the distribution. Range: c > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Stable.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Stable.IsValidParameterSet(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="alpha">The stability (α) of the distribution. Range: 2 ≥ α > 0.</param>
            <param name="beta">The skewness (β) of the distribution. Range: 1 ≥ β ≥ -1.</param>
            <param name="scale">The scale (c) of the distribution. Range: c > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Stable.Alpha">
            <summary>
            Gets the stability (α) of the distribution. Range: 2 ≥ α > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Stable.Beta">
            <summary>
            Gets The skewness (β) of the distribution. Range: 1 ≥ β ≥ -1.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Stable.Scale">
            <summary>
            Gets the scale (c) of the distribution. Range: c > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Stable.Location">
            <summary>
            Gets the location (μ) of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Stable.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Stable.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Stable.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Stable.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Stable.Entropy">
            <summary>
            Gets he entropy of the distribution.
            </summary>
            <remarks>Always throws a not supported exception.</remarks>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Stable.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
            <remarks>Throws a not supported exception of <c>Alpha</c> != 2.</remarks>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Stable.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
            <remarks>Throws a not supported exception if <c>Beta != 0</c>.</remarks>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Stable.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
            <remarks>Throws a not supported exception if <c>Beta != 0</c>.</remarks>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Stable.Minimum">
            <summary>
            Gets the minimum of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Stable.Maximum">
            <summary>
            Gets the maximum of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Stable.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Stable.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Stable.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <remarks>Throws a not supported exception if <c>Alpha != 2</c>, <c>(Alpha != 1 and Beta !=0)</c>, or <c>(Alpha != 0.5 and Beta != 1)</c></remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Stable.SampleUnchecked(System.Random,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Samples the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="alpha">The stability (α) of the distribution. Range: 2 ≥ α > 0.</param>
            <param name="beta">The skewness (β) of the distribution. Range: 1 ≥ β ≥ -1.</param>
            <param name="scale">The scale (c) of the distribution. Range: c > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <returns>a random number from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Stable.Sample">
            <summary>
            Draws a random sample from the distribution.
            </summary>
            <returns>A random number from this distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Stable.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Stable.Samples">
            <summary>
            Generates a sequence of samples from the Stable distribution.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Stable.PDF(System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="alpha">The stability (α) of the distribution. Range: 2 ≥ α > 0.</param>
            <param name="beta">The skewness (β) of the distribution. Range: 1 ≥ β ≥ -1.</param>
            <param name="scale">The scale (c) of the distribution. Range: c > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Stable.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Stable.PDFLn(System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="alpha">The stability (α) of the distribution. Range: 2 ≥ α > 0.</param>
            <param name="beta">The skewness (β) of the distribution. Range: 1 ≥ β ≥ -1.</param>
            <param name="scale">The scale (c) of the distribution. Range: c > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Stable.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Stable.CDF(System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="alpha">The stability (α) of the distribution. Range: 2 ≥ α > 0.</param>
            <param name="beta">The skewness (β) of the distribution. Range: 1 ≥ β ≥ -1.</param>
            <param name="scale">The scale (c) of the distribution. Range: c > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Stable.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Stable.Sample(System.Random,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="alpha">The stability (α) of the distribution. Range: 2 ≥ α > 0.</param>
            <param name="beta">The skewness (β) of the distribution. Range: 1 ≥ β ≥ -1.</param>
            <param name="scale">The scale (c) of the distribution. Range: c > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Stable.Samples(System.Random,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="alpha">The stability (α) of the distribution. Range: 2 ≥ α > 0.</param>
            <param name="beta">The skewness (β) of the distribution. Range: 1 ≥ β ≥ -1.</param>
            <param name="scale">The scale (c) of the distribution. Range: c > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Stable.Samples(System.Random,System.Double[],System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="alpha">The stability (α) of the distribution. Range: 2 ≥ α > 0.</param>
            <param name="beta">The skewness (β) of the distribution. Range: 1 ≥ β ≥ -1.</param>
            <param name="scale">The scale (c) of the distribution. Range: c > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Stable.Sample(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sample from the distribution.
            </summary>
            <param name="alpha">The stability (α) of the distribution. Range: 2 ≥ α > 0.</param>
            <param name="beta">The skewness (β) of the distribution. Range: 1 ≥ β ≥ -1.</param>
            <param name="scale">The scale (c) of the distribution. Range: c > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Stable.Samples(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the distribution.
            </summary>
            <param name="alpha">The stability (α) of the distribution. Range: 2 ≥ α > 0.</param>
            <param name="beta">The skewness (β) of the distribution. Range: 1 ≥ β ≥ -1.</param>
            <param name="scale">The scale (c) of the distribution. Range: c > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Stable.Samples(System.Double[],System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="alpha">The stability (α) of the distribution. Range: 2 ≥ α > 0.</param>
            <param name="beta">The skewness (β) of the distribution. Range: 1 ≥ β ≥ -1.</param>
            <param name="scale">The scale (c) of the distribution. Range: c > 0.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.StudentT">
            <summary>
            Continuous Univariate Student's T-distribution.
            Implements the univariate Student t-distribution. For details about this
            distribution, see
            <a href="http://en.wikipedia.org/wiki/Student%27s_t-distribution">
            Wikipedia - Student's t-distribution</a>.
            </summary>
            <remarks><para>We use a slightly generalized version (compared to
            Wikipedia) of the Student t-distribution. Namely, one which also
            parameterizes the location and scale. See the book "Bayesian Data
            Analysis" by Gelman et al. for more details.</para>
            <para>The density of the Student t-distribution  p(x|mu,scale,dof) =
            Gamma((dof+1)/2) (1 + (x - mu)^2 / (scale * scale * dof))^(-(dof+1)/2) /
            (Gamma(dof/2)*Sqrt(dof*pi*scale)).</para>
            <para>The distribution will use the <see cref="T:System.Random"/> by
            default.  Users can get/set the random number generator by using the
            <see cref="P:MathNet.Numerics.Distributions.StudentT.RandomSource"/> property.</para>
            <para>The statistics classes will check all the incoming parameters
            whether they are in the allowed range. This might involve heavy
            computation. Optionally, by setting Control.CheckDistributionParameters
            to <c>false</c>, all parameter checks can be turned off.</para></remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.#ctor">
            <summary>
            Initializes a new instance of the StudentT class. This is a Student t-distribution with location 0.0
            scale 1.0 and degrees of freedom 1.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.#ctor(System.Double,System.Double,System.Double)">
            <summary>
            Initializes a new instance of the StudentT class with a particular location, scale and degrees of
            freedom.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="freedom">The degrees of freedom (ν) for the distribution. Range: ν > 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.#ctor(System.Double,System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the StudentT class with a particular location, scale and degrees of
            freedom.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="freedom">The degrees of freedom (ν) for the distribution. Range: ν > 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.IsValidParameterSet(System.Double,System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="freedom">The degrees of freedom (ν) for the distribution. Range: ν > 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.StudentT.Location">
            <summary>
            Gets the location (μ) of the Student t-distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.StudentT.Scale">
            <summary>
            Gets the scale (σ) of the Student t-distribution. Range: σ > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.StudentT.DegreesOfFreedom">
            <summary>
            Gets the degrees of freedom (ν) of the Student t-distribution. Range: ν > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.StudentT.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.StudentT.Mean">
            <summary>
            Gets the mean of the Student t-distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.StudentT.Variance">
            <summary>
            Gets the variance of the Student t-distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.StudentT.StdDev">
            <summary>
            Gets the standard deviation of the Student t-distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.StudentT.Entropy">
            <summary>
            Gets the entropy of the Student t-distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.StudentT.Skewness">
            <summary>
            Gets the skewness of the Student t-distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.StudentT.Mode">
            <summary>
            Gets the mode of the Student t-distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.StudentT.Median">
            <summary>
            Gets the median of the Student t-distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.StudentT.Minimum">
            <summary>
            Gets the minimum of the Student t-distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.StudentT.Maximum">
            <summary>
            Gets the maximum of the Student t-distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.InverseCumulativeDistribution(System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.StudentT.InvCDF(System.Double,System.Double,System.Double,System.Double)"/>
            <remarks>WARNING: currently not an explicit implementation, hence slow and unreliable.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.SampleUnchecked(System.Random,System.Double,System.Double,System.Double)">
            <summary>
            Samples student-t distributed random variables.
            </summary>
            <remarks>The algorithm is method 2 in section 5, chapter 9
            in L. Devroye's "Non-Uniform Random Variate Generation"</remarks>
            <param name="rnd">The random number generator to use.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="freedom">The degrees of freedom (ν) for the distribution. Range: ν > 0.</param>
            <returns>a random number from the standard student-t distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.Sample">
            <summary>
            Generates a sample from the Student t-distribution.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.Samples">
            <summary>
            Generates a sequence of samples from the Student t-distribution.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.PDF(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="freedom">The degrees of freedom (ν) for the distribution. Range: ν > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.StudentT.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.PDFLn(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="freedom">The degrees of freedom (ν) for the distribution. Range: ν > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.StudentT.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.CDF(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="freedom">The degrees of freedom (ν) for the distribution. Range: ν > 0.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.StudentT.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.InvCDF(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="freedom">The degrees of freedom (ν) for the distribution. Range: ν > 0.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.StudentT.InverseCumulativeDistribution(System.Double)"/>
            <remarks>WARNING: currently not an explicit implementation, hence slow and unreliable.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.Sample(System.Random,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sample from the Student t-distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="freedom">The degrees of freedom (ν) for the distribution. Range: ν > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.Samples(System.Random,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the Student t-distribution using the <i>Box-Muller</i> algorithm.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="freedom">The degrees of freedom (ν) for the distribution. Range: ν > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.Samples(System.Random,System.Double[],System.Double,System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="freedom">The degrees of freedom (ν) for the distribution. Range: ν > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.Sample(System.Double,System.Double,System.Double)">
            <summary>
            Generates a sample from the Student t-distribution.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="freedom">The degrees of freedom (ν) for the distribution. Range: ν > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.Samples(System.Double,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the Student t-distribution using the <i>Box-Muller</i> algorithm.
            </summary>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="freedom">The degrees of freedom (ν) for the distribution. Range: ν > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.StudentT.Samples(System.Double[],System.Double,System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="location">The location (μ) of the distribution.</param>
            <param name="scale">The scale (σ) of the distribution. Range: σ > 0.</param>
            <param name="freedom">The degrees of freedom (ν) for the distribution. Range: ν > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Triangular">
            <summary>
            Triangular distribution.
            For details, see <a href="https://en.wikipedia.org/wiki/Triangular_distribution">Wikipedia - Triangular distribution</a>.
            </summary>
            <remarks><para>The distribution will use the <see cref="T:System.Random"/> by default.
            Users can get/set the random number generator by using the <see cref="P:MathNet.Numerics.Distributions.Triangular.RandomSource"/> property.</para>
            <para>The statistics classes will check whether all the incoming parameters are in the allowed range. This might involve heavy computation. Optionally, by setting Control.CheckDistributionParameters
            to <c>false</c>, all parameter checks can be turned off.</para></remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.#ctor(System.Double,System.Double,System.Double)">
            <summary>
            Initializes a new instance of the Triangular class with the given lower bound, upper bound and mode.
            </summary>
            <param name="lower">Lower bound. Range: lower ≤ mode ≤ upper</param>
            <param name="upper">Upper bound. Range: lower ≤ mode ≤ upper</param>
            <param name="mode">Mode (most frequent value).  Range: lower ≤ mode ≤ upper</param>
            <exception cref="T:System.ArgumentException">If the upper bound is smaller than the mode or if the mode is smaller than the lower bound.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.#ctor(System.Double,System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the Triangular class with the given lower bound, upper bound and mode.
            </summary>
            <param name="lower">Lower bound. Range: lower ≤ mode ≤ upper</param>
            <param name="upper">Upper bound. Range: lower ≤ mode ≤ upper</param>
            <param name="mode">Mode (most frequent value).  Range: lower ≤ mode ≤ upper</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
            <exception cref="T:System.ArgumentException">If the upper bound is smaller than the mode or if the mode is smaller than the lower bound.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.IsValidParameterSet(System.Double,System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="lower">Lower bound. Range: lower ≤ mode ≤ upper</param>
            <param name="upper">Upper bound. Range: lower ≤ mode ≤ upper</param>
            <param name="mode">Mode (most frequent value).  Range: lower ≤ mode ≤ upper</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Triangular.LowerBound">
            <summary>
            Gets the lower bound of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Triangular.UpperBound">
            <summary>
            Gets the upper bound of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Triangular.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Triangular.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Triangular.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Triangular.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Triangular.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
            <value></value>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Triangular.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Triangular.Mode">
            <summary>
            Gets or sets the mode of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Triangular.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
            <value></value>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Triangular.Minimum">
            <summary>
            Gets the minimum of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Triangular.Maximum">
            <summary>
            Gets the maximum of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Triangular.PDF(System.Double,System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Triangular.PDFLn(System.Double,System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Triangular.CDF(System.Double,System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.InverseCumulativeDistribution(System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Triangular.InvCDF(System.Double,System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.Sample">
            <summary>
            Generates a sample from the <c>Triangular</c> distribution.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.Samples">
            <summary>
            Generates a sequence of samples from the <c>Triangular</c> distribution.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.PDF(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="lower">Lower bound. Range: lower ≤ mode ≤ upper</param>
            <param name="upper">Upper bound. Range: lower ≤ mode ≤ upper</param>
            <param name="mode">Mode (most frequent value).  Range: lower ≤ mode ≤ upper</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Triangular.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.PDFLn(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="lower">Lower bound. Range: lower ≤ mode ≤ upper</param>
            <param name="upper">Upper bound. Range: lower ≤ mode ≤ upper</param>
            <param name="mode">Mode (most frequent value).  Range: lower ≤ mode ≤ upper</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Triangular.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.CDF(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="lower">Lower bound. Range: lower ≤ mode ≤ upper</param>
            <param name="upper">Upper bound. Range: lower ≤ mode ≤ upper</param>
            <param name="mode">Mode (most frequent value).  Range: lower ≤ mode ≤ upper</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Triangular.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.InvCDF(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
            at the given probability. This is also known as the quantile or percent point function.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative density.</param>
            <param name="lower">Lower bound. Range: lower ≤ mode ≤ upper</param>
            <param name="upper">Upper bound. Range: lower ≤ mode ≤ upper</param>
            <param name="mode">Mode (most frequent value).  Range: lower ≤ mode ≤ upper</param>
            <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Triangular.InverseCumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.Sample(System.Random,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sample from the <c>Triangular</c> distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="lower">Lower bound. Range: lower ≤ mode ≤ upper</param>
            <param name="upper">Upper bound. Range: lower ≤ mode ≤ upper</param>
            <param name="mode">Mode (most frequent value).  Range: lower ≤ mode ≤ upper</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.Samples(System.Random,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the <c>Triangular</c> distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="lower">Lower bound. Range: lower ≤ mode ≤ upper</param>
            <param name="upper">Upper bound. Range: lower ≤ mode ≤ upper</param>
            <param name="mode">Mode (most frequent value).  Range: lower ≤ mode ≤ upper</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.Samples(System.Random,System.Double[],System.Double,System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="lower">Lower bound. Range: lower ≤ mode ≤ upper</param>
            <param name="upper">Upper bound. Range: lower ≤ mode ≤ upper</param>
            <param name="mode">Mode (most frequent value).  Range: lower ≤ mode ≤ upper</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.Sample(System.Double,System.Double,System.Double)">
            <summary>
            Generates a sample from the <c>Triangular</c> distribution.
            </summary>
            <param name="lower">Lower bound. Range: lower ≤ mode ≤ upper</param>
            <param name="upper">Upper bound. Range: lower ≤ mode ≤ upper</param>
            <param name="mode">Mode (most frequent value).  Range: lower ≤ mode ≤ upper</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.Samples(System.Double,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the <c>Triangular</c> distribution.
            </summary>
            <param name="lower">Lower bound. Range: lower ≤ mode ≤ upper</param>
            <param name="upper">Upper bound. Range: lower ≤ mode ≤ upper</param>
            <param name="mode">Mode (most frequent value).  Range: lower ≤ mode ≤ upper</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Triangular.Samples(System.Double[],System.Double,System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="lower">Lower bound. Range: lower ≤ mode ≤ upper</param>
            <param name="upper">Upper bound. Range: lower ≤ mode ≤ upper</param>
            <param name="mode">Mode (most frequent value).  Range: lower ≤ mode ≤ upper</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.TruncatedPareto.#ctor(System.Double,System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the TruncatedPareto class.
            </summary>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="truncation">The truncation (T) of the distribution. Range: T > xm.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
            <exception cref="T:System.ArgumentException">If <paramref name="scale"/> or <paramref name="shape"/> are non-positive or if T ≤ xm.</exception>
        </member>
        <member name="M:MathNet.Numerics.Distributions.TruncatedPareto.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.TruncatedPareto.IsValidParameterSet(System.Double,System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="truncation">The truncation (T) of the distribution. Range: T > xm.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.TruncatedPareto.RandomSource">
            <summary>
            Gets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.TruncatedPareto.Scale">
            <summary>
            Gets the scale (xm) of the distribution. Range: xm > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.TruncatedPareto.Shape">
            <summary>
            Gets the shape (α) of the distribution. Range: α > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.TruncatedPareto.Truncation">
            <summary>
            Gets the truncation (T) of the distribution. Range: T > 0.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.TruncatedPareto.GetMoment(System.Int32)">
            <summary>
            Gets the n-th raw moment of the distribution.
            </summary>
            <param name="n">The order (n) of the moment. Range: n ≥ 1.</param>
            <returns>the n-th moment of the distribution.</returns>
        </member>
        <member name="P:MathNet.Numerics.Distributions.TruncatedPareto.Mean">
            <summary>
            Gets the mean of the truncated Pareto distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.TruncatedPareto.Variance">
            <summary>
            Gets the variance of the truncated Pareto distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.TruncatedPareto.StdDev">
            <summary>
            Gets the standard deviation of the truncated Pareto distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.TruncatedPareto.Mode">
            <summary>
            Gets the mode of the truncated Pareto distribution (not supported).
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.TruncatedPareto.Minimum">
            <summary>
            Gets the minimum of the truncated Pareto distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.TruncatedPareto.Maximum">
            <summary>
            Gets the maximum of the truncated Pareto distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.TruncatedPareto.Entropy">
            <summary>
            Gets the entropy of the truncated Pareto distribution (not supported).
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.TruncatedPareto.Skewness">
            <summary>
            Gets the skewness of the truncated Pareto distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.TruncatedPareto.Median">
            <summary>
            Gets the median of the truncated Pareto distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.TruncatedPareto.Sample">
            <summary>
            Generates a sample from the truncated Pareto distribution.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.TruncatedPareto.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.TruncatedPareto.Samples">
            <summary>
            Generates a sequence of samples from the truncated Pareto distribution.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.TruncatedPareto.Sample(System.Random,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sample from the truncated Pareto distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="truncation">The truncation (T) of the distribution. Range: T > xm.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.TruncatedPareto.Samples(System.Random,System.Double[],System.Double,System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="truncation">The truncation (T) of the distribution. Range: T > xm.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.TruncatedPareto.Samples(System.Random,System.Double,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the truncated Pareto distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="truncation">The truncation (T) of the distribution. Range: T > xm.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.TruncatedPareto.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.TruncatedPareto.PDF(System.Double,System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.TruncatedPareto.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.TruncatedPareto.PDFLn(System.Double,System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.TruncatedPareto.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.TruncatedPareto.CDF(System.Double,System.Double,System.Double,System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.TruncatedPareto.InvCDF(System.Double)">
            <summary>
            Computes the inverse cumulative distribution (CDF) of the distribution at p, i.e. solving for P(X ≤ x) = p.
            </summary>
            <param name="p">The location at which to compute the inverse cumulative distribution function.</param>
            <returns>the inverse cumulative distribution at location <paramref name="p"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.TruncatedPareto.InvCDF(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the inverse cumulative distribution (CDF) of the distribution at p, i.e. solving for P(X ≤ x) = p.
            </summary>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="truncation">The truncation (T) of the distribution. Range: T > xm.</param>
            <param name="p">The location at which to compute the inverse cumulative distribution function.</param>
             <returns>the inverse cumulative distribution at location <paramref name="p"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.TruncatedPareto.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.TruncatedPareto.PDF(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="truncation">The truncation (T) of the distribution. Range: T > xm.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.TruncatedPareto.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.TruncatedPareto.PDFLn(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="truncation">The truncation (T) of the distribution. Range: T > xm.</param>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.TruncatedPareto.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.TruncatedPareto.CDF(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="scale">The scale (xm) of the distribution. Range: xm > 0.</param>
            <param name="shape">The shape (α) of the distribution. Range: α > 0.</param>
            <param name="truncation">The truncation (T) of the distribution. Range: T > xm.</param>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.TruncatedPareto.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Weibull">
            <summary>
            Continuous Univariate Weibull distribution.
            For details about this distribution, see
            <a href="http://en.wikipedia.org/wiki/Weibull_distribution">Wikipedia - Weibull distribution</a>.
            </summary>
            <remarks>
            The Weibull distribution is parametrized by a shape and scale parameter.
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.Distributions.Weibull._scalePowShapeInv">
            <summary>
            Reusable intermediate result 1 / (_scale ^ _shape)
            </summary>
            <remarks>
            By caching this parameter we can get slightly better numerics precision
            in certain constellations without any additional computations.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Weibull.#ctor(System.Double,System.Double)">
            <summary>
            Initializes a new instance of the Weibull class.
            </summary>
            <param name="shape">The shape (k) of the Weibull distribution. Range: k > 0.</param>
            <param name="scale">The scale (λ) of the Weibull distribution. Range: λ > 0.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Weibull.#ctor(System.Double,System.Double,System.Random)">
            <summary>
            Initializes a new instance of the Weibull class.
            </summary>
            <param name="shape">The shape (k) of the Weibull distribution. Range: k > 0.</param>
            <param name="scale">The scale (λ) of the Weibull distribution. Range: λ > 0.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Weibull.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Weibull.IsValidParameterSet(System.Double,System.Double)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="shape">The shape (k) of the Weibull distribution. Range: k > 0.</param>
            <param name="scale">The scale (λ) of the Weibull distribution. Range: λ > 0.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Weibull.Shape">
            <summary>
            Gets the shape (k) of the Weibull distribution. Range: k > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Weibull.Scale">
            <summary>
            Gets the scale (λ) of the Weibull distribution. Range: λ > 0.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Weibull.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Weibull.Mean">
            <summary>
            Gets the mean of the Weibull distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Weibull.Variance">
            <summary>
            Gets the variance of the Weibull distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Weibull.StdDev">
            <summary>
            Gets the standard deviation of the Weibull distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Weibull.Entropy">
            <summary>
            Gets the entropy of the Weibull distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Weibull.Skewness">
            <summary>
            Gets the skewness of the Weibull distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Weibull.Mode">
            <summary>
            Gets the mode of the Weibull distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Weibull.Median">
            <summary>
            Gets the median of the Weibull distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Weibull.Minimum">
            <summary>
            Gets the minimum of the Weibull distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Weibull.Maximum">
            <summary>
            Gets the maximum of the Weibull distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Weibull.Density(System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Weibull.DensityLn(System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="x">The location at which to compute the log density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Weibull.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Weibull.Sample">
            <summary>
            Generates a sample from the Weibull distribution.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Weibull.Samples(System.Double[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Weibull.Samples">
            <summary>
            Generates a sequence of samples from the Weibull distribution.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Weibull.PDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
            </summary>
            <param name="shape">The shape (k) of the Weibull distribution. Range: k > 0.</param>
            <param name="scale">The scale (λ) of the Weibull distribution. Range: λ > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Weibull.Density(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Weibull.PDFLn(System.Double,System.Double,System.Double)">
            <summary>
            Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
            </summary>
            <param name="shape">The shape (k) of the Weibull distribution. Range: k > 0.</param>
            <param name="scale">The scale (λ) of the Weibull distribution. Range: λ > 0.</param>
            <param name="x">The location at which to compute the density.</param>
            <returns>the log density at <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Weibull.DensityLn(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Weibull.CDF(System.Double,System.Double,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="shape">The shape (k) of the Weibull distribution. Range: k > 0.</param>
            <param name="scale">The scale (λ) of the Weibull distribution. Range: λ > 0.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Weibull.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Weibull.Estimate(System.Collections.Generic.IEnumerable{System.Double},System.Random)">
            <summary>
            Implemented according to: Parameter estimation of the Weibull probability distribution, 1994, Hongzhu Qiao, Chris P. Tsokos
            </summary>
            <param name="samples"></param>
            <param name="randomSource"></param>
            <returns>Returns a Weibull distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Weibull.Sample(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sample from the Weibull distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="shape">The shape (k) of the Weibull distribution. Range: k > 0.</param>
            <param name="scale">The scale (λ) of the Weibull distribution. Range: λ > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Weibull.Samples(System.Random,System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the Weibull distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="shape">The shape (k) of the Weibull distribution. Range: k > 0.</param>
            <param name="scale">The scale (λ) of the Weibull distribution. Range: λ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Weibull.Samples(System.Random,System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="shape">The shape (k) of the Weibull distribution. Range: k > 0.</param>
            <param name="scale">The scale (λ) of the Weibull distribution. Range: λ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Weibull.Sample(System.Double,System.Double)">
            <summary>
            Generates a sample from the Weibull distribution.
            </summary>
            <param name="shape">The shape (k) of the Weibull distribution. Range: k > 0.</param>
            <param name="scale">The scale (λ) of the Weibull distribution. Range: λ > 0.</param>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Weibull.Samples(System.Double,System.Double)">
            <summary>
            Generates a sequence of samples from the Weibull distribution.
            </summary>
            <param name="shape">The shape (k) of the Weibull distribution. Range: k > 0.</param>
            <param name="scale">The scale (λ) of the Weibull distribution. Range: λ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Weibull.Samples(System.Double[],System.Double,System.Double)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="shape">The shape (k) of the Weibull distribution. Range: k > 0.</param>
            <param name="scale">The scale (λ) of the Weibull distribution. Range: λ > 0.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Wishart">
            <summary>
            Multivariate Wishart distribution. This distribution is
            parameterized by the degrees of freedom nu and the scale matrix S. The Wishart distribution
            is the conjugate prior for the precision (inverse covariance) matrix of the multivariate
            normal distribution.
            <a href="http://en.wikipedia.org/wiki/Wishart_distribution">Wikipedia - Wishart distribution</a>.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Distributions.Wishart._degreesOfFreedom">
            <summary>
            The degrees of freedom for the Wishart distribution.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Distributions.Wishart._scale">
            <summary>
            The scale matrix for the Wishart distribution.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Distributions.Wishart._chol">
            <summary>
            Caches the Cholesky factorization of the scale matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Wishart.#ctor(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Wishart"/> class.
            </summary>
            <param name="degreesOfFreedom">The degrees of freedom (n) for the Wishart distribution.</param>
            <param name="scale">The scale matrix (V) for the Wishart distribution.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Wishart.#ctor(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double},System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Wishart"/> class.
            </summary>
            <param name="degreesOfFreedom">The degrees of freedom (n) for the Wishart distribution.</param>
            <param name="scale">The scale matrix (V) for the Wishart distribution.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Wishart.IsValidParameterSet(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="degreesOfFreedom">The degrees of freedom (n) for the Wishart distribution.</param>
            <param name="scale">The scale matrix (V) for the Wishart distribution.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Wishart.DegreesOfFreedom">
            <summary>
            Gets or sets the degrees of freedom (n) for the Wishart distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Wishart.Scale">
            <summary>
            Gets or sets the scale matrix (V) for the Wishart distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Wishart.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Wishart.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Wishart.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
            <value>The mean of the distribution.</value>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Wishart.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
            <value>The mode of the distribution.</value>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Wishart.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
            <value>The variance  of the distribution.</value>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Wishart.Density(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Evaluates the probability density function for the Wishart distribution.
            </summary>
            <param name="x">The matrix at which to evaluate the density at.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If the argument does not have the same dimensions as the scale matrix.</exception>
            <returns>the density at <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Wishart.Sample">
            <summary>
            Samples a Wishart distributed random variable using the method
                Algorithm AS 53: Wishart Variate Generator
                W. B. Smith and R. R. Hocking
                Applied Statistics, Vol. 21, No. 3 (1972), pp. 341-345
            </summary>
            <returns>A random number from this distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Wishart.Sample(System.Random,System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Samples a Wishart distributed random variable using the method
                Algorithm AS 53: Wishart Variate Generator
                W. B. Smith and R. R. Hocking
                Applied Statistics, Vol. 21, No. 3 (1972), pp. 341-345
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="degreesOfFreedom">The degrees of freedom (n) for the Wishart distribution.</param>
            <param name="scale">The scale matrix (V) for the Wishart distribution.</param>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Wishart.DoSample(System.Random,System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Factorization.Cholesky{System.Double})">
            <summary>
            Samples the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="degreesOfFreedom">The degrees of freedom (n) for the Wishart distribution.</param>
            <param name="scale">The scale matrix (V) for the Wishart distribution.</param>
            <param name="chol">The cholesky decomposition to use.</param>
            <returns>a random number from the distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Distributions.Zipf">
            <summary>
            Discrete Univariate Zipf distribution.
            Zipf's law, an empirical law formulated using mathematical statistics, refers to the fact
            that many types of data studied in the physical and social sciences can be approximated with
            a Zipfian distribution, one of a family of related discrete power law probability distributions.
            For details about this distribution, see
            <a href="http://en.wikipedia.org/wiki/Zipf%27s_law">Wikipedia - Zipf distribution</a>.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Distributions.Zipf._s">
            <summary>
            The s parameter of the distribution.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Distributions.Zipf._n">
            <summary>
            The n parameter of the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Zipf.#ctor(System.Double,System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Zipf"/> class.
            </summary>
            <param name="s">The s parameter of the distribution.</param>
            <param name="n">The n parameter of the distribution.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Zipf.#ctor(System.Double,System.Int32,System.Random)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Distributions.Zipf"/> class.
            </summary>
            <param name="s">The s parameter of the distribution.</param>
            <param name="n">The n parameter of the distribution.</param>
            <param name="randomSource">The random number generator which is used to draw random samples.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Zipf.ToString">
            <summary>
            A string representation of the distribution.
            </summary>
            <returns>a string representation of the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Zipf.IsValidParameterSet(System.Double,System.Int32)">
            <summary>
            Tests whether the provided values are valid parameters for this distribution.
            </summary>
            <param name="s">The s parameter of the distribution.</param>
            <param name="n">The n parameter of the distribution.</param>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Zipf.S">
            <summary>
            Gets or sets the s parameter of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Zipf.N">
            <summary>
            Gets or sets the n parameter of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Zipf.RandomSource">
            <summary>
            Gets or sets the random number generator which is used to draw random samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Zipf.Mean">
            <summary>
            Gets the mean of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Zipf.Variance">
            <summary>
            Gets the variance of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Zipf.StdDev">
            <summary>
            Gets the standard deviation of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Zipf.Entropy">
            <summary>
            Gets the entropy of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Zipf.Skewness">
            <summary>
            Gets the skewness of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Zipf.Mode">
            <summary>
            Gets the mode of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Zipf.Median">
            <summary>
            Gets the median of the distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Zipf.Minimum">
            <summary>
            Gets the smallest element in the domain of the distributions which can be represented by an integer.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Distributions.Zipf.Maximum">
            <summary>
            Gets the largest element in the domain of the distributions which can be represented by an integer.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Zipf.Probability(System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Zipf.ProbabilityLn(System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Zipf.CumulativeDistribution(System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Zipf.PMF(System.Double,System.Int32,System.Int32)">
            <summary>
            Computes the probability mass (PMF) at k, i.e. P(X = k).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the probability mass function.</param>
            <param name="s">The s parameter of the distribution.</param>
            <param name="n">The n parameter of the distribution.</param>
            <returns>the probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Zipf.PMFLn(System.Double,System.Int32,System.Int32)">
            <summary>
            Computes the log probability mass (lnPMF) at k, i.e. ln(P(X = k)).
            </summary>
            <param name="k">The location in the domain where we want to evaluate the log probability mass function.</param>
            <param name="s">The s parameter of the distribution.</param>
            <param name="n">The n parameter of the distribution.</param>
            <returns>the log probability mass at location <paramref name="k"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Zipf.CDF(System.Double,System.Int32,System.Double)">
            <summary>
            Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
            </summary>
            <param name="x">The location at which to compute the cumulative distribution function.</param>
            <param name="s">The s parameter of the distribution.</param>
            <param name="n">The n parameter of the distribution.</param>
            <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
            <seealso cref="M:MathNet.Numerics.Distributions.Zipf.CumulativeDistribution(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Zipf.SampleUnchecked(System.Random,System.Double,System.Int32)">
            <summary>
            Generates a sample from the Zipf distribution without doing parameter checking.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="s">The s parameter of the distribution.</param>
            <param name="n">The n parameter of the distribution.</param>
            <returns>a random number from the Zipf distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Zipf.Sample">
            <summary>
            Draws a random sample from the distribution.
            </summary>
            <returns>a sample from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Zipf.Samples(System.Int32[])">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Zipf.Samples">
            <summary>
            Samples an array of zipf distributed random variables.
            </summary>
            <returns>a sequence of samples from the distribution.</returns>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Zipf.Sample(System.Random,System.Double,System.Int32)">
            <summary>
            Samples a random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="s">The s parameter of the distribution.</param>
            <param name="n">The n parameter of the distribution.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Zipf.Samples(System.Random,System.Double,System.Int32)">
            <summary>
            Samples a sequence of this random variable.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="s">The s parameter of the distribution.</param>
            <param name="n">The n parameter of the distribution.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Zipf.Samples(System.Random,System.Int32[],System.Double,System.Int32)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="rnd">The random number generator to use.</param>
            <param name="values">The array to fill with the samples.</param>
            <param name="s">The s parameter of the distribution.</param>
            <param name="n">The n parameter of the distribution.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Zipf.Sample(System.Double,System.Int32)">
            <summary>
            Samples a random variable.
            </summary>
            <param name="s">The s parameter of the distribution.</param>
            <param name="n">The n parameter of the distribution.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Zipf.Samples(System.Double,System.Int32)">
            <summary>
            Samples a sequence of this random variable.
            </summary>
            <param name="s">The s parameter of the distribution.</param>
            <param name="n">The n parameter of the distribution.</param>
        </member>
        <member name="M:MathNet.Numerics.Distributions.Zipf.Samples(System.Int32[],System.Double,System.Int32)">
            <summary>
            Fills an array with samples generated from the distribution.
            </summary>
            <param name="values">The array to fill with the samples.</param>
            <param name="s">The s parameter of the distribution.</param>
            <param name="n">The n parameter of the distribution.</param>
        </member>
        <member name="T:MathNet.Numerics.Euclid">
            <summary>
            Integer number theory functions.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Euclid.Modulus(System.Double,System.Double)">
            <summary>
            Canonical Modulus. The result has the sign of the divisor.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Euclid.Modulus(System.Single,System.Single)">
            <summary>
            Canonical Modulus. The result has the sign of the divisor.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Euclid.Modulus(System.Int32,System.Int32)">
            <summary>
            Canonical Modulus. The result has the sign of the divisor.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Euclid.Modulus(System.Int64,System.Int64)">
            <summary>
            Canonical Modulus. The result has the sign of the divisor.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Euclid.Modulus(System.Numerics.BigInteger,System.Numerics.BigInteger)">
            <summary>
            Canonical Modulus. The result has the sign of the divisor.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Euclid.Remainder(System.Double,System.Double)">
            <summary>
            Remainder (% operator). The result has the sign of the dividend.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Euclid.Remainder(System.Single,System.Single)">
            <summary>
            Remainder (% operator). The result has the sign of the dividend.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Euclid.Remainder(System.Int32,System.Int32)">
            <summary>
            Remainder (% operator). The result has the sign of the dividend.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Euclid.Remainder(System.Int64,System.Int64)">
            <summary>
            Remainder (% operator). The result has the sign of the dividend.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Euclid.Remainder(System.Numerics.BigInteger,System.Numerics.BigInteger)">
            <summary>
            Remainder (% operator). The result has the sign of the dividend.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Euclid.IsEven(System.Int32)">
            <summary>
            Find out whether the provided 32 bit integer is an even number.
            </summary>
            <param name="number">The number to very whether it's even.</param>
            <returns>True if and only if it is an even number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Euclid.IsEven(System.Int64)">
            <summary>
            Find out whether the provided 64 bit integer is an even number.
            </summary>
            <param name="number">The number to very whether it's even.</param>
            <returns>True if and only if it is an even number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Euclid.IsOdd(System.Int32)">
            <summary>
            Find out whether the provided 32 bit integer is an odd number.
            </summary>
            <param name="number">The number to very whether it's odd.</param>
            <returns>True if and only if it is an odd number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Euclid.IsOdd(System.Int64)">
            <summary>
            Find out whether the provided 64 bit integer is an odd number.
            </summary>
            <param name="number">The number to very whether it's odd.</param>
            <returns>True if and only if it is an odd number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Euclid.IsPowerOfTwo(System.Int32)">
            <summary>
            Find out whether the provided 32 bit integer is a perfect power of two.
            </summary>
            <param name="number">The number to very whether it's a power of two.</param>
            <returns>True if and only if it is a power of two.</returns>
        </member>
        <member name="M:MathNet.Numerics.Euclid.IsPowerOfTwo(System.Int64)">
            <summary>
            Find out whether the provided 64 bit integer is a perfect power of two.
            </summary>
            <param name="number">The number to very whether it's a power of two.</param>
            <returns>True if and only if it is a power of two.</returns>
        </member>
        <member name="M:MathNet.Numerics.Euclid.IsPerfectSquare(System.Int32)">
            <summary>
            Find out whether the provided 32 bit integer is a perfect square, i.e. a square of an integer.
            </summary>
            <param name="number">The number to very whether it's a perfect square.</param>
            <returns>True if and only if it is a perfect square.</returns>
        </member>
        <member name="M:MathNet.Numerics.Euclid.IsPerfectSquare(System.Int64)">
            <summary>
            Find out whether the provided 64 bit integer is a perfect square, i.e. a square of an integer.
            </summary>
            <param name="number">The number to very whether it's a perfect square.</param>
            <returns>True if and only if it is a perfect square.</returns>
        </member>
        <member name="M:MathNet.Numerics.Euclid.PowerOfTwo(System.Int32)">
            <summary>
            Raises 2 to the provided integer exponent (0 &lt;= exponent &lt; 31).
            </summary>
            <param name="exponent">The exponent to raise 2 up to.</param>
            <returns>2 ^ exponent.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException"/>
        </member>
        <member name="M:MathNet.Numerics.Euclid.PowerOfTwo(System.Int64)">
            <summary>
            Raises 2 to the provided integer exponent (0 &lt;= exponent &lt; 63).
            </summary>
            <param name="exponent">The exponent to raise 2 up to.</param>
            <returns>2 ^ exponent.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException"/>
        </member>
        <member name="M:MathNet.Numerics.Euclid.Log2(System.Int32)">
            <summary>
            Evaluate the binary logarithm of an integer number.
            </summary>
            <remarks>Two-step method using a De Bruijn-like sequence table lookup.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Euclid.CeilingToPowerOfTwo(System.Int32)">
            <summary>
            Find the closest perfect power of two that is larger or equal to the provided
            32 bit integer.
            </summary>
            <param name="number">The number of which to find the closest upper power of two.</param>
            <returns>A power of two.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException"/>
        </member>
        <member name="M:MathNet.Numerics.Euclid.CeilingToPowerOfTwo(System.Int64)">
            <summary>
            Find the closest perfect power of two that is larger or equal to the provided
            64 bit integer.
            </summary>
            <param name="number">The number of which to find the closest upper power of two.</param>
            <returns>A power of two.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException"/>
        </member>
        <member name="M:MathNet.Numerics.Euclid.GreatestCommonDivisor(System.Int64,System.Int64)">
            <summary>
            Returns the greatest common divisor (<c>gcd</c>) of two integers using Euclid's algorithm.
            </summary>
            <param name="a">First Integer: a.</param>
            <param name="b">Second Integer: b.</param>
            <returns>Greatest common divisor <c>gcd</c>(a,b)</returns>
        </member>
        <member name="M:MathNet.Numerics.Euclid.GreatestCommonDivisor(System.Collections.Generic.IList{System.Int64})">
            <summary>
            Returns the greatest common divisor (<c>gcd</c>) of a set of integers using Euclid's
            algorithm.
            </summary>
            <param name="integers">List of Integers.</param>
            <returns>Greatest common divisor <c>gcd</c>(list of integers)</returns>
        </member>
        <member name="M:MathNet.Numerics.Euclid.GreatestCommonDivisor(System.Int64[])">
            <summary>
            Returns the greatest common divisor (<c>gcd</c>) of a set of integers using Euclid's algorithm.
            </summary>
            <param name="integers">List of Integers.</param>
            <returns>Greatest common divisor <c>gcd</c>(list of integers)</returns>
        </member>
        <member name="M:MathNet.Numerics.Euclid.ExtendedGreatestCommonDivisor(System.Int64,System.Int64,System.Int64@,System.Int64@)">
            <summary>
            Computes the extended greatest common divisor, such that a*x + b*y = <c>gcd</c>(a,b).
            </summary>
            <param name="a">First Integer: a.</param>
            <param name="b">Second Integer: b.</param>
            <param name="x">Resulting x, such that a*x + b*y = <c>gcd</c>(a,b).</param>
            <param name="y">Resulting y, such that a*x + b*y = <c>gcd</c>(a,b)</param>
            <returns>Greatest common divisor <c>gcd</c>(a,b)</returns>
            <example>
            <code>
            long x,y,d;
            d = Fn.GreatestCommonDivisor(45,18,out x, out y);
            -> d == 9 &amp;&amp; x == 1 &amp;&amp; y == -2
            </code>
            The <c>gcd</c> of 45 and 18 is 9: 18 = 2*9, 45 = 5*9. 9 = 1*45 -2*18, therefore x=1 and y=-2.
            </example>
        </member>
        <member name="M:MathNet.Numerics.Euclid.LeastCommonMultiple(System.Int64,System.Int64)">
            <summary>
            Returns the least common multiple (<c>lcm</c>) of two integers using Euclid's algorithm.
            </summary>
            <param name="a">First Integer: a.</param>
            <param name="b">Second Integer: b.</param>
            <returns>Least common multiple <c>lcm</c>(a,b)</returns>
        </member>
        <member name="M:MathNet.Numerics.Euclid.LeastCommonMultiple(System.Collections.Generic.IList{System.Int64})">
            <summary>
            Returns the least common multiple (<c>lcm</c>) of a set of integers using Euclid's algorithm.
            </summary>
            <param name="integers">List of Integers.</param>
            <returns>Least common multiple <c>lcm</c>(list of integers)</returns>
        </member>
        <member name="M:MathNet.Numerics.Euclid.LeastCommonMultiple(System.Int64[])">
            <summary>
            Returns the least common multiple (<c>lcm</c>) of a set of integers using Euclid's algorithm.
            </summary>
            <param name="integers">List of Integers.</param>
            <returns>Least common multiple <c>lcm</c>(list of integers)</returns>
        </member>
        <member name="M:MathNet.Numerics.Euclid.GreatestCommonDivisor(System.Numerics.BigInteger,System.Numerics.BigInteger)">
            <summary>
            Returns the greatest common divisor (<c>gcd</c>) of two big integers.
            </summary>
            <param name="a">First Integer: a.</param>
            <param name="b">Second Integer: b.</param>
            <returns>Greatest common divisor <c>gcd</c>(a,b)</returns>
        </member>
        <member name="M:MathNet.Numerics.Euclid.GreatestCommonDivisor(System.Collections.Generic.IList{System.Numerics.BigInteger})">
            <summary>
            Returns the greatest common divisor (<c>gcd</c>) of a set of big integers.
            </summary>
            <param name="integers">List of Integers.</param>
            <returns>Greatest common divisor <c>gcd</c>(list of integers)</returns>
        </member>
        <member name="M:MathNet.Numerics.Euclid.GreatestCommonDivisor(System.Numerics.BigInteger[])">
            <summary>
            Returns the greatest common divisor (<c>gcd</c>) of a set of big integers.
            </summary>
            <param name="integers">List of Integers.</param>
            <returns>Greatest common divisor <c>gcd</c>(list of integers)</returns>
        </member>
        <member name="M:MathNet.Numerics.Euclid.ExtendedGreatestCommonDivisor(System.Numerics.BigInteger,System.Numerics.BigInteger,System.Numerics.BigInteger@,System.Numerics.BigInteger@)">
            <summary>
            Computes the extended greatest common divisor, such that a*x + b*y = <c>gcd</c>(a,b).
            </summary>
            <param name="a">First Integer: a.</param>
            <param name="b">Second Integer: b.</param>
            <param name="x">Resulting x, such that a*x + b*y = <c>gcd</c>(a,b).</param>
            <param name="y">Resulting y, such that a*x + b*y = <c>gcd</c>(a,b)</param>
            <returns>Greatest common divisor <c>gcd</c>(a,b)</returns>
            <example>
            <code>
            long x,y,d;
            d = Fn.GreatestCommonDivisor(45,18,out x, out y);
            -> d == 9 &amp;&amp; x == 1 &amp;&amp; y == -2
            </code>
            The <c>gcd</c> of 45 and 18 is 9: 18 = 2*9, 45 = 5*9. 9 = 1*45 -2*18, therefore x=1 and y=-2.
            </example>
        </member>
        <member name="M:MathNet.Numerics.Euclid.LeastCommonMultiple(System.Numerics.BigInteger,System.Numerics.BigInteger)">
            <summary>
            Returns the least common multiple (<c>lcm</c>) of two big integers.
            </summary>
            <param name="a">First Integer: a.</param>
            <param name="b">Second Integer: b.</param>
            <returns>Least common multiple <c>lcm</c>(a,b)</returns>
        </member>
        <member name="M:MathNet.Numerics.Euclid.LeastCommonMultiple(System.Collections.Generic.IList{System.Numerics.BigInteger})">
            <summary>
            Returns the least common multiple (<c>lcm</c>) of a set of big integers.
            </summary>
            <param name="integers">List of Integers.</param>
            <returns>Least common multiple <c>lcm</c>(list of integers)</returns>
        </member>
        <member name="M:MathNet.Numerics.Euclid.LeastCommonMultiple(System.Numerics.BigInteger[])">
            <summary>
            Returns the least common multiple (<c>lcm</c>) of a set of big integers.
            </summary>
            <param name="integers">List of Integers.</param>
            <returns>Least common multiple <c>lcm</c>(list of integers)</returns>
        </member>
        <member name="T:MathNet.Numerics.ExcelFunctions">
            <summary>
            Collection of functions equivalent to those provided by Microsoft Excel
            but backed instead by Math.NET Numerics.
            We do not recommend to use them except in an intermediate phase when
            porting over solutions previously implemented in Excel.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.NonConvergenceException">
            <summary>
            An algorithm failed to converge.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.NumericalBreakdownException">
            <summary>
            An algorithm failed to converge due to a numerical breakdown.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.NativeInterfaceException">
            <summary>
            An error occurred calling native provider function.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.InvalidParameterException">
            <summary>
            An error occurred calling native provider function.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.MemoryAllocationException">
            <summary>
            Native provider was unable to allocate sufficient memory.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.SingularUMatrixException">
            <summary>
            Native provider failed LU inversion do to a singular U matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Financial.AbsoluteReturnMeasures.CompoundReturn(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Compound Monthly Return or Geometric Return or Annualized Return
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Financial.AbsoluteReturnMeasures.GainMean(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Average Gain or Gain Mean
            This is a simple average (arithmetic mean) of the periods with a gain. It is calculated by summing the returns for gain periods (return 0)
            and then dividing the total by the number of gain periods.
            </summary>
            <remarks>http://www.offshore-library.com/kb/statistics.php</remarks>
        </member>
        <member name="M:MathNet.Numerics.Financial.AbsoluteReturnMeasures.LossMean(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Average Loss or LossMean
            This is a simple average (arithmetic mean) of the periods with a loss. It is calculated by summing the returns for loss periods (return &lt; 0)
            and then dividing the total by the number of loss periods.
            </summary>
            <remarks>http://www.offshore-library.com/kb/statistics.php</remarks>
        </member>
        <member name="M:MathNet.Numerics.Financial.AbsoluteRiskMeasures.GainStandardDeviation(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Calculation is similar to Standard Deviation , except it calculates an average (mean) return only for periods with a gain
            and measures the variation of only the gain periods around the gain mean. Measures the volatility of upside performance.
            © Copyright 1996, 1999 Gary L.Gastineau. First Edition. © 1992 Swiss Bank Corporation.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Financial.AbsoluteRiskMeasures.LossStandardDeviation(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Similar to standard deviation, except this statistic calculates an average (mean) return for only the periods with a loss and then
            measures the variation of only the losing periods around this loss mean. This statistic measures the volatility of downside performance.
            </summary>
            <remarks>http://www.offshore-library.com/kb/statistics.php</remarks>
        </member>
        <member name="M:MathNet.Numerics.Financial.AbsoluteRiskMeasures.DownsideDeviation(System.Collections.Generic.IEnumerable{System.Double},System.Double)">
            <summary>
            This measure is similar to the loss standard deviation except the downside deviation
            considers only returns that fall below a defined minimum acceptable return (MAR) rather than the arithmetic mean.
            For example, if the MAR is 7%, the downside deviation would measure the variation of each period that falls below
            7%. (The loss standard deviation, on the other hand, would take only losing periods, calculate an average return for
            the losing periods, and then measure the variation between each losing return and the losing return average).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Financial.AbsoluteRiskMeasures.SemiDeviation(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            A measure of volatility in returns below the mean. It's similar to standard deviation, but it only
            looks at periods where the investment return was less than average return.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Financial.AbsoluteRiskMeasures.GainLossRatio(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Measures a fund’s average gain in a gain period divided by the fund’s average loss in a losing
            period. Periods can be monthly or quarterly depending on the data frequency.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.FindMinimum.OfScalarFunctionConstrained(System.Func{System.Double,System.Double},System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Find value x that minimizes the scalar function f(x), constrained within bounds, using the Golden Section algorithm.
            For more options and diagnostics consider to use <see cref="T:MathNet.Numerics.Optimization.GoldenSectionMinimizer"/> directly.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.FindMinimum.OfScalarFunction(System.Func{System.Double,System.Double},System.Double,System.Double,System.Int32)">
            <summary>
            Find vector x that minimizes the function f(x) using the Nelder-Mead Simplex algorithm.
            For more options and diagnostics consider to use <see cref="T:MathNet.Numerics.Optimization.NelderMeadSimplex"/> directly.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.FindMinimum.OfFunction(System.Func{System.Double,System.Double,System.Double},System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Find vector x that minimizes the function f(x) using the Nelder-Mead Simplex algorithm.
            For more options and diagnostics consider to use <see cref="T:MathNet.Numerics.Optimization.NelderMeadSimplex"/> directly.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.FindMinimum.OfFunction(System.Func{System.Double,System.Double,System.Double,System.Double},System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Find vector x that minimizes the function f(x) using the Nelder-Mead Simplex algorithm.
            For more options and diagnostics consider to use <see cref="T:MathNet.Numerics.Optimization.NelderMeadSimplex"/> directly.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.FindMinimum.OfFunction(System.Func{System.Double,System.Double,System.Double,System.Double,System.Double},System.Double,System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Find vector x that minimizes the function f(x) using the Nelder-Mead Simplex algorithm.
            For more options and diagnostics consider to use <see cref="T:MathNet.Numerics.Optimization.NelderMeadSimplex"/> directly.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.FindMinimum.OfFunction(System.Func{System.Double,System.Double,System.Double,System.Double,System.Double,System.Double},System.Double,System.Double,System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Find vector x that minimizes the function f(x) using the Nelder-Mead Simplex algorithm.
            For more options and diagnostics consider to use <see cref="T:MathNet.Numerics.Optimization.NelderMeadSimplex"/> directly.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.FindMinimum.OfFunction(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double,System.Int32)">
            <summary>
            Find vector x that minimizes the function f(x) using the Nelder-Mead Simplex algorithm.
            For more options and diagnostics consider to use <see cref="T:MathNet.Numerics.Optimization.NelderMeadSimplex"/> directly.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.FindMinimum.OfFunctionConstrained(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Find vector x that minimizes the function f(x), constrained within bounds, using the Broyden–Fletcher–Goldfarb–Shanno Bounded (BFGS-B) algorithm.
            The missing gradient is evaluated numerically (forward difference).
            For more options and diagnostics consider to use <see cref="T:MathNet.Numerics.Optimization.BfgsBMinimizer"/> directly.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.FindMinimum.OfFunctionGradient(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double},System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double}},MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Find vector x that minimizes the function f(x) using the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm.
            For more options and diagnostics consider to use <see cref="T:MathNet.Numerics.Optimization.BfgsMinimizer"/> directly.
            An alternative routine using conjugate gradients (CG) is available in <see cref="T:MathNet.Numerics.Optimization.ConjugateGradientMinimizer"/>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.FindMinimum.OfFunctionGradient(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.ValueTuple{System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double}}},MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Find vector x that minimizes the function f(x) using the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm.
            For more options and diagnostics consider to use <see cref="T:MathNet.Numerics.Optimization.BfgsMinimizer"/> directly.
            An alternative routine using conjugate gradients (CG) is available in <see cref="T:MathNet.Numerics.Optimization.ConjugateGradientMinimizer"/>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.FindMinimum.OfFunctionGradientConstrained(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double},System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double}},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Find vector x that minimizes the function f(x), constrained within bounds, using the Broyden–Fletcher–Goldfarb–Shanno Bounded (BFGS-B) algorithm.
            For more options and diagnostics consider to use <see cref="T:MathNet.Numerics.Optimization.BfgsBMinimizer"/> directly.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.FindMinimum.OfFunctionGradientConstrained(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.ValueTuple{System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double}}},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Find vector x that minimizes the function f(x), constrained within bounds, using the Broyden–Fletcher–Goldfarb–Shanno Bounded (BFGS-B) algorithm.
            For more options and diagnostics consider to use <see cref="T:MathNet.Numerics.Optimization.BfgsBMinimizer"/> directly.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.FindMinimum.OfFunctionGradientHessian(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double},System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double}},System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double}},MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double,System.Int32)">
            <summary>
            Find vector x that minimizes the function f(x) using the Newton algorithm.
            For more options and diagnostics consider to use <see cref="T:MathNet.Numerics.Optimization.NewtonMinimizer"/> directly.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.FindMinimum.OfFunctionGradientHessian(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.ValueTuple{System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double}}},MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double,System.Int32)">
            <summary>
            Find vector x that minimizes the function f(x) using the Newton algorithm.
            For more options and diagnostics consider to use <see cref="T:MathNet.Numerics.Optimization.NewtonMinimizer"/> directly.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.FindRoots.OfFunction(System.Func{System.Double,System.Double},System.Double,System.Double,System.Double,System.Int32)">
            <summary>Find a solution of the equation f(x)=0.</summary>
            <param name="f">The function to find roots from.</param>
            <param name="lowerBound">The low value of the range where the root is supposed to be.</param>
            <param name="upperBound">The high value of the range where the root is supposed to be.</param>
            <param name="accuracy">Desired accuracy. The root will be refined until the accuracy or the maximum number of iterations is reached. Example: 1e-14.</param>
            <param name="maxIterations">Maximum number of iterations. Example: 100.</param>
        </member>
        <member name="M:MathNet.Numerics.FindRoots.OfFunctionDerivative(System.Func{System.Double,System.Double},System.Func{System.Double,System.Double},System.Double,System.Double,System.Double,System.Int32)">
            <summary>Find a solution of the equation f(x)=0.</summary>
            <param name="f">The function to find roots from.</param>
            <param name="df">The first derivative of the function to find roots from.</param>
            <param name="lowerBound">The low value of the range where the root is supposed to be.</param>
            <param name="upperBound">The high value of the range where the root is supposed to be.</param>
            <param name="accuracy">Desired accuracy. The root will be refined until the accuracy or the maximum number of iterations is reached. Example: 1e-14.</param>
            <param name="maxIterations">Maximum number of iterations. Example: 100.</param>
        </member>
        <member name="M:MathNet.Numerics.FindRoots.Quadratic(System.Double,System.Double,System.Double)">
            <summary>
            Find both complex roots of the quadratic equation c + b*x + a*x^2 = 0.
            Note the special coefficient order ascending by exponent (consistent with polynomials).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.FindRoots.Cubic(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Find all three complex roots of the cubic equation d + c*x + b*x^2 + a*x^3 = 0.
            Note the special coefficient order ascending by exponent (consistent with polynomials).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.FindRoots.Polynomial(System.Double[])">
            <summary>
            Find all roots of a polynomial by calculating the characteristic polynomial of the companion matrix
            </summary>
            <param name="coefficients">The coefficients of the polynomial in ascending order, e.g. new double[] {5, 0, 2} = "5 + 0 x^1 + 2 x^2"</param>
            <returns>The roots of the polynomial</returns>
        </member>
        <member name="M:MathNet.Numerics.FindRoots.Polynomial(MathNet.Numerics.Polynomial)">
            <summary>
            Find all roots of a polynomial by calculating the characteristic polynomial of the companion matrix
            </summary>
            <param name="polynomial">The polynomial.</param>
            <returns>The roots of the polynomial</returns>
        </member>
        <member name="M:MathNet.Numerics.FindRoots.ChebychevPolynomialFirstKind(System.Int32,System.Double,System.Double)">
            <summary>
            Find all roots of the Chebychev polynomial of the first kind.
            </summary>
            <param name="degree">The polynomial order and therefore the number of roots.</param>
            <param name="intervalBegin">The real domain interval begin where to start sampling.</param>
            <param name="intervalEnd">The real domain interval end where to stop sampling.</param>
            <returns>Samples in [a,b] at (b+a)/2+(b-1)/2*cos(pi*(2i-1)/(2n))</returns>
        </member>
        <member name="M:MathNet.Numerics.FindRoots.ChebychevPolynomialSecondKind(System.Int32,System.Double,System.Double)">
            <summary>
            Find all roots of the Chebychev polynomial of the second kind.
            </summary>
            <param name="degree">The polynomial order and therefore the number of roots.</param>
            <param name="intervalBegin">The real domain interval begin where to start sampling.</param>
            <param name="intervalEnd">The real domain interval end where to stop sampling.</param>
            <returns>Samples in [a,b] at (b+a)/2+(b-1)/2*cos(pi*i/(n-1))</returns>
        </member>
        <member name="T:MathNet.Numerics.Fit">
            <summary>
            Least-Squares Curve Fitting Routines
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.Line(System.Double[],System.Double[])">
            <summary>
            Least-Squares fitting the points (x,y) to a line y : x -> a+b*x,
            returning its best fitting parameters as (a, b) tuple,
            where a is the intercept and b the slope.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.LineFunc(System.Double[],System.Double[])">
            <summary>
            Least-Squares fitting the points (x,y) to a line y : x -> a+b*x,
            returning a function y' for the best fitting line.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.LineThroughOrigin(System.Double[],System.Double[])">
            <summary>
            Least-Squares fitting the points (x,y) to a line through origin y : x -> b*x,
            returning its best fitting parameter b,
            where the intercept is zero and b the slope.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.LineThroughOriginFunc(System.Double[],System.Double[])">
            <summary>
            Least-Squares fitting the points (x,y) to a line through origin y : x -> b*x,
            returning a function y' for the best fitting line.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.Exponential(System.Double[],System.Double[],MathNet.Numerics.LinearRegression.DirectRegressionMethod)">
            <summary>
            Least-Squares fitting the points (x,y) to an exponential y : x -> a*exp(r*x),
            returning its best fitting parameters as (a, r) tuple.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.ExponentialFunc(System.Double[],System.Double[],MathNet.Numerics.LinearRegression.DirectRegressionMethod)">
            <summary>
            Least-Squares fitting the points (x,y) to an exponential y : x -> a*exp(r*x),
            returning a function y' for the best fitting line.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.Logarithm(System.Double[],System.Double[],MathNet.Numerics.LinearRegression.DirectRegressionMethod)">
            <summary>
            Least-Squares fitting the points (x,y) to a logarithm y : x -> a + b*ln(x),
            returning its best fitting parameters as (a, b) tuple.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.LogarithmFunc(System.Double[],System.Double[],MathNet.Numerics.LinearRegression.DirectRegressionMethod)">
            <summary>
            Least-Squares fitting the points (x,y) to a logarithm y : x -> a + b*ln(x),
            returning a function y' for the best fitting line.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.Power(System.Double[],System.Double[],MathNet.Numerics.LinearRegression.DirectRegressionMethod)">
            <summary>
            Least-Squares fitting the points (x,y) to a power y : x -> a*x^b,
            returning its best fitting parameters as (a, b) tuple.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.PowerFunc(System.Double[],System.Double[],MathNet.Numerics.LinearRegression.DirectRegressionMethod)">
            <summary>
            Least-Squares fitting the points (x,y) to a power y : x -> a*x^b,
            returning a function y' for the best fitting line.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.Polynomial(System.Double[],System.Double[],System.Int32,MathNet.Numerics.LinearRegression.DirectRegressionMethod)">
            <summary>
            Least-Squares fitting the points (x,y) to a k-order polynomial y : x -> p0 + p1*x + p2*x^2 + ... + pk*x^k,
            returning its best fitting parameters as [p0, p1, p2, ..., pk] array, compatible with Polynomial.Evaluate.
            A polynomial with order/degree k has (k+1) coefficients and thus requires at least (k+1) samples.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.PolynomialFunc(System.Double[],System.Double[],System.Int32,MathNet.Numerics.LinearRegression.DirectRegressionMethod)">
            <summary>
            Least-Squares fitting the points (x,y) to a k-order polynomial y : x -> p0 + p1*x + p2*x^2 + ... + pk*x^k,
            returning a function y' for the best fitting polynomial.
            A polynomial with order/degree k has (k+1) coefficients and thus requires at least (k+1) samples.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.PolynomialWeighted(System.Double[],System.Double[],System.Double[],System.Int32)">
            <summary>
            Weighted Least-Squares fitting the points (x,y) and weights w to a k-order polynomial y : x -> p0 + p1*x + p2*x^2 + ... + pk*x^k,
            returning its best fitting parameters as [p0, p1, p2, ..., pk] array, compatible with Polynomial.Evaluate.
            A polynomial with order/degree k has (k+1) coefficients and thus requires at least (k+1) samples.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.LinearCombination(System.Double[],System.Double[],System.Func{System.Double,System.Double}[])">
            <summary>
            Least-Squares fitting the points (x,y) to an arbitrary linear combination y : x -> p0*f0(x) + p1*f1(x) + ... + pk*fk(x),
            returning its best fitting parameters as [p0, p1, p2, ..., pk] array.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.LinearCombinationFunc(System.Double[],System.Double[],System.Func{System.Double,System.Double}[])">
            <summary>
            Least-Squares fitting the points (x,y) to an arbitrary linear combination y : x -> p0*f0(x) + p1*f1(x) + ... + pk*fk(x),
            returning a function y' for the best fitting combination.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.LinearCombination(System.Double[],System.Double[],MathNet.Numerics.LinearRegression.DirectRegressionMethod,System.Func{System.Double,System.Double}[])">
            <summary>
            Least-Squares fitting the points (x,y) to an arbitrary linear combination y : x -> p0*f0(x) + p1*f1(x) + ... + pk*fk(x),
            returning its best fitting parameters as [p0, p1, p2, ..., pk] array.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.LinearCombinationFunc(System.Double[],System.Double[],MathNet.Numerics.LinearRegression.DirectRegressionMethod,System.Func{System.Double,System.Double}[])">
            <summary>
            Least-Squares fitting the points (x,y) to an arbitrary linear combination y : x -> p0*f0(x) + p1*f1(x) + ... + pk*fk(x),
            returning a function y' for the best fitting combination.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.MultiDim(System.Double[][],System.Double[],System.Boolean,MathNet.Numerics.LinearRegression.DirectRegressionMethod)">
            <summary>
            Least-Squares fitting the points (X,y) = ((x0,x1,..,xk),y) to a linear surface y : X -> p0*x0 + p1*x1 + ... + pk*xk,
            returning its best fitting parameters as [p0, p1, p2, ..., pk] array.
            If an intercept is added, its coefficient will be prepended to the resulting parameters.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.MultiDimFunc(System.Double[][],System.Double[],System.Boolean,MathNet.Numerics.LinearRegression.DirectRegressionMethod)">
            <summary>
            Least-Squares fitting the points (X,y) = ((x0,x1,..,xk),y) to a linear surface y : X -> p0*x0 + p1*x1 + ... + pk*xk,
            returning a function y' for the best fitting combination.
            If an intercept is added, its coefficient will be prepended to the resulting parameters.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.MultiDimWeighted(System.Double[][],System.Double[],System.Double[])">
            <summary>
            Weighted Least-Squares fitting the points (X,y) = ((x0,x1,..,xk),y) and weights w to a linear surface y : X -> p0*x0 + p1*x1 + ... + pk*xk,
            returning its best fitting parameters as [p0, p1, p2, ..., pk] array.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.LinearMultiDim(System.Double[][],System.Double[],System.Func{System.Double[],System.Double}[])">
            <summary>
            Least-Squares fitting the points (X,y) = ((x0,x1,..,xk),y) to an arbitrary linear combination y : X -> p0*f0(x) + p1*f1(x) + ... + pk*fk(x),
            returning its best fitting parameters as [p0, p1, p2, ..., pk] array.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.LinearMultiDimFunc(System.Double[][],System.Double[],System.Func{System.Double[],System.Double}[])">
            <summary>
            Least-Squares fitting the points (X,y) = ((x0,x1,..,xk),y) to an arbitrary linear combination y : X -> p0*f0(x) + p1*f1(x) + ... + pk*fk(x),
            returning a function y' for the best fitting combination.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.LinearMultiDim(System.Double[][],System.Double[],MathNet.Numerics.LinearRegression.DirectRegressionMethod,System.Func{System.Double[],System.Double}[])">
            <summary>
            Least-Squares fitting the points (X,y) = ((x0,x1,..,xk),y) to an arbitrary linear combination y : X -> p0*f0(x) + p1*f1(x) + ... + pk*fk(x),
            returning its best fitting parameters as [p0, p1, p2, ..., pk] array.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.LinearMultiDimFunc(System.Double[][],System.Double[],MathNet.Numerics.LinearRegression.DirectRegressionMethod,System.Func{System.Double[],System.Double}[])">
            <summary>
            Least-Squares fitting the points (X,y) = ((x0,x1,..,xk),y) to an arbitrary linear combination y : X -> p0*f0(x) + p1*f1(x) + ... + pk*fk(x),
            returning a function y' for the best fitting combination.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.LinearGeneric``1(``0[],System.Double[],System.Func{``0,System.Double}[])">
            <summary>
            Least-Squares fitting the points (T,y) = (T,y) to an arbitrary linear combination y : X -> p0*f0(T) + p1*f1(T) + ... + pk*fk(T),
            returning its best fitting parameters as [p0, p1, p2, ..., pk] array.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.LinearGenericFunc``1(``0[],System.Double[],System.Func{``0,System.Double}[])">
            <summary>
            Least-Squares fitting the points (T,y) = (T,y) to an arbitrary linear combination y : X -> p0*f0(T) + p1*f1(T) + ... + pk*fk(T),
            returning a function y' for the best fitting combination.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.LinearGeneric``1(``0[],System.Double[],MathNet.Numerics.LinearRegression.DirectRegressionMethod,System.Func{``0,System.Double}[])">
            <summary>
            Least-Squares fitting the points (T,y) = (T,y) to an arbitrary linear combination y : X -> p0*f0(T) + p1*f1(T) + ... + pk*fk(T),
            returning its best fitting parameters as [p0, p1, p2, ..., pk] array.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.LinearGenericFunc``1(``0[],System.Double[],MathNet.Numerics.LinearRegression.DirectRegressionMethod,System.Func{``0,System.Double}[])">
            <summary>
            Least-Squares fitting the points (T,y) = (T,y) to an arbitrary linear combination y : X -> p0*f0(T) + p1*f1(T) + ... + pk*fk(T),
            returning a function y' for the best fitting combination.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.Curve(System.Double[],System.Double[],System.Func{System.Double,System.Double,System.Double},System.Double,System.Double,System.Int32)">
            <summary>
            Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p, x),
            returning its best fitting parameter p.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.Curve(System.Double[],System.Double[],System.Func{System.Double,System.Double,System.Double,System.Double},System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, x),
            returning its best fitting parameter p0 and p1.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.Curve(System.Double[],System.Double[],System.Func{System.Double,System.Double,System.Double,System.Double,System.Double},System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, p2, x),
            returning its best fitting parameter p0, p1 and p2.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.Curve(System.Double[],System.Double[],System.Func{System.Double,System.Double,System.Double,System.Double,System.Double,System.Double},System.Double,System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, p2, p3, x),
            returning its best fitting parameter p0, p1, p2 and p3.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.Curve(System.Double[],System.Double[],System.Func{System.Double,System.Double,System.Double,System.Double,System.Double,System.Double,System.Double},System.Double,System.Double,System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, p2, p3, p4, x),
            returning its best fitting parameter p0, p1, p2, p3 and p4.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.CurveFunc(System.Double[],System.Double[],System.Func{System.Double,System.Double,System.Double},System.Double,System.Double,System.Int32)">
            <summary>
            Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p, x),
            returning a function y' for the best fitting curve.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.CurveFunc(System.Double[],System.Double[],System.Func{System.Double,System.Double,System.Double,System.Double},System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, x),
            returning a function y' for the best fitting curve.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.CurveFunc(System.Double[],System.Double[],System.Func{System.Double,System.Double,System.Double,System.Double,System.Double},System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, p2, x),
            returning a function y' for the best fitting curve.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.CurveFunc(System.Double[],System.Double[],System.Func{System.Double,System.Double,System.Double,System.Double,System.Double,System.Double},System.Double,System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, p2, p3, x),
            returning a function y' for the best fitting curve.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Fit.CurveFunc(System.Double[],System.Double[],System.Func{System.Double,System.Double,System.Double,System.Double,System.Double,System.Double,System.Double},System.Double,System.Double,System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, p2, p3, p4, x),
            returning a function y' for the best fitting curve.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.Map``2(``0[],System.Func{``0,``1})">
            <summary>
            Generate samples by sampling a function at the provided points.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.MapSequence``2(System.Collections.Generic.IEnumerable{``0},System.Func{``0,``1})">
            <summary>
            Generate a sample sequence by sampling a function at the provided point sequence.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.Map2``3(``0[],``1[],System.Func{``0,``1,``2})">
            <summary>
            Generate samples by sampling a function at the provided points.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.Map2Sequence``3(System.Collections.Generic.IEnumerable{``0},System.Collections.Generic.IEnumerable{``1},System.Func{``0,``1,``2})">
            <summary>
            Generate a sample sequence by sampling a function at the provided point sequence.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.LinearSpaced(System.Int32,System.Double,System.Double)">
            <summary>
            Generate a linearly spaced sample vector of the given length between the specified values (inclusive).
            Equivalent to MATLAB linspace but with the length as first instead of last argument.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.LinearSpacedMap``1(System.Int32,System.Double,System.Double,System.Func{System.Double,``0})">
            <summary>
            Generate samples by sampling a function at linearly spaced points between the specified values (inclusive).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.LogSpaced(System.Int32,System.Double,System.Double)">
            <summary>
            Generate a base 10 logarithmically spaced sample vector of the given length between the specified decade exponents (inclusive).
            Equivalent to MATLAB logspace but with the length as first instead of last argument.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.LogSpacedMap``1(System.Int32,System.Double,System.Double,System.Func{System.Double,``0})">
            <summary>
            Generate samples by sampling a function at base 10 logarithmically spaced points between the specified decade exponents (inclusive).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.LinearRange(System.Int32,System.Int32)">
            <summary>
            Generate a linearly spaced sample vector within the inclusive interval (start, stop) and step 1.
            Equivalent to MATLAB colon operator (:).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.LinearRangeInt32(System.Int32,System.Int32)">
            <summary>
            Generate a linearly spaced sample vector within the inclusive interval (start, stop) and step 1.
            Equivalent to MATLAB colon operator (:).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.LinearRange(System.Int32,System.Int32,System.Int32)">
            <summary>
            Generate a linearly spaced sample vector within the inclusive interval (start, stop) and the provided step.
            The start value is aways included as first value, but stop is only included if it stop-start is a multiple of step.
            Equivalent to MATLAB double colon operator (::).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.LinearRangeInt32(System.Int32,System.Int32,System.Int32)">
            <summary>
            Generate a linearly spaced sample vector within the inclusive interval (start, stop) and the provided step.
            The start value is aways included as first value, but stop is only included if it stop-start is a multiple of step.
            Equivalent to MATLAB double colon operator (::).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.LinearRange(System.Double,System.Double,System.Double)">
            <summary>
            Generate a linearly spaced sample vector within the inclusive interval (start, stop) and the provide step.
            The start value is aways included as first value, but stop is only included if it stop-start is a multiple of step.
            Equivalent to MATLAB double colon operator (::).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.LinearRangeMap``1(System.Double,System.Double,System.Double,System.Func{System.Double,``0})">
            <summary>
            Generate samples by sampling a function at linearly spaced points within the inclusive interval (start, stop) and the provide step.
            The start value is aways included as first value, but stop is only included if it stop-start is a multiple of step.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.Periodic(System.Int32,System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Create a periodic wave.
            </summary>
            <param name="length">The number of samples to generate.</param>
            <param name="samplingRate">Samples per time unit (Hz). Must be larger than twice the frequency to satisfy the Nyquist criterion.</param>
            <param name="frequency">Frequency in periods per time unit (Hz).</param>
            <param name="amplitude">The length of the period when sampled at one sample per time unit. This is the interval of the periodic domain, a typical value is 1.0, or 2*Pi for angular functions.</param>
            <param name="phase">Optional phase offset.</param>
            <param name="delay">Optional delay, relative to the phase.</param>
        </member>
        <member name="M:MathNet.Numerics.Generate.PeriodicMap``1(System.Int32,System.Func{System.Double,``0},System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Create a periodic wave.
            </summary>
            <param name="length">The number of samples to generate.</param>
            <param name="map">The function to apply to each of the values and evaluate the resulting sample.</param>
            <param name="samplingRate">Samples per time unit (Hz). Must be larger than twice the frequency to satisfy the Nyquist criterion.</param>
            <param name="frequency">Frequency in periods per time unit (Hz).</param>
            <param name="amplitude">The length of the period when sampled at one sample per time unit. This is the interval of the periodic domain, a typical value is 1.0, or 2*Pi for angular functions.</param>
            <param name="phase">Optional phase offset.</param>
            <param name="delay">Optional delay, relative to the phase.</param>
        </member>
        <member name="M:MathNet.Numerics.Generate.PeriodicSequence(System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Create an infinite periodic wave sequence.
            </summary>
            <param name="samplingRate">Samples per time unit (Hz). Must be larger than twice the frequency to satisfy the Nyquist criterion.</param>
            <param name="frequency">Frequency in periods per time unit (Hz).</param>
            <param name="amplitude">The length of the period when sampled at one sample per time unit. This is the interval of the periodic domain, a typical value is 1.0, or 2*Pi for angular functions.</param>
            <param name="phase">Optional phase offset.</param>
            <param name="delay">Optional delay, relative to the phase.</param>
        </member>
        <member name="M:MathNet.Numerics.Generate.PeriodicMapSequence``1(System.Func{System.Double,``0},System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Create an infinite periodic wave sequence.
            </summary>
            <param name="map">The function to apply to each of the values and evaluate the resulting sample.</param>
            <param name="samplingRate">Samples per time unit (Hz). Must be larger than twice the frequency to satisfy the Nyquist criterion.</param>
            <param name="frequency">Frequency in periods per time unit (Hz).</param>
            <param name="amplitude">The length of the period when sampled at one sample per time unit. This is the interval of the periodic domain, a typical value is 1.0, or 2*Pi for angular functions.</param>
            <param name="phase">Optional phase offset.</param>
            <param name="delay">Optional delay, relative to the phase.</param>
        </member>
        <member name="M:MathNet.Numerics.Generate.Sinusoidal(System.Int32,System.Double,System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Create a Sine wave.
            </summary>
            <param name="length">The number of samples to generate.</param>
            <param name="samplingRate">Samples per time unit (Hz). Must be larger than twice the frequency to satisfy the Nyquist criterion.</param>
            <param name="frequency">Frequency in periods per time unit (Hz).</param>
            <param name="amplitude">The maximal reached peak.</param>
            <param name="mean">The mean, or DC part, of the signal.</param>
            <param name="phase">Optional phase offset.</param>
            <param name="delay">Optional delay, relative to the phase.</param>
        </member>
        <member name="M:MathNet.Numerics.Generate.SinusoidalSequence(System.Double,System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Create an infinite Sine wave sequence.
            </summary>
            <param name="samplingRate">Samples per unit.</param>
            <param name="frequency">Frequency in samples per unit.</param>
            <param name="amplitude">The maximal reached peak.</param>
            <param name="mean">The mean, or DC part, of the signal.</param>
            <param name="phase">Optional phase offset.</param>
            <param name="delay">Optional delay, relative to the phase.</param>
        </member>
        <member name="M:MathNet.Numerics.Generate.Square(System.Int32,System.Int32,System.Int32,System.Double,System.Double,System.Int32)">
            <summary>
            Create a periodic square wave, starting with the high phase.
            </summary>
            <param name="length">The number of samples to generate.</param>
            <param name="highDuration">Number of samples of the high phase.</param>
            <param name="lowDuration">Number of samples of the low phase.</param>
            <param name="lowValue">Sample value to be emitted during the low phase.</param>
            <param name="highValue">Sample value to be emitted during the high phase.</param>
            <param name="delay">Optional delay.</param>
        </member>
        <member name="M:MathNet.Numerics.Generate.SquareSequence(System.Int32,System.Int32,System.Double,System.Double,System.Int32)">
            <summary>
            Create an infinite periodic square wave sequence, starting with the high phase.
            </summary>
            <param name="highDuration">Number of samples of the high phase.</param>
            <param name="lowDuration">Number of samples of the low phase.</param>
            <param name="lowValue">Sample value to be emitted during the low phase.</param>
            <param name="highValue">Sample value to be emitted during the high phase.</param>
            <param name="delay">Optional delay.</param>
        </member>
        <member name="M:MathNet.Numerics.Generate.Triangle(System.Int32,System.Int32,System.Int32,System.Double,System.Double,System.Int32)">
            <summary>
            Create a periodic triangle wave, starting with the raise phase from the lowest sample.
            </summary>
            <param name="length">The number of samples to generate.</param>
            <param name="raiseDuration">Number of samples of the raise phase.</param>
            <param name="fallDuration">Number of samples of the fall phase.</param>
            <param name="lowValue">Lowest sample value.</param>
            <param name="highValue">Highest sample value.</param>
            <param name="delay">Optional delay.</param>
        </member>
        <member name="M:MathNet.Numerics.Generate.TriangleSequence(System.Int32,System.Int32,System.Double,System.Double,System.Int32)">
            <summary>
            Create an infinite periodic triangle wave sequence, starting with the raise phase from the lowest sample.
            </summary>
            <param name="raiseDuration">Number of samples of the raise phase.</param>
            <param name="fallDuration">Number of samples of the fall phase.</param>
            <param name="lowValue">Lowest sample value.</param>
            <param name="highValue">Highest sample value.</param>
            <param name="delay">Optional delay.</param>
        </member>
        <member name="M:MathNet.Numerics.Generate.Sawtooth(System.Int32,System.Int32,System.Double,System.Double,System.Int32)">
            <summary>
            Create a periodic sawtooth wave, starting with the lowest sample.
            </summary>
            <param name="length">The number of samples to generate.</param>
            <param name="period">Number of samples a full sawtooth period.</param>
            <param name="lowValue">Lowest sample value.</param>
            <param name="highValue">Highest sample value.</param>
            <param name="delay">Optional delay.</param>
        </member>
        <member name="M:MathNet.Numerics.Generate.SawtoothSequence(System.Int32,System.Double,System.Double,System.Int32)">
            <summary>
            Create an infinite periodic sawtooth wave sequence, starting with the lowest sample.
            </summary>
            <param name="period">Number of samples a full sawtooth period.</param>
            <param name="lowValue">Lowest sample value.</param>
            <param name="highValue">Highest sample value.</param>
            <param name="delay">Optional delay.</param>
        </member>
        <member name="M:MathNet.Numerics.Generate.Repeat``1(System.Int32,``0)">
            <summary>
            Create an array with each field set to the same value.
            </summary>
            <param name="length">The number of samples to generate.</param>
            <param name="value">The value that each field should be set to.</param>
        </member>
        <member name="M:MathNet.Numerics.Generate.RepeatSequence``1(``0)">
            <summary>
            Create an infinite sequence where each element has the same value.
            </summary>
            <param name="value">The value that each element should be set to.</param>
        </member>
        <member name="M:MathNet.Numerics.Generate.Step(System.Int32,System.Double,System.Int32)">
            <summary>
            Create a Heaviside Step sample vector.
            </summary>
            <param name="length">The number of samples to generate.</param>
            <param name="amplitude">The maximal reached peak.</param>
            <param name="delay">Offset to the time axis.</param>
        </member>
        <member name="M:MathNet.Numerics.Generate.StepSequence(System.Double,System.Int32)">
            <summary>
            Create an infinite Heaviside Step sample sequence.
            </summary>
            <param name="amplitude">The maximal reached peak.</param>
            <param name="delay">Offset to the time axis.</param>
        </member>
        <member name="M:MathNet.Numerics.Generate.Impulse(System.Int32,System.Double,System.Int32)">
            <summary>
            Create a Kronecker Delta impulse sample vector.
            </summary>
            <param name="length">The number of samples to generate.</param>
            <param name="amplitude">The maximal reached peak.</param>
            <param name="delay">Offset to the time axis. Zero or positive.</param>
        </member>
        <member name="M:MathNet.Numerics.Generate.ImpulseSequence(System.Double,System.Int32)">
            <summary>
            Create a Kronecker Delta impulse sample vector.
            </summary>
            <param name="amplitude">The maximal reached peak.</param>
            <param name="delay">Offset to the time axis, hence the sample index of the impulse.</param>
        </member>
        <member name="M:MathNet.Numerics.Generate.PeriodicImpulse(System.Int32,System.Int32,System.Double,System.Int32)">
            <summary>
            Create a periodic Kronecker Delta impulse sample vector.
            </summary>
            <param name="length">The number of samples to generate.</param>
            <param name="period">impulse sequence period.</param>
            <param name="amplitude">The maximal reached peak.</param>
            <param name="delay">Offset to the time axis. Zero or positive.</param>
        </member>
        <member name="M:MathNet.Numerics.Generate.PeriodicImpulseSequence(System.Int32,System.Double,System.Int32)">
            <summary>
            Create a Kronecker Delta impulse sample vector.
            </summary>
            <param name="period">impulse sequence period.</param>
            <param name="amplitude">The maximal reached peak.</param>
            <param name="delay">Offset to the time axis. Zero or positive.</param>
        </member>
        <member name="M:MathNet.Numerics.Generate.Unfold``2(System.Int32,System.Func{``1,System.Tuple{``0,``1}},``1)">
            <summary>
            Generate samples generated by the given computation.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.Unfold``2(System.Int32,System.Func{``1,System.ValueTuple{``0,``1}},``1)">
            <summary>
            Generate samples generated by the given computation.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.UnfoldSequence``2(System.Func{``1,System.Tuple{``0,``1}},``1)">
            <summary>
            Generate an infinite sequence generated by the given computation.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.UnfoldSequence``2(System.Func{``1,System.ValueTuple{``0,``1}},``1)">
            <summary>
            Generate an infinite sequence generated by the given computation.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.Fibonacci(System.Int32)">
            <summary>
            Generate a Fibonacci sequence, including zero as first value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.FibonacciSequence">
            <summary>
            Generate an infinite Fibonacci sequence, including zero as first value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.Uniform(System.Int32)">
            <summary>
            Create random samples, uniform between 0 and 1.
            Faster than other methods but with reduced guarantees on randomness.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.UniformSequence">
            <summary>
            Create an infinite random sample sequence, uniform between 0 and 1.
            Faster than other methods but with reduced guarantees on randomness.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.UniformMap``1(System.Int32,System.Func{System.Double,``0})">
            <summary>
            Generate samples by sampling a function at samples from a probability distribution, uniform between 0 and 1.
            Faster than other methods but with reduced guarantees on randomness.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.UniformMapSequence``1(System.Func{System.Double,``0})">
            <summary>
            Generate a sample sequence by sampling a function at samples from a probability distribution, uniform between 0 and 1.
            Faster than other methods but with reduced guarantees on randomness.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.UniformMap2``1(System.Int32,System.Func{System.Double,System.Double,``0})">
            <summary>
            Generate samples by sampling a function at sample pairs from a probability distribution, uniform between 0 and 1.
            Faster than other methods but with reduced guarantees on randomness.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.UniformMap2Sequence``1(System.Func{System.Double,System.Double,``0})">
            <summary>
            Generate a sample sequence by sampling a function at sample pairs from a probability distribution, uniform between 0 and 1.
            Faster than other methods but with reduced guarantees on randomness.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.Standard(System.Int32)">
            <summary>
            Create samples with independent amplitudes of standard distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.StandardSequence">
            <summary>
            Create an infinite sample sequence with independent amplitudes of standard distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.Normal(System.Int32,System.Double,System.Double)">
            <summary>
            Create samples with independent amplitudes of normal distribution and a flat spectral density.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.NormalSequence(System.Double,System.Double)">
            <summary>
            Create an infinite sample sequence with independent amplitudes of normal distribution and a flat spectral density.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.Random(System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create random samples.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.Random(MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create an infinite random sample sequence.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.RandomSingle(System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create random samples.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.RandomSingle(MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create an infinite random sample sequence.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.RandomComplex(System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create random samples.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.RandomComplex(MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create an infinite random sample sequence.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.RandomComplex32(System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create random samples.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.RandomComplex32(MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create an infinite random sample sequence.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.RandomMap``1(System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution,System.Func{System.Double,``0})">
            <summary>
            Generate samples by sampling a function at samples from a probability distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.RandomMapSequence``1(MathNet.Numerics.Distributions.IContinuousDistribution,System.Func{System.Double,``0})">
            <summary>
            Generate a sample sequence by sampling a function at samples from a probability distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.RandomMap2``1(System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution,System.Func{System.Double,System.Double,``0})">
            <summary>
            Generate samples by sampling a function at sample pairs from a probability distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Generate.RandomMap2Sequence``1(MathNet.Numerics.Distributions.IContinuousDistribution,System.Func{System.Double,System.Double,``0})">
            <summary>
            Generate a sample sequence by sampling a function at sample pairs from a probability distribution.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.GlobalizationHelper">
            <summary>
            Globalized String Handling Helpers
            </summary>
        </member>
        <member name="M:MathNet.Numerics.GlobalizationHelper.GetCultureInfo(System.IFormatProvider)">
            <summary>
            Tries to get a <see cref="T:System.Globalization.CultureInfo"/> from the format provider,
            returning the current culture if it fails.
            </summary>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific
            formatting information.
            </param>
            <returns>A <see cref="T:System.Globalization.CultureInfo"/> instance.</returns>
        </member>
        <member name="M:MathNet.Numerics.GlobalizationHelper.GetNumberFormatInfo(System.IFormatProvider)">
            <summary>
            Tries to get a <see cref="T:System.Globalization.NumberFormatInfo"/> from the format
            provider, returning the current culture if it fails.
            </summary>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific
            formatting information.
            </param>
            <returns>A <see cref="T:System.Globalization.NumberFormatInfo"/> instance.</returns>
        </member>
        <member name="M:MathNet.Numerics.GlobalizationHelper.GetTextInfo(System.IFormatProvider)">
            <summary>
            Tries to get a <see cref="T:System.Globalization.TextInfo"/> from the format provider, returning the current culture if it fails.
            </summary>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific
            formatting information.
            </param>
            <returns>A <see cref="T:System.Globalization.TextInfo"/> instance.</returns>
        </member>
        <member name="M:MathNet.Numerics.GlobalizationHelper.Tokenize(System.Collections.Generic.LinkedListNode{System.String},System.String[],System.Int32)">
            <summary>
            Globalized Parsing: Tokenize a node by splitting it into several nodes.
            </summary>
            <param name="node">Node that contains the trimmed string to be tokenized.</param>
            <param name="keywords">List of keywords to tokenize by.</param>
            <param name="skip">keywords to skip looking for (because they've already been handled).</param>
        </member>
        <member name="M:MathNet.Numerics.GlobalizationHelper.ParseDouble(System.Collections.Generic.LinkedListNode{System.String}@,System.Globalization.CultureInfo)">
            <summary>
            Globalized Parsing: Parse a double number
            </summary>
            <param name="token">First token of the number.</param>
            <param name="culture">Culture Info.</param>
            <returns>The parsed double number using the given culture information.</returns>
            <exception cref="T:System.FormatException" />
        </member>
        <member name="M:MathNet.Numerics.GlobalizationHelper.ParseSingle(System.Collections.Generic.LinkedListNode{System.String}@,System.Globalization.CultureInfo)">
            <summary>
            Globalized Parsing: Parse a float number
            </summary>
            <param name="token">First token of the number.</param>
            <param name="culture">Culture Info.</param>
            <returns>The parsed float number using the given culture information.</returns>
            <exception cref="T:System.FormatException" />
        </member>
        <member name="M:MathNet.Numerics.GoodnessOfFit.RSquared(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Calculates r^2, the square of the sample correlation coefficient between
            the observed outcomes and the observed predictor values.
            Not to be confused with R^2, the coefficient of determination, see <see cref="M:MathNet.Numerics.GoodnessOfFit.CoefficientOfDetermination(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})"/>.
            </summary>
            <param name="modelledValues">The modelled/predicted values</param>
            <param name="observedValues">The observed/actual values</param>
            <returns>Squared Person product-momentum correlation coefficient.</returns>
        </member>
        <member name="M:MathNet.Numerics.GoodnessOfFit.R(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Calculates r, the sample correlation coefficient between the observed outcomes
            and the observed predictor values.
            </summary>
            <param name="modelledValues">The modelled/predicted values</param>
            <param name="observedValues">The observed/actual values</param>
            <returns>Person product-momentum correlation coefficient.</returns>
        </member>
        <member name="M:MathNet.Numerics.GoodnessOfFit.PopulationStandardError(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Calculates the Standard Error of the regression, given a sequence of
            modeled/predicted values, and a sequence of actual/observed values
            </summary>
            <param name="modelledValues">The modelled/predicted values</param>
            <param name="observedValues">The observed/actual values</param>
            <returns>The Standard Error of the regression</returns>
        </member>
        <member name="M:MathNet.Numerics.GoodnessOfFit.StandardError(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double},System.Int32)">
            <summary>
            Calculates the Standard Error of the regression, given a sequence of
            modeled/predicted values, and a sequence of actual/observed values
            </summary>
            <param name="modelledValues">The modelled/predicted values</param>
            <param name="observedValues">The observed/actual values</param>
            <param name="degreesOfFreedom">The degrees of freedom by which the
            number of samples is reduced for performing the Standard Error calculation</param>
            <returns>The Standard Error of the regression</returns>
        </member>
        <member name="M:MathNet.Numerics.GoodnessOfFit.CoefficientOfDetermination(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Calculates the R-Squared value, also known as coefficient of determination,
            given some modelled and observed values.
            </summary>
            <param name="modelledValues">The values expected from the model.</param>
            <param name="observedValues">The actual values obtained.</param>
            <returns>Coefficient of determination.</returns>
        </member>
        <member name="T:MathNet.Numerics.IntegralTransforms.Fourier">
            <summary>
            Complex Fast (FFT) Implementation of the Discrete Fourier Transform (DFT).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.Forward(MathNet.Numerics.Complex32[])">
            <summary>
            Applies the forward Fast Fourier Transform (FFT) to arbitrary-length sample vectors.
            </summary>
            <param name="samples">Sample vector, where the FFT is evaluated in place.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.Forward(System.Numerics.Complex[])">
            <summary>
            Applies the forward Fast Fourier Transform (FFT) to arbitrary-length sample vectors.
            </summary>
            <param name="samples">Sample vector, where the FFT is evaluated in place.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.Forward(MathNet.Numerics.Complex32[],MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Applies the forward Fast Fourier Transform (FFT) to arbitrary-length sample vectors.
            </summary>
            <param name="samples">Sample vector, where the FFT is evaluated in place.</param>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.Forward(System.Numerics.Complex[],MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Applies the forward Fast Fourier Transform (FFT) to arbitrary-length sample vectors.
            </summary>
            <param name="samples">Sample vector, where the FFT is evaluated in place.</param>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.Forward(System.Single[],System.Single[],MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Applies the forward Fast Fourier Transform (FFT) to arbitrary-length sample vectors.
            </summary>
            <param name="real">Real part of the sample vector, where the FFT is evaluated in place.</param>
            <param name="imaginary">Imaginary part of the sample vector, where the FFT is evaluated in place.</param>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.Forward(System.Double[],System.Double[],MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Applies the forward Fast Fourier Transform (FFT) to arbitrary-length sample vectors.
            </summary>
            <param name="real">Real part of the sample vector, where the FFT is evaluated in place.</param>
            <param name="imaginary">Imaginary part of the sample vector, where the FFT is evaluated in place.</param>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.ForwardReal(System.Single[],System.Int32,MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Packed Real-Complex forward Fast Fourier Transform (FFT) to arbitrary-length sample vectors.
            Since for real-valued time samples the complex spectrum is conjugate-even (symmetry),
            the spectrum can be fully reconstructed from the positive frequencies only (first half).
            The data array needs to be N+2 (if N is even) or N+1 (if N is odd) long in order to support such a packed spectrum.
            </summary>
            <param name="data">Data array of length N+2 (if N is even) or N+1 (if N is odd).</param>
            <param name="n">The number of samples.</param>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.ForwardReal(System.Double[],System.Int32,MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Packed Real-Complex forward Fast Fourier Transform (FFT) to arbitrary-length sample vectors.
            Since for real-valued time samples the complex spectrum is conjugate-even (symmetry),
            the spectrum can be fully reconstructed form the positive frequencies only (first half).
            The data array needs to be N+2 (if N is even) or N+1 (if N is odd) long in order to support such a packed spectrum.
            </summary>
            <param name="data">Data array of length N+2 (if N is even) or N+1 (if N is odd).</param>
            <param name="n">The number of samples.</param>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.ForwardMultiDim(MathNet.Numerics.Complex32[],System.Int32[],MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Applies the forward Fast Fourier Transform (FFT) to multiple dimensional sample data.
            </summary>
            <param name="samples">Sample data, where the FFT is evaluated in place.</param>
            <param name="dimensions">
            The data size per dimension. The first dimension is the major one.
            For example, with two dimensions "rows" and "columns" the samples are assumed to be organized row by row.
            </param>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.ForwardMultiDim(System.Numerics.Complex[],System.Int32[],MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Applies the forward Fast Fourier Transform (FFT) to multiple dimensional sample data.
            </summary>
            <param name="samples">Sample data, where the FFT is evaluated in place.</param>
            <param name="dimensions">
            The data size per dimension. The first dimension is the major one.
            For example, with two dimensions "rows" and "columns" the samples are assumed to be organized row by row.
            </param>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.Forward2D(MathNet.Numerics.Complex32[],System.Int32,System.Int32,MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Applies the forward Fast Fourier Transform (FFT) to two dimensional sample data.
            </summary>
            <param name="samplesRowWise">Sample data, organized row by row, where the FFT is evaluated in place</param>
            <param name="rows">The number of rows.</param>
            <param name="columns">The number of columns.</param>
            <remarks>Data available organized column by column instead of row by row can be processed directly by swapping the rows and columns arguments.</remarks>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.Forward2D(System.Numerics.Complex[],System.Int32,System.Int32,MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Applies the forward Fast Fourier Transform (FFT) to two dimensional sample data.
            </summary>
            <param name="samplesRowWise">Sample data, organized row by row, where the FFT is evaluated in place</param>
            <param name="rows">The number of rows.</param>
            <param name="columns">The number of columns.</param>
            <remarks>Data available organized column by column instead of row by row can be processed directly by swapping the rows and columns arguments.</remarks>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.Forward2D(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Applies the forward Fast Fourier Transform (FFT) to a two dimensional data in form of a matrix.
            </summary>
            <param name="samples">Sample matrix, where the FFT is evaluated in place</param>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.Forward2D(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Applies the forward Fast Fourier Transform (FFT) to a two dimensional data in form of a matrix.
            </summary>
            <param name="samples">Sample matrix, where the FFT is evaluated in place</param>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.Inverse(MathNet.Numerics.Complex32[])">
            <summary>
            Applies the inverse Fast Fourier Transform (iFFT) to arbitrary-length sample vectors.
            </summary>
            <param name="spectrum">Spectrum data, where the iFFT is evaluated in place.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.Inverse(System.Numerics.Complex[])">
            <summary>
            Applies the inverse Fast Fourier Transform (iFFT) to arbitrary-length sample vectors.
            </summary>
            <param name="spectrum">Spectrum data, where the iFFT is evaluated in place.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.Inverse(MathNet.Numerics.Complex32[],MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Applies the inverse Fast Fourier Transform (iFFT) to arbitrary-length sample vectors.
            </summary>
            <param name="spectrum">Spectrum data, where the iFFT is evaluated in place.</param>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.Inverse(System.Numerics.Complex[],MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Applies the inverse Fast Fourier Transform (iFFT) to arbitrary-length sample vectors.
            </summary>
            <param name="spectrum">Spectrum data, where the iFFT is evaluated in place.</param>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.Inverse(System.Single[],System.Single[],MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Applies the inverse Fast Fourier Transform (iFFT) to arbitrary-length sample vectors.
            </summary>
            <param name="real">Real part of the sample vector, where the iFFT is evaluated in place.</param>
            <param name="imaginary">Imaginary part of the sample vector, where the iFFT is evaluated in place.</param>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.Inverse(System.Double[],System.Double[],MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Applies the inverse Fast Fourier Transform (iFFT) to arbitrary-length sample vectors.
            </summary>
            <param name="real">Real part of the sample vector, where the iFFT is evaluated in place.</param>
            <param name="imaginary">Imaginary part of the sample vector, where the iFFT is evaluated in place.</param>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.InverseReal(System.Single[],System.Int32,MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Packed Real-Complex inverse Fast Fourier Transform (iFFT) to arbitrary-length sample vectors.
            Since for real-valued time samples the complex spectrum is conjugate-even (symmetry),
            the spectrum can be fully reconstructed form the positive frequencies only (first half).
            The data array needs to be N+2 (if N is even) or N+1 (if N is odd) long in order to support such a packed spectrum.
            </summary>
            <param name="data">Data array of length N+2 (if N is even) or N+1 (if N is odd).</param>
            <param name="n">The number of samples.</param>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.InverseReal(System.Double[],System.Int32,MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Packed Real-Complex inverse Fast Fourier Transform (iFFT) to arbitrary-length sample vectors.
            Since for real-valued time samples the complex spectrum is conjugate-even (symmetry),
            the spectrum can be fully reconstructed form the positive frequencies only (first half).
            The data array needs to be N+2 (if N is even) or N+1 (if N is odd) long in order to support such a packed spectrum.
            </summary>
            <param name="data">Data array of length N+2 (if N is even) or N+1 (if N is odd).</param>
            <param name="n">The number of samples.</param>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.InverseMultiDim(MathNet.Numerics.Complex32[],System.Int32[],MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Applies the inverse Fast Fourier Transform (iFFT) to multiple dimensional sample data.
            </summary>
            <param name="spectrum">Spectrum data, where the iFFT is evaluated in place.</param>
            <param name="dimensions">
            The data size per dimension. The first dimension is the major one.
            For example, with two dimensions "rows" and "columns" the samples are assumed to be organized row by row.
            </param>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.InverseMultiDim(System.Numerics.Complex[],System.Int32[],MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Applies the inverse Fast Fourier Transform (iFFT) to multiple dimensional sample data.
            </summary>
            <param name="spectrum">Spectrum data, where the iFFT is evaluated in place.</param>
            <param name="dimensions">
            The data size per dimension. The first dimension is the major one.
            For example, with two dimensions "rows" and "columns" the samples are assumed to be organized row by row.
            </param>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.Inverse2D(MathNet.Numerics.Complex32[],System.Int32,System.Int32,MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Applies the inverse Fast Fourier Transform (iFFT) to two dimensional sample data.
            </summary>
            <param name="spectrumRowWise">Sample data, organized row by row, where the iFFT is evaluated in place</param>
            <param name="rows">The number of rows.</param>
            <param name="columns">The number of columns.</param>
            <remarks>Data available organized column by column instead of row by row can be processed directly by swapping the rows and columns arguments.</remarks>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.Inverse2D(System.Numerics.Complex[],System.Int32,System.Int32,MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Applies the inverse Fast Fourier Transform (iFFT) to two dimensional sample data.
            </summary>
            <param name="spectrumRowWise">Sample data, organized row by row, where the iFFT is evaluated in place</param>
            <param name="rows">The number of rows.</param>
            <param name="columns">The number of columns.</param>
            <remarks>Data available organized column by column instead of row by row can be processed directly by swapping the rows and columns arguments.</remarks>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.Inverse2D(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Applies the inverse Fast Fourier Transform (iFFT) to a two dimensional data in form of a matrix.
            </summary>
            <param name="spectrum">Sample matrix, where the iFFT is evaluated in place</param>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.Inverse2D(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.IntegralTransforms.FourierOptions)">
            <summary>
            Applies the inverse Fast Fourier Transform (iFFT) to a two dimensional data in form of a matrix.
            </summary>
            <param name="spectrum">Sample matrix, where the iFFT is evaluated in place</param>
            <param name="options">Fourier Transform Convention Options.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Fourier.FrequencyScale(System.Int32,System.Double)">
            <summary>
            Generate the frequencies corresponding to each index in frequency space.
            The frequency space has a resolution of sampleRate/N.
            Index 0 corresponds to the DC part, the following indices correspond to
            the positive frequencies up to the Nyquist frequency (sampleRate/2),
            followed by the negative frequencies wrapped around.
            </summary>
            <param name="length">Number of samples.</param>
            <param name="sampleRate">The sampling rate of the time-space data.</param>
        </member>
        <member name="T:MathNet.Numerics.IntegralTransforms.FourierOptions">
            <summary>
            Fourier Transform Convention
            </summary>
        </member>
        <member name="F:MathNet.Numerics.IntegralTransforms.FourierOptions.InverseExponent">
            <summary>
            Inverse integrand exponent (forward: positive sign; inverse: negative sign).
            </summary>
        </member>
        <member name="F:MathNet.Numerics.IntegralTransforms.FourierOptions.AsymmetricScaling">
            <summary>
            Only scale by 1/N in the inverse direction; No scaling in forward direction.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.IntegralTransforms.FourierOptions.NoScaling">
            <summary>
            Don't scale at all (neither on forward nor on inverse transformation).
            </summary>
        </member>
        <member name="F:MathNet.Numerics.IntegralTransforms.FourierOptions.Default">
            <summary>
            Universal; Symmetric scaling and common exponent (used in Maple).
            </summary>
        </member>
        <member name="F:MathNet.Numerics.IntegralTransforms.FourierOptions.Matlab">
            <summary>
            Only scale by 1/N in the inverse direction; No scaling in forward direction (used in Matlab). [= AsymmetricScaling]
            </summary>
        </member>
        <member name="F:MathNet.Numerics.IntegralTransforms.FourierOptions.NumericalRecipes">
            <summary>
            Inverse integrand exponent; No scaling at all (used in all Numerical Recipes based implementations). [= InverseExponent | NoScaling]
            </summary>
        </member>
        <member name="T:MathNet.Numerics.IntegralTransforms.Hartley">
            <summary>
            Fast (FHT) Implementation of the Discrete Hartley Transform (DHT).
            </summary>
            <summary>
            Fast (FHT) Implementation of the Discrete Hartley Transform (DHT).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Hartley.NaiveForward(System.Double[],MathNet.Numerics.IntegralTransforms.HartleyOptions)">
            <summary>
            Naive forward DHT, useful e.g. to verify faster algorithms.
            </summary>
            <param name="timeSpace">Time-space sample vector.</param>
            <param name="options">Hartley Transform Convention Options.</param>
            <returns>Corresponding frequency-space vector.</returns>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Hartley.NaiveInverse(System.Double[],MathNet.Numerics.IntegralTransforms.HartleyOptions)">
            <summary>
            Naive inverse DHT, useful e.g. to verify faster algorithms.
            </summary>
            <param name="frequencySpace">Frequency-space sample vector.</param>
            <param name="options">Hartley Transform Convention Options.</param>
            <returns>Corresponding time-space vector.</returns>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Hartley.ForwardScaleByOptions(MathNet.Numerics.IntegralTransforms.HartleyOptions,System.Double[])">
            <summary>
            Rescale FFT-the resulting vector according to the provided convention options.
            </summary>
            <param name="options">Fourier Transform Convention Options.</param>
            <param name="samples">Sample Vector.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Hartley.InverseScaleByOptions(MathNet.Numerics.IntegralTransforms.HartleyOptions,System.Double[])">
            <summary>
            Rescale the iFFT-resulting vector according to the provided convention options.
            </summary>
            <param name="options">Fourier Transform Convention Options.</param>
            <param name="samples">Sample Vector.</param>
        </member>
        <member name="M:MathNet.Numerics.IntegralTransforms.Hartley.Naive(System.Double[])">
            <summary>
            Naive generic DHT, useful e.g. to verify faster algorithms.
            </summary>
            <param name="samples">Time-space sample vector.</param>
            <returns>Corresponding frequency-space vector.</returns>
        </member>
        <member name="T:MathNet.Numerics.IntegralTransforms.HartleyOptions">
            <summary>
            Hartley Transform Convention
            </summary>
        </member>
        <member name="F:MathNet.Numerics.IntegralTransforms.HartleyOptions.AsymmetricScaling">
            <summary>
            Only scale by 1/N in the inverse direction; No scaling in forward direction.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.IntegralTransforms.HartleyOptions.NoScaling">
            <summary>
            Don't scale at all (neither on forward nor on inverse transformation).
            </summary>
        </member>
        <member name="F:MathNet.Numerics.IntegralTransforms.HartleyOptions.Default">
            <summary>
            Universal; Symmetric scaling.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Integrate">
            <summary>
            Numerical Integration (Quadrature).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Integrate.OnClosedInterval(System.Func{System.Double,System.Double},System.Double,System.Double,System.Double)">
            <summary>
            Approximation of the definite integral of an analytic smooth function on a closed interval.
            </summary>
            <param name="f">The analytic smooth function to integrate.</param>
            <param name="intervalBegin">Where the interval starts, inclusive and finite.</param>
            <param name="intervalEnd">Where the interval stops, inclusive and finite.</param>
            <param name="targetAbsoluteError">The expected relative accuracy of the approximation.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integrate.OnClosedInterval(System.Func{System.Double,System.Double},System.Double,System.Double)">
            <summary>
            Approximation of the definite integral of an analytic smooth function on a closed interval.
            </summary>
            <param name="f">The analytic smooth function to integrate.</param>
            <param name="intervalBegin">Where the interval starts, inclusive and finite.</param>
            <param name="intervalEnd">Where the interval stops, inclusive and finite.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integrate.OnRectangle(System.Func{System.Double,System.Double,System.Double},System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Approximates a 2-dimensional definite integral using an Nth order Gauss-Legendre rule over the rectangle [a,b] x [c,d].
            </summary>
            <param name="f">The 2-dimensional analytic smooth function to integrate.</param>
            <param name="invervalBeginA">Where the interval starts for the first (inside) integral, exclusive and finite.</param>
            <param name="invervalEndA">Where the interval ends for the first (inside) integral, exclusive and finite.</param>
            <param name="invervalBeginB">Where the interval starts for the second (outside) integral, exclusive and finite.</param>
            /// <param name="invervalEndB">Where the interval ends for the second (outside) integral, exclusive and finite.</param>
            <param name="order">Defines an Nth order Gauss-Legendre rule. The order also defines the number of abscissas and weights for the rule. Precomputed Gauss-Legendre abscissas/weights for orders 2-20, 32, 64, 96, 100, 128, 256, 512, 1024 are used, otherwise they're calculated on the fly.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integrate.OnRectangle(System.Func{System.Double,System.Double,System.Double},System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Approximates a 2-dimensional definite integral using an Nth order Gauss-Legendre rule over the rectangle [a,b] x [c,d].
            </summary>
            <param name="f">The 2-dimensional analytic smooth function to integrate.</param>
            <param name="invervalBeginA">Where the interval starts for the first (inside) integral, exclusive and finite.</param>
            <param name="invervalEndA">Where the interval ends for the first (inside) integral, exclusive and finite.</param>
            <param name="invervalBeginB">Where the interval starts for the second (outside) integral, exclusive and finite.</param>
            /// <param name="invervalEndB">Where the interval ends for the second (outside) integral, exclusive and finite.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integrate.DoubleExponential(System.Func{System.Double,System.Double},System.Double,System.Double,System.Double)">
            <summary>
            Approximation of the definite integral of an analytic smooth function by double-exponential quadrature. When either or both limits are infinite, the integrand is assumed rapidly decayed to zero as x -> infinity.
            </summary>
            <param name="f">The analytic smooth function to integrate.</param>
            <param name="intervalBegin">Where the interval starts.</param>
            <param name="intervalEnd">Where the interval stops.</param>
            <param name="targetAbsoluteError">The expected relative accuracy of the approximation.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integrate.GaussLegendre(System.Func{System.Double,System.Double},System.Double,System.Double,System.Int32)">
            <summary>
            Approximation of the definite integral of an analytic smooth function by Gauss-Legendre quadrature. When either or both limits are infinite, the integrand is assumed rapidly decayed to zero as x -> infinity.
            </summary>
            <param name="f">The analytic smooth function to integrate.</param>
            <param name="intervalBegin">Where the interval starts.</param>
            <param name="intervalEnd">Where the interval stops.</param>
            <param name="order">Defines an Nth order Gauss-Legendre rule. The order also defines the number of abscissas and weights for the rule. Precomputed Gauss-Legendre abscissas/weights for orders 2-20, 32, 64, 96, 100, 128, 256, 512, 1024 are used, otherwise they're calculated on the fly.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integrate.GaussKronrod(System.Func{System.Double,System.Double},System.Double,System.Double,System.Double,System.Int32,System.Int32)">
            <summary>
            Approximation of the definite integral of an analytic smooth function by Gauss-Kronrod quadrature. When either or both limits are infinite, the integrand is assumed rapidly decayed to zero as x -> infinity.
            </summary>
            <param name="f">The analytic smooth function to integrate.</param>
            <param name="intervalBegin">Where the interval starts.</param>
            <param name="intervalEnd">Where the interval stops.</param>
            <param name="targetRelativeError">The expected relative accuracy of the approximation.</param>
            <param name="maximumDepth">The maximum number of interval splittings permitted before stopping.</param>
            <param name="order">The number of Gauss-Kronrod points. Pre-computed for 15, 21, 31, 41, 51 and 61 points.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integrate.GaussKronrod(System.Func{System.Double,System.Double},System.Double,System.Double,System.Double@,System.Double@,System.Double,System.Int32,System.Int32)">
            <summary>
            Approximation of the definite integral of an analytic smooth function by Gauss-Kronrod quadrature. When either or both limits are infinite, the integrand is assumed rapidly decayed to zero as x -> infinity.
            </summary>
            <param name="f">The analytic smooth function to integrate.</param>
            <param name="intervalBegin">Where the interval starts.</param>
            <param name="intervalEnd">Where the interval stops.</param>
            <param name="error">The difference between the (N-1)/2 point Gauss approximation and the N-point Gauss-Kronrod approximation</param>
            <param name="L1Norm">The L1 norm of the result, if there is a significant difference between this and the returned value, then the result is likely to be ill-conditioned.</param>
            <param name="targetRelativeError">The expected relative accuracy of the approximation.</param>
            <param name="maximumDepth">The maximum number of interval splittings permitted before stopping</param>
            <param name="order">The number of Gauss-Kronrod points. Pre-computed for 15, 21, 31, 41, 51 and 61 points</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="T:MathNet.Numerics.ContourIntegrate">
            <summary>
            Numerical Contour Integration of a complex-valued function over a real variable,.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.ContourIntegrate.DoubleExponential(System.Func{System.Double,System.Numerics.Complex},System.Double,System.Double,System.Double)">
            <summary>
            Approximation of the definite integral of an analytic smooth complex function by double-exponential quadrature. When either or both limits are infinite, the integrand is assumed rapidly decayed to zero as x -> infinity.
            </summary>
            <param name="f">The analytic smooth complex function to integrate, defined on the real domain.</param>
            <param name="intervalBegin">Where the interval starts.</param>
            <param name="intervalEnd">Where the interval stops.</param>
            <param name="targetAbsoluteError">The expected relative accuracy of the approximation.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.ContourIntegrate.GaussLegendre(System.Func{System.Double,System.Numerics.Complex},System.Double,System.Double,System.Int32)">
            <summary>
            Approximation of the definite integral of an analytic smooth complex function by double-exponential quadrature. When either or both limits are infinite, the integrand is assumed rapidly decayed to zero as x -> infinity.
            </summary>
            <param name="f">The analytic smooth complex function to integrate, defined on the real domain.</param>
            <param name="intervalBegin">Where the interval starts.</param>
            <param name="intervalEnd">Where the interval stops.</param>
            <param name="order">Defines an Nth order Gauss-Legendre rule. The order also defines the number of abscissas and weights for the rule. Precomputed Gauss-Legendre abscissas/weights for orders 2-20, 32, 64, 96, 100, 128, 256, 512, 1024 are used, otherwise they're calculated on the fly.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.ContourIntegrate.GaussKronrod(System.Func{System.Double,System.Numerics.Complex},System.Double,System.Double,System.Double,System.Int32,System.Int32)">
            <summary>
            Approximation of the definite integral of an analytic smooth function by Gauss-Kronrod quadrature. When either or both limits are infinite, the integrand is assumed rapidly decayed to zero as x -> infinity.
            </summary>
            <param name="f">The analytic smooth complex function to integrate, defined on the real domain.</param>
            <param name="intervalBegin">Where the interval starts.</param>
            <param name="intervalEnd">Where the interval stops.</param>
            <param name="targetRelativeError">The expected relative accuracy of the approximation.</param>
            <param name="maximumDepth">The maximum number of interval splittings permitted before stopping</param>
            <param name="order">The number of Gauss-Kronrod points. Pre-computed for 15, 21, 31, 41, 51 and 61 points</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.ContourIntegrate.GaussKronrod(System.Func{System.Double,System.Numerics.Complex},System.Double,System.Double,System.Double@,System.Double@,System.Double,System.Int32,System.Int32)">
            <summary>
            Approximation of the definite integral of an analytic smooth function by Gauss-Kronrod quadrature. When either or both limits are infinite, the integrand is assumed rapidly decayed to zero as x -> infinity.
            </summary>
            <param name="f">The analytic smooth complex function to integrate, defined on the real domain.</param>
            <param name="intervalBegin">Where the interval starts.</param>
            <param name="intervalEnd">Where the interval stops.</param>
            <param name="error">The difference between the (N-1)/2 point Gauss approximation and the N-point Gauss-Kronrod approximation</param>
            <param name="L1Norm">The L1 norm of the result, if there is a significant difference between this and the returned value, then the result is likely to be ill-conditioned.</param>
            <param name="targetRelativeError">The expected relative accuracy of the approximation.</param>
            <param name="maximumDepth">The maximum number of interval splittings permitted before stopping</param>
            <param name="order">The number of Gauss-Kronrod points. Pre-computed for 15, 21, 31, 41, 51 and 61 points</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="T:MathNet.Numerics.Integration.DoubleExponentialTransformation">
            <summary>
            Analytic integration algorithm for smooth functions with no discontinuities
            or derivative discontinuities and no poles inside the interval.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Integration.DoubleExponentialTransformation.NumberOfMaximumLevels">
            <summary>
            Maximum number of iterations, until the asked
            maximum error is (likely to be) satisfied.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Integration.DoubleExponentialTransformation.Integrate(System.Func{System.Double,System.Double},System.Double,System.Double,System.Double)">
            <summary>
            Approximate the integral by the double exponential transformation
            </summary>
            <param name="f">The analytic smooth function to integrate.</param>
            <param name="intervalBegin">Where the interval starts, inclusive and finite.</param>
            <param name="intervalEnd">Where the interval stops, inclusive and finite.</param>
            <param name="targetRelativeError">The expected relative accuracy of the approximation.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integration.DoubleExponentialTransformation.ContourIntegrate(System.Func{System.Double,System.Numerics.Complex},System.Double,System.Double,System.Double)">
            <summary>
            Approximate the integral by the double exponential transformation
            </summary>
            <param name="f">The analytic smooth complex function to integrate, defined on the real domain.</param>
            <param name="intervalBegin">Where the interval starts, inclusive and finite.</param>
            <param name="intervalEnd">Where the interval stops, inclusive and finite.</param>
            <param name="targetRelativeError">The expected relative accuracy of the approximation.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integration.DoubleExponentialTransformation.EvaluateAbcissas(System.Int32)">
            <summary>
            Compute the abscissa vector for a single level.
            </summary>
            <param name="level">The level to evaluate the abscissa vector for.</param>
            <returns>Abscissa Vector.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integration.DoubleExponentialTransformation.EvaluateWeights(System.Int32)">
            <summary>
            Compute the weight vector for a single level.
            </summary>
            <param name="level">The level to evaluate the weight vector for.</param>
            <returns>Weight Vector.</returns>
        </member>
        <member name="F:MathNet.Numerics.Integration.DoubleExponentialTransformation.PrecomputedAbscissas">
            <summary>
            Precomputed abscissa vector per level.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Integration.DoubleExponentialTransformation.PrecomputedWeights">
            <summary>
            Precomputed weight vector per level.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Integration.GaussKronrodRule.Order">
            <summary>
            Getter for the order.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Integration.GaussKronrodRule.KronrodAbscissas">
            <summary>
            Getter that returns a clone of the array containing the Kronrod abscissas.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Integration.GaussKronrodRule.KronrodWeights">
            <summary>
            Getter that returns a clone of the array containing the Kronrod weights.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Integration.GaussKronrodRule.GaussWeights">
            <summary>
            Getter that returns a clone of the array containing the Gauss weights.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Integration.GaussKronrodRule.Integrate(System.Func{System.Double,System.Double},System.Double,System.Double,System.Double@,System.Double@,System.Double,System.Int32,System.Int32)">
            <summary>
            Performs adaptive Gauss-Kronrod quadrature on function f over the range (a,b)
            </summary>
            <param name="f">The analytic smooth function to integrate</param>
            <param name="intervalBegin">Where the interval starts</param>
            <param name="intervalEnd">Where the interval stops</param>
            <param name="error">The difference between the (N-1)/2 point Gauss approximation and the N-point Gauss-Kronrod approximation</param>
            <param name="L1Norm">The L1 norm of the result, if there is a significant difference between this and the returned value, then the result is likely to be ill-conditioned.</param>
            <param name="targetRelativeError">The maximum relative error in the result</param>
            <param name="maximumDepth">The maximum number of interval splittings permitted before stopping</param>
            <param name="order">The number of Gauss-Kronrod points. Pre-computed for 15, 21, 31, 41, 51 and 61 points</param>
        </member>
        <member name="M:MathNet.Numerics.Integration.GaussKronrodRule.ContourIntegrate(System.Func{System.Double,System.Numerics.Complex},System.Double,System.Double,System.Double@,System.Double@,System.Double,System.Int32,System.Int32)">
            <summary>
            Performs adaptive Gauss-Kronrod quadrature on function f over the range (a,b)
            </summary>
            <param name="f">The analytic smooth complex function to integrate, defined on the real axis.</param>
            <param name="intervalBegin">Where the interval starts</param>
            <param name="intervalEnd">Where the interval stops</param>
            <param name="error">The difference between the (N-1)/2 point Gauss approximation and the N-point Gauss-Kronrod approximation</param>
            <param name="L1Norm">The L1 norm of the result, if there is a significant difference between this and the returned value, then the result is likely to be ill-conditioned.</param>
            <param name="targetRelativeError">The maximum relative error in the result</param>
            <param name="maximumDepth">The maximum number of interval splittings permitted before stopping</param>
            <param name="order">The number of Gauss-Kronrod points. Pre-computed for 15, 21, 31, 41, 51 and 61 points</param>
            <returns></returns>
        </member>
        <member name="T:MathNet.Numerics.Integration.GaussLegendreRule">
            <summary>
            Approximates a definite integral using an Nth order Gauss-Legendre rule. Precomputed Gauss-Legendre abscissas/weights for orders 2-20, 32, 64, 96, 100, 128, 256, 512, 1024 are used, otherwise they're calculated on the fly.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Integration.GaussLegendreRule.#ctor(System.Double,System.Double,System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Integration.GaussLegendreRule"/> class.
            </summary>
            <param name="intervalBegin">Where the interval starts, inclusive and finite.</param>
            <param name="intervalEnd">Where the interval stops, inclusive and finite.</param>
            <param name="order">Defines an Nth order Gauss-Legendre rule. The order also defines the number of abscissas and weights for the rule. Precomputed Gauss-Legendre abscissas/weights for orders 2-20, 32, 64, 96, 100, 128, 256, 512, 1024 are used, otherwise they're calculated on the fly.</param>
        </member>
        <member name="M:MathNet.Numerics.Integration.GaussLegendreRule.GetAbscissa(System.Int32)">
            <summary>
            Gettter for the ith abscissa.
            </summary>
            <param name="index">Index of the ith abscissa.</param>
            <returns>The ith abscissa.</returns>
        </member>
        <member name="P:MathNet.Numerics.Integration.GaussLegendreRule.Abscissas">
            <summary>
            Getter that returns a clone of the array containing the abscissas.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Integration.GaussLegendreRule.GetWeight(System.Int32)">
            <summary>
            Getter for the ith weight.
            </summary>
            <param name="index">Index of the ith weight.</param>
            <returns>The ith weight.</returns>
        </member>
        <member name="P:MathNet.Numerics.Integration.GaussLegendreRule.Weights">
            <summary>
            Getter that returns a clone of the array containing the weights.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Integration.GaussLegendreRule.Order">
            <summary>
            Getter for the order.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Integration.GaussLegendreRule.IntervalBegin">
            <summary>
            Getter for the InvervalBegin.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Integration.GaussLegendreRule.IntervalEnd">
            <summary>
            Getter for the InvervalEnd.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Integration.GaussLegendreRule.Integrate(System.Func{System.Double,System.Double},System.Double,System.Double,System.Int32)">
            <summary>
            Approximates a definite integral using an Nth order Gauss-Legendre rule.
            </summary>
            <param name="f">The analytic smooth function to integrate.</param>
            <param name="invervalBegin">Where the interval starts, exclusive and finite.</param>
            <param name="invervalEnd">Where the interval ends, exclusive and finite.</param>
            <param name="order">Defines an Nth order Gauss-Legendre rule. The order also defines the number of abscissas and weights for the rule. Precomputed Gauss-Legendre abscissas/weights for orders 2-20, 32, 64, 96, 100, 128, 256, 512, 1024 are used, otherwise they're calculated on the fly.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integration.GaussLegendreRule.ContourIntegrate(System.Func{System.Double,System.Numerics.Complex},System.Double,System.Double,System.Int32)">
            <summary>
            Approximates a definite integral using an Nth order Gauss-Legendre rule.
            </summary>
            <param name="f">The analytic smooth complex function to integrate, defined on the real domain.</param>
            <param name="invervalBegin">Where the interval starts, exclusive and finite.</param>
            <param name="invervalEnd">Where the interval ends, exclusive and finite.</param>
            <param name="order">Defines an Nth order Gauss-Legendre rule. The order also defines the number of abscissas and weights for the rule. Precomputed Gauss-Legendre abscissas/weights for orders 2-20, 32, 64, 96, 100, 128, 256, 512, 1024 are used, otherwise they're calculated on the fly.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integration.GaussLegendreRule.Integrate(System.Func{System.Double,System.Double,System.Double},System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Approximates a 2-dimensional definite integral using an Nth order Gauss-Legendre rule over the rectangle [a,b] x [c,d].
            </summary>
            <param name="f">The 2-dimensional analytic smooth function to integrate.</param>
            <param name="invervalBeginA">Where the interval starts for the first (inside) integral, exclusive and finite.</param>
            <param name="invervalEndA">Where the interval ends for the first (inside) integral, exclusive and finite.</param>
            <param name="invervalBeginB">Where the interval starts for the second (outside) integral, exclusive and finite.</param>
            /// <param name="invervalEndB">Where the interval ends for the second (outside) integral, exclusive and finite.</param>
            <param name="order">Defines an Nth order Gauss-Legendre rule. The order also defines the number of abscissas and weights for the rule. Precomputed Gauss-Legendre abscissas/weights for orders 2-20, 32, 64, 96, 100, 128, 256, 512, 1024 are used, otherwise they're calculated on the fly.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="T:MathNet.Numerics.Integration.GaussRule.GaussKronrodPoint">
            <summary>
            Contains a method to compute the Gauss-Kronrod abscissas/weights and precomputed abscissas/weights for orders 15, 21, 31, 41, 51, 61.
            </summary>
            <summary>
            Contains a method to compute the Gauss-Kronrod abscissas/weights.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Integration.GaussRule.GaussKronrodPoint.PreComputed">
            <summary>
            Precomputed abscissas/weights for orders 15, 21, 31, 41, 51, 61.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Integration.GaussRule.GaussKronrodPoint.Generate(System.Int32,System.Double)">
            <summary>
            Computes the Gauss-Kronrod abscissas/weights and Gauss weights.
            </summary>
            <param name="order">Defines an Nth order Gauss-Kronrod rule. The order also defines the number of abscissas and weights for the rule.</param>
            <param name="eps">Required precision to compute the abscissas/weights.</param>
            <returns>Object containing the non-negative abscissas/weights, order.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integration.GaussRule.GaussKronrodPoint.StieltjesP(System.Int32)">
            <summary>
            Returns coefficients of a Stieltjes polynomial in terms of Legendre polynomials.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Integration.GaussRule.GaussKronrodPoint.LegendreSeries(System.Double[],System.Double)">
            <summary>
            Return value and derivative of a Legendre series at given points.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Integration.GaussRule.GaussKronrodPoint.LegendreP(System.Int32,System.Double)">
            <summary>
            Return value and derivative of a Legendre polynomial of order at given points.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Integration.GaussRule.GaussKronrodPointFactory">
            <summary>
            Creates a Gauss-Kronrod point.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Integration.GaussRule.GaussKronrodPointFactory.GetGaussPoint(System.Int32)">
            <summary>
            Getter for the GaussKronrodPoint.
            </summary>
            <param name="order">Defines an Nth order Gauss-Kronrod rule. Precomputed Gauss-Kronrod abscissas/weights for orders 15, 21, 31, 41, 51, 61 are used, otherwise they're calculated on the fly.</param>
            <returns>Object containing the non-negative abscissas/weights, and order.</returns>
        </member>
        <member name="T:MathNet.Numerics.Integration.GaussRule.GaussLegendrePoint">
            <summary>
            Contains a method to compute the Gauss-Legendre abscissas/weights and precomputed abscissas/weights for orders 2-20, 32, 64, 96, 100, 128, 256, 512, 1024.
            </summary>
            <summary>
            Contains a method to compute the Gauss-Legendre abscissas/weights and precomputed abscissas/weights for orders 2-20, 32, 64, 96, 100, 128, 256, 512, 1024.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Integration.GaussRule.GaussLegendrePoint.PreComputed">
            <summary>
            Precomputed abscissas/weights for orders 2-20, 32, 64, 96, 100, 128, 256, 512, 1024.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Integration.GaussRule.GaussLegendrePoint.Generate(System.Int32,System.Double)">
            <summary>
            Computes the Gauss-Legendre abscissas/weights.
            See <see href="http://www.holoborodko.com/pavel/numerical-methods/numerical-integration/" >Pavel Holoborodko</see> for a description of the algorithm.
            </summary>
            <param name="order">Defines an Nth order Gauss-Legendre rule. The order also defines the number of abscissas and weights for the rule.</param>
            <param name="eps">Required precision to compute the abscissas/weights. 1e-10 is usually fine.</param>
            <returns>Object containing the non-negative abscissas/weights, order, and intervalBegin/intervalEnd. The non-negative abscissas/weights are generated over the interval [-1,1] for the given order.</returns>
        </member>
        <member name="T:MathNet.Numerics.Integration.GaussRule.GaussLegendrePointFactory">
            <summary>
            Creates and maps a Gauss-Legendre point.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Integration.GaussRule.GaussLegendrePointFactory.GetGaussPoint(System.Int32)">
            <summary>
            Getter for the GaussPoint.
            </summary>
            <param name="order">Defines an Nth order Gauss-Legendre rule. Precomputed Gauss-Legendre abscissas/weights for orders 2-20, 32, 64, 96, 100, 128, 256, 512, 1024 are used, otherwise they're calculated on the fly.</param>
            <returns>Object containing the non-negative abscissas/weights, order, and intervalBegin/intervalEnd. The non-negative abscissas/weights are generated over the interval [-1,1] for the given order.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integration.GaussRule.GaussLegendrePointFactory.GetGaussPoint(System.Double,System.Double,System.Int32)">
            <summary>
            Getter for the GaussPoint.
            </summary>
            <param name="intervalBegin">Where the interval starts, inclusive and finite.</param>
            <param name="intervalEnd">Where the interval stops, inclusive and finite.</param>
            <param name="order">Defines an Nth order Gauss-Legendre rule. Precomputed Gauss-Legendre abscissas/weights for orders 2-20, 32, 64, 96, 100, 128, 256, 512, 1024 are used, otherwise they're calculated on the fly.</param>
            <returns>Object containing the abscissas/weights, order, and intervalBegin/intervalEnd.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integration.GaussRule.GaussLegendrePointFactory.Map(System.Double,System.Double,MathNet.Numerics.Integration.GaussRule.GaussPoint)">
            <summary>
            Maps the non-negative abscissas/weights from the interval [-1, 1] to the interval [intervalBegin, intervalEnd].
            </summary>
            <param name="intervalBegin">Where the interval starts, inclusive and finite.</param>
            <param name="intervalEnd">Where the interval stops, inclusive and finite.</param>
            <param name="gaussPoint">Object containing the non-negative abscissas/weights, order, and intervalBegin/intervalEnd. The non-negative abscissas/weights are generated over the interval [-1,1] for the given order.</param>
            <returns>Object containing the abscissas/weights, order, and intervalBegin/intervalEnd.</returns>
        </member>
        <member name="T:MathNet.Numerics.Integration.GaussRule.GaussPoint">
            <summary>
            Contains the abscissas/weights, order, and intervalBegin/intervalEnd.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Integration.GaussRule.GaussPointPair">
            <summary>
            Contains two GaussPoint.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Integration.NewtonCotesTrapeziumRule">
            <summary>
            Approximation algorithm for definite integrals by the Trapezium rule of the Newton-Cotes family.
            </summary>
            <remarks>
            <a href="http://en.wikipedia.org/wiki/Trapezium_rule">Wikipedia - Trapezium Rule</a>
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Integration.NewtonCotesTrapeziumRule.IntegrateTwoPoint(System.Func{System.Double,System.Double},System.Double,System.Double)">
            <summary>
            Direct 2-point approximation of the definite integral in the provided interval by the trapezium rule.
            </summary>
            <param name="f">The analytic smooth function to integrate.</param>
            <param name="intervalBegin">Where the interval starts, inclusive and finite.</param>
            <param name="intervalEnd">Where the interval stops, inclusive and finite.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integration.NewtonCotesTrapeziumRule.ContourIntegrateTwoPoint(System.Func{System.Double,System.Numerics.Complex},System.Double,System.Double)">
            <summary>
            Direct 2-point approximation of the definite integral in the provided interval by the trapezium rule.
            </summary>
            <param name="f">The analytic smooth complex function to integrate, defined on real domain.</param>
            <param name="intervalBegin">Where the interval starts, inclusive and finite.</param>
            <param name="intervalEnd">Where the interval stops, inclusive and finite.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integration.NewtonCotesTrapeziumRule.IntegrateComposite(System.Func{System.Double,System.Double},System.Double,System.Double,System.Int32)">
            <summary>
            Composite N-point approximation of the definite integral in the provided interval by the trapezium rule.
            </summary>
            <param name="f">The analytic smooth function to integrate.</param>
            <param name="intervalBegin">Where the interval starts, inclusive and finite.</param>
            <param name="intervalEnd">Where the interval stops, inclusive and finite.</param>
            <param name="numberOfPartitions">Number of composite subdivision partitions.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integration.NewtonCotesTrapeziumRule.ContourIntegrateComposite(System.Func{System.Double,System.Numerics.Complex},System.Double,System.Double,System.Int32)">
            <summary>
            Composite N-point approximation of the definite integral in the provided interval by the trapezium rule.
            </summary>
            <param name="f">The analytic smooth complex function to integrate, defined on real domain.</param>
            <param name="intervalBegin">Where the interval starts, inclusive and finite.</param>
            <param name="intervalEnd">Where the interval stops, inclusive and finite.</param>
            <param name="numberOfPartitions">Number of composite subdivision partitions.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integration.NewtonCotesTrapeziumRule.IntegrateAdaptive(System.Func{System.Double,System.Double},System.Double,System.Double,System.Double)">
            <summary>
            Adaptive approximation of the definite integral in the provided interval by the trapezium rule.
            </summary>
            <param name="f">The analytic smooth function to integrate.</param>
            <param name="intervalBegin">Where the interval starts, inclusive and finite.</param>
            <param name="intervalEnd">Where the interval stops, inclusive and finite.</param>
            <param name="targetError">The expected accuracy of the approximation.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integration.NewtonCotesTrapeziumRule.ContourIntegrateAdaptive(System.Func{System.Double,System.Numerics.Complex},System.Double,System.Double,System.Double)">
            <summary>
            Adaptive approximation of the definite integral in the provided interval by the trapezium rule.
            </summary>
            <param name="f">The analytic smooth complex function to integrate, define don real domain.</param>
            <param name="intervalBegin">Where the interval starts, inclusive and finite.</param>
            <param name="intervalEnd">Where the interval stops, inclusive and finite.</param>
            <param name="targetError">The expected accuracy of the approximation.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integration.NewtonCotesTrapeziumRule.IntegrateAdaptiveTransformedOdd(System.Func{System.Double,System.Double},System.Double,System.Double,System.Collections.Generic.IEnumerable{System.Double[]},System.Collections.Generic.IEnumerable{System.Double[]},System.Double,System.Double)">
            <summary>
            Adaptive approximation of the definite integral by the trapezium rule.
            </summary>
            <param name="f">The analytic smooth function to integrate.</param>
            <param name="intervalBegin">Where the interval starts, inclusive and finite.</param>
            <param name="intervalEnd">Where the interval stops, inclusive and finite.</param>
            <param name="levelAbscissas">Abscissa vector per level provider.</param>
            <param name="levelWeights">Weight vector per level provider.</param>
            <param name="levelOneStep">First Level Step</param>
            <param name="targetRelativeError">The expected relative accuracy of the approximation.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integration.NewtonCotesTrapeziumRule.ContourIntegrateAdaptiveTransformedOdd(System.Func{System.Double,System.Numerics.Complex},System.Double,System.Double,System.Collections.Generic.IEnumerable{System.Double[]},System.Collections.Generic.IEnumerable{System.Double[]},System.Double,System.Double)">
            <summary>
            Adaptive approximation of the definite integral by the trapezium rule.
            </summary>
            <param name="f">The analytic smooth complex function to integrate, defined on the real domain.</param>
            <param name="intervalBegin">Where the interval starts, inclusive and finite.</param>
            <param name="intervalEnd">Where the interval stops, inclusive and finite.</param>
            <param name="levelAbscissas">Abscissa vector per level provider.</param>
            <param name="levelWeights">Weight vector per level provider.</param>
            <param name="levelOneStep">First Level Step</param>
            <param name="targetRelativeError">The expected relative accuracy of the approximation.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="T:MathNet.Numerics.Integration.SimpsonRule">
            <summary>
            Approximation algorithm for definite integrals by Simpson's rule.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Integration.SimpsonRule.IntegrateThreePoint(System.Func{System.Double,System.Double},System.Double,System.Double)">
            <summary>
            Direct 3-point approximation of the definite integral in the provided interval by Simpson's rule.
            </summary>
            <param name="f">The analytic smooth function to integrate.</param>
            <param name="intervalBegin">Where the interval starts, inclusive and finite.</param>
            <param name="intervalEnd">Where the interval stops, inclusive and finite.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="M:MathNet.Numerics.Integration.SimpsonRule.IntegrateComposite(System.Func{System.Double,System.Double},System.Double,System.Double,System.Int32)">
            <summary>
            Composite N-point approximation of the definite integral in the provided interval by Simpson's rule.
            </summary>
            <param name="f">The analytic smooth function to integrate.</param>
            <param name="intervalBegin">Where the interval starts, inclusive and finite.</param>
            <param name="intervalEnd">Where the interval stops, inclusive and finite.</param>
            <param name="numberOfPartitions">Even number of composite subdivision partitions.</param>
            <returns>Approximation of the finite integral in the given interval.</returns>
        </member>
        <member name="T:MathNet.Numerics.Interpolate">
            <summary>
            Interpolation Factory.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolate.Common(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Creates an interpolation based on arbitrary points.
            </summary>
            <param name="points">The sample points t.</param>
            <param name="values">The sample point values x(t).</param>
            <returns>
            An interpolation scheme optimized for the given sample points and values,
            which can then be used to compute interpolations and extrapolations
            on arbitrary points.
            </returns>
            <remarks>
            if your data is already sorted in arrays, consider to use
            MathNet.Numerics.Interpolation.Barycentric.InterpolateRationalFloaterHormannSorted
            instead, which is more efficient.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Interpolate.RationalWithoutPoles(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a Floater-Hormann rational pole-free interpolation based on arbitrary points.
            </summary>
            <param name="points">The sample points t.</param>
            <param name="values">The sample point values x(t).</param>
            <returns>
            An interpolation scheme optimized for the given sample points and values,
            which can then be used to compute interpolations and extrapolations
            on arbitrary points.
            </returns>
            <remarks>
            if your data is already sorted in arrays, consider to use
            MathNet.Numerics.Interpolation.Barycentric.InterpolateRationalFloaterHormannSorted
            instead, which is more efficient.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Interpolate.RationalWithPoles(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a Bulirsch Stoer rational interpolation based on arbitrary points.
            </summary>
            <param name="points">The sample points t.</param>
            <param name="values">The sample point values x(t).</param>
            <returns>
            An interpolation scheme optimized for the given sample points and values,
            which can then be used to compute interpolations and extrapolations
            on arbitrary points.
            </returns>
            <remarks>
            if your data is already sorted in arrays, consider to use
            MathNet.Numerics.Interpolation.BulirschStoerRationalInterpolation.InterpolateSorted
            instead, which is more efficient.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Interpolate.PolynomialEquidistant(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a barycentric polynomial interpolation where the given sample points are equidistant.
            </summary>
            <param name="points">The sample points t, must be equidistant.</param>
            <param name="values">The sample point values x(t).</param>
            <returns>
            An interpolation scheme optimized for the given sample points and values,
            which can then be used to compute interpolations and extrapolations
            on arbitrary points.
            </returns>
            <remarks>
            if your data is already sorted in arrays, consider to use
            MathNet.Numerics.Interpolation.Barycentric.InterpolatePolynomialEquidistantSorted
            instead, which is more efficient.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Interpolate.Polynomial(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a Neville polynomial interpolation based on arbitrary points.
            If the points happen to be equidistant, consider to use the much more robust PolynomialEquidistant instead.
            Otherwise, consider whether RationalWithoutPoles would not be a more robust alternative.
            </summary>
            <param name="points">The sample points t.</param>
            <param name="values">The sample point values x(t).</param>
            <returns>
            An interpolation scheme optimized for the given sample points and values,
            which can then be used to compute interpolations and extrapolations
            on arbitrary points.
            </returns>
            <remarks>
            if your data is already sorted in arrays, consider to use
            MathNet.Numerics.Interpolation.NevillePolynomialInterpolation.InterpolateSorted
            instead, which is more efficient.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Interpolate.Linear(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a piecewise linear interpolation based on arbitrary points.
            </summary>
            <param name="points">The sample points t.</param>
            <param name="values">The sample point values x(t).</param>
            <returns>
            An interpolation scheme optimized for the given sample points and values,
            which can then be used to compute interpolations and extrapolations
            on arbitrary points.
            </returns>
            <remarks>
            if your data is already sorted in arrays, consider to use
            MathNet.Numerics.Interpolation.LinearSpline.InterpolateSorted
            instead, which is more efficient.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Interpolate.LogLinear(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create piecewise log-linear interpolation based on arbitrary points.
            </summary>
            <param name="points">The sample points t.</param>
            <param name="values">The sample point values x(t).</param>
            <returns>
            An interpolation scheme optimized for the given sample points and values,
            which can then be used to compute interpolations and extrapolations
            on arbitrary points.
            </returns>
            <remarks>
            if your data is already sorted in arrays, consider to use
            MathNet.Numerics.Interpolation.LogLinear.InterpolateSorted
            instead, which is more efficient.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Interpolate.CubicSpline(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create an piecewise natural cubic spline interpolation based on arbitrary points,
            with zero secondary derivatives at the boundaries.
            </summary>
            <param name="points">The sample points t.</param>
            <param name="values">The sample point values x(t).</param>
            <returns>
            An interpolation scheme optimized for the given sample points and values,
            which can then be used to compute interpolations and extrapolations
            on arbitrary points.
            </returns>
            <remarks>
            if your data is already sorted in arrays, consider to use
            MathNet.Numerics.Interpolation.CubicSpline.InterpolateNaturalSorted
            instead, which is more efficient.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Interpolate.CubicSplineRobust(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a piecewise cubic Akima spline interpolation based on arbitrary points.
            Akima splines are robust to outliers.
            </summary>
            <param name="points">The sample points t.</param>
            <param name="values">The sample point values x(t).</param>
            <returns>
            An interpolation scheme optimized for the given sample points and values,
            which can then be used to compute interpolations and extrapolations
            on arbitrary points.
            </returns>
            <remarks>
            if your data is already sorted in arrays, consider to use
            MathNet.Numerics.Interpolation.CubicSpline.InterpolateAkimaSorted
            instead, which is more efficient.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Interpolate.CubicSplineMonotone(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a piecewise cubic monotone spline interpolation based on arbitrary points.
            This is a shape-preserving spline with continuous first derivative.
            </summary>
            <param name="points">The sample points t.</param>
            <param name="values">The sample point values x(t).</param>
            <returns>
            An interpolation scheme optimized for the given sample points and values,
            which can then be used to compute interpolations and extrapolations
            on arbitrary points.
            </returns>
            <remarks>
            if your data is already sorted in arrays, consider to use
            MathNet.Numerics.Interpolation.CubicSpline.InterpolatePchipSorted
            instead, which is more efficient.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Interpolate.CubicSplineWithDerivatives(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a piecewise cubic Hermite spline interpolation based on arbitrary points
            and their slopes/first derivative.
            </summary>
            <param name="points">The sample points t.</param>
            <param name="values">The sample point values x(t).</param>
            <param name="firstDerivatives">The slope at the sample points. Optimized for arrays.</param>
            <returns>
            An interpolation scheme optimized for the given sample points and values,
            which can then be used to compute interpolations and extrapolations
            on arbitrary points.
            </returns>
            <remarks>
            if your data is already sorted in arrays, consider to use
            MathNet.Numerics.Interpolation.CubicSpline.InterpolateHermiteSorted
            instead, which is more efficient.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Interpolate.Step(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a step-interpolation based on arbitrary points.
            </summary>
            <param name="points">The sample points t.</param>
            <param name="values">The sample point values x(t).</param>
            <returns>
            An interpolation scheme optimized for the given sample points and values,
            which can then be used to compute interpolations and extrapolations
            on arbitrary points.
            </returns>
            <remarks>
            if your data is already sorted in arrays, consider to use
            MathNet.Numerics.Interpolation.StepInterpolation.InterpolateSorted
            instead, which is more efficient.
            </remarks>
        </member>
        <member name="T:MathNet.Numerics.Interpolation.Barycentric">
            <summary>
            Barycentric Interpolation Algorithm.
            </summary>
            <remarks>Supports neither differentiation nor integration.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.Barycentric.#ctor(System.Double[],System.Double[],System.Double[])">
            <param name="x">Sample points (N), sorted ascendingly.</param>
            <param name="y">Sample values (N), sorted ascendingly by x.</param>
            <param name="w">Barycentric weights (N), sorted ascendingly by x.</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.Barycentric.InterpolatePolynomialEquidistantSorted(System.Double[],System.Double[])">
            <summary>
            Create a barycentric polynomial interpolation from a set of (x,y) value pairs with equidistant x, sorted ascendingly by x.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.Barycentric.InterpolatePolynomialEquidistantInplace(System.Double[],System.Double[])">
            <summary>
            Create a barycentric polynomial interpolation from an unordered set of (x,y) value pairs with equidistant x.
            WARNING: Works in-place and can thus causes the data array to be reordered.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.Barycentric.InterpolatePolynomialEquidistant(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a barycentric polynomial interpolation from an unsorted set of (x,y) value pairs with equidistant x.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.Barycentric.InterpolatePolynomialEquidistant(System.Double,System.Double,System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a barycentric polynomial interpolation from a set of values related to linearly/equidistant spaced points within an interval.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.Barycentric.InterpolateRationalFloaterHormannSorted(System.Double[],System.Double[],System.Int32)">
            <summary>
            Create a barycentric rational interpolation without poles, using Mike Floater and Kai Hormann's Algorithm.
            The values are assumed to be sorted ascendingly by x.
            </summary>
            <param name="x">Sample points (N), sorted ascendingly.</param>
            <param name="y">Sample values (N), sorted ascendingly by x.</param>
            <param name="order">
            Order of the interpolation scheme, 0 &lt;= order &lt;= N.
            In most cases a value between 3 and 8 gives good results.
            </param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.Barycentric.InterpolateRationalFloaterHormannInplace(System.Double[],System.Double[],System.Int32)">
            <summary>
            Create a barycentric rational interpolation without poles, using Mike Floater and Kai Hormann's Algorithm.
            WARNING: Works in-place and can thus causes the data array to be reordered.
            </summary>
            <param name="x">Sample points (N), no sorting assumed.</param>
            <param name="y">Sample values (N).</param>
            <param name="order">
            Order of the interpolation scheme, 0 &lt;= order &lt;= N.
            In most cases a value between 3 and 8 gives good results.
            </param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.Barycentric.InterpolateRationalFloaterHormann(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double},System.Int32)">
            <summary>
            Create a barycentric rational interpolation without poles, using Mike Floater and Kai Hormann's Algorithm.
            </summary>
            <param name="x">Sample points (N), no sorting assumed.</param>
            <param name="y">Sample values (N).</param>
            <param name="order">
            Order of the interpolation scheme, 0 &lt;= order &lt;= N.
            In most cases a value between 3 and 8 gives good results.
            </param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.Barycentric.InterpolateRationalFloaterHormannSorted(System.Double[],System.Double[])">
            <summary>
            Create a barycentric rational interpolation without poles, using Mike Floater and Kai Hormann's Algorithm.
            The values are assumed to be sorted ascendingly by x.
            </summary>
            <param name="x">Sample points (N), sorted ascendingly.</param>
            <param name="y">Sample values (N), sorted ascendingly by x.</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.Barycentric.InterpolateRationalFloaterHormannInplace(System.Double[],System.Double[])">
            <summary>
            Create a barycentric rational interpolation without poles, using Mike Floater and Kai Hormann's Algorithm.
            WARNING: Works in-place and can thus causes the data array to be reordered.
            </summary>
            <param name="x">Sample points (N), no sorting assumed.</param>
            <param name="y">Sample values (N).</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.Barycentric.InterpolateRationalFloaterHormann(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a barycentric rational interpolation without poles, using Mike Floater and Kai Hormann's Algorithm.
            </summary>
            <param name="x">Sample points (N), no sorting assumed.</param>
            <param name="y">Sample values (N).</param>
        </member>
        <member name="P:MathNet.Numerics.Interpolation.Barycentric.MathNet#Numerics#Interpolation#IInterpolation#SupportsDifferentiation">
            <summary>
            Gets a value indicating whether the algorithm supports differentiation (interpolated derivative).
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Interpolation.Barycentric.MathNet#Numerics#Interpolation#IInterpolation#SupportsIntegration">
            <summary>
            Gets a value indicating whether the algorithm supports integration (interpolated quadrature).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.Barycentric.Interpolate(System.Double)">
            <summary>
            Interpolate at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated value x(t).</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.Barycentric.MathNet#Numerics#Interpolation#IInterpolation#Differentiate(System.Double)">
            <summary>
            Differentiate at point t. NOT SUPPORTED.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated first derivative at point t.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.Barycentric.MathNet#Numerics#Interpolation#IInterpolation#Differentiate2(System.Double)">
            <summary>
            Differentiate twice at point t. NOT SUPPORTED.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated second derivative at point t.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.Barycentric.MathNet#Numerics#Interpolation#IInterpolation#Integrate(System.Double)">
            <summary>
            Indefinite integral at point t. NOT SUPPORTED.
            </summary>
            <param name="t">Point t to integrate at.</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.Barycentric.MathNet#Numerics#Interpolation#IInterpolation#Integrate(System.Double,System.Double)">
            <summary>
            Definite integral between points a and b. NOT SUPPORTED.
            </summary>
            <param name="a">Left bound of the integration interval [a,b].</param>
            <param name="b">Right bound of the integration interval [a,b].</param>
        </member>
        <member name="T:MathNet.Numerics.Interpolation.BulirschStoerRationalInterpolation">
            <summary>
            Rational Interpolation (with poles) using Roland Bulirsch and Josef Stoer's Algorithm.
            </summary>
            <remarks>
            <para>
            This algorithm supports neither differentiation nor integration.
            </para>
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.BulirschStoerRationalInterpolation.#ctor(System.Double[],System.Double[])">
            <param name="x">Sample Points t, sorted ascendingly.</param>
            <param name="y">Sample Values x(t), sorted ascendingly by x.</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.BulirschStoerRationalInterpolation.InterpolateSorted(System.Double[],System.Double[])">
            <summary>
            Create a Bulirsch-Stoer rational interpolation from a set of (x,y) value pairs, sorted ascendingly by x.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.BulirschStoerRationalInterpolation.InterpolateInplace(System.Double[],System.Double[])">
            <summary>
            Create a Bulirsch-Stoer rational interpolation from an unsorted set of (x,y) value pairs.
            WARNING: Works in-place and can thus causes the data array to be reordered.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.BulirschStoerRationalInterpolation.Interpolate(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a Bulirsch-Stoer rational interpolation from an unsorted set of (x,y) value pairs.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Interpolation.BulirschStoerRationalInterpolation.MathNet#Numerics#Interpolation#IInterpolation#SupportsDifferentiation">
            <summary>
            Gets a value indicating whether the algorithm supports differentiation (interpolated derivative).
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Interpolation.BulirschStoerRationalInterpolation.MathNet#Numerics#Interpolation#IInterpolation#SupportsIntegration">
            <summary>
            Gets a value indicating whether the algorithm supports integration (interpolated quadrature).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.BulirschStoerRationalInterpolation.Interpolate(System.Double)">
            <summary>
            Interpolate at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated value x(t).</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.BulirschStoerRationalInterpolation.MathNet#Numerics#Interpolation#IInterpolation#Differentiate(System.Double)">
            <summary>
            Differentiate at point t. NOT SUPPORTED.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated first derivative at point t.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.BulirschStoerRationalInterpolation.MathNet#Numerics#Interpolation#IInterpolation#Differentiate2(System.Double)">
            <summary>
            Differentiate twice at point t. NOT SUPPORTED.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated second derivative at point t.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.BulirschStoerRationalInterpolation.MathNet#Numerics#Interpolation#IInterpolation#Integrate(System.Double)">
            <summary>
            Indefinite integral at point t. NOT SUPPORTED.
            </summary>
            <param name="t">Point t to integrate at.</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.BulirschStoerRationalInterpolation.MathNet#Numerics#Interpolation#IInterpolation#Integrate(System.Double,System.Double)">
            <summary>
            Definite integral between points a and b. NOT SUPPORTED.
            </summary>
            <param name="a">Left bound of the integration interval [a,b].</param>
            <param name="b">Right bound of the integration interval [a,b].</param>
        </member>
        <member name="T:MathNet.Numerics.Interpolation.CubicSpline">
            <summary>
            Cubic Spline Interpolation.
            </summary>
            <remarks>Supports both differentiation and integration.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.#ctor(System.Double[],System.Double[],System.Double[],System.Double[],System.Double[])">
            <param name="x">sample points (N+1), sorted ascending</param>
            <param name="c0">Zero order spline coefficients (N)</param>
            <param name="c1">First order spline coefficients (N)</param>
            <param name="c2">second order spline coefficients (N)</param>
            <param name="c3">third order spline coefficients (N)</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.InterpolateHermiteSorted(System.Double[],System.Double[],System.Double[])">
            <summary>
            Create a Hermite cubic spline interpolation from a set of (x,y) value pairs and their slope (first derivative), sorted ascendingly by x.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.InterpolateHermiteInplace(System.Double[],System.Double[],System.Double[])">
            <summary>
            Create a Hermite cubic spline interpolation from an unsorted set of (x,y) value pairs and their slope (first derivative).
            WARNING: Works in-place and can thus causes the data array to be reordered.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.InterpolateHermite(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a Hermite cubic spline interpolation from an unsorted set of (x,y) value pairs and their slope (first derivative).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.InterpolateAkimaSorted(System.Double[],System.Double[])">
            <summary>
            Create an Akima cubic spline interpolation from a set of (x,y) value pairs, sorted ascendingly by x.
            Akima splines are robust to outliers.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.InterpolateAkimaInplace(System.Double[],System.Double[])">
            <summary>
            Create an Akima cubic spline interpolation from an unsorted set of (x,y) value pairs.
            Akima splines are robust to outliers.
            WARNING: Works in-place and can thus causes the data array to be reordered.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.InterpolateAkima(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create an Akima cubic spline interpolation from an unsorted set of (x,y) value pairs.
            Akima splines are robust to outliers.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.InterpolatePchipSorted(System.Double[],System.Double[])">
            <summary>
            Create a piecewise cubic Hermite interpolating polynomial from an unsorted set of (x,y) value pairs.
            Monotone-preserving interpolation with continuous first derivative.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.InterpolatePchipInplace(System.Double[],System.Double[])">
            <summary>
            Create a piecewise cubic Hermite interpolating polynomial from an unsorted set of (x,y) value pairs.
            Monotone-preserving interpolation with continuous first derivative.
            WARNING: Works in-place and can thus causes the data array to be reordered.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.InterpolatePchip(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a piecewise cubic Hermite interpolating polynomial from an unsorted set of (x,y) value pairs.
            Monotone-preserving interpolation with continuous first derivative.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.InterpolateBoundariesSorted(System.Double[],System.Double[],MathNet.Numerics.Interpolation.SplineBoundaryCondition,System.Double,MathNet.Numerics.Interpolation.SplineBoundaryCondition,System.Double)">
            <summary>
            Create a cubic spline interpolation from a set of (x,y) value pairs, sorted ascendingly by x,
            and custom boundary/termination conditions.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.InterpolateBoundariesInplace(System.Double[],System.Double[],MathNet.Numerics.Interpolation.SplineBoundaryCondition,System.Double,MathNet.Numerics.Interpolation.SplineBoundaryCondition,System.Double)">
            <summary>
            Create a cubic spline interpolation from an unsorted set of (x,y) value pairs and custom boundary/termination conditions.
            WARNING: Works in-place and can thus causes the data array to be reordered.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.InterpolateBoundaries(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double},MathNet.Numerics.Interpolation.SplineBoundaryCondition,System.Double,MathNet.Numerics.Interpolation.SplineBoundaryCondition,System.Double)">
            <summary>
            Create a cubic spline interpolation from an unsorted set of (x,y) value pairs and custom boundary/termination conditions.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.InterpolateNaturalSorted(System.Double[],System.Double[])">
            <summary>
            Create a natural cubic spline interpolation from a set of (x,y) value pairs
            and zero second derivatives at the two boundaries, sorted ascendingly by x.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.InterpolateNaturalInplace(System.Double[],System.Double[])">
            <summary>
            Create a natural cubic spline interpolation from an unsorted set of (x,y) value pairs
            and zero second derivatives at the two boundaries.
            WARNING: Works in-place and can thus causes the data array to be reordered.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.InterpolateNatural(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a natural cubic spline interpolation from an unsorted set of (x,y) value pairs
            and zero second derivatives at the two boundaries.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.DifferentiateThreePoint(System.Double[],System.Double[],System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Three-Point Differentiation Helper.
            </summary>
            <param name="xx">Sample Points t.</param>
            <param name="yy">Sample Values x(t).</param>
            <param name="indexT">Index of the point of the differentiation.</param>
            <param name="index0">Index of the first sample.</param>
            <param name="index1">Index of the second sample.</param>
            <param name="index2">Index of the third sample.</param>
            <returns>The derivative approximation.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.SolveTridiagonal(System.Double[],System.Double[],System.Double[],System.Double[])">
            <summary>
            Tridiagonal Solve Helper.
            </summary>
            <param name="a">The a-vector[n].</param>
            <param name="b">The b-vector[n], will be modified by this function.</param>
            <param name="c">The c-vector[n].</param>
            <param name="d">The d-vector[n], will be modified by this function.</param>
            <returns>The x-vector[n]</returns>
        </member>
        <member name="P:MathNet.Numerics.Interpolation.CubicSpline.MathNet#Numerics#Interpolation#IInterpolation#SupportsDifferentiation">
            <summary>
            Gets a value indicating whether the algorithm supports differentiation (interpolated derivative).
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Interpolation.CubicSpline.MathNet#Numerics#Interpolation#IInterpolation#SupportsIntegration">
            <summary>
            Gets a value indicating whether the algorithm supports integration (interpolated quadrature).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.Interpolate(System.Double)">
            <summary>
            Interpolate at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated value x(t).</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.Differentiate(System.Double)">
            <summary>
            Differentiate at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated first derivative at point t.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.Differentiate2(System.Double)">
            <summary>
            Differentiate twice at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated second derivative at point t.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.Integrate(System.Double)">
            <summary>
            Indefinite integral at point t.
            </summary>
            <param name="t">Point t to integrate at.</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.Integrate(System.Double,System.Double)">
            <summary>
            Definite integral between points a and b.
            </summary>
            <param name="a">Left bound of the integration interval [a,b].</param>
            <param name="b">Right bound of the integration interval [a,b].</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.LeftSegmentIndex(System.Double)">
            <summary>
            Find the index of the greatest sample point smaller than t,
            or the left index of the closest segment for extrapolation.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.StationaryPoints">
            <summary>
            Gets all the t values where the derivative is 0
            see: https://mathworld.wolfram.com/StationaryPoint.html
            </summary>
            <returns>An array of t values (in the domain of the function) where the derivative of the spline is 0</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.CubicSpline.Extrema">
            <summary>
            Returns the t values in the domain of the spline for which it takes the minimum and maximum value.
            </summary>
            <returns>A tuple containing the t value for which the spline is minimum in the first component and maximum in the second component </returns>
        </member>
        <member name="T:MathNet.Numerics.Interpolation.IInterpolation">
            <summary>
            Interpolation within the range of a discrete set of known data points.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Interpolation.IInterpolation.SupportsDifferentiation">
            <summary>
            Gets a value indicating whether the algorithm supports differentiation (interpolated derivative).
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Interpolation.IInterpolation.SupportsIntegration">
            <summary>
            Gets a value indicating whether the algorithm supports integration (interpolated quadrature).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.IInterpolation.Interpolate(System.Double)">
            <summary>
            Interpolate at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated value x(t).</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.IInterpolation.Differentiate(System.Double)">
            <summary>
            Differentiate at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated first derivative at point t.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.IInterpolation.Differentiate2(System.Double)">
            <summary>
            Differentiate twice at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated second derivative at point t.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.IInterpolation.Integrate(System.Double)">
            <summary>
            Indefinite integral at point t.
            </summary>
            <param name="t">Point t to integrate at.</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.IInterpolation.Integrate(System.Double,System.Double)">
            <summary>
            Definite integral between points a and b.
            </summary>
            <param name="a">Left bound of the integration interval [a,b].</param>
            <param name="b">Right bound of the integration interval [a,b].</param>
        </member>
        <member name="T:MathNet.Numerics.Interpolation.LinearSpline">
            <summary>
            Piece-wise Linear Interpolation.
            </summary>
            <remarks>Supports both differentiation and integration.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.LinearSpline.#ctor(System.Double[],System.Double[],System.Double[])">
            <param name="x">Sample points (N+1), sorted ascending</param>
            <param name="c0">Sample values (N or N+1) at the corresponding points; intercept, zero order coefficients</param>
            <param name="c1">Slopes (N) at the sample points (first order coefficients): N</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.LinearSpline.InterpolateSorted(System.Double[],System.Double[])">
            <summary>
            Create a linear spline interpolation from a set of (x,y) value pairs, sorted ascendingly by x.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.LinearSpline.InterpolateInplace(System.Double[],System.Double[])">
            <summary>
            Create a linear spline interpolation from an unsorted set of (x,y) value pairs.
            WARNING: Works in-place and can thus causes the data array to be reordered.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.LinearSpline.Interpolate(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a linear spline interpolation from an unsorted set of (x,y) value pairs.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Interpolation.LinearSpline.MathNet#Numerics#Interpolation#IInterpolation#SupportsDifferentiation">
            <summary>
            Gets a value indicating whether the algorithm supports differentiation (interpolated derivative).
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Interpolation.LinearSpline.MathNet#Numerics#Interpolation#IInterpolation#SupportsIntegration">
            <summary>
            Gets a value indicating whether the algorithm supports integration (interpolated quadrature).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.LinearSpline.Interpolate(System.Double)">
            <summary>
            Interpolate at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated value x(t).</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.LinearSpline.Differentiate(System.Double)">
            <summary>
            Differentiate at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated first derivative at point t.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.LinearSpline.Differentiate2(System.Double)">
            <summary>
            Differentiate twice at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated second derivative at point t.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.LinearSpline.Integrate(System.Double)">
            <summary>
            Indefinite integral at point t.
            </summary>
            <param name="t">Point t to integrate at.</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.LinearSpline.Integrate(System.Double,System.Double)">
            <summary>
            Definite integral between points a and b.
            </summary>
            <param name="a">Left bound of the integration interval [a,b].</param>
            <param name="b">Right bound of the integration interval [a,b].</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.LinearSpline.LeftSegmentIndex(System.Double)">
            <summary>
            Find the index of the greatest sample point smaller than t,
            or the left index of the closest segment for extrapolation.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Interpolation.LogLinear">
            <summary>
            Piece-wise Log-Linear Interpolation
            </summary>
            <remarks>This algorithm supports differentiation, not integration.</remarks>
        </member>
        <member name="F:MathNet.Numerics.Interpolation.LogLinear._spline">
            <summary>
            Internal Spline Interpolation
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.LogLinear.#ctor(System.Double[],System.Double[])">
            <param name="x">Sample points (N), sorted ascending</param>
            <param name="logy">Natural logarithm of the sample values (N) at the corresponding points</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.LogLinear.InterpolateSorted(System.Double[],System.Double[])">
            <summary>
            Create a piecewise log-linear interpolation from a set of (x,y) value pairs, sorted ascendingly by x.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.LogLinear.InterpolateInplace(System.Double[],System.Double[])">
            <summary>
            Create a piecewise log-linear interpolation from an unsorted set of (x,y) value pairs.
            WARNING: Works in-place and can thus causes the data array to be reordered and modified.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.LogLinear.Interpolate(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a piecewise log-linear interpolation from an unsorted set of (x,y) value pairs.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Interpolation.LogLinear.MathNet#Numerics#Interpolation#IInterpolation#SupportsDifferentiation">
            <summary>
            Gets a value indicating whether the algorithm supports differentiation (interpolated derivative).
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Interpolation.LogLinear.MathNet#Numerics#Interpolation#IInterpolation#SupportsIntegration">
            <summary>
            Gets a value indicating whether the algorithm supports integration (interpolated quadrature).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.LogLinear.Interpolate(System.Double)">
            <summary>
            Interpolate at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated value x(t).</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.LogLinear.Differentiate(System.Double)">
            <summary>
            Differentiate at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated first derivative at point t.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.LogLinear.Differentiate2(System.Double)">
            <summary>
            Differentiate twice at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated second derivative at point t.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.LogLinear.MathNet#Numerics#Interpolation#IInterpolation#Integrate(System.Double)">
            <summary>
            Indefinite integral at point t.
            </summary>
            <param name="t">Point t to integrate at.</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.LogLinear.MathNet#Numerics#Interpolation#IInterpolation#Integrate(System.Double,System.Double)">
            <summary>
            Definite integral between points a and b.
            </summary>
            <param name="a">Left bound of the integration interval [a,b].</param>
            <param name="b">Right bound of the integration interval [a,b].</param>
        </member>
        <member name="T:MathNet.Numerics.Interpolation.NevillePolynomialInterpolation">
            <summary>
            Lagrange Polynomial Interpolation using Neville's Algorithm.
            </summary>
            <remarks>
            <para>
            This algorithm supports differentiation, but doesn't support integration.
            </para>
            <para>
            When working with equidistant or Chebyshev sample points it is
            recommended to use the barycentric algorithms specialized for
            these cases instead of this arbitrary Neville algorithm.
            </para>
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.NevillePolynomialInterpolation.#ctor(System.Double[],System.Double[])">
            <param name="x">Sample Points t, sorted ascendingly.</param>
            <param name="y">Sample Values x(t), sorted ascendingly by x.</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.NevillePolynomialInterpolation.InterpolateSorted(System.Double[],System.Double[])">
            <summary>
            Create a Neville polynomial interpolation from a set of (x,y) value pairs, sorted ascendingly by x.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.NevillePolynomialInterpolation.InterpolateInplace(System.Double[],System.Double[])">
            <summary>
            Create a Neville polynomial interpolation from an unsorted set of (x,y) value pairs.
            WARNING: Works in-place and can thus causes the data array to be reordered.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.NevillePolynomialInterpolation.Interpolate(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a Neville polynomial interpolation from an unsorted set of (x,y) value pairs.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Interpolation.NevillePolynomialInterpolation.MathNet#Numerics#Interpolation#IInterpolation#SupportsDifferentiation">
            <summary>
            Gets a value indicating whether the algorithm supports differentiation (interpolated derivative).
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Interpolation.NevillePolynomialInterpolation.MathNet#Numerics#Interpolation#IInterpolation#SupportsIntegration">
            <summary>
            Gets a value indicating whether the algorithm supports integration (interpolated quadrature).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.NevillePolynomialInterpolation.Interpolate(System.Double)">
            <summary>
            Interpolate at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated value x(t).</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.NevillePolynomialInterpolation.Differentiate(System.Double)">
            <summary>
            Differentiate at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated first derivative at point t.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.NevillePolynomialInterpolation.Differentiate2(System.Double)">
            <summary>
            Differentiate twice at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated second derivative at point t.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.NevillePolynomialInterpolation.MathNet#Numerics#Interpolation#IInterpolation#Integrate(System.Double)">
            <summary>
            Indefinite integral at point t. NOT SUPPORTED.
            </summary>
            <param name="t">Point t to integrate at.</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.NevillePolynomialInterpolation.MathNet#Numerics#Interpolation#IInterpolation#Integrate(System.Double,System.Double)">
            <summary>
            Definite integral between points a and b. NOT SUPPORTED.
            </summary>
            <param name="a">Left bound of the integration interval [a,b].</param>
            <param name="b">Right bound of the integration interval [a,b].</param>
        </member>
        <member name="T:MathNet.Numerics.Interpolation.QuadraticSpline">
            <summary>
            Quadratic Spline Interpolation.
            </summary>
            <remarks>Supports both differentiation and integration.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.QuadraticSpline.#ctor(System.Double[],System.Double[],System.Double[],System.Double[])">
            <param name="x">sample points (N+1), sorted ascending</param>
            <param name="c0">Zero order spline coefficients (N)</param>
            <param name="c1">First order spline coefficients (N)</param>
            <param name="c2">second order spline coefficients (N)</param>
        </member>
        <member name="P:MathNet.Numerics.Interpolation.QuadraticSpline.MathNet#Numerics#Interpolation#IInterpolation#SupportsDifferentiation">
            <summary>
            Gets a value indicating whether the algorithm supports differentiation (interpolated derivative).
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Interpolation.QuadraticSpline.MathNet#Numerics#Interpolation#IInterpolation#SupportsIntegration">
            <summary>
            Gets a value indicating whether the algorithm supports integration (interpolated quadrature).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.QuadraticSpline.Interpolate(System.Double)">
            <summary>
            Interpolate at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated value x(t).</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.QuadraticSpline.Differentiate(System.Double)">
            <summary>
            Differentiate at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated first derivative at point t.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.QuadraticSpline.Differentiate2(System.Double)">
            <summary>
            Differentiate twice at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated second derivative at point t.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.QuadraticSpline.Integrate(System.Double)">
            <summary>
            Indefinite integral at point t.
            </summary>
            <param name="t">Point t to integrate at.</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.QuadraticSpline.Integrate(System.Double,System.Double)">
            <summary>
            Definite integral between points a and b.
            </summary>
            <param name="a">Left bound of the integration interval [a,b].</param>
            <param name="b">Right bound of the integration interval [a,b].</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.QuadraticSpline.LeftSegmentIndex(System.Double)">
            <summary>
            Find the index of the greatest sample point smaller than t,
            or the left index of the closest segment for extrapolation.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Interpolation.SplineBoundaryCondition">
            <summary>
            Left and right boundary conditions.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Interpolation.SplineBoundaryCondition.Natural">
            <summary>
            Natural Boundary (Zero second derivative).
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Interpolation.SplineBoundaryCondition.ParabolicallyTerminated">
            <summary>
            Parabolically Terminated boundary.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Interpolation.SplineBoundaryCondition.FirstDerivative">
            <summary>
            Fixed first derivative at the boundary.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Interpolation.SplineBoundaryCondition.SecondDerivative">
            <summary>
            Fixed second derivative at the boundary.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Interpolation.StepInterpolation">
            <summary>
            A step function where the start of each segment is included, and the last segment is open-ended.
            Segment i is [x_i, x_i+1) for i &lt; N, or [x_i, infinity] for i = N.
            The domain of the function is all real numbers, such that y = 0 where x &lt;.
            </summary>
            <remarks>Supports both differentiation and integration.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.StepInterpolation.#ctor(System.Double[],System.Double[])">
            <param name="x">Sample points (N), sorted ascending</param>
            <param name="sy">Samples values (N) of each segment starting at the corresponding sample point.</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.StepInterpolation.InterpolateSorted(System.Double[],System.Double[])">
            <summary>
            Create a linear spline interpolation from a set of (x,y) value pairs, sorted ascendingly by x.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.StepInterpolation.InterpolateInplace(System.Double[],System.Double[])">
            <summary>
            Create a linear spline interpolation from an unsorted set of (x,y) value pairs.
            WARNING: Works in-place and can thus causes the data array to be reordered.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.StepInterpolation.Interpolate(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a linear spline interpolation from an unsorted set of (x,y) value pairs.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.StepInterpolation.Interpolate(System.Double)">
            <summary>
            Interpolate at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated value x(t).</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.StepInterpolation.Differentiate(System.Double)">
            <summary>
            Differentiate at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated first derivative at point t.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.StepInterpolation.Differentiate2(System.Double)">
            <summary>
            Differentiate twice at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated second derivative at point t.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.StepInterpolation.Integrate(System.Double)">
            <summary>
            Indefinite integral at point t.
            </summary>
            <param name="t">Point t to integrate at.</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.StepInterpolation.Integrate(System.Double,System.Double)">
            <summary>
            Definite integral between points a and b.
            </summary>
            <param name="a">Left bound of the integration interval [a,b].</param>
            <param name="b">Right bound of the integration interval [a,b].</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.StepInterpolation.LeftBracketIndex(System.Double)">
            <summary>
            Find the index of the greatest sample point smaller than t.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Interpolation.TransformedInterpolation">
            <summary>
            Wraps an interpolation with a transformation of the interpolated values.
            </summary>
            <remarks>Neither differentiation nor integration is supported.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.TransformedInterpolation.InterpolateSorted(System.Func{System.Double,System.Double},System.Func{System.Double,System.Double},System.Double[],System.Double[])">
            <summary>
            Create a linear spline interpolation from a set of (x,y) value pairs, sorted ascendingly by x.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.TransformedInterpolation.InterpolateInplace(System.Func{System.Double,System.Double},System.Func{System.Double,System.Double},System.Double[],System.Double[])">
            <summary>
            Create a linear spline interpolation from an unsorted set of (x,y) value pairs.
            WARNING: Works in-place and can thus causes the data array to be reordered and modified.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.TransformedInterpolation.Interpolate(System.Func{System.Double,System.Double},System.Func{System.Double,System.Double},System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a linear spline interpolation from an unsorted set of (x,y) value pairs.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Interpolation.TransformedInterpolation.MathNet#Numerics#Interpolation#IInterpolation#SupportsDifferentiation">
            <summary>
            Gets a value indicating whether the algorithm supports differentiation (interpolated derivative).
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Interpolation.TransformedInterpolation.MathNet#Numerics#Interpolation#IInterpolation#SupportsIntegration">
            <summary>
            Gets a value indicating whether the algorithm supports integration (interpolated quadrature).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.TransformedInterpolation.Interpolate(System.Double)">
            <summary>
            Interpolate at point t.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated value x(t).</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.TransformedInterpolation.MathNet#Numerics#Interpolation#IInterpolation#Differentiate(System.Double)">
            <summary>
            Differentiate at point t. NOT SUPPORTED.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated first derivative at point t.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.TransformedInterpolation.MathNet#Numerics#Interpolation#IInterpolation#Differentiate2(System.Double)">
            <summary>
            Differentiate twice at point t. NOT SUPPORTED.
            </summary>
            <param name="t">Point t to interpolate at.</param>
            <returns>Interpolated second derivative at point t.</returns>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.TransformedInterpolation.MathNet#Numerics#Interpolation#IInterpolation#Integrate(System.Double)">
            <summary>
            Indefinite integral at point t. NOT SUPPORTED.
            </summary>
            <param name="t">Point t to integrate at.</param>
        </member>
        <member name="M:MathNet.Numerics.Interpolation.TransformedInterpolation.MathNet#Numerics#Interpolation#IInterpolation#Integrate(System.Double,System.Double)">
            <summary>
            Definite integral between points a and b. NOT SUPPORTED.
            </summary>
            <param name="a">Left bound of the integration interval [a,b].</param>
            <param name="b">Right bound of the integration interval [a,b].</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix">
            <summary>
            A Matrix class with dense storage. The underlying storage is a one dimensional array in column-major order (column by column).
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix._rowCount">
            <summary>
            Number of rows.
            </summary>
            <remarks>Using this instead of the RowCount property to speed up calculating
            a matrix index in the data array.</remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix._columnCount">
            <summary>
            Number of columns.
            </summary>
            <remarks>Using this instead of the ColumnCount property to speed up calculating
            a matrix index in the data array.</remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix._values">
            <summary>
            Gets the matrix's data.
            </summary>
            <value>The matrix's data.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.#ctor(MathNet.Numerics.LinearAlgebra.Storage.DenseColumnMajorMatrixStorage{System.Double})">
            <summary>
            Create a new dense matrix straight from an initialized matrix storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.#ctor(System.Int32)">
            <summary>
            Create a new square dense matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the order is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.#ctor(System.Int32,System.Int32)">
            <summary>
            Create a new dense matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the row or column count is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.#ctor(System.Int32,System.Int32,System.Double[])">
            <summary>
            Create a new dense matrix with the given number of rows and columns directly binding to a raw array.
            The array is assumed to be in column-major order (column by column) and is used directly without copying.
            Very efficient, but changes to the array and the matrix will affect each other.
            </summary>
            <seealso href="http://en.wikipedia.org/wiki/Row-major_order"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfMatrix(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Create a new dense matrix as a copy of the given other matrix.
            This new matrix will be independent from the other matrix.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfArray(System.Double[0:,0:])">
            <summary>
            Create a new dense matrix as a copy of the given two-dimensional array.
            This new matrix will be independent from the provided array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfIndexed(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Int32,System.Double}})">
            <summary>
            Create a new dense matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfIndexed(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Int32,System.Double}})">
            <summary>
            Create a new dense matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfColumnMajor(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable.
            The enumerable is assumed to be in column-major order (column by column).
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfColumns(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Double}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfColumns(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Double}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfColumnArrays(System.Double[][])">
            <summary>
            Create a new dense matrix as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfColumnArrays(System.Collections.Generic.IEnumerable{System.Double[]})">
            <summary>
            Create a new dense matrix as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfColumnVectors(MathNet.Numerics.LinearAlgebra.Vector{System.Double}[])">
            <summary>
            Create a new dense matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfColumnVectors(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{System.Double}})">
            <summary>
            Create a new dense matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfRows(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Double}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfRows(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Double}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfRowArrays(System.Double[][])">
            <summary>
            Create a new dense matrix as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfRowArrays(System.Collections.Generic.IEnumerable{System.Double[]})">
            <summary>
            Create a new dense matrix as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfRowVectors(MathNet.Numerics.LinearAlgebra.Vector{System.Double}[])">
            <summary>
            Create a new dense matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfRowVectors(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{System.Double}})">
            <summary>
            Create a new dense matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfDiagonalVector(MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfDiagonalVector(System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfDiagonalArray(System.Double[])">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfDiagonalArray(System.Int32,System.Int32,System.Double[])">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.Create(System.Int32,System.Int32,System.Double)">
            <summary>
            Create a new dense matrix and initialize each value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.Create(System.Int32,System.Int32,System.Func{System.Int32,System.Int32,System.Double})">
            <summary>
            Create a new dense matrix and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.CreateDiagonal(System.Int32,System.Int32,System.Double)">
            <summary>
            Create a new diagonal dense matrix and initialize each diagonal value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.CreateDiagonal(System.Int32,System.Int32,System.Func{System.Int32,System.Double})">
            <summary>
            Create a new diagonal dense matrix and initialize each diagonal value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.CreateIdentity(System.Int32)">
            <summary>
            Create a new square sparse identity matrix where each diagonal value is set to One.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.CreateRandom(System.Int32,System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create a new dense matrix with values sampled from the provided random distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.Values">
            <summary>
            Gets the matrix's data.
            </summary>
            <value>The matrix's data.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.L1Norm">
            <summary>Calculates the induced L1 norm of this matrix.</summary>
            <returns>The maximum absolute column sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.InfinityNorm">
            <summary>Calculates the induced infinity norm of this matrix.</summary>
            <returns>The maximum absolute row sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.FrobeniusNorm">
            <summary>Calculates the entry-wise Frobenius norm of this matrix.</summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.DoNegate(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Negate each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the negation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.DoAdd(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Add a scalar to each element of the matrix and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to add.</param>
            <param name="result">The matrix to store the result of the addition.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.DoAdd(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Adds another matrix to this matrix.
            </summary>
            <param name="other">The matrix to add to this matrix.</param>
            <param name="result">The matrix to store the result of add</param>
            <exception cref="T:System.ArgumentNullException">If the other matrix is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.DoSubtract(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Subtracts a scalar from each element of the matrix and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to subtract.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.DoSubtract(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Subtracts another matrix from this matrix.
            </summary>
            <param name="other">The matrix to subtract.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.DoMultiply(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Multiplies each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="scalar">The scalar to multiply the matrix with.</param>
            <param name="result">The matrix to store the result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Multiplies this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Multiplies this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.DoTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Multiplies this matrix with transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Multiplies the transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.DoDivide(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Divides each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="divisor">The scalar to divide the matrix with.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Pointwise multiplies this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="other">The matrix to pointwise multiply with this one.</param>
            <param name="result">The matrix to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Pointwise divide this matrix by another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The matrix to pointwise divide this one by.</param>
            <param name="result">The matrix to store the result of the pointwise division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.DoPointwisePower(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Pointwise raise this matrix to an exponent and store the result into the result matrix.
            </summary>
            <param name="exponent">The exponent to raise this matrix values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.DoModulus(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.DoModulusByThis(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given dividend for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.DoRemainder(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.DoRemainderByThis(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given dividend for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.Trace">
            <summary>
            Computes the trace of this matrix.
            </summary>
            <returns>The trace of this matrix</returns>
            <exception cref="T:System.ArgumentException">If the matrix is not square</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.op_Addition(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix,MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)">
            <summary>
            Adds two matrices together and returns the results.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to add.</param>
            <param name="rightSide">The right matrix to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> don't have the same dimensions.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.op_UnaryPlus(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)">
            <summary>
            Returns a <strong>Matrix</strong> containing the same values of <paramref name="rightSide"/>.
            </summary>
            <param name="rightSide">The matrix to get the values from.</param>
            <returns>A matrix containing a the same values as <paramref name="rightSide"/>.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.op_Subtraction(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix,MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)">
            <summary>
            Subtracts two matrices together and returns the results.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to subtract.</param>
            <param name="rightSide">The right matrix to subtract.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> don't have the same dimensions.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.op_UnaryNegation(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)">
            <summary>
            Negates each element of the matrix.
            </summary>
            <param name="rightSide">The matrix to negate.</param>
            <returns>A matrix containing the negated values.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix,System.Double)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.op_Multiply(System.Double,MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix,MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)">
            <summary>
            Multiplies two matrices.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to multiply.</param>
            <param name="rightSide">The right matrix to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the dimensions of <paramref name="leftSide"/> or <paramref name="rightSide"/> don't conform.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix,MathNet.Numerics.LinearAlgebra.Double.DenseVector)">
            <summary>
            Multiplies a <strong>Matrix</strong> and a Vector.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The vector to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Double.DenseVector,MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)">
            <summary>
            Multiplies a Vector and a <strong>Matrix</strong>.
            </summary>
            <param name="leftSide">The vector to multiply.</param>
            <param name="rightSide">The matrix to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.op_Modulus(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix,System.Double)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.IsSymmetric">
            <summary>
            Evaluates whether this matrix is symmetric.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.DenseVector">
            <summary>
            A vector using dense storage.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.DenseVector._length">
            <summary>
            Number of elements
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.DenseVector._values">
            <summary>
            Gets the vector's data.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.#ctor(MathNet.Numerics.LinearAlgebra.Storage.DenseVectorStorage{System.Double})">
            <summary>
            Create a new dense vector straight from an initialized vector storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.#ctor(System.Int32)">
            <summary>
            Create a new dense vector with the given length.
            All cells of the vector will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If length is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.#ctor(System.Double[])">
            <summary>
            Create a new dense vector directly binding to a raw array.
            The array is used directly without copying.
            Very efficient, but changes to the array and the vector will affect each other.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.OfVector(MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Create a new dense vector as a copy of the given other vector.
            This new vector will be independent from the other vector.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.OfArray(System.Double[])">
            <summary>
            Create a new dense vector as a copy of the given array.
            This new vector will be independent from the array.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.OfEnumerable(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a new dense vector as a copy of the given enumerable.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.OfIndexedEnumerable(System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Double}})">
            <summary>
            Create a new dense vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.OfIndexedEnumerable(System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Double}})">
            <summary>
            Create a new dense vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.Create(System.Int32,System.Double)">
            <summary>
            Create a new dense vector and initialize each value using the provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.Create(System.Int32,System.Func{System.Int32,System.Double})">
            <summary>
            Create a new dense vector and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.CreateRandom(System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create a new dense vector with values sampled from the provided random distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.DenseVector.Values">
            <summary>
            Gets the vector's data.
            </summary>
            <value>The vector's data.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.op_Explicit(MathNet.Numerics.LinearAlgebra.Double.DenseVector)~System.Double[]">
            <summary>
            Returns a reference to the internal data structure.
            </summary>
            <param name="vector">The <c>DenseVector</c> whose internal data we are
            returning.</param>
            <returns>
            A reference to the internal date of the given vector.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.op_Implicit(System.Double[])~MathNet.Numerics.LinearAlgebra.Double.DenseVector">
            <summary>
            Returns a vector bound directly to a reference of the provided array.
            </summary>
            <param name="array">The array to bind to the <c>DenseVector</c> object.</param>
            <returns>
            A <c>DenseVector</c> whose values are bound to the given array.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.DoAdd(System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Adds a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to add.</param>
            <param name="result">The vector to store the result of the addition.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.DoAdd(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Adds another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to add to this one.</param>
            <param name="result">The vector to store the result of the addition.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.op_Addition(MathNet.Numerics.LinearAlgebra.Double.DenseVector,MathNet.Numerics.LinearAlgebra.Double.DenseVector)">
            <summary>
            Adds two <strong>Vectors</strong> together and returns the results.
            </summary>
            <param name="leftSide">One of the vectors to add.</param>
            <param name="rightSide">The other vector to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.DoSubtract(System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Subtracts a scalar from each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to subtract.</param>
            <param name="result">The vector to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.DoSubtract(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Subtracts another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to subtract from this one.</param>
            <param name="result">The vector to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.op_UnaryNegation(MathNet.Numerics.LinearAlgebra.Double.DenseVector)">
            <summary>
            Returns a <strong>Vector</strong> containing the negated values of <paramref name="rightSide"/>.
            </summary>
            <param name="rightSide">The vector to get the values from.</param>
            <returns>A vector containing the negated values as <paramref name="rightSide"/>.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.op_Subtraction(MathNet.Numerics.LinearAlgebra.Double.DenseVector,MathNet.Numerics.LinearAlgebra.Double.DenseVector)">
            <summary>
            Subtracts two <strong>Vectors</strong> and returns the results.
            </summary>
            <param name="leftSide">The vector to subtract from.</param>
            <param name="rightSide">The vector to subtract.</param>
            <returns>The result of the subtraction.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.DoNegate(MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Negates vector and saves result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.DoMultiply(System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Multiplies a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to multiply.</param>
            <param name="result">The vector to store the result of the multiplication.</param>
            <remarks></remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.DoDotProduct(MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Computes the dot product between this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of a[i]*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.op_Multiply(MathNet.Numerics.LinearAlgebra.Double.DenseVector,System.Double)">
            <summary>
            Multiplies a vector with a scalar.
            </summary>
            <param name="leftSide">The vector to scale.</param>
            <param name="rightSide">The scalar value.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.op_Multiply(System.Double,MathNet.Numerics.LinearAlgebra.Double.DenseVector)">
            <summary>
            Multiplies a vector with a scalar.
            </summary>
            <param name="leftSide">The scalar value.</param>
            <param name="rightSide">The vector to scale.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.op_Multiply(MathNet.Numerics.LinearAlgebra.Double.DenseVector,MathNet.Numerics.LinearAlgebra.Double.DenseVector)">
            <summary>
            Computes the dot product between two <strong>Vectors</strong>.
            </summary>
            <param name="leftSide">The left row vector.</param>
            <param name="rightSide">The right column vector.</param>
            <returns>The dot product between the two vectors.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.op_Division(MathNet.Numerics.LinearAlgebra.Double.DenseVector,System.Double)">
            <summary>
            Divides a vector with a scalar.
            </summary>
            <param name="leftSide">The vector to divide.</param>
            <param name="rightSide">The scalar value.</param>
            <returns>The result of the division.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.DoModulus(System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The divisor to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.DoRemainder(System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The divisor to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.op_Modulus(MathNet.Numerics.LinearAlgebra.Double.DenseVector,System.Double)">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            of each element of the vector of the given divisor.
            </summary>
            <param name="leftSide">The vector whose elements we want to compute the remainder of.</param>
            <param name="rightSide">The divisor to use,</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.AbsoluteMinimumIndex">
            <summary>
            Returns the index of the absolute minimum element.
            </summary>
            <returns>The index of absolute minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.AbsoluteMaximumIndex">
            <summary>
            Returns the index of the absolute maximum element.
            </summary>
            <returns>The index of absolute maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.MaximumIndex">
            <summary>
            Returns the index of the maximum element.
            </summary>
            <returns>The index of maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.MinimumIndex">
            <summary>
            Returns the index of the minimum element.
            </summary>
            <returns>The index of minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.Sum">
            <summary>
            Computes the sum of the vector's elements.
            </summary>
            <returns>The sum of the vector's elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.L1Norm">
            <summary>
            Calculates the L1 norm of the vector, also known as Manhattan norm.
            </summary>
            <returns>The sum of the absolute values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.L2Norm">
            <summary>
            Calculates the L2 norm of the vector, also known as Euclidean norm.
            </summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.InfinityNorm">
            <summary>
            Calculates the infinity norm of the vector.
            </summary>
            <returns>The maximum absolute value.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.Norm(System.Double)">
            <summary>
            Computes the p-Norm.
            </summary>
            <param name="p">The p value.</param>
            <returns>Scalar <c>ret = ( ∑|this[i]|^p )^(1/p)</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Pointwise divide this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to pointwise divide this one by.</param>
            <param name="result">The vector to store the result of the pointwise division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Pointwise divide this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The vector to pointwise divide this one by.</param>
            <param name="result">The vector to store the result of the pointwise division.</param>
            <remarks></remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.DoPointwisePower(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Pointwise raise this vector to an exponent vector and store the result into the result vector.
            </summary>
            <param name="exponent">The exponent vector to raise this vector values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.Parse(System.String,System.IFormatProvider)">
            <summary>
            Creates a double dense vector based on a string. The string can be in the following formats (without the
            quotes): 'n', 'n,n,..', '(n,n,..)', '[n,n,...]', where n is a double.
            </summary>
            <returns>
            A double dense vector containing the values specified by the given string.
            </returns>
            <param name="value">
            the string to parse.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific formatting information.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.TryParse(System.String,MathNet.Numerics.LinearAlgebra.Double.DenseVector@)">
            <summary>
            Converts the string representation of a real dense vector to double-precision dense vector equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a real vector to convert.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will be <c>null</c>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DenseVector.TryParse(System.String,System.IFormatProvider,MathNet.Numerics.LinearAlgebra.Double.DenseVector@)">
            <summary>
            Converts the string representation of a real dense vector to double-precision dense vector equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a real vector to convert.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific formatting information about value.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will be <c>null</c>.
            </returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix">
            <summary>
            A matrix type for diagonal matrices.
            </summary>
            <remarks>
            Diagonal matrices can be non-square matrices but the diagonal always starts
            at element 0,0. A diagonal matrix will throw an exception if non diagonal
            entries are set. The exception to this is when the off diagonal elements are
            0.0 or NaN; these settings will cause no change to the diagonal matrix.
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix._data">
            <summary>
            Gets the matrix's data.
            </summary>
            <value>The matrix's data.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.#ctor(MathNet.Numerics.LinearAlgebra.Storage.DiagonalMatrixStorage{System.Double})">
            <summary>
            Create a new diagonal matrix straight from an initialized matrix storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.#ctor(System.Int32)">
            <summary>
            Create a new square diagonal matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the order is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.#ctor(System.Int32,System.Int32)">
            <summary>
            Create a new diagonal matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the row or column count is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.#ctor(System.Int32,System.Int32,System.Double)">
            <summary>
            Create a new diagonal matrix with the given number of rows and columns.
            All diagonal cells of the matrix will be initialized to the provided value, all non-diagonal ones to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the row or column count is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.#ctor(System.Int32,System.Int32,System.Double[])">
            <summary>
            Create a new diagonal matrix with the given number of rows and columns directly binding to a raw array.
            The array is assumed to contain the diagonal elements only and is used directly without copying.
            Very efficient, but changes to the array and the matrix will affect each other.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.OfMatrix(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Create a new diagonal matrix as a copy of the given other matrix.
            This new matrix will be independent from the other matrix.
            The matrix to copy from must be diagonal as well.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.OfArray(System.Double[0:,0:])">
            <summary>
            Create a new diagonal matrix as a copy of the given two-dimensional array.
            This new matrix will be independent from the provided array.
            The array to copy from must be diagonal as well.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.OfIndexedDiagonal(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Double}})">
            <summary>
            Create a new diagonal matrix and initialize each diagonal value from the provided indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.OfIndexedDiagonal(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Double}})">
            <summary>
            Create a new diagonal matrix and initialize each diagonal value from the provided indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.OfDiagonal(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a new diagonal matrix and initialize each diagonal value from the provided enumerable.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.Create(System.Int32,System.Int32,System.Func{System.Int32,System.Double})">
            <summary>
            Create a new diagonal matrix and initialize each diagonal value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.CreateIdentity(System.Int32)">
            <summary>
            Create a new square sparse identity matrix where each diagonal value is set to One.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.CreateRandom(System.Int32,System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create a new diagonal matrix with diagonal values sampled from the provided random distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.DoNegate(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Negate each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the negation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.DoAdd(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Adds another matrix to this matrix.
            </summary>
            <param name="other">The matrix to add to this matrix.</param>
            <param name="result">The matrix to store the result of the addition.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.DoSubtract(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Subtracts another matrix from this matrix.
            </summary>
            <param name="other">The matrix to subtract.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.DoMultiply(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Multiplies each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="scalar">The scalar to multiply the matrix with.</param>
            <param name="result">The matrix to store the result of the multiplication.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Multiplies this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Multiplies this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.DoTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Multiplies this matrix with transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Multiplies the transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.DoDivide(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Divides each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="divisor">The scalar to divide the matrix with.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.DoDivideByThis(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Divides a scalar by each element of the matrix and stores the result in the result matrix.
            </summary>
            <param name="dividend">The scalar to add.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.Determinant">
            <summary>
            Computes the determinant of this matrix.
            </summary>
            <returns>The determinant of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.Diagonal">
            <summary>
            Returns the elements of the diagonal in a <see cref="T:MathNet.Numerics.LinearAlgebra.Double.DenseVector"/>.
            </summary>
            <returns>The elements of the diagonal.</returns>
            <remarks>For non-square matrices, the method returns Min(Rows, Columns) elements where
            i == j (i is the row index, and j is the column index).</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.SetDiagonal(System.Double[])">
            <summary>
            Copies the values of the given array to the diagonal.
            </summary>
            <param name="source">The array to copy the values from. The length of the vector should be
            Min(Rows, Columns).</param>
            <exception cref="T:System.ArgumentException">If the length of <paramref name="source"/> does not
            equal Min(Rows, Columns).</exception>
            <remarks>For non-square matrices, the elements of <paramref name="source"/> are copied to
            this[i,i].</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.SetDiagonal(MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Copies the values of the given <see cref="T:MathNet.Numerics.LinearAlgebra.Vector`1"/> to the diagonal.
            </summary>
            <param name="source">The vector to copy the values from. The length of the vector should be
            Min(Rows, Columns).</param>
            <exception cref="T:System.ArgumentException">If the length of <paramref name="source"/> does not
            equal Min(Rows, Columns).</exception>
            <remarks>For non-square matrices, the elements of <paramref name="source"/> are copied to
            this[i,i].</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.L1Norm">
            <summary>Calculates the induced L1 norm of this matrix.</summary>
            <returns>The maximum absolute column sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.L2Norm">
            <summary>Calculates the induced L2 norm of the matrix.</summary>
            <returns>The largest singular value of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.InfinityNorm">
            <summary>Calculates the induced infinity norm of this matrix.</summary>
            <returns>The maximum absolute row sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.FrobeniusNorm">
            <summary>Calculates the entry-wise Frobenius norm of this matrix.</summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.ConditionNumber">
            <summary>Calculates the condition number of this matrix.</summary>
            <returns>The condition number of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.Inverse">
            <summary>Computes the inverse of this matrix.</summary>
            <exception cref="T:System.ArgumentException">If <see cref="T:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <see cref="T:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix"/> is singular.</exception>
            <returns>The inverse of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.LowerTriangle">
            <summary>
            Returns a new matrix containing the lower triangle of this matrix.
            </summary>
            <returns>The lower triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.LowerTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Puts the lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.StrictlyLowerTriangle">
            <summary>
            Returns a new matrix containing the lower triangle of this matrix. The new matrix
            does not contain the diagonal elements of this matrix.
            </summary>
            <returns>The lower triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.StrictlyLowerTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Puts the strictly lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.UpperTriangle">
            <summary>
            Returns a new matrix containing the upper triangle of this matrix.
            </summary>
            <returns>The upper triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.UpperTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Puts the upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.StrictlyUpperTriangle">
            <summary>
            Returns a new matrix containing the upper triangle of this matrix. The new matrix
            does not contain the diagonal elements of this matrix.
            </summary>
            <returns>The upper triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.StrictlyUpperTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Puts the strictly upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.SubMatrix(System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Creates a matrix that contains the values from the requested sub-matrix.
            </summary>
            <param name="rowIndex">The row to start copying from.</param>
            <param name="rowCount">The number of rows to copy. Must be positive.</param>
            <param name="columnIndex">The column to start copying from.</param>
            <param name="columnCount">The number of columns to copy. Must be positive.</param>
            <returns>The requested sub-matrix.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If: <list><item><paramref name="rowIndex"/> is
            negative, or greater than or equal to the number of rows.</item>
            <item><paramref name="columnIndex"/> is negative, or greater than or equal to the number
            of columns.</item>
            <item><c>(columnIndex + columnLength) &gt;= Columns</c></item>
            <item><c>(rowIndex + rowLength) &gt;= Rows</c></item></list></exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="rowCount"/> or <paramref name="columnCount"/>
            is not positive.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.PermuteColumns(MathNet.Numerics.Permutation)">
            <summary>
            Permute the columns of a matrix according to a permutation.
            </summary>
            <param name="p">The column permutation to apply to this matrix.</param>
            <exception cref="T:System.InvalidOperationException">Always thrown</exception>
            <remarks>Permutation in diagonal matrix are senseless, because of matrix nature</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.PermuteRows(MathNet.Numerics.Permutation)">
            <summary>
            Permute the rows of a matrix according to a permutation.
            </summary>
            <param name="p">The row permutation to apply to this matrix.</param>
            <exception cref="T:System.InvalidOperationException">Always thrown</exception>
            <remarks>Permutation in diagonal matrix are senseless, because of matrix nature</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.IsSymmetric">
            <summary>
            Evaluates whether this matrix is symmetric.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.DoModulus(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.DoModulusByThis(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given dividend for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.DoRemainder(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix.DoRemainderByThis(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given dividend for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.Cholesky">
            <summary>
            <para>A class which encapsulates the functionality of a Cholesky factorization.</para>
            <para>For a symmetric, positive definite matrix A, the Cholesky factorization
            is an lower triangular matrix L so that A = L*L'.</para>
            </summary>
            <remarks>
            The computation of the Cholesky factorization is done at construction time. If the matrix is not symmetric
            or positive definite, the constructor will throw an exception.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Factorization.Cholesky.Determinant">
            <summary>
            Gets the determinant of the matrix for which the Cholesky matrix was computed.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Factorization.Cholesky.DeterminantLn">
            <summary>
            Gets the log determinant of the matrix for which the Cholesky matrix was computed.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseCholesky">
            <summary>
            <para>A class which encapsulates the functionality of a Cholesky factorization for dense matrices.</para>
            <para>For a symmetric, positive definite matrix A, the Cholesky factorization
            is an lower triangular matrix L so that A = L*L'.</para>
            </summary>
            <remarks>
            The computation of the Cholesky factorization is done at construction time. If the matrix is not symmetric
            or positive definite, the constructor will throw an exception.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseCholesky.Create(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseCholesky"/> class. This object will compute the
            Cholesky factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not positive definite.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseCholesky.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A Cholesky factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseCholesky.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A Cholesky factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseCholesky.Factorize(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Calculates the Cholesky factorization of the input matrix.
            </summary>
            <param name="matrix">The matrix to be factorized<see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not positive definite.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="matrix"/> does not have the same dimensions as the existing factor.</exception>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseEvd">
            <summary>
            Eigenvalues and eigenvectors of a real matrix.
            </summary>
            <remarks>
            If A is symmetric, then A = V*D*V' where the eigenvalue matrix D is
            diagonal and the eigenvector matrix V is orthogonal.
            I.e. A = V*D*V' and V*VT=I.
            If A is not symmetric, then the eigenvalue matrix D is block diagonal
            with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues,
            lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda].  The
            columns of V represent the eigenvectors in the sense that A*V = V*D,
            i.e. A.Multiply(V) equals V.Multiply(D).  The matrix V may be badly
            conditioned, or even singular, so the validity of the equation
            A = V*D*Inverse(V) depends upon V.Condition().
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseEvd.Create(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix,MathNet.Numerics.LinearAlgebra.Symmetricity)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseEvd"/> class. This object will compute the
            the eigenvalue decomposition when the constructor is called and cache it's decomposition.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="symmetricity">If it is known whether the matrix is symmetric or not the routine can skip checking it itself.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If EVD algorithm failed to converge with matrix <paramref name="matrix"/>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseEvd.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseEvd.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A EVD factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseGramSchmidt">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition Modified Gram-Schmidt Orthogonalization.</para>
            <para>Any real square matrix A may be decomposed as A = QR where Q is an orthogonal mxn matrix and R is an nxn upper triangular matrix.</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by modified Gram-Schmidt Orthogonalization.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseGramSchmidt.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseGramSchmidt"/> class. This object creates an orthogonal matrix
            using the modified Gram-Schmidt method.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> row count is less then column count</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is rank deficient</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseGramSchmidt.Factorize(System.Double[],System.Int32,System.Int32,System.Double[])">
            <summary>
            Factorize matrix using the modified Gram-Schmidt method.
            </summary>
            <param name="q">Initial matrix. On exit is replaced by <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/> Q.</param>
            <param name="rowsQ">Number of rows in <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/> Q.</param>
            <param name="columnsQ">Number of columns in <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/> Q.</param>
            <param name="r">On exit is filled by <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/> R.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseGramSchmidt.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseGramSchmidt.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseLU">
            <summary>
            <para>A class which encapsulates the functionality of an LU factorization.</para>
            <para>For a matrix A, the LU factorization is a pair of lower triangular matrix L and
            upper triangular matrix U so that A = L*U.</para>
            </summary>
            <remarks>
            The computation of the LU factorization is done at construction time.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseLU.Create(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseLU"/> class. This object will compute the
            LU factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseLU.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Solves a system of linear equations, <c>AX = B</c>, with A LU factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>B</c>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>X</c>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseLU.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Solves a system of linear equations, <c>Ax = b</c>, with A LU factorized.
            </summary>
            <param name="input">The right hand side vector, <c>b</c>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>x</c>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseLU.Inverse">
            <summary>
            Returns the inverse of this matrix. The inverse is calculated using LU decomposition.
            </summary>
            <returns>The inverse of this matrix.</returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseQR">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition.</para>
            <para>Any real square matrix A may be decomposed as A = QR where Q is an orthogonal matrix
            (its columns are orthogonal unit vectors meaning QTQ = I) and R is an upper triangular matrix
            (also called right triangular matrix).</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by Householder transformation.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseQR.Tau">
            <summary>
             Gets or sets Tau vector. Contains additional information on Q - used for native solver.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseQR.Create(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix,MathNet.Numerics.LinearAlgebra.Factorization.QRMethod)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseQR"/> class. This object will compute the
            QR factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="method">The type of QR factorization to perform.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> row count is less then column count</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseQR.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseQR.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseSvd">
            <summary>
            <para>A class which encapsulates the functionality of the singular value decomposition (SVD) for <see cref="T:MathNet.Numerics.LinearAlgebra.Double.DenseMatrix"/>.</para>
            <para>Suppose M is an m-by-n matrix whose entries are real numbers.
            Then there exists a factorization of the form M = UΣVT where:
            - U is an m-by-m unitary matrix;
            - Σ is m-by-n diagonal matrix with nonnegative real numbers on the diagonal;
            - VT denotes transpose of V, an n-by-n unitary matrix;
            Such a factorization is called a singular-value decomposition of M. A common convention is to order the diagonal
            entries Σ(i,i) in descending order. In this case, the diagonal matrix Σ is uniquely determined
            by M (though the matrices U and V are not). The diagonal entries of Σ are known as the singular values of M.</para>
            </summary>
            <remarks>
            The computation of the singular value decomposition is done at construction time.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseSvd.Create(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix,System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseSvd"/> class. This object will compute the
            the singular value decomposition when the constructor is called and cache it's decomposition.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="computeVectors">Compute the singular U and VT vectors or not.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If SVD algorithm failed to converge with matrix <paramref name="matrix"/>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseSvd.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.DenseSvd.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.Evd">
            <summary>
            Eigenvalues and eigenvectors of a real matrix.
            </summary>
            <remarks>
            If A is symmetric, then A = V*D*V' where the eigenvalue matrix D is
            diagonal and the eigenvector matrix V is orthogonal.
            I.e. A = V*D*V' and V*VT=I.
            If A is not symmetric, then the eigenvalue matrix D is block diagonal
            with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues,
            lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda].  The
            columns of V represent the eigenvectors in the sense that A*V = V*D,
            i.e. A.Multiply(V) equals V.Multiply(D).  The matrix V may be badly
            conditioned, or even singular, so the validity of the equation
            A = V*D*Inverse(V) depends upon V.Condition().
            Matrix V is encoded in the property EigenVectors in the way that:
             - column corresponding to real eigenvalue represents real eigenvector,
             - columns corresponding to the pair of complex conjugate eigenvalues
               lambda[i] and lambda[i+1] encode real and imaginary parts of eigenvectors.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Factorization.Evd.Determinant">
            <summary>
            Gets the absolute value of determinant of the square matrix for which the EVD was computed.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Factorization.Evd.Rank">
            <summary>
            Gets the effective numerical matrix rank.
            </summary>
            <value>The number of non-negligible singular values.</value>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Factorization.Evd.IsFullRank">
            <summary>
            Gets a value indicating whether the matrix is full rank or not.
            </summary>
            <value><c>true</c> if the matrix is full rank; otherwise <c>false</c>.</value>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.GramSchmidt">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition Modified Gram-Schmidt Orthogonalization.</para>
            <para>Any real square matrix A may be decomposed as A = QR where Q is an orthogonal mxn matrix and R is an nxn upper triangular matrix.</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by modified Gram-Schmidt Orthogonalization.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Factorization.GramSchmidt.Determinant">
            <summary>
            Gets the absolute determinant value of the matrix for which the QR matrix was computed.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Factorization.GramSchmidt.IsFullRank">
            <summary>
            Gets a value indicating whether the matrix is full rank or not.
            </summary>
            <value><c>true</c> if the matrix is full rank; otherwise <c>false</c>.</value>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.LU">
            <summary>
            <para>A class which encapsulates the functionality of an LU factorization.</para>
            <para>For a matrix A, the LU factorization is a pair of lower triangular matrix L and
            upper triangular matrix U so that A = L*U.</para>
            <para>In the Math.NET implementation we also store a set of pivot elements for increased
            numerical stability. The pivot elements encode a permutation matrix P such that P*A = L*U.</para>
            </summary>
            <remarks>
            The computation of the LU factorization is done at construction time.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Factorization.LU.Determinant">
            <summary>
            Gets the determinant of the matrix for which the LU factorization was computed.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.QR">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition.</para>
            <para>Any real square matrix A (m x n) may be decomposed as A = QR where Q is an orthogonal matrix
            (its columns are orthogonal unit vectors meaning QTQ = I) and R is an upper triangular matrix
            (also called right triangular matrix).</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by Householder transformation.
            If a <seealso cref="F:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod.Full"/> factorization is performed, the resulting Q matrix is an m x m matrix
            and the R matrix is an m x n matrix. If a <seealso cref="F:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod.Thin"/> factorization is performed, the
            resulting Q matrix is an m x n matrix and the R matrix is an n x n matrix.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Factorization.QR.Determinant">
            <summary>
            Gets the absolute determinant value of the matrix for which the QR matrix was computed.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Factorization.QR.IsFullRank">
            <summary>
            Gets a value indicating whether the matrix is full rank or not.
            </summary>
            <value><c>true</c> if the matrix is full rank; otherwise <c>false</c>.</value>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.Svd">
            <summary>
            <para>A class which encapsulates the functionality of the singular value decomposition (SVD).</para>
            <para>Suppose M is an m-by-n matrix whose entries are real numbers.
            Then there exists a factorization of the form M = UΣVT where:
            - U is an m-by-m unitary matrix;
            - Σ is m-by-n diagonal matrix with nonnegative real numbers on the diagonal;
            - VT denotes transpose of V, an n-by-n unitary matrix;
            Such a factorization is called a singular-value decomposition of M. A common convention is to order the diagonal
            entries Σ(i,i) in descending order. In this case, the diagonal matrix Σ is uniquely determined
            by M (though the matrices U and V are not). The diagonal entries of Σ are known as the singular values of M.</para>
            </summary>
            <remarks>
            The computation of the singular value decomposition is done at construction time.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Factorization.Svd.Rank">
            <summary>
            Gets the effective numerical matrix rank.
            </summary>
            <value>The number of non-negligible singular values.</value>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Factorization.Svd.L2Norm">
            <summary>
            Gets the two norm of the <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.
            </summary>
            <returns>The 2-norm of the <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.</returns>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Factorization.Svd.ConditionNumber">
            <summary>
            Gets the condition number <b>max(S) / min(S)</b>
            </summary>
            <returns>The condition number.</returns>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Factorization.Svd.Determinant">
            <summary>
            Gets the determinant of the square matrix for which the SVD was computed.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserCholesky">
            <summary>
            <para>A class which encapsulates the functionality of a Cholesky factorization for user matrices.</para>
            <para>For a symmetric, positive definite matrix A, the Cholesky factorization
            is an lower triangular matrix L so that A = L*L'.</para>
            </summary>
            <remarks>
            The computation of the Cholesky factorization is done at construction time. If the matrix is not symmetric
            or positive definite, the constructor will throw an exception.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserCholesky.DoCholesky(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Computes the Cholesky factorization in-place.
            </summary>
            <param name="factor">On entry, the matrix to factor. On exit, the Cholesky factor matrix</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="factor"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="factor"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="factor"/> is not positive definite.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserCholesky.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserCholesky"/> class. This object will compute the
            Cholesky factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not positive definite.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserCholesky.Factorize(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Calculates the Cholesky factorization of the input matrix.
            </summary>
            <param name="matrix">The matrix to be factorized<see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not positive definite.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="matrix"/> does not have the same dimensions as the existing factor.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserCholesky.DoCholeskyStep(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},System.Int32,System.Int32,System.Int32,System.Double[],System.Int32)">
            <summary>
            Calculate Cholesky step
            </summary>
            <param name="data">Factor matrix</param>
            <param name="rowDim">Number of rows</param>
            <param name="firstCol">Column start</param>
            <param name="colLimit">Total columns</param>
            <param name="multipliers">Multipliers calculated previously</param>
            <param name="availableCores">Number of available processors</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserCholesky.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A Cholesky factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserCholesky.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A Cholesky factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserEvd">
            <summary>
            Eigenvalues and eigenvectors of a real matrix.
            </summary>
            <remarks>
            If A is symmetric, then A = V*D*V' where the eigenvalue matrix D is
            diagonal and the eigenvector matrix V is orthogonal.
            I.e. A = V*D*V' and V*VT=I.
            If A is not symmetric, then the eigenvalue matrix D is block diagonal
            with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues,
            lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda].  The
            columns of V represent the eigenvectors in the sense that A*V = V*D,
            i.e. A.Multiply(V) equals V.Multiply(D).  The matrix V may be badly
            conditioned, or even singular, so the validity of the equation
            A = V*D*Inverse(V) depends upon V.Condition().
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserEvd.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Symmetricity)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserEvd"/> class. This object will compute the
            the eigenvalue decomposition when the constructor is called and cache it's decomposition.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="symmetricity">If it is known whether the matrix is symmetric or not the routine can skip checking it itself.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If EVD algorithm failed to converge with matrix <paramref name="matrix"/>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserEvd.SymmetricTridiagonalize(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},System.Double[],System.Double[],System.Int32)">
            <summary>
            Symmetric Householder reduction to tridiagonal form.
            </summary>
            <param name="eigenVectors">The eigen vectors to work on.</param>
            <param name="d">Arrays for internal storage of real parts of eigenvalues</param>
            <param name="e">Arrays for internal storage of imaginary parts of eigenvalues</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures tred2 by
            Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
            Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserEvd.SymmetricDiagonalize(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},System.Double[],System.Double[],System.Int32)">
            <summary>
            Symmetric tridiagonal QL algorithm.
            </summary>
            <param name="eigenVectors">The eigen vectors to work on.</param>
            <param name="d">Arrays for internal storage of real parts of eigenvalues</param>
            <param name="e">Arrays for internal storage of imaginary parts of eigenvalues</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures tql2, by
            Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
            Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserEvd.NonsymmetricReduceToHessenberg(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},System.Double[0:,0:],System.Int32)">
            <summary>
            Nonsymmetric reduction to Hessenberg form.
            </summary>
            <param name="eigenVectors">The eigen vectors to work on.</param>
            <param name="matrixH">Array for internal storage of nonsymmetric Hessenberg form.</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures orthes and ortran,
            by Martin and Wilkinson, Handbook for Auto. Comp.,
            Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutines in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserEvd.NonsymmetricReduceHessenberToRealSchur(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},System.Double[0:,0:],System.Double[],System.Double[],System.Int32)">
            <summary>
            Nonsymmetric reduction from Hessenberg to real Schur form.
            </summary>
            <param name="eigenVectors">The eigen vectors to work on.</param>
            <param name="matrixH">Array for internal storage of nonsymmetric Hessenberg form.</param>
            <param name="d">Arrays for internal storage of real parts of eigenvalues</param>
            <param name="e">Arrays for internal storage of imaginary parts of eigenvalues</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedure hqr2,
            by Martin and Wilkinson, Handbook for Auto. Comp.,
            Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserEvd.Cdiv(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Complex scalar division X/Y.
            </summary>
            <param name="xreal">Real part of X</param>
            <param name="ximag">Imaginary part of X</param>
            <param name="yreal">Real part of Y</param>
            <param name="yimag">Imaginary part of Y</param>
            <returns>Division result as a <see cref="T:System.Numerics.Complex"/> number.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserEvd.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserEvd.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A EVD factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserGramSchmidt">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition Modified Gram-Schmidt Orthogonalization.</para>
            <para>Any real square matrix A may be decomposed as A = QR where Q is an orthogonal mxn matrix and R is an nxn upper triangular matrix.</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by modified Gram-Schmidt Orthogonalization.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserGramSchmidt.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserGramSchmidt"/> class. This object creates an orthogonal matrix
            using the modified Gram-Schmidt method.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> row count is less then column count</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is rank deficient</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserGramSchmidt.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserGramSchmidt.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserLU">
            <summary>
            <para>A class which encapsulates the functionality of an LU factorization.</para>
            <para>For a matrix A, the LU factorization is a pair of lower triangular matrix L and
            upper triangular matrix U so that A = L*U.</para>
            </summary>
            <remarks>
            The computation of the LU factorization is done at construction time.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserLU.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserLU"/> class. This object will compute the
            LU factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserLU.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Solves a system of linear equations, <c>AX = B</c>, with A LU factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>B</c>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>X</c>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserLU.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Solves a system of linear equations, <c>Ax = b</c>, with A LU factorized.
            </summary>
            <param name="input">The right hand side vector, <c>b</c>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>x</c>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserLU.Inverse">
            <summary>
            Returns the inverse of this matrix. The inverse is calculated using LU decomposition.
            </summary>
            <returns>The inverse of this matrix.</returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserQR">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition.</para>
            <para>Any real square matrix A may be decomposed as A = QR where Q is an orthogonal matrix
            (its columns are orthogonal unit vectors meaning QTQ = I) and R is an upper triangular matrix
            (also called right triangular matrix).</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by Householder transformation.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserQR.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Factorization.QRMethod)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserQR"/> class. This object will compute the
            QR factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="method">The QR factorization method to use.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserQR.GenerateColumn(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},System.Int32,System.Int32)">
            <summary>
            Generate column from initial matrix to work array
            </summary>
            <param name="a">Initial matrix</param>
            <param name="row">The first row</param>
            <param name="column">Column index</param>
            <returns>Generated vector</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserQR.ComputeQR(System.Double[],MathNet.Numerics.LinearAlgebra.Matrix{System.Double},System.Int32,System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Perform calculation of Q or R
            </summary>
            <param name="u">Work array</param>
            <param name="a">Q or R matrices</param>
            <param name="rowStart">The first row</param>
            <param name="rowDim">The last row</param>
            <param name="columnStart">The first column</param>
            <param name="columnDim">The last column</param>
            <param name="availableCores">Number of available CPUs</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserQR.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserQR.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserSvd">
            <summary>
            <para>A class which encapsulates the functionality of the singular value decomposition (SVD) for <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.</para>
            <para>Suppose M is an m-by-n matrix whose entries are real numbers.
            Then there exists a factorization of the form M = UΣVT where:
            - U is an m-by-m unitary matrix;
            - Σ is m-by-n diagonal matrix with nonnegative real numbers on the diagonal;
            - VT denotes transpose of V, an n-by-n unitary matrix;
            Such a factorization is called a singular-value decomposition of M. A common convention is to order the diagonal
            entries Σ(i,i) in descending order. In this case, the diagonal matrix Σ is uniquely determined
            by M (though the matrices U and V are not). The diagonal entries of Σ are known as the singular values of M.</para>
            </summary>
            <remarks>
            The computation of the singular value decomposition is done at construction time.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserSvd.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserSvd"/> class. This object will compute the
            the singular value decomposition when the constructor is called and cache it's decomposition.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="computeVectors">Compute the singular U and VT vectors or not.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserSvd.Dsign(System.Double,System.Double)">
            <summary>
            Calculates absolute value of <paramref name="z1"/> multiplied on signum function of <paramref name="z2"/>
            </summary>
            <param name="z1">Double value z1</param>
            <param name="z2">Double value z2</param>
            <returns>Result multiplication of signum function and absolute value</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserSvd.Dswap(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},System.Int32,System.Int32,System.Int32)">
            <summary>
            Swap column  <paramref name="columnA"/>  and  <paramref name="columnB"/>
            </summary>
            <param name="a">Source matrix</param>
            <param name="rowCount">The number of rows in <paramref name="a"/></param>
            <param name="columnA">Column A index to swap</param>
            <param name="columnB">Column B index to swap</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserSvd.DscalColumn(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},System.Int32,System.Int32,System.Int32,System.Double)">
            <summary>
            Scale column <paramref name="column"/> by <paramref name="z"/> starting from row <paramref name="rowStart"/>
            </summary>
            <param name="a">Source matrix</param>
            <param name="rowCount">The number of rows in <paramref name="a"/> </param>
            <param name="column">Column to scale</param>
            <param name="rowStart">Row to scale from</param>
            <param name="z">Scale value</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserSvd.DscalVector(System.Double[],System.Int32,System.Double)">
            <summary>
            Scale vector <paramref name="a"/> by <paramref name="z"/> starting from index <paramref name="start"/>
            </summary>
            <param name="a">Source vector</param>
            <param name="start">Row to scale from</param>
            <param name="z">Scale value</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserSvd.Drotg(System.Double@,System.Double@,System.Double@,System.Double@)">
            <summary>
            Given the Cartesian coordinates (da, db) of a point p, these function return the parameters da, db, c, and s
            associated with the Givens rotation that zeros the y-coordinate of the point.
            </summary>
            <param name="da">Provides the x-coordinate of the point p. On exit contains the parameter r associated with the Givens rotation</param>
            <param name="db">Provides the y-coordinate of the point p. On exit contains the parameter z associated with the Givens rotation</param>
            <param name="c">Contains the parameter c associated with the Givens rotation</param>
            <param name="s">Contains the parameter s associated with the Givens rotation</param>
            <remarks>This is equivalent to the DROTG LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserSvd.Dnrm2Column(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},System.Int32,System.Int32,System.Int32)">
            <summary>
            Calculate Norm 2 of the column <paramref name="column"/> in matrix <paramref name="a"/> starting from row <paramref name="rowStart"/>
            </summary>
            <param name="a">Source matrix</param>
            <param name="rowCount">The number of rows in <paramref name="a"/></param>
            <param name="column">Column index</param>
            <param name="rowStart">Start row index</param>
            <returns>Norm2 (Euclidean norm) of the column</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserSvd.Dnrm2Vector(System.Double[],System.Int32)">
            <summary>
            Calculate Norm 2 of the vector <paramref name="a"/> starting from index <paramref name="rowStart"/>
            </summary>
            <param name="a">Source vector</param>
            <param name="rowStart">Start index</param>
            <returns>Norm2 (Euclidean norm) of the vector</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserSvd.Ddot(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Calculate dot product of <paramref name="columnA"/> and <paramref name="columnB"/>
            </summary>
            <param name="a">Source matrix</param>
            <param name="rowCount">The number of rows in <paramref name="a"/></param>
            <param name="columnA">Index of column A</param>
            <param name="columnB">Index of column B</param>
            <param name="rowStart">Starting row index</param>
            <returns>Dot product value</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserSvd.Drot(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},System.Int32,System.Int32,System.Int32,System.Double,System.Double)">
            <summary>
            Performs rotation of points in the plane. Given two vectors x <paramref name="columnA"/> and y <paramref name="columnB"/>,
            each vector element of these vectors is replaced as follows: x(i) = c*x(i) + s*y(i); y(i) = c*y(i) - s*x(i)
            </summary>
            <param name="a">Source matrix</param>
            <param name="rowCount">The number of rows in <paramref name="a"/></param>
            <param name="columnA">Index of column A</param>
            <param name="columnB">Index of column B</param>
            <param name="c">Scalar "c" value</param>
            <param name="s">Scalar "s" value</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserSvd.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Factorization.UserSvd.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Matrix">
            <summary>
            <c>double</c> version of the <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/> class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.#ctor(MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage{System.Double})">
            <summary>
            Initializes a new instance of the Matrix class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.CoerceZero(System.Double)">
            <summary>
            Set all values whose absolute value is smaller than the threshold to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.ConjugateTranspose">
            <summary>
            Returns the conjugate transpose of this matrix.
            </summary>
            <returns>The conjugate transpose of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.ConjugateTranspose(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Puts the conjugate transpose of this matrix into the result matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoConjugate(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Complex conjugates each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the conjugation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoNegate(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Negate each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the negation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoAdd(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Add a scalar to each element of the matrix and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to add.</param>
            <param name="result">The matrix to store the result of the addition.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoAdd(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Adds another matrix to this matrix.
            </summary>
            <param name="other">The matrix to add to this matrix.</param>
            <param name="result">The matrix to store the result of the addition.</param>
            <exception cref="T:System.ArgumentNullException">If the other matrix is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoSubtract(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Subtracts a scalar from each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to subtract.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoSubtract(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Subtracts another matrix from this matrix.
            </summary>
            <param name="other">The matrix to subtract to this matrix.</param>
            <param name="result">The matrix to store the result of subtraction.</param>
            <exception cref="T:System.ArgumentNullException">If the other matrix is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoMultiply(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Multiplies each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="scalar">The scalar to multiply the matrix with.</param>
            <param name="result">The matrix to store the result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Multiplies this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoDivide(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Divides each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="divisor">The scalar to divide the matrix with.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoDivideByThis(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Divides a scalar by each element of the matrix and stores the result in the result matrix.
            </summary>
            <param name="dividend">The scalar to divide by each element of the matrix.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Multiplies this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Multiplies this matrix with transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoConjugateTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Multiplies this matrix with the conjugate transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Multiplies the transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Multiplies the conjugate transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoModulus(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoModulusByThis(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given dividend for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoRemainder(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoRemainderByThis(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given dividend for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Pointwise multiplies this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="other">The matrix to pointwise multiply with this one.</param>
            <param name="result">The matrix to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Pointwise divide this matrix by another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The matrix to pointwise divide this one by.</param>
            <param name="result">The matrix to store the result of the pointwise division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoPointwisePower(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Pointwise raise this matrix to an exponent and store the result into the result matrix.
            </summary>
            <param name="exponent">The exponent to raise this matrix values to.</param>
            <param name="result">The matrix to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoPointwisePower(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Pointwise raise this matrix to an exponent and store the result into the result matrix.
            </summary>
            <param name="exponent">The exponent to raise this matrix values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoPointwiseModulus(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Pointwise canonical modulus, where the result has the sign of the divisor,
            of this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The pointwise denominator matrix to use</param>
            <param name="result">The result of the modulus.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoPointwiseRemainder(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Pointwise remainder (% operator), where the result has the sign of the dividend,
            of this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The pointwise denominator matrix to use</param>
            <param name="result">The result of the modulus.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoPointwiseExp(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Pointwise applies the exponential function to each value and stores the result into the result matrix.
            </summary>
            <param name="result">The matrix to store the result.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.DoPointwiseLog(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Pointwise applies the natural logarithm function to each value and stores the result into the result matrix.
            </summary>
            <param name="result">The matrix to store the result.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.PseudoInverse">
            <summary>
            Computes the Moore-Penrose Pseudo-Inverse of this matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.Trace">
            <summary>
            Computes the trace of this matrix.
            </summary>
            <returns>The trace of this matrix</returns>
            <exception cref="T:System.ArgumentException">If the matrix is not square</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.L1Norm">
            <summary>Calculates the induced L1 norm of this matrix.</summary>
            <returns>The maximum absolute column sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.InfinityNorm">
            <summary>Calculates the induced infinity norm of this matrix.</summary>
            <returns>The maximum absolute row sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.FrobeniusNorm">
            <summary>Calculates the entry-wise Frobenius norm of this matrix.</summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.RowNorms(System.Double)">
            <summary>
            Calculates the p-norms of all row vectors.
            Typical values for p are 1.0 (L1, Manhattan norm), 2.0 (L2, Euclidean norm) and positive infinity (infinity norm)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.ColumnNorms(System.Double)">
            <summary>
            Calculates the p-norms of all column vectors.
            Typical values for p are 1.0 (L1, Manhattan norm), 2.0 (L2, Euclidean norm) and positive infinity (infinity norm)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.NormalizeRows(System.Double)">
            <summary>
            Normalizes all row vectors to a unit p-norm.
            Typical values for p are 1.0 (L1, Manhattan norm), 2.0 (L2, Euclidean norm) and positive infinity (infinity norm)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.NormalizeColumns(System.Double)">
            <summary>
            Normalizes all column vectors to a unit p-norm.
            Typical values for p are 1.0 (L1, Manhattan norm), 2.0 (L2, Euclidean norm) and positive infinity (infinity norm)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.RowSums">
            <summary>
            Calculates the value sum of each row vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.RowAbsoluteSums">
            <summary>
            Calculates the absolute value sum of each row vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.ColumnSums">
            <summary>
            Calculates the value sum of each column vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.ColumnAbsoluteSums">
            <summary>
            Calculates the absolute value sum of each column vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Matrix.IsHermitian">
            <summary>
            Evaluates whether this matrix is Hermitian (conjugate symmetric).
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Solvers.BiCgStab">
            <summary>
            A Bi-Conjugate Gradient stabilized iterative matrix solver.
            </summary>
            <remarks>
            <para>
            The Bi-Conjugate Gradient Stabilized (BiCGStab) solver is an 'improvement'
            of the standard Conjugate Gradient (CG) solver. Unlike the CG solver the
            BiCGStab can be used on non-symmetric matrices. <br/>
            Note that much of the success of the solver depends on the selection of the
            proper preconditioner.
            </para>
            <para>
            The Bi-CGSTAB algorithm was taken from: <br/>
            Templates for the solution of linear systems: Building blocks
            for iterative methods
            <br/>
            Richard Barrett, Michael Berry, Tony F. Chan, James Demmel,
            June M. Donato, Jack Dongarra, Victor Eijkhout, Roldan Pozo,
            Charles Romine and Henk van der Vorst
            <br/>
            Url: <a href="http://www.netlib.org/templates/Templates.html">http://www.netlib.org/templates/Templates.html</a>
            <br/>
            Algorithm is described in Chapter 2, section 2.3.8, page 27
            </para>
            <para>
            The example code below provides an indication of the possible use of the
            solver.
            </para>
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.BiCgStab.CalculateTrueResidual(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Calculates the <c>true</c> residual of the matrix equation Ax = b according to: residual = b - Ax
            </summary>
            <param name="matrix">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Matrix"/> A.</param>
            <param name="residual">Residual values in <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/>.</param>
            <param name="x">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/> x.</param>
            <param name="b">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/> b.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.BiCgStab.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{System.Double},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{System.Double})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Matrix"/>, <c>A</c>.</param>
            <param name="input">The solution <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/>, <c>b</c>.</param>
            <param name="result">The result <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/>, <c>x</c>.</param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Solvers.CompositeSolver">
            <summary>
            A composite matrix solver. The actual solver is made by a sequence of
            matrix solvers.
            </summary>
            <remarks>
            <para>
            Solver based on:<br />
            Faster PDE-based simulations using robust composite linear solvers<br />
            S. Bhowmicka, P. Raghavan a,*, L. McInnes b, B. Norris<br />
            Future Generation Computer Systems, Vol 20, 2004, pp 373�387<br />
            </para>
            <para>
            Note that if an iterator is passed to this solver it will be used for all the sub-solvers.
            </para>
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.Solvers.CompositeSolver._solvers">
            <summary>
            The collection of solvers that will be used
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.CompositeSolver.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{System.Double},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{System.Double})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient matrix, <c>A</c>.</param>
            <param name="input">The solution vector, <c>b</c></param>
            <param name="result">The result vector, <c>x</c></param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Solvers.DiagonalPreconditioner">
            <summary>
            A diagonal preconditioner. The preconditioner uses the inverse
            of the matrix diagonal as preconditioning values.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.Solvers.DiagonalPreconditioner._inverseDiagonals">
            <summary>
            The inverse of the matrix diagonal.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.DiagonalPreconditioner.DiagonalEntries">
            <summary>
            Returns the decomposed matrix diagonal.
            </summary>
            <returns>The matrix diagonal.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.DiagonalPreconditioner.Initialize(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Initializes the preconditioner and loads the internal data structures.
            </summary>
            <param name="matrix">
            The <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Matrix"/> upon which this preconditioner is based.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <see langword="null" />. </exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.DiagonalPreconditioner.Approximate(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Approximates the solution to the matrix equation <b>Ax = b</b>.
            </summary>
            <param name="rhs">The right hand side vector.</param>
            <param name="lhs">The left hand side vector. Also known as the result vector.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Solvers.GpBiCg">
            <summary>
            A Generalized Product Bi-Conjugate Gradient iterative matrix solver.
            </summary>
            <remarks>
            <para>
            The Generalized Product Bi-Conjugate Gradient (GPBiCG) solver is an
            alternative version of the Bi-Conjugate Gradient stabilized (CG) solver.
            Unlike the CG solver the GPBiCG solver can be used on
            non-symmetric matrices. <br/>
            Note that much of the success of the solver depends on the selection of the
            proper preconditioner.
            </para>
            <para>
            The GPBiCG algorithm was taken from: <br/>
            GPBiCG(m,l): A hybrid of BiCGSTAB and GPBiCG methods with
            efficiency and robustness
            <br/>
            S. Fujino
            <br/>
            Applied Numerical Mathematics, Volume 41, 2002, pp 107 - 117
            <br/>
            </para>
            <para>
            The example code below provides an indication of the possible use of the
            solver.
            </para>
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.Solvers.GpBiCg._numberOfBiCgStabSteps">
            <summary>
            Indicates the number of <c>BiCGStab</c> steps should be taken
            before switching.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.Solvers.GpBiCg._numberOfGpbiCgSteps">
            <summary>
            Indicates the number of <c>GPBiCG</c> steps should be taken
            before switching.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Solvers.GpBiCg.NumberOfBiCgStabSteps">
            <summary>
            Gets or sets the number of steps taken with the <c>BiCgStab</c> algorithm
            before switching over to the <c>GPBiCG</c> algorithm.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Solvers.GpBiCg.NumberOfGpBiCgSteps">
            <summary>
            Gets or sets the number of steps taken with the <c>GPBiCG</c> algorithm
            before switching over to the <c>BiCgStab</c> algorithm.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.GpBiCg.CalculateTrueResidual(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Calculates the true residual of the matrix equation Ax = b according to: residual = b - Ax
            </summary>
            <param name="matrix">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Matrix"/> A.</param>
            <param name="residual">Residual values in <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/>.</param>
            <param name="x">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/> x.</param>
            <param name="b">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/> b.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.GpBiCg.ShouldRunBiCgStabSteps(System.Int32)">
            <summary>
            Decide if to do steps with BiCgStab
            </summary>
            <param name="iterationNumber">Number of iteration</param>
            <returns><c>true</c> if yes, otherwise <c>false</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.GpBiCg.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{System.Double},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{System.Double})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient matrix, <c>A</c>.</param>
            <param name="input">The solution vector, <c>b</c></param>
            <param name="result">The result vector, <c>x</c></param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILU0Preconditioner">
            <summary>
            An incomplete, level 0, LU factorization preconditioner.
            </summary>
            <remarks>
            The ILU(0) algorithm was taken from: <br/>
            Iterative methods for sparse linear systems <br/>
            Yousef Saad <br/>
            Algorithm is described in Chapter 10, section 10.3.2, page 275 <br/>
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILU0Preconditioner._decompositionLU">
            <summary>
            The matrix holding the lower (L) and upper (U) matrices. The
            decomposition matrices are combined to reduce storage.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILU0Preconditioner.UpperTriangle">
            <summary>
            Returns the upper triagonal matrix that was created during the LU decomposition.
            </summary>
            <returns>A new matrix containing the upper triagonal elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILU0Preconditioner.LowerTriangle">
            <summary>
            Returns the lower triagonal matrix that was created during the LU decomposition.
            </summary>
            <returns>A new matrix containing the lower triagonal elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILU0Preconditioner.Initialize(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Initializes the preconditioner and loads the internal data structures.
            </summary>
            <param name="matrix">The matrix upon which the preconditioner is based. </param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILU0Preconditioner.Approximate(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Approximates the solution to the matrix equation <b>Ax = b</b>.
            </summary>
            <param name="rhs">The right hand side vector.</param>
            <param name="lhs">The left hand side vector. Also known as the result vector.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner">
            <summary>
            This class performs an Incomplete LU factorization with drop tolerance
            and partial pivoting. The drop tolerance indicates which additional entries
            will be dropped from the factorized LU matrices.
            </summary>
            <remarks>
            The ILUTP-Mem algorithm was taken from: <br/>
            ILUTP_Mem: a Space-Efficient Incomplete LU Preconditioner
            <br/>
            Tzu-Yi Chen, Department of Mathematics and Computer Science, <br/>
            Pomona College, Claremont CA 91711, USA <br/>
            Published in: <br/>
            Lecture Notes in Computer Science <br/>
            Volume 3046 / 2004 <br/>
            pp. 20 - 28 <br/>
            Algorithm is described in Section 2, page 22
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner.DefaultFillLevel">
            <summary>
            The default fill level.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner.DefaultDropTolerance">
            <summary>
            The default drop tolerance.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner._upper">
            <summary>
            The decomposed upper triangular matrix.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner._lower">
            <summary>
            The decomposed lower triangular matrix.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner._pivots">
            <summary>
            The array containing the pivot values.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner._fillLevel">
            <summary>
            The fill level.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner._dropTolerance">
            <summary>
            The drop tolerance.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner._pivotTolerance">
            <summary>
            The pivot tolerance.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner"/> class with the default settings.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner.#ctor(System.Double,System.Double,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner"/> class with the specified settings.
            </summary>
            <param name="fillLevel">
            The amount of fill that is allowed in the matrix. The value is a fraction of
            the number of non-zero entries in the original matrix. Values should be positive.
            </param>
            <param name="dropTolerance">
            The absolute drop tolerance which indicates below what absolute value an entry
            will be dropped from the matrix. A drop tolerance of 0.0 means that no values
            will be dropped. Values should always be positive.
            </param>
            <param name="pivotTolerance">
            The pivot tolerance which indicates at what level pivoting will take place. A
            value of 0.0 means that no pivoting will take place.
            </param>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner.FillLevel">
            <summary>
            Gets or sets the amount of fill that is allowed in the matrix. The
            value is a fraction of the number of non-zero entries in the original
            matrix. The standard value is 200.
            </summary>
            <remarks>
            <para>
            Values should always be positive and can be higher than 1.0. A value lower
            than 1.0 means that the eventual preconditioner matrix will have fewer
            non-zero entries as the original matrix. A value higher than 1.0 means that
            the eventual preconditioner can have more non-zero values than the original
            matrix.
            </para>
            <para>
            Note that any changes to the <b>FillLevel</b> after creating the preconditioner
            will invalidate the created preconditioner and will require a re-initialization of
            the preconditioner.
            </para>
            </remarks>
            <exception cref="T:System.ArgumentOutOfRangeException">Thrown if a negative value is provided.</exception>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner.DropTolerance">
            <summary>
            Gets or sets the absolute drop tolerance which indicates below what absolute value
            an entry will be dropped from the matrix. The standard value is 0.0001.
            </summary>
            <remarks>
            <para>
            The values should always be positive and can be larger than 1.0. A low value will
            keep more small numbers in the preconditioner matrix. A high value will remove
            more small numbers from the preconditioner matrix.
            </para>
            <para>
            Note that any changes to the <b>DropTolerance</b> after creating the preconditioner
            will invalidate the created preconditioner and will require a re-initialization of
            the preconditioner.
            </para>
            </remarks>
            <exception cref="T:System.ArgumentOutOfRangeException">Thrown if a negative value is provided.</exception>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner.PivotTolerance">
            <summary>
            Gets or sets the pivot tolerance which indicates at what level pivoting will
            take place. The standard value is 0.0 which means pivoting will never take place.
            </summary>
            <remarks>
            <para>
            The pivot tolerance is used to calculate if pivoting is necessary. Pivoting
            will take place if any of the values in a row is bigger than the
            diagonal value of that row divided by the pivot tolerance, i.e. pivoting
            will take place if <b>row(i,j) > row(i,i) / PivotTolerance</b> for
            any <b>j</b> that is not equal to <b>i</b>.
            </para>
            <para>
            Note that any changes to the <b>PivotTolerance</b> after creating the preconditioner
            will invalidate the created preconditioner and will require a re-initialization of
            the preconditioner.
            </para>
            </remarks>
            <exception cref="T:System.ArgumentOutOfRangeException">Thrown if a negative value is provided.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner.UpperTriangle">
            <summary>
            Returns the upper triagonal matrix that was created during the LU decomposition.
            </summary>
            <remarks>
            This method is used for debugging purposes only and should normally not be used.
            </remarks>
            <returns>A new matrix containing the upper triagonal elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner.LowerTriangle">
            <summary>
            Returns the lower triagonal matrix that was created during the LU decomposition.
            </summary>
            <remarks>
            This method is used for debugging purposes only and should normally not be used.
            </remarks>
            <returns>A new matrix containing the lower triagonal elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner.Pivots">
            <summary>
            Returns the pivot array. This array is not needed for normal use because
            the preconditioner will return the solution vector values in the proper order.
            </summary>
            <remarks>
            This method is used for debugging purposes only and should normally not be used.
            </remarks>
            <returns>The pivot array.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner.Initialize(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Initializes the preconditioner and loads the internal data structures.
            </summary>
            <param name="matrix">
            The <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Matrix"/> upon which this preconditioner is based. Note that the
            method takes a general matrix type. However internally the data is stored
            as a sparse matrix. Therefore it is not recommended to pass a dense matrix.
            </param>
            <exception cref="T:System.ArgumentNullException"> If <paramref name="matrix"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner.PivotRow(MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Pivot elements in the <paramref name="row"/> according to internal pivot array
            </summary>
            <param name="row">Row <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/> to pivot in</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner.PivotMapFound(System.Collections.Generic.Dictionary{System.Int32,System.Int32},System.Int32)">
            <summary>
            Was pivoting already performed
            </summary>
            <param name="knownPivots">Pivots already done</param>
            <param name="currentItem">Current item to pivot</param>
            <returns><c>true</c> if performed, otherwise <c>false</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner.SwapColumns(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},System.Int32,System.Int32)">
            <summary>
            Swap columns in the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Matrix"/>
            </summary>
            <param name="matrix">Source <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Matrix"/>.</param>
            <param name="firstColumn">First column index to swap</param>
            <param name="secondColumn">Second column index to swap</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner.FindLargestItems(System.Int32,System.Int32,System.Int32[],MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Sort vector descending, not changing vector but placing sorted indices to <paramref name="sortedIndices"/>
            </summary>
            <param name="lowerBound">Start sort form</param>
            <param name="upperBound">Sort till upper bound</param>
            <param name="sortedIndices">Array with sorted vector indices</param>
            <param name="values">Source <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner.Approximate(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Approximates the solution to the matrix equation <b>Ax = b</b>.
            </summary>
            <param name="rhs">The right hand side vector.</param>
            <param name="lhs">The left hand side vector. Also known as the result vector.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner.Pivot(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Pivot elements in <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/> according to internal pivot array
            </summary>
            <param name="vector">Source <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/>.</param>
            <param name="result">Result <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/> after pivoting.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPElementSorter">
            <summary>
            An element sort algorithm for the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPPreconditioner"/> class.
            </summary>
            <remarks>
            This sort algorithm is used to sort the columns in a sparse matrix based on
            the value of the element on the diagonal of the matrix.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPElementSorter.SortDoubleIndicesDecreasing(System.Int32,System.Int32,System.Int32[],MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Sorts the elements of the <paramref name="values"/> vector in decreasing
            fashion. The vector itself is not affected.
            </summary>
            <param name="lowerBound">The starting index.</param>
            <param name="upperBound">The stopping index.</param>
            <param name="sortedIndices">An array that will contain the sorted indices once the algorithm finishes.</param>
            <param name="values">The <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/> that contains the values that need to be sorted.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPElementSorter.HeapSortDoublesIndices(System.Int32,System.Int32,System.Int32[],MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Sorts the elements of the <paramref name="values"/> vector in decreasing
            fashion using heap sort algorithm. The vector itself is not affected.
            </summary>
            <param name="lowerBound">The starting index.</param>
            <param name="upperBound">The stopping index.</param>
            <param name="sortedIndices">An array that will contain the sorted indices once the algorithm finishes.</param>
            <param name="values">The <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/> that contains the values that need to be sorted.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPElementSorter.BuildDoubleIndexHeap(System.Int32,System.Int32,System.Int32[],MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Build heap for double indices
            </summary>
            <param name="start">Root position</param>
            <param name="count">Length of <paramref name="values"/></param>
            <param name="sortedIndices">Indices of <paramref name="values"/></param>
            <param name="values">Target <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPElementSorter.SiftDoubleIndices(System.Int32[],MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Int32,System.Int32)">
            <summary>
            Sift double indices
            </summary>
            <param name="sortedIndices">Indices of <paramref name="values"/></param>
            <param name="values">Target <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/></param>
            <param name="begin">Root position</param>
            <param name="count">Length of <paramref name="values"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPElementSorter.SortIntegersDecreasing(System.Int32[])">
            <summary>
            Sorts the given integers in a decreasing fashion.
            </summary>
            <param name="values">The values.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPElementSorter.HeapSortIntegers(System.Int32[],System.Int32)">
            <summary>
            Sort the given integers in a decreasing fashion using heapsort algorithm
            </summary>
            <param name="values">Array of values to sort</param>
            <param name="count">Length of <paramref name="values"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPElementSorter.BuildHeap(System.Int32[],System.Int32,System.Int32)">
            <summary>
            Build heap
            </summary>
            <param name="values">Target values array</param>
            <param name="start">Root position</param>
            <param name="count">Length of <paramref name="values"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPElementSorter.Sift(System.Int32[],System.Int32,System.Int32)">
            <summary>
            Sift values
            </summary>
            <param name="values">Target value array</param>
            <param name="start">Root position</param>
            <param name="count">Length of <paramref name="values"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.ILUTPElementSorter.Exchange(System.Int32[],System.Int32,System.Int32)">
            <summary>
            Exchange values in array
            </summary>
            <param name="values">Target values array</param>
            <param name="first">First value to exchange</param>
            <param name="second">Second value to exchange</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Solvers.MILU0Preconditioner">
            <summary>
            A simple milu(0) preconditioner.
            </summary>
            <remarks>
            Original Fortran code by Yousef Saad (07 January 2004)
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.MILU0Preconditioner.#ctor(System.Boolean)">
            <param name="modified">Use modified or standard ILU(0)</param>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Solvers.MILU0Preconditioner.UseModified">
            <summary>
            Gets or sets a value indicating whether to use modified or standard ILU(0).
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Solvers.MILU0Preconditioner.IsInitialized">
            <summary>
            Gets a value indicating whether the preconditioner is initialized.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.MILU0Preconditioner.Initialize(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Initializes the preconditioner and loads the internal data structures.
            </summary>
            <param name="matrix">The matrix upon which the preconditioner is based. </param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square or is not an
            instance of SparseCompressedRowMatrixStorage.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.MILU0Preconditioner.Approximate(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Approximates the solution to the matrix equation <b>Ax = b</b>.
            </summary>
            <param name="input">The right hand side vector b.</param>
            <param name="result">The left hand side vector x.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.MILU0Preconditioner.Compute(System.Int32,System.Double[],System.Int32[],System.Int32[],System.Double[],System.Int32[],System.Int32[],System.Boolean)">
            <summary>
            MILU0 is a simple milu(0) preconditioner.
            </summary>
            <param name="n">Order of the matrix.</param>
            <param name="a">Matrix values in CSR format (input).</param>
            <param name="ja">Column indices (input).</param>
            <param name="ia">Row pointers (input).</param>
            <param name="alu">Matrix values in MSR format (output).</param>
            <param name="jlu">Row pointers and column indices (output).</param>
            <param name="ju">Pointer to diagonal elements (output).</param>
            <param name="modified">True if the modified/MILU algorithm should be used (recommended)</param>
            <returns>Returns 0 on success or k > 0 if a zero pivot was encountered at step k.</returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Solvers.MlkBiCgStab">
            <summary>
            A Multiple-Lanczos Bi-Conjugate Gradient stabilized iterative matrix solver.
            </summary>
            <remarks>
            <para>
            The Multiple-Lanczos Bi-Conjugate Gradient stabilized (ML(k)-BiCGStab) solver is an 'improvement'
            of the standard BiCgStab solver.
            </para>
            <para>
            The algorithm was taken from: <br/>
            ML(k)BiCGSTAB: A BiCGSTAB variant based on multiple Lanczos starting vectors
            <br/>
            Man-Chung Yeung and Tony F. Chan
            <br/>
            SIAM Journal of Scientific Computing
            <br/>
            Volume 21, Number 4, pp. 1263 - 1290
            </para>
            <para>
            The example code below provides an indication of the possible use of the
            solver.
            </para>
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.Solvers.MlkBiCgStab.DefaultNumberOfStartingVectors">
            <summary>
            The default number of starting vectors.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.Solvers.MlkBiCgStab._startingVectors">
            <summary>
            The collection of starting vectors which are used as the basis for the Krylov sub-space.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Double.Solvers.MlkBiCgStab._numberOfStartingVectors">
            <summary>
            The number of starting vectors used by the algorithm
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Solvers.MlkBiCgStab.NumberOfStartingVectors">
            <summary>
            Gets or sets the number of starting vectors.
            </summary>
            <remarks>
            Must be larger than 1 and smaller than the number of variables in the matrix that
            for which this solver will be used.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.MlkBiCgStab.ResetNumberOfStartingVectors">
            <summary>
            Resets the number of starting vectors to the default value.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.Solvers.MlkBiCgStab.StartingVectors">
            <summary>
            Gets or sets a series of orthonormal vectors which will be used as basis for the
            Krylov sub-space.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.MlkBiCgStab.NumberOfStartingVectorsToCreate(System.Int32,System.Int32)">
            <summary>
            Gets the number of starting vectors to create
            </summary>
            <param name="maximumNumberOfStartingVectors">Maximum number</param>
            <param name="numberOfVariables">Number of variables</param>
            <returns>Number of starting vectors to create</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.MlkBiCgStab.CreateStartingVectors(System.Int32,System.Int32)">
            <summary>
            Returns an array of starting vectors.
            </summary>
            <param name="maximumNumberOfStartingVectors">The maximum number of starting vectors that should be created.</param>
            <param name="numberOfVariables">The number of variables.</param>
            <returns>
             An array with starting vectors. The array will never be larger than the
             <paramref name="maximumNumberOfStartingVectors"/> but it may be smaller if
             the <paramref name="numberOfVariables"/> is smaller than
             the <paramref name="maximumNumberOfStartingVectors"/>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.MlkBiCgStab.CreateVectorArray(System.Int32,System.Int32)">
            <summary>
            Create random vectors array
            </summary>
            <param name="arraySize">Number of vectors</param>
            <param name="vectorSize">Size of each vector</param>
            <returns>Array of random vectors</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.MlkBiCgStab.CalculateTrueResidual(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Calculates the true residual of the matrix equation Ax = b according to: residual = b - Ax
            </summary>
            <param name="matrix">Source <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Matrix"/>A.</param>
            <param name="residual">Residual <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/> data.</param>
            <param name="x">x <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/> data.</param>
            <param name="b">b <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/> data.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.MlkBiCgStab.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{System.Double},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{System.Double})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient matrix, <c>A</c>.</param>
            <param name="input">The solution vector, <c>b</c></param>
            <param name="result">The result vector, <c>x</c></param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Solvers.TFQMR">
            <summary>
            A Transpose Free Quasi-Minimal Residual (TFQMR) iterative matrix solver.
            </summary>
            <remarks>
            <para>
            The TFQMR algorithm was taken from: <br/>
            Iterative methods for sparse linear systems.
            <br/>
            Yousef Saad
            <br/>
            Algorithm is described in Chapter 7, section 7.4.3, page 219
            </para>
            <para>
            The example code below provides an indication of the possible use of the
            solver.
            </para>
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.TFQMR.CalculateTrueResidual(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Calculates the <c>true</c> residual of the matrix equation Ax = b according to: residual = b - Ax
            </summary>
            <param name="matrix">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Matrix"/> A.</param>
            <param name="residual">Residual values in <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/>.</param>
            <param name="x">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/> x.</param>
            <param name="b">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Double.Vector"/> b.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.TFQMR.IsEven(System.Int32)">
            <summary>
            Is <paramref name="number"/> even?
            </summary>
            <param name="number">Number to check</param>
            <returns><c>true</c> if <paramref name="number"/> even, otherwise <c>false</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Solvers.TFQMR.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{System.Double},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{System.Double})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient matrix, <c>A</c>.</param>
            <param name="input">The solution vector, <c>b</c></param>
            <param name="result">The result vector, <c>x</c></param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix">
            <summary>
            A Matrix with sparse storage, intended for very large matrices where most of the cells are zero.
            The underlying storage scheme is 3-array compressed-sparse-row (CSR) Format.
            <a href="http://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_.28CSR_or_CRS.29">Wikipedia - CSR</a>.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.NonZerosCount">
            <summary>
            Gets the number of non zero elements in the matrix.
            </summary>
            <value>The number of non zero elements.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.#ctor(MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage{System.Double})">
            <summary>
            Create a new sparse matrix straight from an initialized matrix storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.#ctor(System.Int32)">
            <summary>
            Create a new square sparse matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the order is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.#ctor(System.Int32,System.Int32)">
            <summary>
            Create a new sparse matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the row or column count is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfMatrix(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Create a new sparse matrix as a copy of the given other matrix.
            This new matrix will be independent from the other matrix.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfArray(System.Double[0:,0:])">
            <summary>
            Create a new sparse matrix as a copy of the given two-dimensional array.
            This new matrix will be independent from the provided array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfIndexed(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Int32,System.Double}})">
            <summary>
            Create a new sparse matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfIndexed(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Int32,System.Double}})">
            <summary>
            Create a new sparse matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfRowMajor(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable.
            The enumerable is assumed to be in row-major order (row by row).
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
            <seealso href="http://en.wikipedia.org/wiki/Row-major_order"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfColumnMajor(System.Int32,System.Int32,System.Collections.Generic.IList{System.Double})">
            <summary>
            Create a new sparse matrix with the given number of rows and columns as a copy of the given array.
            The array is assumed to be in column-major order (column by column).
            This new matrix will be independent from the provided array.
            A new memory block will be allocated for storing the matrix.
            </summary>
            <seealso href="http://en.wikipedia.org/wiki/Row-major_order"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfColumns(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Double}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfColumns(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Double}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfColumnArrays(System.Double[][])">
            <summary>
            Create a new sparse matrix as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfColumnArrays(System.Collections.Generic.IEnumerable{System.Double[]})">
            <summary>
            Create a new sparse matrix as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfColumnVectors(MathNet.Numerics.LinearAlgebra.Vector{System.Double}[])">
            <summary>
            Create a new sparse matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfColumnVectors(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{System.Double}})">
            <summary>
            Create a new sparse matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfRows(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Double}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfRows(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Double}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfRowArrays(System.Double[][])">
            <summary>
            Create a new sparse matrix as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfRowArrays(System.Collections.Generic.IEnumerable{System.Double[]})">
            <summary>
            Create a new sparse matrix as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfRowVectors(MathNet.Numerics.LinearAlgebra.Vector{System.Double}[])">
            <summary>
            Create a new sparse matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfRowVectors(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{System.Double}})">
            <summary>
            Create a new sparse matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfDiagonalVector(MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfDiagonalVector(System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfDiagonalArray(System.Double[])">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.OfDiagonalArray(System.Int32,System.Int32,System.Double[])">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.Create(System.Int32,System.Int32,System.Double)">
            <summary>
            Create a new sparse matrix and initialize each value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.Create(System.Int32,System.Int32,System.Func{System.Int32,System.Int32,System.Double})">
            <summary>
            Create a new sparse matrix and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.CreateDiagonal(System.Int32,System.Int32,System.Double)">
            <summary>
            Create a new diagonal sparse matrix and initialize each diagonal value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.CreateDiagonal(System.Int32,System.Int32,System.Func{System.Int32,System.Double})">
            <summary>
            Create a new diagonal sparse matrix and initialize each diagonal value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.CreateIdentity(System.Int32)">
            <summary>
            Create a new square sparse identity matrix where each diagonal value is set to One.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.LowerTriangle">
            <summary>
            Returns a new matrix containing the lower triangle of this matrix.
            </summary>
            <returns>The lower triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.LowerTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Puts the lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="result"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.LowerTriangleImpl(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Puts the lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.UpperTriangle">
            <summary>
            Returns a new matrix containing the upper triangle of this matrix.
            </summary>
            <returns>The upper triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.UpperTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Puts the upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="result"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.UpperTriangleImpl(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Puts the upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.StrictlyLowerTriangle">
            <summary>
            Returns a new matrix containing the lower triangle of this matrix. The new matrix
            does not contain the diagonal elements of this matrix.
            </summary>
            <returns>The lower triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.StrictlyLowerTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Puts the strictly lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="result"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.StrictlyLowerTriangleImpl(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Puts the strictly lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.StrictlyUpperTriangle">
            <summary>
            Returns a new matrix containing the upper triangle of this matrix. The new matrix
            does not contain the diagonal elements of this matrix.
            </summary>
            <returns>The upper triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.StrictlyUpperTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Puts the strictly upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="result"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.StrictlyUpperTriangleImpl(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Puts the strictly upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.DoNegate(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Negate each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the negation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.InfinityNorm">
            <summary>Calculates the induced infinity norm of this matrix.</summary>
            <returns>The maximum absolute row sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.FrobeniusNorm">
            <summary>Calculates the entry-wise Frobenius norm of this matrix.</summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.DoAdd(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Adds another matrix to this matrix.
            </summary>
            <param name="other">The matrix to add to this matrix.</param>
            <param name="result">The matrix to store the result of the addition.</param>
            <exception cref="T:System.ArgumentNullException">If the other matrix is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.DoSubtract(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Subtracts another matrix from this matrix.
            </summary>
            <param name="other">The matrix to subtract to this matrix.</param>
            <param name="result">The matrix to store the result of subtraction.</param>
            <exception cref="T:System.ArgumentNullException">If the other matrix is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.DoMultiply(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Multiplies each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="scalar">The scalar to multiply the matrix with.</param>
            <param name="result">The matrix to store the result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Multiplies this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Multiplies this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.DoTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Multiplies this matrix with transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Multiplies the transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Pointwise multiplies this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="other">The matrix to pointwise multiply with this one.</param>
            <param name="result">The matrix to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Matrix{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Pointwise divide this matrix by another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The matrix to pointwise divide this one by.</param>
            <param name="result">The matrix to store the result of the pointwise division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.DoModulus(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.DoRemainder(System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.IsSymmetric">
            <summary>
            Evaluates whether this matrix is symmetric.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.op_Addition(MathNet.Numerics.LinearAlgebra.Double.SparseMatrix,MathNet.Numerics.LinearAlgebra.Double.SparseMatrix)">
            <summary>
            Adds two matrices together and returns the results.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to add.</param>
            <param name="rightSide">The right matrix to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> don't have the same dimensions.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.op_UnaryPlus(MathNet.Numerics.LinearAlgebra.Double.SparseMatrix)">
            <summary>
            Returns a <strong>Matrix</strong> containing the same values of <paramref name="rightSide"/>.
            </summary>
            <param name="rightSide">The matrix to get the values from.</param>
            <returns>A matrix containing a the same values as <paramref name="rightSide"/>.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.op_Subtraction(MathNet.Numerics.LinearAlgebra.Double.SparseMatrix,MathNet.Numerics.LinearAlgebra.Double.SparseMatrix)">
            <summary>
            Subtracts two matrices together and returns the results.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to subtract.</param>
            <param name="rightSide">The right matrix to subtract.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> don't have the same dimensions.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.op_UnaryNegation(MathNet.Numerics.LinearAlgebra.Double.SparseMatrix)">
            <summary>
            Negates each element of the matrix.
            </summary>
            <param name="rightSide">The matrix to negate.</param>
            <returns>A matrix containing the negated values.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Double.SparseMatrix,System.Double)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.op_Multiply(System.Double,MathNet.Numerics.LinearAlgebra.Double.SparseMatrix)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Double.SparseMatrix,MathNet.Numerics.LinearAlgebra.Double.SparseMatrix)">
            <summary>
            Multiplies two matrices.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to multiply.</param>
            <param name="rightSide">The right matrix to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the dimensions of <paramref name="leftSide"/> or <paramref name="rightSide"/> don't conform.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Double.SparseMatrix,MathNet.Numerics.LinearAlgebra.Double.SparseVector)">
            <summary>
            Multiplies a <strong>Matrix</strong> and a Vector.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The vector to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Double.SparseVector,MathNet.Numerics.LinearAlgebra.Double.SparseMatrix)">
            <summary>
            Multiplies a Vector and a <strong>Matrix</strong>.
            </summary>
            <param name="leftSide">The vector to multiply.</param>
            <param name="rightSide">The matrix to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseMatrix.op_Modulus(MathNet.Numerics.LinearAlgebra.Double.SparseMatrix,System.Double)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.SparseVector">
            <summary>
            A vector with sparse storage, intended for very large vectors where most of the cells are zero.
            </summary>
            <remarks>The sparse vector is not thread safe.</remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Double.SparseVector.NonZerosCount">
            <summary>
            Gets the number of non zero elements in the vector.
            </summary>
            <value>The number of non zero elements.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.#ctor(MathNet.Numerics.LinearAlgebra.Storage.SparseVectorStorage{System.Double})">
            <summary>
            Create a new sparse vector straight from an initialized vector storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.#ctor(System.Int32)">
            <summary>
            Create a new sparse vector with the given length.
            All cells of the vector will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If length is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.OfVector(MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Create a new sparse vector as a copy of the given other vector.
            This new vector will be independent from the other vector.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.OfEnumerable(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a new sparse vector as a copy of the given enumerable.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.OfIndexedEnumerable(System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Double}})">
            <summary>
            Create a new sparse vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.OfIndexedEnumerable(System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Double}})">
            <summary>
            Create a new sparse vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.Create(System.Int32,System.Double)">
            <summary>
            Create a new sparse vector and initialize each value using the provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.Create(System.Int32,System.Func{System.Int32,System.Double})">
            <summary>
            Create a new sparse vector and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.DoAdd(System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Adds a scalar to each element of the vector and stores the result in the result vector.
            Warning, the new 'sparse vector' with a non-zero scalar added to it will be a 100% filled
            sparse vector and very inefficient. Would be better to work with a dense vector instead.
            </summary>
            <param name="scalar">
            The scalar to add.
            </param>
            <param name="result">
            The vector to store the result of the addition.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.DoAdd(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Adds another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">
            The vector to add to this one.
            </param>
            <param name="result">
            The vector to store the result of the addition.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.DoSubtract(System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Subtracts a scalar from each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">
            The scalar to subtract.
            </param>
            <param name="result">
            The vector to store the result of the subtraction.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.DoSubtract(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Subtracts another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">
            The vector to subtract from this one.
            </param>
            <param name="result">
            The vector to store the result of the subtraction.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.DoNegate(MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Negates vector and saves result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.DoMultiply(System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Multiplies a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">
            The scalar to multiply.
            </param>
            <param name="result">
            The vector to store the result of the multiplication.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.DoDotProduct(MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Computes the dot product between this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of a[i]*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.DoModulus(System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.DoRemainder(System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.op_Addition(MathNet.Numerics.LinearAlgebra.Double.SparseVector,MathNet.Numerics.LinearAlgebra.Double.SparseVector)">
            <summary>
            Adds two <strong>Vectors</strong> together and returns the results.
            </summary>
            <param name="leftSide">One of the vectors to add.</param>
            <param name="rightSide">The other vector to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.op_UnaryNegation(MathNet.Numerics.LinearAlgebra.Double.SparseVector)">
            <summary>
            Returns a <strong>Vector</strong> containing the negated values of <paramref name="rightSide"/>.
            </summary>
            <param name="rightSide">The vector to get the values from.</param>
            <returns>A vector containing the negated values as <paramref name="rightSide"/>.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.op_Subtraction(MathNet.Numerics.LinearAlgebra.Double.SparseVector,MathNet.Numerics.LinearAlgebra.Double.SparseVector)">
            <summary>
            Subtracts two <strong>Vectors</strong> and returns the results.
            </summary>
            <param name="leftSide">The vector to subtract from.</param>
            <param name="rightSide">The vector to subtract.</param>
            <returns>The result of the subtraction.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.op_Multiply(MathNet.Numerics.LinearAlgebra.Double.SparseVector,System.Double)">
            <summary>
            Multiplies a vector with a scalar.
            </summary>
            <param name="leftSide">The vector to scale.</param>
            <param name="rightSide">The scalar value.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.op_Multiply(System.Double,MathNet.Numerics.LinearAlgebra.Double.SparseVector)">
            <summary>
            Multiplies a vector with a scalar.
            </summary>
            <param name="leftSide">The scalar value.</param>
            <param name="rightSide">The vector to scale.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.op_Multiply(MathNet.Numerics.LinearAlgebra.Double.SparseVector,MathNet.Numerics.LinearAlgebra.Double.SparseVector)">
            <summary>
            Computes the dot product between two <strong>Vectors</strong>.
            </summary>
            <param name="leftSide">The left row vector.</param>
            <param name="rightSide">The right column vector.</param>
            <returns>The dot product between the two vectors.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.op_Division(MathNet.Numerics.LinearAlgebra.Double.SparseVector,System.Double)">
            <summary>
            Divides a vector with a scalar.
            </summary>
            <param name="leftSide">The vector to divide.</param>
            <param name="rightSide">The scalar value.</param>
            <returns>The result of the division.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.op_Modulus(MathNet.Numerics.LinearAlgebra.Double.SparseVector,System.Double)">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            of each element of the vector of the given divisor.
            </summary>
            <param name="leftSide">The vector whose elements we want to compute the modulus of.</param>
            <param name="rightSide">The divisor to use,</param>
            <returns>The result of the calculation</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.AbsoluteMinimumIndex">
            <summary>
            Returns the index of the absolute minimum element.
            </summary>
            <returns>The index of absolute minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.AbsoluteMaximumIndex">
            <summary>
            Returns the index of the absolute maximum element.
            </summary>
            <returns>The index of absolute maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.MaximumIndex">
            <summary>
            Returns the index of the maximum element.
            </summary>
            <returns>The index of maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.MinimumIndex">
            <summary>
            Returns the index of the minimum element.
            </summary>
            <returns>The index of minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.Sum">
            <summary>
            Computes the sum of the vector's elements.
            </summary>
            <returns>The sum of the vector's elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.L1Norm">
            <summary>
            Calculates the L1 norm of the vector, also known as Manhattan norm.
            </summary>
            <returns>The sum of the absolute values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.InfinityNorm">
            <summary>
            Calculates the infinity norm of the vector.
            </summary>
            <returns>The maximum absolute value.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.Norm(System.Double)">
            <summary>
            Computes the p-Norm.
            </summary>
            <param name="p">The p value.</param>
            <returns>Scalar <c>ret = ( ∑|this[i]|^p )^(1/p)</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Pointwise multiplies this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to pointwise multiply with this one.</param>
            <param name="result">The vector to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.Parse(System.String,System.IFormatProvider)">
            <summary>
            Creates a double sparse vector based on a string. The string can be in the following formats (without the
            quotes): 'n', 'n,n,..', '(n,n,..)', '[n,n,...]', where n is a double.
            </summary>
            <returns>
            A double sparse vector containing the values specified by the given string.
            </returns>
            <param name="value">
            the string to parse.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific formatting information.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.TryParse(System.String,MathNet.Numerics.LinearAlgebra.Double.SparseVector@)">
            <summary>
            Converts the string representation of a real sparse vector to double-precision sparse vector equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a real vector to convert.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will be <c>null</c>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.SparseVector.TryParse(System.String,System.IFormatProvider,MathNet.Numerics.LinearAlgebra.Double.SparseVector@)">
            <summary>
            Converts the string representation of a real sparse vector to double-precision sparse vector equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a real vector to convert.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific formatting information about value.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will be <c>null</c>.
            </returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Double.Vector">
            <summary>
            <c>double</c> version of the <see cref="T:MathNet.Numerics.LinearAlgebra.Vector`1"/> class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.#ctor(MathNet.Numerics.LinearAlgebra.Storage.VectorStorage{System.Double})">
            <summary>
            Initializes a new instance of the Vector class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.CoerceZero(System.Double)">
            <summary>
            Set all values whose absolute value is smaller than the threshold to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoConjugate(MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Conjugates vector and save result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoNegate(MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Negates vector and saves result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoAdd(System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Adds a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">
            The scalar to add.
            </param>
            <param name="result">
            The vector to store the result of the addition.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoAdd(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Adds another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">
            The vector to add to this one.
            </param>
            <param name="result">
            The vector to store the result of the addition.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoSubtract(System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Subtracts a scalar from each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">
            The scalar to subtract.
            </param>
            <param name="result">
            The vector to store the result of the subtraction.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoSubtract(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Subtracts another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">
            The vector to subtract from this one.
            </param>
            <param name="result">
            The vector to store the result of the subtraction.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoMultiply(System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Multiplies a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">
            The scalar to multiply.
            </param>
            <param name="result">
            The vector to store the result of the multiplication.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoDivide(System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Divides each element of the vector by a scalar and stores the result in the result vector.
            </summary>
            <param name="divisor">
            The scalar to divide with.
            </param>
            <param name="result">
            The vector to store the result of the division.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoDivideByThis(System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Divides a scalar by each element of the vector and stores the result in the result vector.
            </summary>
            <param name="dividend">The scalar to divide.</param>
            <param name="result">The vector to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Pointwise multiplies this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to pointwise multiply with this one.</param>
            <param name="result">The vector to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Pointwise divide this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The vector to pointwise divide this one by.</param>
            <param name="result">The vector to store the result of the pointwise division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoPointwisePower(System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Pointwise raise this vector to an exponent and store the result into the result vector.
            </summary>
            <param name="exponent">The exponent to raise this vector values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoPointwisePower(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Pointwise raise this vector to an exponent vector and store the result into the result vector.
            </summary>
            <param name="exponent">The exponent vector to raise this vector values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoPointwiseModulus(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Pointwise canonical modulus, where the result has the sign of the divisor,
            of this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The pointwise denominator vector to use.</param>
            <param name="result">The result of the modulus.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoPointwiseRemainder(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Pointwise remainder (% operator), where the result has the sign of the dividend,
            of this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The pointwise denominator vector to use.</param>
            <param name="result">The result of the modulus.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoPointwiseExp(MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Pointwise applies the exponential function to each value and stores the result into the result vector.
            </summary>
            <param name="result">The vector to store the result.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoPointwiseLog(MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Pointwise applies the natural logarithm function to each value and stores the result into the result vector.
            </summary>
            <param name="result">The vector to store the result.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoDotProduct(MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Computes the dot product between this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of a[i]*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoConjugateDotProduct(MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Computes the dot product between the conjugate of this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of conj(a[i])*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoModulus(System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoModulusByThis(System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given dividend for each element of the vector.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoRemainder(System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.DoRemainderByThis(System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given dividend for each element of the vector.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.AbsoluteMinimum">
            <summary>
            Returns the value of the absolute minimum element.
            </summary>
            <returns>The value of the absolute minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.AbsoluteMinimumIndex">
            <summary>
            Returns the index of the absolute minimum element.
            </summary>
            <returns>The index of absolute minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.AbsoluteMaximum">
            <summary>
            Returns the value of the absolute maximum element.
            </summary>
            <returns>The value of the absolute maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.AbsoluteMaximumIndex">
            <summary>
            Returns the index of the absolute maximum element.
            </summary>
            <returns>The index of absolute maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.Sum">
            <summary>
            Computes the sum of the vector's elements.
            </summary>
            <returns>The sum of the vector's elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.L1Norm">
            <summary>
            Calculates the L1 norm of the vector, also known as Manhattan norm.
            </summary>
            <returns>The sum of the absolute values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.L2Norm">
            <summary>
            Calculates the L2 norm of the vector, also known as Euclidean norm.
            </summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.InfinityNorm">
            <summary>
            Calculates the infinity norm of the vector.
            </summary>
            <returns>The maximum absolute value.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.Norm(System.Double)">
            <summary>
            Computes the p-Norm.
            </summary>
            <param name="p">
            The p value.
            </param>
            <returns>
            <c>Scalar ret = ( ∑|At(i)|^p )^(1/p)</c>
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.MaximumIndex">
            <summary>
            Returns the index of the maximum element.
            </summary>
            <returns>The index of maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.MinimumIndex">
            <summary>
            Returns the index of the minimum element.
            </summary>
            <returns>The index of minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Double.Vector.Normalize(System.Double)">
            <summary>
            Normalizes this vector to a unit vector with respect to the p-norm.
            </summary>
            <param name="p">
            The p value.
            </param>
            <returns>
            This vector normalized to a unit vector with respect to the p-norm.
            </returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix">
            <summary>
            A Matrix class with dense storage. The underlying storage is a one dimensional array in column-major order (column by column).
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix._rowCount">
            <summary>
            Number of rows.
            </summary>
            <remarks>Using this instead of the RowCount property to speed up calculating
            a matrix index in the data array.</remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix._columnCount">
            <summary>
            Number of columns.
            </summary>
            <remarks>Using this instead of the ColumnCount property to speed up calculating
            a matrix index in the data array.</remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix._values">
            <summary>
            Gets the matrix's data.
            </summary>
            <value>The matrix's data.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.#ctor(MathNet.Numerics.LinearAlgebra.Storage.DenseColumnMajorMatrixStorage{System.Single})">
            <summary>
            Create a new dense matrix straight from an initialized matrix storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.#ctor(System.Int32)">
            <summary>
            Create a new square dense matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the order is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.#ctor(System.Int32,System.Int32)">
            <summary>
            Create a new dense matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the row or column count is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.#ctor(System.Int32,System.Int32,System.Single[])">
            <summary>
            Create a new dense matrix with the given number of rows and columns directly binding to a raw array.
            The array is assumed to be in column-major order (column by column) and is used directly without copying.
            Very efficient, but changes to the array and the matrix will affect each other.
            </summary>
            <seealso href="http://en.wikipedia.org/wiki/Row-major_order"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfMatrix(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Create a new dense matrix as a copy of the given other matrix.
            This new matrix will be independent from the other matrix.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfArray(System.Single[0:,0:])">
            <summary>
            Create a new dense matrix as a copy of the given two-dimensional array.
            This new matrix will be independent from the provided array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfIndexed(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Int32,System.Single}})">
            <summary>
            Create a new dense matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfIndexed(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Int32,System.Single}})">
            <summary>
            Create a new dense matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfColumnMajor(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable.
            The enumerable is assumed to be in column-major order (column by column).
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfColumns(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Single}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfColumns(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Single}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfColumnArrays(System.Single[][])">
            <summary>
            Create a new dense matrix as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfColumnArrays(System.Collections.Generic.IEnumerable{System.Single[]})">
            <summary>
            Create a new dense matrix as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfColumnVectors(MathNet.Numerics.LinearAlgebra.Vector{System.Single}[])">
            <summary>
            Create a new dense matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfColumnVectors(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{System.Single}})">
            <summary>
            Create a new dense matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfRows(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Single}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfRows(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Single}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfRowArrays(System.Single[][])">
            <summary>
            Create a new dense matrix as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfRowArrays(System.Collections.Generic.IEnumerable{System.Single[]})">
            <summary>
            Create a new dense matrix as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfRowVectors(MathNet.Numerics.LinearAlgebra.Vector{System.Single}[])">
            <summary>
            Create a new dense matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfRowVectors(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{System.Single}})">
            <summary>
            Create a new dense matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfDiagonalVector(MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfDiagonalVector(System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfDiagonalArray(System.Single[])">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.OfDiagonalArray(System.Int32,System.Int32,System.Single[])">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.Create(System.Int32,System.Int32,System.Single)">
            <summary>
            Create a new dense matrix and initialize each value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.Create(System.Int32,System.Int32,System.Func{System.Int32,System.Int32,System.Single})">
            <summary>
            Create a new dense matrix and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.CreateDiagonal(System.Int32,System.Int32,System.Single)">
            <summary>
            Create a new diagonal dense matrix and initialize each diagonal value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.CreateDiagonal(System.Int32,System.Int32,System.Func{System.Int32,System.Single})">
            <summary>
            Create a new diagonal dense matrix and initialize each diagonal value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.CreateIdentity(System.Int32)">
            <summary>
            Create a new square sparse identity matrix where each diagonal value is set to One.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.CreateRandom(System.Int32,System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create a new dense matrix with values sampled from the provided random distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.Values">
            <summary>
            Gets the matrix's data.
            </summary>
            <value>The matrix's data.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.L1Norm">
            <summary>Calculates the induced L1 norm of this matrix.</summary>
            <returns>The maximum absolute column sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.InfinityNorm">
            <summary>Calculates the induced infinity norm of this matrix.</summary>
            <returns>The maximum absolute row sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.FrobeniusNorm">
            <summary>Calculates the entry-wise Frobenius norm of this matrix.</summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.DoNegate(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Negate each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the negation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.DoAdd(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Add a scalar to each element of the matrix and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to add.</param>
            <param name="result">The matrix to store the result of the addition.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.DoAdd(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Adds another matrix to this matrix.
            </summary>
            <param name="other">The matrix to add to this matrix.</param>
            <param name="result">The matrix to store the result of add</param>
            <exception cref="T:System.ArgumentNullException">If the other matrix is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.DoSubtract(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Subtracts a scalar from each element of the matrix and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to subtract.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.DoSubtract(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Subtracts another matrix from this matrix.
            </summary>
            <param name="other">The matrix to subtract.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.DoMultiply(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Multiplies each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="scalar">The scalar to multiply the matrix with.</param>
            <param name="result">The matrix to store the result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Multiplies this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Multiplies this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.DoTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Multiplies this matrix with transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Multiplies the transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.DoDivide(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Divides each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="divisor">The scalar to divide the matrix with.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Pointwise multiplies this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="other">The matrix to pointwise multiply with this one.</param>
            <param name="result">The matrix to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Pointwise divide this matrix by another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The matrix to pointwise divide this one by.</param>
            <param name="result">The matrix to store the result of the pointwise division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.DoPointwisePower(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Pointwise raise this matrix to an exponent and store the result into the result matrix.
            </summary>
            <param name="exponent">The exponent to raise this matrix values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.DoModulus(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.DoModulusByThis(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given dividend for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.DoRemainder(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.DoRemainderByThis(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given dividend for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.Trace">
            <summary>
            Computes the trace of this matrix.
            </summary>
            <returns>The trace of this matrix</returns>
            <exception cref="T:System.ArgumentException">If the matrix is not square</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.op_Addition(MathNet.Numerics.LinearAlgebra.Single.DenseMatrix,MathNet.Numerics.LinearAlgebra.Single.DenseMatrix)">
            <summary>
            Adds two matrices together and returns the results.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to add.</param>
            <param name="rightSide">The right matrix to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> don't have the same dimensions.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.op_UnaryPlus(MathNet.Numerics.LinearAlgebra.Single.DenseMatrix)">
            <summary>
            Returns a <strong>Matrix</strong> containing the same values of <paramref name="rightSide"/>.
            </summary>
            <param name="rightSide">The matrix to get the values from.</param>
            <returns>A matrix containing a the same values as <paramref name="rightSide"/>.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.op_Subtraction(MathNet.Numerics.LinearAlgebra.Single.DenseMatrix,MathNet.Numerics.LinearAlgebra.Single.DenseMatrix)">
            <summary>
            Subtracts two matrices together and returns the results.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to subtract.</param>
            <param name="rightSide">The right matrix to subtract.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> don't have the same dimensions.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.op_UnaryNegation(MathNet.Numerics.LinearAlgebra.Single.DenseMatrix)">
            <summary>
            Negates each element of the matrix.
            </summary>
            <param name="rightSide">The matrix to negate.</param>
            <returns>A matrix containing the negated values.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Single.DenseMatrix,System.Single)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.op_Multiply(System.Single,MathNet.Numerics.LinearAlgebra.Single.DenseMatrix)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Single.DenseMatrix,MathNet.Numerics.LinearAlgebra.Single.DenseMatrix)">
            <summary>
            Multiplies two matrices.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to multiply.</param>
            <param name="rightSide">The right matrix to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the dimensions of <paramref name="leftSide"/> or <paramref name="rightSide"/> don't conform.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Single.DenseMatrix,MathNet.Numerics.LinearAlgebra.Single.DenseVector)">
            <summary>
            Multiplies a <strong>Matrix</strong> and a Vector.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The vector to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Single.DenseVector,MathNet.Numerics.LinearAlgebra.Single.DenseMatrix)">
            <summary>
            Multiplies a Vector and a <strong>Matrix</strong>.
            </summary>
            <param name="leftSide">The vector to multiply.</param>
            <param name="rightSide">The matrix to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.op_Modulus(MathNet.Numerics.LinearAlgebra.Single.DenseMatrix,System.Single)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix.IsSymmetric">
            <summary>
            Evaluates whether this matrix is symmetric.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.DenseVector">
            <summary>
            A vector using dense storage.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.DenseVector._length">
            <summary>
            Number of elements
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.DenseVector._values">
            <summary>
            Gets the vector's data.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.#ctor(MathNet.Numerics.LinearAlgebra.Storage.DenseVectorStorage{System.Single})">
            <summary>
            Create a new dense vector straight from an initialized vector storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.#ctor(System.Int32)">
            <summary>
            Create a new dense vector with the given length.
            All cells of the vector will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If length is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.#ctor(System.Single[])">
            <summary>
            Create a new dense vector directly binding to a raw array.
            The array is used directly without copying.
            Very efficient, but changes to the array and the vector will affect each other.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.OfVector(MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Create a new dense vector as a copy of the given other vector.
            This new vector will be independent from the other vector.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.OfArray(System.Single[])">
            <summary>
            Create a new dense vector as a copy of the given array.
            This new vector will be independent from the array.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.OfEnumerable(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Create a new dense vector as a copy of the given enumerable.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.OfIndexedEnumerable(System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Single}})">
            <summary>
            Create a new dense vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.OfIndexedEnumerable(System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Single}})">
            <summary>
            Create a new dense vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.Create(System.Int32,System.Single)">
            <summary>
            Create a new dense vector and initialize each value using the provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.Create(System.Int32,System.Func{System.Int32,System.Single})">
            <summary>
            Create a new dense vector and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.CreateRandom(System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create a new dense vector with values sampled from the provided random distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.DenseVector.Values">
            <summary>
            Gets the vector's data.
            </summary>
            <value>The vector's data.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.op_Explicit(MathNet.Numerics.LinearAlgebra.Single.DenseVector)~System.Single[]">
            <summary>
            Returns a reference to the internal data structure.
            </summary>
            <param name="vector">The <c>DenseVector</c> whose internal data we are
            returning.</param>
            <returns>
            A reference to the internal date of the given vector.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.op_Implicit(System.Single[])~MathNet.Numerics.LinearAlgebra.Single.DenseVector">
            <summary>
            Returns a vector bound directly to a reference of the provided array.
            </summary>
            <param name="array">The array to bind to the <c>DenseVector</c> object.</param>
            <returns>
            A <c>DenseVector</c> whose values are bound to the given array.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.DoAdd(System.Single,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Adds a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to add.</param>
            <param name="result">The vector to store the result of the addition.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.DoAdd(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Adds another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to add to this one.</param>
            <param name="result">The vector to store the result of the addition.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.op_Addition(MathNet.Numerics.LinearAlgebra.Single.DenseVector,MathNet.Numerics.LinearAlgebra.Single.DenseVector)">
            <summary>
            Adds two <strong>Vectors</strong> together and returns the results.
            </summary>
            <param name="leftSide">One of the vectors to add.</param>
            <param name="rightSide">The other vector to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.DoSubtract(System.Single,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Subtracts a scalar from each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to subtract.</param>
            <param name="result">The vector to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.DoSubtract(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Subtracts another vector from this vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to subtract from this one.</param>
            <param name="result">The vector to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.op_UnaryNegation(MathNet.Numerics.LinearAlgebra.Single.DenseVector)">
            <summary>
            Returns a <strong>Vector</strong> containing the negated values of <paramref name="rightSide"/>.
            </summary>
            <param name="rightSide">The vector to get the values from.</param>
            <returns>A vector containing the negated values as <paramref name="rightSide"/>.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.op_Subtraction(MathNet.Numerics.LinearAlgebra.Single.DenseVector,MathNet.Numerics.LinearAlgebra.Single.DenseVector)">
            <summary>
            Subtracts two <strong>Vectors</strong> and returns the results.
            </summary>
            <param name="leftSide">The vector to subtract from.</param>
            <param name="rightSide">The vector to subtract.</param>
            <returns>The result of the subtraction.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.DoNegate(MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Negates vector and saves result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.DoMultiply(System.Single,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Multiplies a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to multiply.</param>
            <param name="result">The vector to store the result of the multiplication.</param>
            <remarks></remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.DoDotProduct(MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Computes the dot product between this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of a[i]*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.op_Multiply(MathNet.Numerics.LinearAlgebra.Single.DenseVector,System.Single)">
            <summary>
            Multiplies a vector with a scalar.
            </summary>
            <param name="leftSide">The vector to scale.</param>
            <param name="rightSide">The scalar value.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.op_Multiply(System.Single,MathNet.Numerics.LinearAlgebra.Single.DenseVector)">
            <summary>
            Multiplies a vector with a scalar.
            </summary>
            <param name="leftSide">The scalar value.</param>
            <param name="rightSide">The vector to scale.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.op_Multiply(MathNet.Numerics.LinearAlgebra.Single.DenseVector,MathNet.Numerics.LinearAlgebra.Single.DenseVector)">
            <summary>
            Computes the dot product between two <strong>Vectors</strong>.
            </summary>
            <param name="leftSide">The left row vector.</param>
            <param name="rightSide">The right column vector.</param>
            <returns>The dot product between the two vectors.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.op_Division(MathNet.Numerics.LinearAlgebra.Single.DenseVector,System.Single)">
            <summary>
            Divides a vector with a scalar.
            </summary>
            <param name="leftSide">The vector to divide.</param>
            <param name="rightSide">The scalar value.</param>
            <returns>The result of the division.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.DoModulus(System.Single,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.DoRemainder(System.Single,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.op_Modulus(MathNet.Numerics.LinearAlgebra.Single.DenseVector,System.Single)">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            of each element of the vector of the given divisor.
            </summary>
            <param name="leftSide">The vector whose elements we want to compute the modulus of.</param>
            <param name="rightSide">The divisor to use,</param>
            <returns>The result of the calculation</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.AbsoluteMinimumIndex">
            <summary>
            Returns the index of the absolute minimum element.
            </summary>
            <returns>The index of absolute minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.AbsoluteMaximumIndex">
            <summary>
            Returns the index of the absolute maximum element.
            </summary>
            <returns>The index of absolute maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.MaximumIndex">
            <summary>
            Returns the index of the maximum element.
            </summary>
            <returns>The index of maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.MinimumIndex">
            <summary>
            Returns the index of the minimum element.
            </summary>
            <returns>The index of minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.Sum">
            <summary>
            Computes the sum of the vector's elements.
            </summary>
            <returns>The sum of the vector's elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.L1Norm">
            <summary>
            Calculates the L1 norm of the vector, also known as Manhattan norm.
            </summary>
            <returns>The sum of the absolute values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.L2Norm">
            <summary>
            Calculates the L2 norm of the vector, also known as Euclidean norm.
            </summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.InfinityNorm">
            <summary>
            Calculates the infinity norm of the vector.
            </summary>
            <returns>The maximum absolute value.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.Norm(System.Double)">
            <summary>
            Computes the p-Norm.
            </summary>
            <param name="p">The p value.</param>
            <returns>Scalar <c>ret = ( ∑|this[i]|^p )^(1/p)</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Pointwise multiply this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to pointwise multiply this one by.</param>
            <param name="result">The vector to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Pointwise divide this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The vector to pointwise divide this one by.</param>
            <param name="result">The vector to store the result of the pointwise division.</param>
            <remarks></remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.DoPointwisePower(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Pointwise raise this vector to an exponent vector and store the result into the result vector.
            </summary>
            <param name="exponent">The exponent vector to raise this vector values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.Parse(System.String,System.IFormatProvider)">
            <summary>
            Creates a float dense vector based on a string. The string can be in the following formats (without the
            quotes): 'n', 'n,n,..', '(n,n,..)', '[n,n,...]', where n is a float.
            </summary>
            <returns>
            A float dense vector containing the values specified by the given string.
            </returns>
            <param name="value">
            the string to parse.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific formatting information.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.TryParse(System.String,MathNet.Numerics.LinearAlgebra.Single.DenseVector@)">
            <summary>
            Converts the string representation of a real dense vector to float-precision dense vector equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a real vector to convert.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will be <c>null</c>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DenseVector.TryParse(System.String,System.IFormatProvider,MathNet.Numerics.LinearAlgebra.Single.DenseVector@)">
            <summary>
            Converts the string representation of a real dense vector to float-precision dense vector equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a real vector to convert.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific formatting information about value.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will be <c>null</c>.
            </returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix">
            <summary>
            A matrix type for diagonal matrices.
            </summary>
            <remarks>
            Diagonal matrices can be non-square matrices but the diagonal always starts
            at element 0,0. A diagonal matrix will throw an exception if non diagonal
            entries are set. The exception to this is when the off diagonal elements are
            0.0 or NaN; these settings will cause no change to the diagonal matrix.
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix._data">
            <summary>
            Gets the matrix's data.
            </summary>
            <value>The matrix's data.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.#ctor(MathNet.Numerics.LinearAlgebra.Storage.DiagonalMatrixStorage{System.Single})">
            <summary>
            Create a new diagonal matrix straight from an initialized matrix storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.#ctor(System.Int32)">
            <summary>
            Create a new square diagonal matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the order is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.#ctor(System.Int32,System.Int32)">
            <summary>
            Create a new diagonal matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the row or column count is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.#ctor(System.Int32,System.Int32,System.Single)">
            <summary>
            Create a new diagonal matrix with the given number of rows and columns.
            All diagonal cells of the matrix will be initialized to the provided value, all non-diagonal ones to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the row or column count is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.#ctor(System.Int32,System.Int32,System.Single[])">
            <summary>
            Create a new diagonal matrix with the given number of rows and columns directly binding to a raw array.
            The array is assumed to contain the diagonal elements only and is used directly without copying.
            Very efficient, but changes to the array and the matrix will affect each other.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.OfMatrix(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Create a new diagonal matrix as a copy of the given other matrix.
            This new matrix will be independent from the other matrix.
            The matrix to copy from must be diagonal as well.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.OfArray(System.Single[0:,0:])">
            <summary>
            Create a new diagonal matrix as a copy of the given two-dimensional array.
            This new matrix will be independent from the provided array.
            The array to copy from must be diagonal as well.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.OfIndexedDiagonal(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Single}})">
            <summary>
            Create a new diagonal matrix and initialize each diagonal value from the provided indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.OfIndexedDiagonal(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Single}})">
            <summary>
            Create a new diagonal matrix and initialize each diagonal value from the provided indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.OfDiagonal(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Create a new diagonal matrix and initialize each diagonal value from the provided enumerable.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.Create(System.Int32,System.Int32,System.Func{System.Int32,System.Single})">
            <summary>
            Create a new diagonal matrix and initialize each diagonal value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.CreateIdentity(System.Int32)">
            <summary>
            Create a new square sparse identity matrix where each diagonal value is set to One.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.CreateRandom(System.Int32,System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create a new diagonal matrix with diagonal values sampled from the provided random distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.DoNegate(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Negate each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the negation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.DoAdd(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Adds another matrix to this matrix.
            </summary>
            <param name="other">The matrix to add to this matrix.</param>
            <param name="result">The matrix to store the result of the addition.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.DoSubtract(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Subtracts another matrix from this matrix.
            </summary>
            <param name="other">The matrix to subtract.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.DoMultiply(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Multiplies each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="scalar">The scalar to multiply the matrix with.</param>
            <param name="result">The matrix to store the result of the multiplication.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Multiplies this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Multiplies this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.DoTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Multiplies this matrix with transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Multiplies the transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.DoDivide(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Divides each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="divisor">The scalar to divide the matrix with.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.DoDivideByThis(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Divides a scalar by each element of the matrix and stores the result in the result matrix.
            </summary>
            <param name="dividend">The scalar to add.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.Determinant">
            <summary>
            Computes the determinant of this matrix.
            </summary>
            <returns>The determinant of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.Diagonal">
            <summary>
            Returns the elements of the diagonal in a <see cref="T:MathNet.Numerics.LinearAlgebra.Single.DenseVector"/>.
            </summary>
            <returns>The elements of the diagonal.</returns>
            <remarks>For non-square matrices, the method returns Min(Rows, Columns) elements where
            i == j (i is the row index, and j is the column index).</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.SetDiagonal(System.Single[])">
            <summary>
            Copies the values of the given array to the diagonal.
            </summary>
            <param name="source">The array to copy the values from. The length of the vector should be
            Min(Rows, Columns).</param>
            <exception cref="T:System.ArgumentException">If the length of <paramref name="source"/> does not
            equal Min(Rows, Columns).</exception>
            <remarks>For non-square matrices, the elements of <paramref name="source"/> are copied to
            this[i,i].</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.SetDiagonal(MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Copies the values of the given <see cref="T:MathNet.Numerics.LinearAlgebra.Vector`1"/> to the diagonal.
            </summary>
            <param name="source">The vector to copy the values from. The length of the vector should be
            Min(Rows, Columns).</param>
            <exception cref="T:System.ArgumentException">If the length of <paramref name="source"/> does not
            equal Min(Rows, Columns).</exception>
            <remarks>For non-square matrices, the elements of <paramref name="source"/> are copied to
            this[i,i].</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.L1Norm">
            <summary>Calculates the induced L1 norm of this matrix.</summary>
            <returns>The maximum absolute column sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.L2Norm">
            <summary>Calculates the induced L2 norm of the matrix.</summary>
            <returns>The largest singular value of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.InfinityNorm">
            <summary>Calculates the induced infinity norm of this matrix.</summary>
            <returns>The maximum absolute row sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.FrobeniusNorm">
            <summary>Calculates the entry-wise Frobenius norm of this matrix.</summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.ConditionNumber">
            <summary>Calculates the condition number of this matrix.</summary>
            <returns>The condition number of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.Inverse">
            <summary>Computes the inverse of this matrix.</summary>
            <exception cref="T:System.ArgumentException">If <see cref="T:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <see cref="T:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix"/> is singular.</exception>
            <returns>The inverse of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.LowerTriangle">
            <summary>
            Returns a new matrix containing the lower triangle of this matrix.
            </summary>
            <returns>The lower triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.LowerTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Puts the lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.StrictlyLowerTriangle">
            <summary>
            Returns a new matrix containing the lower triangle of this matrix. The new matrix
            does not contain the diagonal elements of this matrix.
            </summary>
            <returns>The lower triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.StrictlyLowerTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Puts the strictly lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.UpperTriangle">
            <summary>
            Returns a new matrix containing the upper triangle of this matrix.
            </summary>
            <returns>The upper triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.UpperTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Puts the upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.StrictlyUpperTriangle">
            <summary>
            Returns a new matrix containing the upper triangle of this matrix. The new matrix
            does not contain the diagonal elements of this matrix.
            </summary>
            <returns>The upper triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.StrictlyUpperTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Puts the strictly upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.SubMatrix(System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Creates a matrix that contains the values from the requested sub-matrix.
            </summary>
            <param name="rowIndex">The row to start copying from.</param>
            <param name="rowCount">The number of rows to copy. Must be positive.</param>
            <param name="columnIndex">The column to start copying from.</param>
            <param name="columnCount">The number of columns to copy. Must be positive.</param>
            <returns>The requested sub-matrix.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If: <list><item><paramref name="rowIndex"/> is
            negative, or greater than or equal to the number of rows.</item>
            <item><paramref name="columnIndex"/> is negative, or greater than or equal to the number
            of columns.</item>
            <item><c>(columnIndex + columnLength) &gt;= Columns</c></item>
            <item><c>(rowIndex + rowLength) &gt;= Rows</c></item></list></exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="rowCount"/> or <paramref name="columnCount"/>
            is not positive.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.PermuteColumns(MathNet.Numerics.Permutation)">
            <summary>
            Permute the columns of a matrix according to a permutation.
            </summary>
            <param name="p">The column permutation to apply to this matrix.</param>
            <exception cref="T:System.InvalidOperationException">Always thrown</exception>
            <remarks>Permutation in diagonal matrix are senseless, because of matrix nature</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.PermuteRows(MathNet.Numerics.Permutation)">
            <summary>
            Permute the rows of a matrix according to a permutation.
            </summary>
            <param name="p">The row permutation to apply to this matrix.</param>
            <exception cref="T:System.InvalidOperationException">Always thrown</exception>
            <remarks>Permutation in diagonal matrix are senseless, because of matrix nature</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.IsSymmetric">
            <summary>
            Evaluates whether this matrix is symmetric.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.DoModulus(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.DoModulusByThis(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given dividend for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.DoRemainder(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.DiagonalMatrix.DoRemainderByThis(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given dividend for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.Cholesky">
            <summary>
            <para>A class which encapsulates the functionality of a Cholesky factorization.</para>
            <para>For a symmetric, positive definite matrix A, the Cholesky factorization
            is an lower triangular matrix L so that A = L*L'.</para>
            </summary>
            <remarks>
            The computation of the Cholesky factorization is done at construction time. If the matrix is not symmetric
            or positive definite, the constructor will throw an exception.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Factorization.Cholesky.Determinant">
            <summary>
            Gets the determinant of the matrix for which the Cholesky matrix was computed.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Factorization.Cholesky.DeterminantLn">
            <summary>
            Gets the log determinant of the matrix for which the Cholesky matrix was computed.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseCholesky">
            <summary>
            <para>A class which encapsulates the functionality of a Cholesky factorization for dense matrices.</para>
            <para>For a symmetric, positive definite matrix A, the Cholesky factorization
            is an lower triangular matrix L so that A = L*L'.</para>
            </summary>
            <remarks>
            The computation of the Cholesky factorization is done at construction time. If the matrix is not symmetric
            or positive definite, the constructor will throw an exception.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseCholesky.Create(MathNet.Numerics.LinearAlgebra.Single.DenseMatrix)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseCholesky"/> class. This object will compute the
            Cholesky factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not positive definite.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseCholesky.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A Cholesky factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseCholesky.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A Cholesky factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseCholesky.Factorize(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Calculates the Cholesky factorization of the input matrix.
            </summary>
            <param name="matrix">The matrix to be factorized<see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not positive definite.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="matrix"/> does not have the same dimensions as the existing factor.</exception>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseEvd">
            <summary>
            Eigenvalues and eigenvectors of a real matrix.
            </summary>
            <remarks>
            If A is symmetric, then A = V*D*V' where the eigenvalue matrix D is
            diagonal and the eigenvector matrix V is orthogonal.
            I.e. A = V*D*V' and V*VT=I.
            If A is not symmetric, then the eigenvalue matrix D is block diagonal
            with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues,
            lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda].  The
            columns of V represent the eigenvectors in the sense that A*V = V*D,
            i.e. A.Multiply(V) equals V.Multiply(D).  The matrix V may be badly
            conditioned, or even singular, so the validity of the equation
            A = V*D*Inverse(V) depends upon V.Condition().
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseEvd.Create(MathNet.Numerics.LinearAlgebra.Single.DenseMatrix,MathNet.Numerics.LinearAlgebra.Symmetricity)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseEvd"/> class. This object will compute the
            the eigenvalue decomposition when the constructor is called and cache it's decomposition.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="symmetricity">If it is known whether the matrix is symmetric or not the routine can skip checking it itself.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If EVD algorithm failed to converge with matrix <paramref name="matrix"/>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseEvd.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseEvd.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A EVD factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseGramSchmidt">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition Modified Gram-Schmidt Orthogonalization.</para>
            <para>Any real square matrix A may be decomposed as A = QR where Q is an orthogonal mxn matrix and R is an nxn upper triangular matrix.</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by modified Gram-Schmidt Orthogonalization.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseGramSchmidt.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseGramSchmidt"/> class. This object creates an orthogonal matrix
            using the modified Gram-Schmidt method.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> row count is less then column count</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is rank deficient</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseGramSchmidt.Factorize(System.Single[],System.Int32,System.Int32,System.Single[])">
            <summary>
            Factorize matrix using the modified Gram-Schmidt method.
            </summary>
            <param name="q">Initial matrix. On exit is replaced by <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/> Q.</param>
            <param name="rowsQ">Number of rows in <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/> Q.</param>
            <param name="columnsQ">Number of columns in <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/> Q.</param>
            <param name="r">On exit is filled by <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/> R.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseGramSchmidt.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseGramSchmidt.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseLU">
            <summary>
            <para>A class which encapsulates the functionality of an LU factorization.</para>
            <para>For a matrix A, the LU factorization is a pair of lower triangular matrix L and
            upper triangular matrix U so that A = L*U.</para>
            </summary>
            <remarks>
            The computation of the LU factorization is done at construction time.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseLU.Create(MathNet.Numerics.LinearAlgebra.Single.DenseMatrix)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseLU"/> class. This object will compute the
            LU factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseLU.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Solves a system of linear equations, <c>AX = B</c>, with A LU factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>B</c>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>X</c>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseLU.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Solves a system of linear equations, <c>Ax = b</c>, with A LU factorized.
            </summary>
            <param name="input">The right hand side vector, <c>b</c>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>x</c>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseLU.Inverse">
            <summary>
            Returns the inverse of this matrix. The inverse is calculated using LU decomposition.
            </summary>
            <returns>The inverse of this matrix.</returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseQR">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition.</para>
            <para>Any real square matrix A may be decomposed as A = QR where Q is an orthogonal matrix
            (its columns are orthogonal unit vectors meaning QTQ = I) and R is an upper triangular matrix
            (also called right triangular matrix).</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by Householder transformation.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseQR.Tau">
            <summary>
             Gets or sets Tau vector. Contains additional information on Q - used for native solver.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseQR.Create(MathNet.Numerics.LinearAlgebra.Single.DenseMatrix,MathNet.Numerics.LinearAlgebra.Factorization.QRMethod)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseQR"/> class. This object will compute the
            QR factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="method">The QR factorization method to use.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> row count is less then column count</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseQR.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseQR.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseSvd">
            <summary>
            <para>A class which encapsulates the functionality of the singular value decomposition (SVD) for <see cref="T:MathNet.Numerics.LinearAlgebra.Single.DenseMatrix"/>.</para>
            <para>Suppose M is an m-by-n matrix whose entries are real numbers.
            Then there exists a factorization of the form M = UΣVT where:
            - U is an m-by-m unitary matrix;
            - Σ is m-by-n diagonal matrix with nonnegative real numbers on the diagonal;
            - VT denotes transpose of V, an n-by-n unitary matrix;
            Such a factorization is called a singular-value decomposition of M. A common convention is to order the diagonal
            entries Σ(i,i) in descending order. In this case, the diagonal matrix Σ is uniquely determined
            by M (though the matrices U and V are not). The diagonal entries of Σ are known as the singular values of M.</para>
            </summary>
            <remarks>
            The computation of the singular value decomposition is done at construction time.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseSvd.Create(MathNet.Numerics.LinearAlgebra.Single.DenseMatrix,System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseSvd"/> class. This object will compute the
            the singular value decomposition when the constructor is called and cache it's decomposition.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="computeVectors">Compute the singular U and VT vectors or not.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If SVD algorithm failed to converge with matrix <paramref name="matrix"/>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseSvd.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.DenseSvd.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.Evd">
            <summary>
            Eigenvalues and eigenvectors of a real matrix.
            </summary>
            <remarks>
            If A is symmetric, then A = V*D*V' where the eigenvalue matrix D is
            diagonal and the eigenvector matrix V is orthogonal.
            I.e. A = V*D*V' and V*VT=I.
            If A is not symmetric, then the eigenvalue matrix D is block diagonal
            with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues,
            lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda].  The
            columns of V represent the eigenvectors in the sense that A*V = V*D,
            i.e. A.Multiply(V) equals V.Multiply(D).  The matrix V may be badly
            conditioned, or even singular, so the validity of the equation
            A = V*D*Inverse(V) depends upon V.Condition().
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Factorization.Evd.Determinant">
            <summary>
            Gets the absolute value of determinant of the square matrix for which the EVD was computed.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Factorization.Evd.Rank">
            <summary>
            Gets the effective numerical matrix rank.
            </summary>
            <value>The number of non-negligible singular values.</value>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Factorization.Evd.IsFullRank">
            <summary>
            Gets a value indicating whether the matrix is full rank or not.
            </summary>
            <value><c>true</c> if the matrix is full rank; otherwise <c>false</c>.</value>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.GramSchmidt">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition Modified Gram-Schmidt Orthogonalization.</para>
            <para>Any real square matrix A may be decomposed as A = QR where Q is an orthogonal mxn matrix and R is an nxn upper triangular matrix.</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by modified Gram-Schmidt Orthogonalization.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Factorization.GramSchmidt.Determinant">
            <summary>
            Gets the absolute determinant value of the matrix for which the QR matrix was computed.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Factorization.GramSchmidt.IsFullRank">
            <summary>
            Gets a value indicating whether the matrix is full rank or not.
            </summary>
            <value><c>true</c> if the matrix is full rank; otherwise <c>false</c>.</value>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.LU">
            <summary>
            <para>A class which encapsulates the functionality of an LU factorization.</para>
            <para>For a matrix A, the LU factorization is a pair of lower triangular matrix L and
            upper triangular matrix U so that A = L*U.</para>
            <para>In the Math.NET implementation we also store a set of pivot elements for increased
            numerical stability. The pivot elements encode a permutation matrix P such that P*A = L*U.</para>
            </summary>
            <remarks>
            The computation of the LU factorization is done at construction time.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Factorization.LU.Determinant">
            <summary>
            Gets the determinant of the matrix for which the LU factorization was computed.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.QR">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition.</para>
            <para>Any real square matrix A (m x n) may be decomposed as A = QR where Q is an orthogonal matrix
            (its columns are orthogonal unit vectors meaning QTQ = I) and R is an upper triangular matrix
            (also called right triangular matrix).</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by Householder transformation.
            If a <seealso cref="F:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod.Full"/> factorization is performed, the resulting Q matrix is an m x m matrix
            and the R matrix is an m x n matrix. If a <seealso cref="F:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod.Thin"/> factorization is performed, the
            resulting Q matrix is an m x n matrix and the R matrix is an n x n matrix.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Factorization.QR.Determinant">
            <summary>
            Gets the absolute determinant value of the matrix for which the QR matrix was computed.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Factorization.QR.IsFullRank">
            <summary>
            Gets a value indicating whether the matrix is full rank or not.
            </summary>
            <value><c>true</c> if the matrix is full rank; otherwise <c>false</c>.</value>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.Svd">
            <summary>
            <para>A class which encapsulates the functionality of the singular value decomposition (SVD).</para>
            <para>Suppose M is an m-by-n matrix whose entries are real numbers.
            Then there exists a factorization of the form M = UΣVT where:
            - U is an m-by-m unitary matrix;
            - Σ is m-by-n diagonal matrix with nonnegative real numbers on the diagonal;
            - VT denotes transpose of V, an n-by-n unitary matrix;
            Such a factorization is called a singular-value decomposition of M. A common convention is to order the diagonal
            entries Σ(i,i) in descending order. In this case, the diagonal matrix Σ is uniquely determined
            by M (though the matrices U and V are not). The diagonal entries of Σ are known as the singular values of M.</para>
            </summary>
            <remarks>
            The computation of the singular value decomposition is done at construction time.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Factorization.Svd.Rank">
            <summary>
            Gets the effective numerical matrix rank.
            </summary>
            <value>The number of non-negligible singular values.</value>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Factorization.Svd.L2Norm">
            <summary>
            Gets the two norm of the <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.
            </summary>
            <returns>The 2-norm of the <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.</returns>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Factorization.Svd.ConditionNumber">
            <summary>
            Gets the condition number <b>max(S) / min(S)</b>
            </summary>
            <returns>The condition number.</returns>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Factorization.Svd.Determinant">
            <summary>
            Gets the determinant of the square matrix for which the SVD was computed.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserCholesky">
            <summary>
            <para>A class which encapsulates the functionality of a Cholesky factorization for user matrices.</para>
            <para>For a symmetric, positive definite matrix A, the Cholesky factorization
            is an lower triangular matrix L so that A = L*L'.</para>
            </summary>
            <remarks>
            The computation of the Cholesky factorization is done at construction time. If the matrix is not symmetric
            or positive definite, the constructor will throw an exception.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserCholesky.DoCholesky(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Computes the Cholesky factorization in-place.
            </summary>
            <param name="factor">On entry, the matrix to factor. On exit, the Cholesky factor matrix</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="factor"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="factor"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="factor"/> is not positive definite.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserCholesky.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserCholesky"/> class. This object will compute the
            Cholesky factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not positive definite.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserCholesky.Factorize(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Calculates the Cholesky factorization of the input matrix.
            </summary>
            <param name="matrix">The matrix to be factorized<see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not positive definite.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="matrix"/> does not have the same dimensions as the existing factor.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserCholesky.DoCholeskyStep(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},System.Int32,System.Int32,System.Int32,System.Single[],System.Int32)">
            <summary>
            Calculate Cholesky step
            </summary>
            <param name="data">Factor matrix</param>
            <param name="rowDim">Number of rows</param>
            <param name="firstCol">Column start</param>
            <param name="colLimit">Total columns</param>
            <param name="multipliers">Multipliers calculated previously</param>
            <param name="availableCores">Number of available processors</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserCholesky.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A Cholesky factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserCholesky.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A Cholesky factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserEvd">
            <summary>
            Eigenvalues and eigenvectors of a real matrix.
            </summary>
            <remarks>
            If A is symmetric, then A = V*D*V' where the eigenvalue matrix D is
            diagonal and the eigenvector matrix V is orthogonal.
            I.e. A = V*D*V' and V*VT=I.
            If A is not symmetric, then the eigenvalue matrix D is block diagonal
            with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues,
            lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda].  The
            columns of V represent the eigenvectors in the sense that A*V = V*D,
            i.e. A.Multiply(V) equals V.Multiply(D).  The matrix V may be badly
            conditioned, or even singular, so the validity of the equation
            A = V*D*Inverse(V) depends upon V.Condition().
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserEvd.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Symmetricity)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserEvd"/> class. This object will compute the
            the eigenvalue decomposition when the constructor is called and cache it's decomposition.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="symmetricity">If it is known whether the matrix is symmetric or not the routine can skip checking it itself.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If EVD algorithm failed to converge with matrix <paramref name="matrix"/>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserEvd.SymmetricTridiagonalize(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},System.Single[],System.Single[],System.Int32)">
            <summary>
            Symmetric Householder reduction to tridiagonal form.
            </summary>
            <param name="eigenVectors">The eigen vectors to work on.</param>
            <param name="d">Arrays for internal storage of real parts of eigenvalues</param>
            <param name="e">Arrays for internal storage of imaginary parts of eigenvalues</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures tred2 by
            Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
            Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserEvd.SymmetricDiagonalize(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},System.Single[],System.Single[],System.Int32)">
            <summary>
            Symmetric tridiagonal QL algorithm.
            </summary>
            <param name="eigenVectors">The eigen vectors to work on.</param>
            <param name="d">Arrays for internal storage of real parts of eigenvalues</param>
            <param name="e">Arrays for internal storage of imaginary parts of eigenvalues</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures tql2, by
            Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
            Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserEvd.NonsymmetricReduceToHessenberg(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},System.Single[0:,0:],System.Int32)">
            <summary>
            Nonsymmetric reduction to Hessenberg form.
            </summary>
            <param name="eigenVectors">The eigen vectors to work on.</param>
            <param name="matrixH">Array for internal storage of nonsymmetric Hessenberg form.</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures orthes and ortran,
            by Martin and Wilkinson, Handbook for Auto. Comp.,
            Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutines in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserEvd.NonsymmetricReduceHessenberToRealSchur(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},System.Single[0:,0:],System.Single[],System.Single[],System.Int32)">
            <summary>
            Nonsymmetric reduction from Hessenberg to real Schur form.
            </summary>
            <param name="eigenVectors">The eigen vectors to work on.</param>
            <param name="matrixH">Array for internal storage of nonsymmetric Hessenberg form.</param>
            <param name="d">Arrays for internal storage of real parts of eigenvalues</param>
            <param name="e">Arrays for internal storage of imaginary parts of eigenvalues</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedure hqr2,
            by Martin and Wilkinson, Handbook for Auto. Comp.,
            Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserEvd.Cdiv(System.Single,System.Single,System.Single,System.Single)">
            <summary>
            Complex scalar division X/Y.
            </summary>
            <param name="xreal">Real part of X</param>
            <param name="ximag">Imaginary part of X</param>
            <param name="yreal">Real part of Y</param>
            <param name="yimag">Imaginary part of Y</param>
            <returns>Division result as a <see cref="T:System.Numerics.Complex"/> number.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserEvd.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserEvd.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A EVD factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserGramSchmidt">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition Modified Gram-Schmidt Orthogonalization.</para>
            <para>Any real square matrix A may be decomposed as A = QR where Q is an orthogonal mxn matrix and R is an nxn upper triangular matrix.</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by modified Gram-Schmidt Orthogonalization.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserGramSchmidt.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserGramSchmidt"/> class. This object creates an orthogonal matrix
            using the modified Gram-Schmidt method.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> row count is less then column count</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is rank deficient</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserGramSchmidt.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserGramSchmidt.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserLU">
            <summary>
            <para>A class which encapsulates the functionality of an LU factorization.</para>
            <para>For a matrix A, the LU factorization is a pair of lower triangular matrix L and
            upper triangular matrix U so that A = L*U.</para>
            </summary>
            <remarks>
            The computation of the LU factorization is done at construction time.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserLU.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserLU"/> class. This object will compute the
            LU factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserLU.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Solves a system of linear equations, <c>AX = B</c>, with A LU factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>B</c>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>X</c>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserLU.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Solves a system of linear equations, <c>Ax = b</c>, with A LU factorized.
            </summary>
            <param name="input">The right hand side vector, <c>b</c>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>x</c>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserLU.Inverse">
            <summary>
            Returns the inverse of this matrix. The inverse is calculated using LU decomposition.
            </summary>
            <returns>The inverse of this matrix.</returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserQR">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition.</para>
            <para>Any real square matrix A may be decomposed as A = QR where Q is an orthogonal matrix
            (its columns are orthogonal unit vectors meaning QTQ = I) and R is an upper triangular matrix
            (also called right triangular matrix).</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by Householder transformation.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserQR.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Factorization.QRMethod)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserQR"/> class. This object will compute the
            QR factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="method">The QR factorization method to use.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserQR.GenerateColumn(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},System.Int32,System.Int32)">
            <summary>
            Generate column from initial matrix to work array
            </summary>
            <param name="a">Initial matrix</param>
            <param name="row">The first row</param>
            <param name="column">Column index</param>
            <returns>Generated vector</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserQR.ComputeQR(System.Single[],MathNet.Numerics.LinearAlgebra.Matrix{System.Single},System.Int32,System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Perform calculation of Q or R
            </summary>
            <param name="u">Work array</param>
            <param name="a">Q or R matrices</param>
            <param name="rowStart">The first row</param>
            <param name="rowDim">The last row</param>
            <param name="columnStart">The first column</param>
            <param name="columnDim">The last column</param>
            <param name="availableCores">Number of available CPUs</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserQR.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserQR.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserSvd">
            <summary>
            <para>A class which encapsulates the functionality of the singular value decomposition (SVD) for <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.</para>
            <para>Suppose M is an m-by-n matrix whose entries are real numbers.
            Then there exists a factorization of the form M = UΣVT where:
            - U is an m-by-m unitary matrix;
            - Σ is m-by-n diagonal matrix with nonnegative real numbers on the diagonal;
            - VT denotes transpose of V, an n-by-n unitary matrix;
            Such a factorization is called a singular-value decomposition of M. A common convention is to order the diagonal
            entries Σ(i,i) in descending order. In this case, the diagonal matrix Σ is uniquely determined
            by M (though the matrices U and V are not). The diagonal entries of Σ are known as the singular values of M.</para>
            </summary>
            <remarks>
            The computation of the singular value decomposition is done at construction time.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserSvd.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserSvd"/> class. This object will compute the
            the singular value decomposition when the constructor is called and cache it's decomposition.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="computeVectors">Compute the singular U and VT vectors or not.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserSvd.Dsign(System.Single,System.Single)">
            <summary>
            Calculates absolute value of <paramref name="z1"/> multiplied on signum function of <paramref name="z2"/>
            </summary>
            <param name="z1">Double value z1</param>
            <param name="z2">Double value z2</param>
            <returns>Result multiplication of signum function and absolute value</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserSvd.Dswap(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},System.Int32,System.Int32,System.Int32)">
            <summary>
            Swap column  <paramref name="columnA"/>  and  <paramref name="columnB"/>
            </summary>
            <param name="a">Source matrix</param>
            <param name="rowCount">The number of rows in <paramref name="a"/></param>
            <param name="columnA">Column A index to swap</param>
            <param name="columnB">Column B index to swap</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserSvd.DscalColumn(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},System.Int32,System.Int32,System.Int32,System.Single)">
            <summary>
            Scale column <paramref name="column"/> by <paramref name="z"/> starting from row <paramref name="rowStart"/>
            </summary>
            <param name="a">Source matrix</param>
            <param name="rowCount">The number of rows in <paramref name="a"/> </param>
            <param name="column">Column to scale</param>
            <param name="rowStart">Row to scale from</param>
            <param name="z">Scale value</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserSvd.DscalVector(System.Single[],System.Int32,System.Single)">
            <summary>
            Scale vector <paramref name="a"/> by <paramref name="z"/> starting from index <paramref name="start"/>
            </summary>
            <param name="a">Source vector</param>
            <param name="start">Row to scale from</param>
            <param name="z">Scale value</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserSvd.Drotg(System.Single@,System.Single@,System.Single@,System.Single@)">
            <summary>
            Given the Cartesian coordinates (da, db) of a point p, these function return the parameters da, db, c, and s
            associated with the Givens rotation that zeros the y-coordinate of the point.
            </summary>
            <param name="da">Provides the x-coordinate of the point p. On exit contains the parameter r associated with the Givens rotation</param>
            <param name="db">Provides the y-coordinate of the point p. On exit contains the parameter z associated with the Givens rotation</param>
            <param name="c">Contains the parameter c associated with the Givens rotation</param>
            <param name="s">Contains the parameter s associated with the Givens rotation</param>
            <remarks>This is equivalent to the DROTG LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserSvd.Dnrm2Column(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},System.Int32,System.Int32,System.Int32)">
            <summary>
            Calculate Norm 2 of the column <paramref name="column"/> in matrix <paramref name="a"/> starting from row <paramref name="rowStart"/>
            </summary>
            <param name="a">Source matrix</param>
            <param name="rowCount">The number of rows in <paramref name="a"/></param>
            <param name="column">Column index</param>
            <param name="rowStart">Start row index</param>
            <returns>Norm2 (Euclidean norm) of the column</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserSvd.Dnrm2Vector(System.Single[],System.Int32)">
            <summary>
            Calculate Norm 2 of the vector <paramref name="a"/> starting from index <paramref name="rowStart"/>
            </summary>
            <param name="a">Source vector</param>
            <param name="rowStart">Start index</param>
            <returns>Norm2 (Euclidean norm) of the vector</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserSvd.Ddot(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Calculate dot product of <paramref name="columnA"/> and <paramref name="columnB"/>
            </summary>
            <param name="a">Source matrix</param>
            <param name="rowCount">The number of rows in <paramref name="a"/></param>
            <param name="columnA">Index of column A</param>
            <param name="columnB">Index of column B</param>
            <param name="rowStart">Starting row index</param>
            <returns>Dot product value</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserSvd.Drot(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},System.Int32,System.Int32,System.Int32,System.Single,System.Single)">
            <summary>
            Performs rotation of points in the plane. Given two vectors x <paramref name="columnA"/> and y <paramref name="columnB"/>,
            each vector element of these vectors is replaced as follows: x(i) = c*x(i) + s*y(i); y(i) = c*y(i) - s*x(i)
            </summary>
            <param name="a">Source matrix</param>
            <param name="rowCount">The number of rows in <paramref name="a"/></param>
            <param name="columnA">Index of column A</param>
            <param name="columnB">Index of column B</param>
            <param name="c">Scalar "c" value</param>
            <param name="s">Scalar "s" value</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserSvd.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Factorization.UserSvd.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Matrix">
            <summary>
            <c>float</c> version of the <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/> class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.#ctor(MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage{System.Single})">
            <summary>
            Initializes a new instance of the Matrix class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.CoerceZero(System.Double)">
            <summary>
            Set all values whose absolute value is smaller than the threshold to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.ConjugateTranspose">
            <summary>
            Returns the conjugate transpose of this matrix.
            </summary>
            <returns>The conjugate transpose of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.ConjugateTranspose(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Puts the conjugate transpose of this matrix into the result matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoConjugate(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Complex conjugates each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the conjugation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoNegate(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Negate each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the negation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoAdd(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Add a scalar to each element of the matrix and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to add.</param>
            <param name="result">The matrix to store the result of the addition.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoAdd(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Adds another matrix to this matrix.
            </summary>
            <param name="other">The matrix to add to this matrix.</param>
            <param name="result">The matrix to store the result of the addition.</param>
            <exception cref="T:System.ArgumentNullException">If the other matrix is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoSubtract(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Subtracts a scalar from each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to subtract.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoSubtract(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Subtracts another matrix from this matrix.
            </summary>
            <param name="other">The matrix to subtract to this matrix.</param>
            <param name="result">The matrix to store the result of subtraction.</param>
            <exception cref="T:System.ArgumentNullException">If the other matrix is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoMultiply(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Multiplies each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="scalar">The scalar to multiply the matrix with.</param>
            <param name="result">The matrix to store the result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Multiplies this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Multiplies this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoDivide(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Divides each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="divisor">The scalar to divide the matrix with.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoDivideByThis(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Divides a scalar by each element of the matrix and stores the result in the result matrix.
            </summary>
            <param name="dividend">The scalar to divide by each element of the matrix.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Multiplies this matrix with transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoConjugateTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Multiplies this matrix with the conjugate transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Multiplies the transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Multiplies the conjugate transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoModulus(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoModulusByThis(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given dividend for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoRemainder(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoRemainderByThis(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given dividend for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Pointwise multiplies this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="other">The matrix to pointwise multiply with this one.</param>
            <param name="result">The matrix to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Pointwise divide this matrix by another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The matrix to pointwise divide this one by.</param>
            <param name="result">The matrix to store the result of the pointwise division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoPointwisePower(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Pointwise raise this matrix to an exponent and store the result into the result matrix.
            </summary>
            <param name="exponent">The exponent to raise this matrix values to.</param>
            <param name="result">The matrix to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoPointwisePower(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Pointwise raise this matrix to an exponent and store the result into the result matrix.
            </summary>
            <param name="exponent">The exponent to raise this matrix values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoPointwiseModulus(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Pointwise canonical modulus, where the result has the sign of the divisor,
            of this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The pointwise denominator matrix to use</param>
            <param name="result">The result of the modulus.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoPointwiseRemainder(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Pointwise remainder (% operator), where the result has the sign of the dividend,
            of this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The pointwise denominator matrix to use</param>
            <param name="result">The result of the modulus.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoPointwiseExp(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Pointwise applies the exponential function to each value and stores the result into the result matrix.
            </summary>
            <param name="result">The matrix to store the result.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.DoPointwiseLog(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Pointwise applies the natural logarithm function to each value and stores the result into the result matrix.
            </summary>
            <param name="result">The matrix to store the result.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.PseudoInverse">
            <summary>
            Computes the Moore-Penrose Pseudo-Inverse of this matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.Trace">
            <summary>
            Computes the trace of this matrix.
            </summary>
            <returns>The trace of this matrix</returns>
            <exception cref="T:System.ArgumentException">If the matrix is not square</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.L1Norm">
            <summary>Calculates the induced L1 norm of this matrix.</summary>
            <returns>The maximum absolute column sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.InfinityNorm">
            <summary>Calculates the induced infinity norm of this matrix.</summary>
            <returns>The maximum absolute row sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.FrobeniusNorm">
            <summary>Calculates the entry-wise Frobenius norm of this matrix.</summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.RowNorms(System.Double)">
            <summary>
            Calculates the p-norms of all row vectors.
            Typical values for p are 1.0 (L1, Manhattan norm), 2.0 (L2, Euclidean norm) and positive infinity (infinity norm)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.ColumnNorms(System.Double)">
            <summary>
            Calculates the p-norms of all column vectors.
            Typical values for p are 1.0 (L1, Manhattan norm), 2.0 (L2, Euclidean norm) and positive infinity (infinity norm)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.NormalizeRows(System.Double)">
            <summary>
            Normalizes all row vectors to a unit p-norm.
            Typical values for p are 1.0 (L1, Manhattan norm), 2.0 (L2, Euclidean norm) and positive infinity (infinity norm)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.NormalizeColumns(System.Double)">
            <summary>
            Normalizes all column vectors to a unit p-norm.
            Typical values for p are 1.0 (L1, Manhattan norm), 2.0 (L2, Euclidean norm) and positive infinity (infinity norm)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.RowSums">
            <summary>
            Calculates the value sum of each row vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.RowAbsoluteSums">
            <summary>
            Calculates the absolute value sum of each row vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.ColumnSums">
            <summary>
            Calculates the value sum of each column vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.ColumnAbsoluteSums">
            <summary>
            Calculates the absolute value sum of each column vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Matrix.IsHermitian">
            <summary>
            Evaluates whether this matrix is Hermitian (conjugate symmetric).
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Solvers.BiCgStab">
            <summary>
            A Bi-Conjugate Gradient stabilized iterative matrix solver.
            </summary>
            <remarks>
            <para>
            The Bi-Conjugate Gradient Stabilized (BiCGStab) solver is an 'improvement'
            of the standard Conjugate Gradient (CG) solver. Unlike the CG solver the
            BiCGStab can be used on non-symmetric matrices. <br/>
            Note that much of the success of the solver depends on the selection of the
            proper preconditioner.
            </para>
            <para>
            The Bi-CGSTAB algorithm was taken from: <br/>
            Templates for the solution of linear systems: Building blocks
            for iterative methods
            <br/>
            Richard Barrett, Michael Berry, Tony F. Chan, James Demmel,
            June M. Donato, Jack Dongarra, Victor Eijkhout, Roldan Pozo,
            Charles Romine and Henk van der Vorst
            <br/>
            Url: <a href="http://www.netlib.org/templates/Templates.html">http://www.netlib.org/templates/Templates.html</a>
            <br/>
            Algorithm is described in Chapter 2, section 2.3.8, page 27
            </para>
            <para>
            The example code below provides an indication of the possible use of the
            solver.
            </para>
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.BiCgStab.CalculateTrueResidual(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Calculates the <c>true</c> residual of the matrix equation Ax = b according to: residual = b - Ax
            </summary>
            <param name="matrix">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Matrix"/> A.</param>
            <param name="residual">Residual values in <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/>.</param>
            <param name="x">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/> x.</param>
            <param name="b">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/> b.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.BiCgStab.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{System.Single},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{System.Single})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Matrix"/>, <c>A</c>.</param>
            <param name="input">The solution <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/>, <c>b</c>.</param>
            <param name="result">The result <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/>, <c>x</c>.</param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Solvers.CompositeSolver">
            <summary>
            A composite matrix solver. The actual solver is made by a sequence of
            matrix solvers.
            </summary>
            <remarks>
            <para>
            Solver based on:<br />
            Faster PDE-based simulations using robust composite linear solvers<br />
            S. Bhowmicka, P. Raghavan a,*, L. McInnes b, B. Norris<br />
            Future Generation Computer Systems, Vol 20, 2004, pp 373�387<br />
            </para>
            <para>
            Note that if an iterator is passed to this solver it will be used for all the sub-solvers.
            </para>
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.Solvers.CompositeSolver._solvers">
            <summary>
            The collection of solvers that will be used
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.CompositeSolver.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{System.Single},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{System.Single})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient matrix, <c>A</c>.</param>
            <param name="input">The solution vector, <c>b</c></param>
            <param name="result">The result vector, <c>x</c></param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Solvers.DiagonalPreconditioner">
            <summary>
            A diagonal preconditioner. The preconditioner uses the inverse
            of the matrix diagonal as preconditioning values.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.Solvers.DiagonalPreconditioner._inverseDiagonals">
            <summary>
            The inverse of the matrix diagonal.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.DiagonalPreconditioner.DiagonalEntries">
            <summary>
            Returns the decomposed matrix diagonal.
            </summary>
            <returns>The matrix diagonal.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.DiagonalPreconditioner.Initialize(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Initializes the preconditioner and loads the internal data structures.
            </summary>
            <param name="matrix">
            The <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Matrix"/> upon which this preconditioner is based.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <see langword="null" />. </exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.DiagonalPreconditioner.Approximate(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Approximates the solution to the matrix equation <b>Ax = b</b>.
            </summary>
            <param name="rhs">The right hand side vector.</param>
            <param name="lhs">The left hand side vector. Also known as the result vector.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Solvers.GpBiCg">
            <summary>
            A Generalized Product Bi-Conjugate Gradient iterative matrix solver.
            </summary>
            <remarks>
            <para>
            The Generalized Product Bi-Conjugate Gradient (GPBiCG) solver is an
            alternative version of the Bi-Conjugate Gradient stabilized (CG) solver.
            Unlike the CG solver the GPBiCG solver can be used on
            non-symmetric matrices. <br/>
            Note that much of the success of the solver depends on the selection of the
            proper preconditioner.
            </para>
            <para>
            The GPBiCG algorithm was taken from: <br/>
            GPBiCG(m,l): A hybrid of BiCGSTAB and GPBiCG methods with
            efficiency and robustness
            <br/>
            S. Fujino
            <br/>
            Applied Numerical Mathematics, Volume 41, 2002, pp 107 - 117
            <br/>
            </para>
            <para>
            The example code below provides an indication of the possible use of the
            solver.
            </para>
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.Solvers.GpBiCg._numberOfBiCgStabSteps">
            <summary>
            Indicates the number of <c>BiCGStab</c> steps should be taken
            before switching.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.Solvers.GpBiCg._numberOfGpbiCgSteps">
            <summary>
            Indicates the number of <c>GPBiCG</c> steps should be taken
            before switching.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Solvers.GpBiCg.NumberOfBiCgStabSteps">
            <summary>
            Gets or sets the number of steps taken with the <c>BiCgStab</c> algorithm
            before switching over to the <c>GPBiCG</c> algorithm.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Solvers.GpBiCg.NumberOfGpBiCgSteps">
            <summary>
            Gets or sets the number of steps taken with the <c>GPBiCG</c> algorithm
            before switching over to the <c>BiCgStab</c> algorithm.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.GpBiCg.CalculateTrueResidual(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Calculates the <c>true</c> residual of the matrix equation Ax = b according to: residual = b - Ax
            </summary>
            <param name="matrix">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Matrix"/> A.</param>
            <param name="residual">Residual values in <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/>.</param>
            <param name="x">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/> x.</param>
            <param name="b">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/> b.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.GpBiCg.ShouldRunBiCgStabSteps(System.Int32)">
            <summary>
            Decide if to do steps with BiCgStab
            </summary>
            <param name="iterationNumber">Number of iteration</param>
            <returns><c>true</c> if yes, otherwise <c>false</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.GpBiCg.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{System.Single},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{System.Single})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient matrix, <c>A</c>.</param>
            <param name="input">The solution vector, <c>b</c></param>
            <param name="result">The result vector, <c>x</c></param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILU0Preconditioner">
            <summary>
            An incomplete, level 0, LU factorization preconditioner.
            </summary>
            <remarks>
            The ILU(0) algorithm was taken from: <br/>
            Iterative methods for sparse linear systems <br/>
            Yousef Saad <br/>
            Algorithm is described in Chapter 10, section 10.3.2, page 275 <br/>
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILU0Preconditioner._decompositionLU">
            <summary>
            The matrix holding the lower (L) and upper (U) matrices. The
            decomposition matrices are combined to reduce storage.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILU0Preconditioner.UpperTriangle">
            <summary>
            Returns the upper triagonal matrix that was created during the LU decomposition.
            </summary>
            <returns>A new matrix containing the upper triagonal elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILU0Preconditioner.LowerTriangle">
            <summary>
            Returns the lower triagonal matrix that was created during the LU decomposition.
            </summary>
            <returns>A new matrix containing the lower triagonal elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILU0Preconditioner.Initialize(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Initializes the preconditioner and loads the internal data structures.
            </summary>
            <param name="matrix">The matrix upon which the preconditioner is based. </param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILU0Preconditioner.Approximate(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Approximates the solution to the matrix equation <b>Ax = b</b>.
            </summary>
            <param name="rhs">The right hand side vector.</param>
            <param name="lhs">The left hand side vector. Also known as the result vector.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner">
            <summary>
            This class performs an Incomplete LU factorization with drop tolerance
            and partial pivoting. The drop tolerance indicates which additional entries
            will be dropped from the factorized LU matrices.
            </summary>
            <remarks>
            The ILUTP-Mem algorithm was taken from: <br/>
            ILUTP_Mem: a Space-Efficient Incomplete LU Preconditioner
            <br/>
            Tzu-Yi Chen, Department of Mathematics and Computer Science, <br/>
            Pomona College, Claremont CA 91711, USA <br/>
            Published in: <br/>
            Lecture Notes in Computer Science <br/>
            Volume 3046 / 2004 <br/>
            pp. 20 - 28 <br/>
            Algorithm is described in Section 2, page 22
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner.DefaultFillLevel">
            <summary>
            The default fill level.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner.DefaultDropTolerance">
            <summary>
            The default drop tolerance.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner._upper">
            <summary>
            The decomposed upper triangular matrix.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner._lower">
            <summary>
            The decomposed lower triangular matrix.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner._pivots">
            <summary>
            The array containing the pivot values.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner._fillLevel">
            <summary>
            The fill level.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner._dropTolerance">
            <summary>
            The drop tolerance.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner._pivotTolerance">
            <summary>
            The pivot tolerance.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner"/> class with the default settings.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner.#ctor(System.Double,System.Double,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner"/> class with the specified settings.
            </summary>
            <param name="fillLevel">
            The amount of fill that is allowed in the matrix. The value is a fraction of
            the number of non-zero entries in the original matrix. Values should be positive.
            </param>
            <param name="dropTolerance">
            The absolute drop tolerance which indicates below what absolute value an entry
            will be dropped from the matrix. A drop tolerance of 0.0 means that no values
            will be dropped. Values should always be positive.
            </param>
            <param name="pivotTolerance">
            The pivot tolerance which indicates at what level pivoting will take place. A
            value of 0.0 means that no pivoting will take place.
            </param>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner.FillLevel">
            <summary>
            Gets or sets the amount of fill that is allowed in the matrix. The
            value is a fraction of the number of non-zero entries in the original
            matrix. The standard value is 200.
            </summary>
            <remarks>
            <para>
            Values should always be positive and can be higher than 1.0. A value lower
            than 1.0 means that the eventual preconditioner matrix will have fewer
            non-zero entries as the original matrix. A value higher than 1.0 means that
            the eventual preconditioner can have more non-zero values than the original
            matrix.
            </para>
            <para>
            Note that any changes to the <b>FillLevel</b> after creating the preconditioner
            will invalidate the created preconditioner and will require a re-initialization of
            the preconditioner.
            </para>
            </remarks>
            <exception cref="T:System.ArgumentOutOfRangeException">Thrown if a negative value is provided.</exception>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner.DropTolerance">
            <summary>
            Gets or sets the absolute drop tolerance which indicates below what absolute value
            an entry will be dropped from the matrix. The standard value is 0.0001.
            </summary>
            <remarks>
            <para>
            The values should always be positive and can be larger than 1.0. A low value will
            keep more small numbers in the preconditioner matrix. A high value will remove
            more small numbers from the preconditioner matrix.
            </para>
            <para>
            Note that any changes to the <b>DropTolerance</b> after creating the preconditioner
            will invalidate the created preconditioner and will require a re-initialization of
            the preconditioner.
            </para>
            </remarks>
            <exception cref="T:System.ArgumentOutOfRangeException">Thrown if a negative value is provided.</exception>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner.PivotTolerance">
            <summary>
            Gets or sets the pivot tolerance which indicates at what level pivoting will
            take place. The standard value is 0.0 which means pivoting will never take place.
            </summary>
            <remarks>
            <para>
            The pivot tolerance is used to calculate if pivoting is necessary. Pivoting
            will take place if any of the values in a row is bigger than the
            diagonal value of that row divided by the pivot tolerance, i.e. pivoting
            will take place if <b>row(i,j) > row(i,i) / PivotTolerance</b> for
            any <b>j</b> that is not equal to <b>i</b>.
            </para>
            <para>
            Note that any changes to the <b>PivotTolerance</b> after creating the preconditioner
            will invalidate the created preconditioner and will require a re-initialization of
            the preconditioner.
            </para>
            </remarks>
            <exception cref="T:System.ArgumentOutOfRangeException">Thrown if a negative value is provided.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner.UpperTriangle">
            <summary>
            Returns the upper triagonal matrix that was created during the LU decomposition.
            </summary>
            <remarks>
            This method is used for debugging purposes only and should normally not be used.
            </remarks>
            <returns>A new matrix containing the upper triagonal elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner.LowerTriangle">
            <summary>
            Returns the lower triagonal matrix that was created during the LU decomposition.
            </summary>
            <remarks>
            This method is used for debugging purposes only and should normally not be used.
            </remarks>
            <returns>A new matrix containing the lower triagonal elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner.Pivots">
            <summary>
            Returns the pivot array. This array is not needed for normal use because
            the preconditioner will return the solution vector values in the proper order.
            </summary>
            <remarks>
            This method is used for debugging purposes only and should normally not be used.
            </remarks>
            <returns>The pivot array.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner.Initialize(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Initializes the preconditioner and loads the internal data structures.
            </summary>
            <param name="matrix">
            The <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Matrix"/> upon which this preconditioner is based. Note that the
            method takes a general matrix type. However internally the data is stored
            as a sparse matrix. Therefore it is not recommended to pass a dense matrix.
            </param>
            <exception cref="T:System.ArgumentNullException"> If <paramref name="matrix"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner.PivotRow(MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Pivot elements in the <paramref name="row"/> according to internal pivot array
            </summary>
            <param name="row">Row <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/> to pivot in</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner.PivotMapFound(System.Collections.Generic.Dictionary{System.Int32,System.Int32},System.Int32)">
            <summary>
            Was pivoting already performed
            </summary>
            <param name="knownPivots">Pivots already done</param>
            <param name="currentItem">Current item to pivot</param>
            <returns><c>true</c> if performed, otherwise <c>false</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner.SwapColumns(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},System.Int32,System.Int32)">
            <summary>
            Swap columns in the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Matrix"/>
            </summary>
            <param name="matrix">Source <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Matrix"/>.</param>
            <param name="firstColumn">First column index to swap</param>
            <param name="secondColumn">Second column index to swap</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner.FindLargestItems(System.Int32,System.Int32,System.Int32[],MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Sort vector descending, not changing vector but placing sorted indices to <paramref name="sortedIndices"/>
            </summary>
            <param name="lowerBound">Start sort form</param>
            <param name="upperBound">Sort till upper bound</param>
            <param name="sortedIndices">Array with sorted vector indices</param>
            <param name="values">Source <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner.Approximate(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Approximates the solution to the matrix equation <b>Ax = b</b>.
            </summary>
            <param name="rhs">The right hand side vector.</param>
            <param name="lhs">The left hand side vector. Also known as the result vector.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner.Pivot(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Pivot elements in <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/> according to internal pivot array
            </summary>
            <param name="vector">Source <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/>.</param>
            <param name="result">Result <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/> after pivoting.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPElementSorter">
            <summary>
            An element sort algorithm for the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPPreconditioner"/> class.
            </summary>
            <remarks>
            This sort algorithm is used to sort the columns in a sparse matrix based on
            the value of the element on the diagonal of the matrix.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPElementSorter.SortDoubleIndicesDecreasing(System.Int32,System.Int32,System.Int32[],MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Sorts the elements of the <paramref name="values"/> vector in decreasing
            fashion. The vector itself is not affected.
            </summary>
            <param name="lowerBound">The starting index.</param>
            <param name="upperBound">The stopping index.</param>
            <param name="sortedIndices">An array that will contain the sorted indices once the algorithm finishes.</param>
            <param name="values">The <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/> that contains the values that need to be sorted.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPElementSorter.HeapSortDoublesIndices(System.Int32,System.Int32,System.Int32[],MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Sorts the elements of the <paramref name="values"/> vector in decreasing
            fashion using heap sort algorithm. The vector itself is not affected.
            </summary>
            <param name="lowerBound">The starting index.</param>
            <param name="upperBound">The stopping index.</param>
            <param name="sortedIndices">An array that will contain the sorted indices once the algorithm finishes.</param>
            <param name="values">The <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/> that contains the values that need to be sorted.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPElementSorter.BuildDoubleIndexHeap(System.Int32,System.Int32,System.Int32[],MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Build heap for double indices
            </summary>
            <param name="start">Root position</param>
            <param name="count">Length of <paramref name="values"/></param>
            <param name="sortedIndices">Indices of <paramref name="values"/></param>
            <param name="values">Target <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPElementSorter.SiftDoubleIndices(System.Int32[],MathNet.Numerics.LinearAlgebra.Vector{System.Single},System.Int32,System.Int32)">
            <summary>
            Sift double indices
            </summary>
            <param name="sortedIndices">Indices of <paramref name="values"/></param>
            <param name="values">Target <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/></param>
            <param name="begin">Root position</param>
            <param name="count">Length of <paramref name="values"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPElementSorter.SortIntegersDecreasing(System.Int32[])">
            <summary>
            Sorts the given integers in a decreasing fashion.
            </summary>
            <param name="values">The values.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPElementSorter.HeapSortIntegers(System.Int32[],System.Int32)">
            <summary>
            Sort the given integers in a decreasing fashion using heapsort algorithm
            </summary>
            <param name="values">Array of values to sort</param>
            <param name="count">Length of <paramref name="values"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPElementSorter.BuildHeap(System.Int32[],System.Int32,System.Int32)">
            <summary>
            Build heap
            </summary>
            <param name="values">Target values array</param>
            <param name="start">Root position</param>
            <param name="count">Length of <paramref name="values"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPElementSorter.Sift(System.Int32[],System.Int32,System.Int32)">
            <summary>
            Sift values
            </summary>
            <param name="values">Target value array</param>
            <param name="start">Root position</param>
            <param name="count">Length of <paramref name="values"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.ILUTPElementSorter.Exchange(System.Int32[],System.Int32,System.Int32)">
            <summary>
            Exchange values in array
            </summary>
            <param name="values">Target values array</param>
            <param name="first">First value to exchange</param>
            <param name="second">Second value to exchange</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Solvers.MILU0Preconditioner">
            <summary>
            A simple milu(0) preconditioner.
            </summary>
            <remarks>
            Original Fortran code by Yousef Saad (07 January 2004)
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.MILU0Preconditioner.#ctor(System.Boolean)">
            <param name="modified">Use modified or standard ILU(0)</param>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Solvers.MILU0Preconditioner.UseModified">
            <summary>
            Gets or sets a value indicating whether to use modified or standard ILU(0).
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Solvers.MILU0Preconditioner.IsInitialized">
            <summary>
            Gets a value indicating whether the preconditioner is initialized.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.MILU0Preconditioner.Initialize(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Initializes the preconditioner and loads the internal data structures.
            </summary>
            <param name="matrix">The matrix upon which the preconditioner is based. </param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square or is not an
            instance of SparseCompressedRowMatrixStorage.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.MILU0Preconditioner.Approximate(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Approximates the solution to the matrix equation <b>Ax = b</b>.
            </summary>
            <param name="input">The right hand side vector b.</param>
            <param name="result">The left hand side vector x.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.MILU0Preconditioner.Compute(System.Int32,System.Single[],System.Int32[],System.Int32[],System.Single[],System.Int32[],System.Int32[],System.Boolean)">
            <summary>
            MILU0 is a simple milu(0) preconditioner.
            </summary>
            <param name="n">Order of the matrix.</param>
            <param name="a">Matrix values in CSR format (input).</param>
            <param name="ja">Column indices (input).</param>
            <param name="ia">Row pointers (input).</param>
            <param name="alu">Matrix values in MSR format (output).</param>
            <param name="jlu">Row pointers and column indices (output).</param>
            <param name="ju">Pointer to diagonal elements (output).</param>
            <param name="modified">True if the modified/MILU algorithm should be used (recommended)</param>
            <returns>Returns 0 on success or k > 0 if a zero pivot was encountered at step k.</returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Solvers.MlkBiCgStab">
            <summary>
            A Multiple-Lanczos Bi-Conjugate Gradient stabilized iterative matrix solver.
            </summary>
            <remarks>
            <para>
            The Multiple-Lanczos Bi-Conjugate Gradient stabilized (ML(k)-BiCGStab) solver is an 'improvement'
            of the standard BiCgStab solver.
            </para>
            <para>
            The algorithm was taken from: <br/>
            ML(k)BiCGSTAB: A BiCGSTAB variant based on multiple Lanczos starting vectors
            <br/>
            Man-Chung Yeung and Tony F. Chan
            <br/>
            SIAM Journal of Scientific Computing
            <br/>
            Volume 21, Number 4, pp. 1263 - 1290
            </para>
            <para>
            The example code below provides an indication of the possible use of the
            solver.
            </para>
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.Solvers.MlkBiCgStab.DefaultNumberOfStartingVectors">
            <summary>
            The default number of starting vectors.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.Solvers.MlkBiCgStab._startingVectors">
            <summary>
            The collection of starting vectors which are used as the basis for the Krylov sub-space.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Single.Solvers.MlkBiCgStab._numberOfStartingVectors">
            <summary>
            The number of starting vectors used by the algorithm
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Solvers.MlkBiCgStab.NumberOfStartingVectors">
            <summary>
            Gets or sets the number of starting vectors.
            </summary>
            <remarks>
            Must be larger than 1 and smaller than the number of variables in the matrix that
            for which this solver will be used.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.MlkBiCgStab.ResetNumberOfStartingVectors">
            <summary>
            Resets the number of starting vectors to the default value.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.Solvers.MlkBiCgStab.StartingVectors">
            <summary>
            Gets or sets a series of orthonormal vectors which will be used as basis for the
            Krylov sub-space.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.MlkBiCgStab.NumberOfStartingVectorsToCreate(System.Int32,System.Int32)">
            <summary>
            Gets the number of starting vectors to create
            </summary>
            <param name="maximumNumberOfStartingVectors">Maximum number</param>
            <param name="numberOfVariables">Number of variables</param>
            <returns>Number of starting vectors to create</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.MlkBiCgStab.CreateStartingVectors(System.Int32,System.Int32)">
            <summary>
            Returns an array of starting vectors.
            </summary>
            <param name="maximumNumberOfStartingVectors">The maximum number of starting vectors that should be created.</param>
            <param name="numberOfVariables">The number of variables.</param>
            <returns>
             An array with starting vectors. The array will never be larger than the
             <paramref name="maximumNumberOfStartingVectors"/> but it may be smaller if
             the <paramref name="numberOfVariables"/> is smaller than
             the <paramref name="maximumNumberOfStartingVectors"/>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.MlkBiCgStab.CreateVectorArray(System.Int32,System.Int32)">
            <summary>
            Create random vectors array
            </summary>
            <param name="arraySize">Number of vectors</param>
            <param name="vectorSize">Size of each vector</param>
            <returns>Array of random vectors</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.MlkBiCgStab.CalculateTrueResidual(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Calculates the <c>true</c> residual of the matrix equation Ax = b according to: residual = b - Ax
            </summary>
            <param name="matrix">Source <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Matrix"/>A.</param>
            <param name="residual">Residual <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/> data.</param>
            <param name="x">x <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/> data.</param>
            <param name="b">b <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/> data.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.MlkBiCgStab.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{System.Single},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{System.Single})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient matrix, <c>A</c>.</param>
            <param name="input">The solution vector, <c>b</c></param>
            <param name="result">The result vector, <c>x</c></param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Solvers.TFQMR">
            <summary>
            A Transpose Free Quasi-Minimal Residual (TFQMR) iterative matrix solver.
            </summary>
            <remarks>
            <para>
            The TFQMR algorithm was taken from: <br/>
            Iterative methods for sparse linear systems.
            <br/>
            Yousef Saad
            <br/>
            Algorithm is described in Chapter 7, section 7.4.3, page 219
            </para>
            <para>
            The example code below provides an indication of the possible use of the
            solver.
            </para>
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.TFQMR.CalculateTrueResidual(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Calculates the <c>true</c> residual of the matrix equation Ax = b according to: residual = b - Ax
            </summary>
            <param name="matrix">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Matrix"/> A.</param>
            <param name="residual">Residual values in <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/>.</param>
            <param name="x">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/> x.</param>
            <param name="b">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Single.Vector"/> b.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.TFQMR.IsEven(System.Int32)">
            <summary>
            Is <paramref name="number"/> even?
            </summary>
            <param name="number">Number to check</param>
            <returns><c>true</c> if <paramref name="number"/> even, otherwise <c>false</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Solvers.TFQMR.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{System.Single},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{System.Single})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient matrix, <c>A</c>.</param>
            <param name="input">The solution vector, <c>b</c></param>
            <param name="result">The result vector, <c>x</c></param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix">
            <summary>
            A Matrix with sparse storage, intended for very large matrices where most of the cells are zero.
            The underlying storage scheme is 3-array compressed-sparse-row (CSR) Format.
            <a href="http://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_.28CSR_or_CRS.29">Wikipedia - CSR</a>.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.NonZerosCount">
            <summary>
            Gets the number of non zero elements in the matrix.
            </summary>
            <value>The number of non zero elements.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.#ctor(MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage{System.Single})">
            <summary>
            Create a new sparse matrix straight from an initialized matrix storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.#ctor(System.Int32)">
            <summary>
            Create a new square sparse matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the order is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.#ctor(System.Int32,System.Int32)">
            <summary>
            Create a new sparse matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the row or column count is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfMatrix(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Create a new sparse matrix as a copy of the given other matrix.
            This new matrix will be independent from the other matrix.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfArray(System.Single[0:,0:])">
            <summary>
            Create a new sparse matrix as a copy of the given two-dimensional array.
            This new matrix will be independent from the provided array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfIndexed(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Int32,System.Single}})">
            <summary>
            Create a new sparse matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfIndexed(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Int32,System.Single}})">
            <summary>
            Create a new sparse matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfRowMajor(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable.
            The enumerable is assumed to be in row-major order (row by row).
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
            <seealso href="http://en.wikipedia.org/wiki/Row-major_order"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfColumnMajor(System.Int32,System.Int32,System.Collections.Generic.IList{System.Single})">
            <summary>
            Create a new sparse matrix with the given number of rows and columns as a copy of the given array.
            The array is assumed to be in column-major order (column by column).
            This new matrix will be independent from the provided array.
            A new memory block will be allocated for storing the matrix.
            </summary>
            <seealso href="http://en.wikipedia.org/wiki/Row-major_order"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfColumns(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Single}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfColumns(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Single}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfColumnArrays(System.Single[][])">
            <summary>
            Create a new sparse matrix as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfColumnArrays(System.Collections.Generic.IEnumerable{System.Single[]})">
            <summary>
            Create a new sparse matrix as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfColumnVectors(MathNet.Numerics.LinearAlgebra.Vector{System.Single}[])">
            <summary>
            Create a new sparse matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfColumnVectors(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{System.Single}})">
            <summary>
            Create a new sparse matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfRows(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Single}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfRows(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Single}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfRowArrays(System.Single[][])">
            <summary>
            Create a new sparse matrix as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfRowArrays(System.Collections.Generic.IEnumerable{System.Single[]})">
            <summary>
            Create a new sparse matrix as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfRowVectors(MathNet.Numerics.LinearAlgebra.Vector{System.Single}[])">
            <summary>
            Create a new sparse matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfRowVectors(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{System.Single}})">
            <summary>
            Create a new sparse matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfDiagonalVector(MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfDiagonalVector(System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfDiagonalArray(System.Single[])">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.OfDiagonalArray(System.Int32,System.Int32,System.Single[])">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.Create(System.Int32,System.Int32,System.Single)">
            <summary>
            Create a new sparse matrix and initialize each value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.Create(System.Int32,System.Int32,System.Func{System.Int32,System.Int32,System.Single})">
            <summary>
            Create a new sparse matrix and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.CreateDiagonal(System.Int32,System.Int32,System.Single)">
            <summary>
            Create a new diagonal sparse matrix and initialize each diagonal value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.CreateDiagonal(System.Int32,System.Int32,System.Func{System.Int32,System.Single})">
            <summary>
            Create a new diagonal sparse matrix and initialize each diagonal value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.CreateIdentity(System.Int32)">
            <summary>
            Create a new square sparse identity matrix where each diagonal value is set to One.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.LowerTriangle">
            <summary>
            Returns a new matrix containing the lower triangle of this matrix.
            </summary>
            <returns>The lower triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.LowerTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Puts the lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="result"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.LowerTriangleImpl(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Puts the lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.UpperTriangle">
            <summary>
            Returns a new matrix containing the upper triangle of this matrix.
            </summary>
            <returns>The upper triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.UpperTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Puts the upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="result"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.UpperTriangleImpl(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Puts the upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.StrictlyLowerTriangle">
            <summary>
            Returns a new matrix containing the lower triangle of this matrix. The new matrix
            does not contain the diagonal elements of this matrix.
            </summary>
            <returns>The lower triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.StrictlyLowerTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Puts the strictly lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="result"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.StrictlyLowerTriangleImpl(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Puts the strictly lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.StrictlyUpperTriangle">
            <summary>
            Returns a new matrix containing the upper triangle of this matrix. The new matrix
            does not contain the diagonal elements of this matrix.
            </summary>
            <returns>The upper triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.StrictlyUpperTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Puts the strictly upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="result"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.StrictlyUpperTriangleImpl(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Puts the strictly upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.DoNegate(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Negate each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the negation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.InfinityNorm">
            <summary>Calculates the induced infinity norm of this matrix.</summary>
            <returns>The maximum absolute row sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.FrobeniusNorm">
            <summary>Calculates the entry-wise Frobenius norm of this matrix.</summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.DoAdd(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Adds another matrix to this matrix.
            </summary>
            <param name="other">The matrix to add to this matrix.</param>
            <param name="result">The matrix to store the result of the addition.</param>
            <exception cref="T:System.ArgumentNullException">If the other matrix is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.DoSubtract(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Subtracts another matrix from this matrix.
            </summary>
            <param name="other">The matrix to subtract to this matrix.</param>
            <param name="result">The matrix to store the result of subtraction.</param>
            <exception cref="T:System.ArgumentNullException">If the other matrix is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.DoMultiply(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Multiplies each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="scalar">The scalar to multiply the matrix with.</param>
            <param name="result">The matrix to store the result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Multiplies this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Multiplies this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.DoTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Multiplies this matrix with transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Multiplies the transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Pointwise multiplies this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="other">The matrix to pointwise multiply with this one.</param>
            <param name="result">The matrix to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Matrix{System.Single},MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Pointwise divide this matrix by another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The matrix to pointwise divide this one by.</param>
            <param name="result">The matrix to store the result of the pointwise division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.DoModulus(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.DoRemainder(System.Single,MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.IsSymmetric">
            <summary>
            Evaluates whether this matrix is symmetric.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.op_Addition(MathNet.Numerics.LinearAlgebra.Single.SparseMatrix,MathNet.Numerics.LinearAlgebra.Single.SparseMatrix)">
            <summary>
            Adds two matrices together and returns the results.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to add.</param>
            <param name="rightSide">The right matrix to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> don't have the same dimensions.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.op_UnaryPlus(MathNet.Numerics.LinearAlgebra.Single.SparseMatrix)">
            <summary>
            Returns a <strong>Matrix</strong> containing the same values of <paramref name="rightSide"/>.
            </summary>
            <param name="rightSide">The matrix to get the values from.</param>
            <returns>A matrix containing a the same values as <paramref name="rightSide"/>.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.op_Subtraction(MathNet.Numerics.LinearAlgebra.Single.SparseMatrix,MathNet.Numerics.LinearAlgebra.Single.SparseMatrix)">
            <summary>
            Subtracts two matrices together and returns the results.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to subtract.</param>
            <param name="rightSide">The right matrix to subtract.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> don't have the same dimensions.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.op_UnaryNegation(MathNet.Numerics.LinearAlgebra.Single.SparseMatrix)">
            <summary>
            Negates each element of the matrix.
            </summary>
            <param name="rightSide">The matrix to negate.</param>
            <returns>A matrix containing the negated values.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Single.SparseMatrix,System.Single)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.op_Multiply(System.Single,MathNet.Numerics.LinearAlgebra.Single.SparseMatrix)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Single.SparseMatrix,MathNet.Numerics.LinearAlgebra.Single.SparseMatrix)">
            <summary>
            Multiplies two matrices.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to multiply.</param>
            <param name="rightSide">The right matrix to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the dimensions of <paramref name="leftSide"/> or <paramref name="rightSide"/> don't conform.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Single.SparseMatrix,MathNet.Numerics.LinearAlgebra.Single.SparseVector)">
            <summary>
            Multiplies a <strong>Matrix</strong> and a Vector.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The vector to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Single.SparseVector,MathNet.Numerics.LinearAlgebra.Single.SparseMatrix)">
            <summary>
            Multiplies a Vector and a <strong>Matrix</strong>.
            </summary>
            <param name="leftSide">The vector to multiply.</param>
            <param name="rightSide">The matrix to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseMatrix.op_Modulus(MathNet.Numerics.LinearAlgebra.Single.SparseMatrix,System.Single)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.SparseVector">
            <summary>
            A vector with sparse storage, intended for very large vectors where most of the cells are zero.
            </summary>
            <remarks>The sparse vector is not thread safe.</remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Single.SparseVector.NonZerosCount">
            <summary>
            Gets the number of non zero elements in the vector.
            </summary>
            <value>The number of non zero elements.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.#ctor(MathNet.Numerics.LinearAlgebra.Storage.SparseVectorStorage{System.Single})">
            <summary>
            Create a new sparse vector straight from an initialized vector storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.#ctor(System.Int32)">
            <summary>
            Create a new sparse vector with the given length.
            All cells of the vector will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If length is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.OfVector(MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Create a new sparse vector as a copy of the given other vector.
            This new vector will be independent from the other vector.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.OfEnumerable(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Create a new sparse vector as a copy of the given enumerable.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.OfIndexedEnumerable(System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Single}})">
            <summary>
            Create a new sparse vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.OfIndexedEnumerable(System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Single}})">
            <summary>
            Create a new sparse vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.Create(System.Int32,System.Single)">
            <summary>
            Create a new sparse vector and initialize each value using the provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.Create(System.Int32,System.Func{System.Int32,System.Single})">
            <summary>
            Create a new sparse vector and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.DoAdd(System.Single,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Adds a scalar to each element of the vector and stores the result in the result vector.
            Warning, the new 'sparse vector' with a non-zero scalar added to it will be a 100% filled
            sparse vector and very inefficient. Would be better to work with a dense vector instead.
            </summary>
            <param name="scalar">
            The scalar to add.
            </param>
            <param name="result">
            The vector to store the result of the addition.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.DoAdd(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Adds another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">
            The vector to add to this one.
            </param>
            <param name="result">
            The vector to store the result of the addition.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.DoSubtract(System.Single,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Subtracts a scalar from each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">
            The scalar to subtract.
            </param>
            <param name="result">
            The vector to store the result of the subtraction.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.DoSubtract(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Subtracts another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">
            The vector to subtract from this one.
            </param>
            <param name="result">
            The vector to store the result of the subtraction.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.DoNegate(MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Negates vector and saves result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.DoMultiply(System.Single,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Multiplies a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">
            The scalar to multiply.
            </param>
            <param name="result">
            The vector to store the result of the multiplication.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.DoDotProduct(MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Computes the dot product between this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of a[i]*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.DoModulus(System.Single,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.DoRemainder(System.Single,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.op_Addition(MathNet.Numerics.LinearAlgebra.Single.SparseVector,MathNet.Numerics.LinearAlgebra.Single.SparseVector)">
            <summary>
            Adds two <strong>Vectors</strong> together and returns the results.
            </summary>
            <param name="leftSide">One of the vectors to add.</param>
            <param name="rightSide">The other vector to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.op_UnaryNegation(MathNet.Numerics.LinearAlgebra.Single.SparseVector)">
            <summary>
            Returns a <strong>Vector</strong> containing the negated values of <paramref name="rightSide"/>.
            </summary>
            <param name="rightSide">The vector to get the values from.</param>
            <returns>A vector containing the negated values as <paramref name="rightSide"/>.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.op_Subtraction(MathNet.Numerics.LinearAlgebra.Single.SparseVector,MathNet.Numerics.LinearAlgebra.Single.SparseVector)">
            <summary>
            Subtracts two <strong>Vectors</strong> and returns the results.
            </summary>
            <param name="leftSide">The vector to subtract from.</param>
            <param name="rightSide">The vector to subtract.</param>
            <returns>The result of the subtraction.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.op_Multiply(MathNet.Numerics.LinearAlgebra.Single.SparseVector,System.Single)">
            <summary>
            Multiplies a vector with a scalar.
            </summary>
            <param name="leftSide">The vector to scale.</param>
            <param name="rightSide">The scalar value.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.op_Multiply(System.Single,MathNet.Numerics.LinearAlgebra.Single.SparseVector)">
            <summary>
            Multiplies a vector with a scalar.
            </summary>
            <param name="leftSide">The scalar value.</param>
            <param name="rightSide">The vector to scale.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.op_Multiply(MathNet.Numerics.LinearAlgebra.Single.SparseVector,MathNet.Numerics.LinearAlgebra.Single.SparseVector)">
            <summary>
            Computes the dot product between two <strong>Vectors</strong>.
            </summary>
            <param name="leftSide">The left row vector.</param>
            <param name="rightSide">The right column vector.</param>
            <returns>The dot product between the two vectors.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.op_Division(MathNet.Numerics.LinearAlgebra.Single.SparseVector,System.Single)">
            <summary>
            Divides a vector with a scalar.
            </summary>
            <param name="leftSide">The vector to divide.</param>
            <param name="rightSide">The scalar value.</param>
            <returns>The result of the division.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.op_Modulus(MathNet.Numerics.LinearAlgebra.Single.SparseVector,System.Single)">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            of each element of the vector of the given divisor.
            </summary>
            <param name="leftSide">The vector whose elements we want to compute the modulus of.</param>
            <param name="rightSide">The divisor to use,</param>
            <returns>The result of the calculation</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.AbsoluteMinimumIndex">
            <summary>
            Returns the index of the absolute minimum element.
            </summary>
            <returns>The index of absolute minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.AbsoluteMaximumIndex">
            <summary>
            Returns the index of the absolute maximum element.
            </summary>
            <returns>The index of absolute maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.MaximumIndex">
            <summary>
            Returns the index of the maximum element.
            </summary>
            <returns>The index of maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.MinimumIndex">
            <summary>
            Returns the index of the minimum element.
            </summary>
            <returns>The index of minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.Sum">
            <summary>
            Computes the sum of the vector's elements.
            </summary>
            <returns>The sum of the vector's elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.L1Norm">
            <summary>
            Calculates the L1 norm of the vector, also known as Manhattan norm.
            </summary>
            <returns>The sum of the absolute values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.InfinityNorm">
            <summary>
            Calculates the infinity norm of the vector.
            </summary>
            <returns>The maximum absolute value.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.Norm(System.Double)">
            <summary>
            Computes the p-Norm.
            </summary>
            <param name="p">The p value.</param>
            <returns>Scalar <c>ret = ( ∑|this[i]|^p )^(1/p)</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Pointwise multiplies this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to pointwise multiply with this one.</param>
            <param name="result">The vector to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.Parse(System.String,System.IFormatProvider)">
            <summary>
            Creates a float sparse vector based on a string. The string can be in the following formats (without the
            quotes): 'n', 'n,n,..', '(n,n,..)', '[n,n,...]', where n is a float.
            </summary>
            <returns>
            A float sparse vector containing the values specified by the given string.
            </returns>
            <param name="value">
            the string to parse.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific formatting information.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.TryParse(System.String,MathNet.Numerics.LinearAlgebra.Single.SparseVector@)">
            <summary>
            Converts the string representation of a real sparse vector to float-precision sparse vector equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a real vector to convert.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will be <c>null</c>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.SparseVector.TryParse(System.String,System.IFormatProvider,MathNet.Numerics.LinearAlgebra.Single.SparseVector@)">
            <summary>
            Converts the string representation of a real sparse vector to float-precision sparse vector equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a real vector to convert.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific formatting information about value.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will be <c>null</c>.
            </returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Single.Vector">
            <summary>
            <c>float</c> version of the <see cref="T:MathNet.Numerics.LinearAlgebra.Vector`1"/> class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.#ctor(MathNet.Numerics.LinearAlgebra.Storage.VectorStorage{System.Single})">
            <summary>
            Initializes a new instance of the Vector class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.CoerceZero(System.Double)">
            <summary>
            Set all values whose absolute value is smaller than the threshold to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoConjugate(MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Conjugates vector and save result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoNegate(MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Negates vector and saves result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoAdd(System.Single,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Adds a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">
            The scalar to add.
            </param>
            <param name="result">
            The vector to store the result of the addition.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoAdd(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Adds another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">
            The vector to add to this one.
            </param>
            <param name="result">
            The vector to store the result of the addition.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoSubtract(System.Single,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Subtracts a scalar from each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">
            The scalar to subtract.
            </param>
            <param name="result">
            The vector to store the result of the subtraction.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoSubtract(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Subtracts another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">
            The vector to subtract from this one.
            </param>
            <param name="result">
            The vector to store the result of the subtraction.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoMultiply(System.Single,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Multiplies a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">
            The scalar to multiply.
            </param>
            <param name="result">
            The vector to store the result of the multiplication.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoDivide(System.Single,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Divides each element of the vector by a scalar and stores the result in the result vector.
            </summary>
            <param name="divisor">
            The scalar to divide with.
            </param>
            <param name="result">
            The vector to store the result of the division.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoDivideByThis(System.Single,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Divides a scalar by each element of the vector and stores the result in the result vector.
            </summary>
            <param name="dividend">The scalar to divide.</param>
            <param name="result">The vector to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Pointwise multiplies this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to pointwise multiply with this one.</param>
            <param name="result">The vector to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Pointwise divide this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The vector to pointwise divide this one by.</param>
            <param name="result">The vector to store the result of the pointwise division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoPointwisePower(System.Single,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Pointwise raise this vector to an exponent and store the result into the result vector.
            </summary>
            <param name="exponent">The exponent to raise this vector values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoPointwisePower(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Pointwise raise this vector to an exponent vector and store the result into the result vector.
            </summary>
            <param name="exponent">The exponent vector to raise this vector values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoPointwiseModulus(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Pointwise canonical modulus, where the result has the sign of the divisor,
            of this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The pointwise denominator vector to use.</param>
            <param name="result">The result of the modulus.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoPointwiseRemainder(MathNet.Numerics.LinearAlgebra.Vector{System.Single},MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Pointwise remainder (% operator), where the result has the sign of the dividend,
            of this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The pointwise denominator vector to use.</param>
            <param name="result">The result of the modulus.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoPointwiseExp(MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Pointwise applies the exponential function to each value and stores the result into the result vector.
            </summary>
            <param name="result">The vector to store the result.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoPointwiseLog(MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Pointwise applies the natural logarithm function to each value and stores the result into the result vector.
            </summary>
            <param name="result">The vector to store the result.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoDotProduct(MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Computes the dot product between this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of a[i]*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoConjugateDotProduct(MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Computes the dot product between the conjugate of this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of conj(a[i])*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoModulus(System.Single,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoModulusByThis(System.Single,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given dividend for each element of the vector.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoRemainder(System.Single,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.DoRemainderByThis(System.Single,MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given dividend for each element of the vector.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.AbsoluteMinimum">
            <summary>
            Returns the value of the absolute minimum element.
            </summary>
            <returns>The value of the absolute minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.AbsoluteMinimumIndex">
            <summary>
            Returns the index of the absolute minimum element.
            </summary>
            <returns>The index of absolute minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.AbsoluteMaximum">
            <summary>
            Returns the value of the absolute maximum element.
            </summary>
            <returns>The value of the absolute maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.AbsoluteMaximumIndex">
            <summary>
            Returns the index of the absolute maximum element.
            </summary>
            <returns>The index of absolute maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.Sum">
            <summary>
            Computes the sum of the vector's elements.
            </summary>
            <returns>The sum of the vector's elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.L1Norm">
            <summary>
            Calculates the L1 norm of the vector, also known as Manhattan norm.
            </summary>
            <returns>The sum of the absolute values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.L2Norm">
            <summary>
            Calculates the L2 norm of the vector, also known as Euclidean norm.
            </summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.InfinityNorm">
            <summary>
            Calculates the infinity norm of the vector.
            </summary>
            <returns>The maximum absolute value.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.Norm(System.Double)">
            <summary>
            Computes the p-Norm.
            </summary>
            <param name="p">
            The p value.
            </param>
            <returns>
            <c>Scalar ret = ( ∑|At(i)|^p )^(1/p)</c>
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.MaximumIndex">
            <summary>
            Returns the index of the maximum element.
            </summary>
            <returns>The index of maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.MinimumIndex">
            <summary>
            Returns the index of the minimum element.
            </summary>
            <returns>The index of minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Single.Vector.Normalize(System.Double)">
            <summary>
            Normalizes this vector to a unit vector with respect to the p-norm.
            </summary>
            <param name="p">
            The p value.
            </param>
            <returns>
            This vector normalized to a unit vector with respect to the p-norm.
            </returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix">
            <summary>
            A Matrix class with dense storage. The underlying storage is a one dimensional array in column-major order (column by column).
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix._rowCount">
            <summary>
            Number of rows.
            </summary>
            <remarks>Using this instead of the RowCount property to speed up calculating
            a matrix index in the data array.</remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix._columnCount">
            <summary>
            Number of columns.
            </summary>
            <remarks>Using this instead of the ColumnCount property to speed up calculating
            a matrix index in the data array.</remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix._values">
            <summary>
            Gets the matrix's data.
            </summary>
            <value>The matrix's data.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.#ctor(MathNet.Numerics.LinearAlgebra.Storage.DenseColumnMajorMatrixStorage{System.Numerics.Complex})">
            <summary>
            Create a new dense matrix straight from an initialized matrix storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.#ctor(System.Int32)">
            <summary>
            Create a new square dense matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the order is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.#ctor(System.Int32,System.Int32)">
            <summary>
            Create a new dense matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the row or column count is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.#ctor(System.Int32,System.Int32,System.Numerics.Complex[])">
            <summary>
            Create a new dense matrix with the given number of rows and columns directly binding to a raw array.
            The array is assumed to be in column-major order (column by column) and is used directly without copying.
            Very efficient, but changes to the array and the matrix will affect each other.
            </summary>
            <seealso href="http://en.wikipedia.org/wiki/Row-major_order"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfMatrix(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Create a new dense matrix as a copy of the given other matrix.
            This new matrix will be independent from the other matrix.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfArray(System.Numerics.Complex[0:,0:])">
            <summary>
            Create a new dense matrix as a copy of the given two-dimensional array.
            This new matrix will be independent from the provided array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfIndexed(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Int32,System.Numerics.Complex}})">
            <summary>
            Create a new dense matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfIndexed(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Int32,System.Numerics.Complex}})">
            <summary>
            Create a new dense matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfColumnMajor(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Numerics.Complex})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable.
            The enumerable is assumed to be in column-major order (column by column).
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfColumns(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Numerics.Complex}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfColumns(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Numerics.Complex}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfColumnArrays(System.Numerics.Complex[][])">
            <summary>
            Create a new dense matrix as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfColumnArrays(System.Collections.Generic.IEnumerable{System.Numerics.Complex[]})">
            <summary>
            Create a new dense matrix as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfColumnVectors(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex}[])">
            <summary>
            Create a new dense matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfColumnVectors(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex}})">
            <summary>
            Create a new dense matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfRows(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Numerics.Complex}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfRows(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Numerics.Complex}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfRowArrays(System.Numerics.Complex[][])">
            <summary>
            Create a new dense matrix as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfRowArrays(System.Collections.Generic.IEnumerable{System.Numerics.Complex[]})">
            <summary>
            Create a new dense matrix as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfRowVectors(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex}[])">
            <summary>
            Create a new dense matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfRowVectors(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex}})">
            <summary>
            Create a new dense matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfDiagonalVector(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfDiagonalVector(System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfDiagonalArray(System.Numerics.Complex[])">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.OfDiagonalArray(System.Int32,System.Int32,System.Numerics.Complex[])">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.Create(System.Int32,System.Int32,System.Numerics.Complex)">
            <summary>
            Create a new dense matrix and initialize each value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.Create(System.Int32,System.Int32,System.Func{System.Int32,System.Int32,System.Numerics.Complex})">
            <summary>
            Create a new dense matrix and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.CreateDiagonal(System.Int32,System.Int32,System.Numerics.Complex)">
            <summary>
            Create a new diagonal dense matrix and initialize each diagonal value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.CreateDiagonal(System.Int32,System.Int32,System.Func{System.Int32,System.Numerics.Complex})">
            <summary>
            Create a new diagonal dense matrix and initialize each diagonal value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.CreateIdentity(System.Int32)">
            <summary>
            Create a new square sparse identity matrix where each diagonal value is set to One.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.CreateRandom(System.Int32,System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create a new dense matrix with values sampled from the provided random distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.Values">
            <summary>
            Gets the matrix's data.
            </summary>
            <value>The matrix's data.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.L1Norm">
            <summary>Calculates the induced L1 norm of this matrix.</summary>
            <returns>The maximum absolute column sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.InfinityNorm">
            <summary>Calculates the induced infinity norm of this matrix.</summary>
            <returns>The maximum absolute row sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.FrobeniusNorm">
            <summary>Calculates the entry-wise Frobenius norm of this matrix.</summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.DoNegate(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Negate each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the negation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.DoConjugate(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Complex conjugates each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the conjugation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.DoAdd(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Add a scalar to each element of the matrix and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to add.</param>
            <param name="result">The matrix to store the result of the addition.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.DoAdd(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Adds another matrix to this matrix.
            </summary>
            <param name="other">The matrix to add to this matrix.</param>
            <param name="result">The matrix to store the result of add</param>
            <exception cref="T:System.ArgumentNullException">If the other matrix is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.DoSubtract(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Subtracts a scalar from each element of the matrix and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to subtract.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.DoSubtract(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Subtracts another matrix from this matrix.
            </summary>
            <param name="other">The matrix to subtract.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.DoMultiply(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="scalar">The scalar to multiply the matrix with.</param>
            <param name="result">The matrix to store the result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Multiplies this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.DoTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies this matrix with transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.DoConjugateTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies this matrix with the conjugate transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Multiplies the transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.DoConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Multiplies the conjugate transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.DoConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.DoDivide(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Divides each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="divisor">The scalar to divide the matrix with.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Pointwise multiplies this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="other">The matrix to pointwise multiply with this one.</param>
            <param name="result">The matrix to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Pointwise divide this matrix by another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The matrix to pointwise divide this one by.</param>
            <param name="result">The matrix to store the result of the pointwise division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.DoPointwisePower(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Pointwise raise this matrix to an exponent and store the result into the result matrix.
            </summary>
            <param name="exponent">The exponent to raise this matrix values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.Trace">
            <summary>
            Computes the trace of this matrix.
            </summary>
            <returns>The trace of this matrix</returns>
            <exception cref="T:System.ArgumentException">If the matrix is not square</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.op_Addition(MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix,MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix)">
            <summary>
            Adds two matrices together and returns the results.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to add.</param>
            <param name="rightSide">The right matrix to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> don't have the same dimensions.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.op_UnaryPlus(MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix)">
            <summary>
            Returns a <strong>Matrix</strong> containing the same values of <paramref name="rightSide"/>.
            </summary>
            <param name="rightSide">The matrix to get the values from.</param>
            <returns>A matrix containing a the same values as <paramref name="rightSide"/>.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.op_Subtraction(MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix,MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix)">
            <summary>
            Subtracts two matrices together and returns the results.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to subtract.</param>
            <param name="rightSide">The right matrix to subtract.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> don't have the same dimensions.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.op_UnaryNegation(MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix)">
            <summary>
            Negates each element of the matrix.
            </summary>
            <param name="rightSide">The matrix to negate.</param>
            <returns>A matrix containing the negated values.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix,System.Numerics.Complex)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.op_Multiply(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix,MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix)">
            <summary>
            Multiplies two matrices.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to multiply.</param>
            <param name="rightSide">The right matrix to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the dimensions of <paramref name="leftSide"/> or <paramref name="rightSide"/> don't conform.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix,MathNet.Numerics.LinearAlgebra.Complex.DenseVector)">
            <summary>
            Multiplies a <strong>Matrix</strong> and a Vector.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The vector to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex.DenseVector,MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix)">
            <summary>
            Multiplies a Vector and a <strong>Matrix</strong>.
            </summary>
            <param name="leftSide">The vector to multiply.</param>
            <param name="rightSide">The matrix to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.op_Modulus(MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix,System.Numerics.Complex)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.IsSymmetric">
            <summary>
            Evaluates whether this matrix is symmetric.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix.IsHermitian">
            <summary>
            Evaluates whether this matrix is Hermitian (conjugate symmetric).
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.DenseVector">
            <summary>
            A vector using dense storage.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.DenseVector._length">
            <summary>
            Number of elements
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.DenseVector._values">
            <summary>
            Gets the vector's data.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.#ctor(MathNet.Numerics.LinearAlgebra.Storage.DenseVectorStorage{System.Numerics.Complex})">
            <summary>
            Create a new dense vector straight from an initialized vector storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.#ctor(System.Int32)">
            <summary>
            Create a new dense vector with the given length.
            All cells of the vector will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If length is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.#ctor(System.Numerics.Complex[])">
            <summary>
            Create a new dense vector directly binding to a raw array.
            The array is used directly without copying.
            Very efficient, but changes to the array and the vector will affect each other.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.OfVector(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Create a new dense vector as a copy of the given other vector.
            This new vector will be independent from the other vector.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.OfArray(System.Numerics.Complex[])">
            <summary>
            Create a new dense vector as a copy of the given array.
            This new vector will be independent from the array.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.OfEnumerable(System.Collections.Generic.IEnumerable{System.Numerics.Complex})">
            <summary>
            Create a new dense vector as a copy of the given enumerable.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.OfIndexedEnumerable(System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Numerics.Complex}})">
            <summary>
            Create a new dense vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.OfIndexedEnumerable(System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Numerics.Complex}})">
            <summary>
            Create a new dense vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.Create(System.Int32,System.Numerics.Complex)">
            <summary>
            Create a new dense vector and initialize each value using the provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.Create(System.Int32,System.Func{System.Int32,System.Numerics.Complex})">
            <summary>
            Create a new dense vector and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.CreateRandom(System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create a new dense vector with values sampled from the provided random distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.Values">
            <summary>
            Gets the vector's data.
            </summary>
            <value>The vector's data.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.op_Explicit(MathNet.Numerics.LinearAlgebra.Complex.DenseVector)~System.Numerics.Complex[]">
            <summary>
            Returns a reference to the internal data structure.
            </summary>
            <param name="vector">The <c>DenseVector</c> whose internal data we are
            returning.</param>
            <returns>
            A reference to the internal date of the given vector.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.op_Implicit(System.Numerics.Complex[])~MathNet.Numerics.LinearAlgebra.Complex.DenseVector">
            <summary>
            Returns a vector bound directly to a reference of the provided array.
            </summary>
            <param name="array">The array to bind to the <c>DenseVector</c> object.</param>
            <returns>
            A <c>DenseVector</c> whose values are bound to the given array.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.DoAdd(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Adds a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to add.</param>
            <param name="result">The vector to store the result of the addition.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.DoAdd(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Adds another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to add to this one.</param>
            <param name="result">The vector to store the result of the addition.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.op_Addition(MathNet.Numerics.LinearAlgebra.Complex.DenseVector,MathNet.Numerics.LinearAlgebra.Complex.DenseVector)">
            <summary>
            Adds two <strong>Vectors</strong> together and returns the results.
            </summary>
            <param name="leftSide">One of the vectors to add.</param>
            <param name="rightSide">The other vector to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.DoSubtract(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Subtracts a scalar from each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to subtract.</param>
            <param name="result">The vector to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.DoSubtract(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Subtracts another vector from this vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to subtract from this one.</param>
            <param name="result">The vector to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.op_UnaryNegation(MathNet.Numerics.LinearAlgebra.Complex.DenseVector)">
            <summary>
            Returns a <strong>Vector</strong> containing the negated values of <paramref name="rightSide"/>.
            </summary>
            <param name="rightSide">The vector to get the values from.</param>
            <returns>A vector containing the negated values as <paramref name="rightSide"/>.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.op_Subtraction(MathNet.Numerics.LinearAlgebra.Complex.DenseVector,MathNet.Numerics.LinearAlgebra.Complex.DenseVector)">
            <summary>
            Subtracts two <strong>Vectors</strong> and returns the results.
            </summary>
            <param name="leftSide">The vector to subtract from.</param>
            <param name="rightSide">The vector to subtract.</param>
            <returns>The result of the subtraction.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.DoNegate(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Negates vector and saves result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.DoConjugate(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Conjugates vector and save result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.DoMultiply(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Multiplies a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to multiply.</param>
            <param name="result">The vector to store the result of the multiplication.</param>
            <remarks></remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.DoDotProduct(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Computes the dot product between this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of a[i]*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.DoConjugateDotProduct(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Computes the dot product between the conjugate of this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of conj(a[i])*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex.DenseVector,System.Numerics.Complex)">
            <summary>
            Multiplies a vector with a complex.
            </summary>
            <param name="leftSide">The vector to scale.</param>
            <param name="rightSide">The Complex value.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.op_Multiply(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Complex.DenseVector)">
            <summary>
            Multiplies a vector with a complex.
            </summary>
            <param name="leftSide">The Complex value.</param>
            <param name="rightSide">The vector to scale.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex.DenseVector,MathNet.Numerics.LinearAlgebra.Complex.DenseVector)">
            <summary>
            Computes the dot product between two <strong>Vectors</strong>.
            </summary>
            <param name="leftSide">The left row vector.</param>
            <param name="rightSide">The right column vector.</param>
            <returns>The dot product between the two vectors.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.op_Division(MathNet.Numerics.LinearAlgebra.Complex.DenseVector,System.Numerics.Complex)">
            <summary>
            Divides a vector with a complex.
            </summary>
            <param name="leftSide">The vector to divide.</param>
            <param name="rightSide">The Complex value.</param>
            <returns>The result of the division.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.AbsoluteMinimumIndex">
            <summary>
            Returns the index of the absolute minimum element.
            </summary>
            <returns>The index of absolute minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.AbsoluteMaximumIndex">
            <summary>
            Returns the index of the absolute maximum element.
            </summary>
            <returns>The index of absolute maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.Sum">
            <summary>
            Computes the sum of the vector's elements.
            </summary>
            <returns>The sum of the vector's elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.L1Norm">
            <summary>
            Calculates the L1 norm of the vector, also known as Manhattan norm.
            </summary>
            <returns>The sum of the absolute values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.L2Norm">
            <summary>
            Calculates the L2 norm of the vector, also known as Euclidean norm.
            </summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.InfinityNorm">
            <summary>
            Calculates the infinity norm of the vector.
            </summary>
            <returns>The maximum absolute value.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.Norm(System.Double)">
            <summary>
            Computes the p-Norm.
            </summary>
            <param name="p">The p value.</param>
            <returns>Scalar <c>ret = ( ∑|this[i]|^p )^(1/p)</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Pointwise divide this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to pointwise divide this one by.</param>
            <param name="result">The vector to store the result of the pointwise division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Pointwise divide this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The vector to pointwise divide this one by.</param>
            <param name="result">The vector to store the result of the pointwise division.</param>
            <remarks></remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.DoPointwisePower(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Pointwise raise this vector to an exponent vector and store the result into the result vector.
            </summary>
            <param name="exponent">The exponent vector to raise this vector values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.Parse(System.String,System.IFormatProvider)">
            <summary>
            Creates a Complex dense vector based on a string. The string can be in the following formats (without the
            quotes): 'n', 'n;n;..', '(n;n;..)', '[n;n;...]', where n is a double.
            </summary>
            <returns>
            A Complex dense vector containing the values specified by the given string.
            </returns>
            <param name="value">
            the string to parse.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific formatting information.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.TryParse(System.String,MathNet.Numerics.LinearAlgebra.Complex.DenseVector@)">
            <summary>
            Converts the string representation of a complex dense vector to double-precision dense vector equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a complex vector to convert.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will be <c>null</c>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DenseVector.TryParse(System.String,System.IFormatProvider,MathNet.Numerics.LinearAlgebra.Complex.DenseVector@)">
            <summary>
            Converts the string representation of a complex dense vector to double-precision dense vector equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a complex vector to convert.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific formatting information about value.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will be <c>null</c>.
            </returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix">
            <summary>
            A matrix type for diagonal matrices.
            </summary>
            <remarks>
            Diagonal matrices can be non-square matrices but the diagonal always starts
            at element 0,0. A diagonal matrix will throw an exception if non diagonal
            entries are set. The exception to this is when the off diagonal elements are
            0.0 or NaN; these settings will cause no change to the diagonal matrix.
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix._data">
            <summary>
            Gets the matrix's data.
            </summary>
            <value>The matrix's data.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.#ctor(MathNet.Numerics.LinearAlgebra.Storage.DiagonalMatrixStorage{System.Numerics.Complex})">
            <summary>
            Create a new diagonal matrix straight from an initialized matrix storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.#ctor(System.Int32)">
            <summary>
            Create a new square diagonal matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the order is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.#ctor(System.Int32,System.Int32)">
            <summary>
            Create a new diagonal matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the row or column count is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.#ctor(System.Int32,System.Int32,System.Numerics.Complex)">
            <summary>
            Create a new diagonal matrix with the given number of rows and columns.
            All diagonal cells of the matrix will be initialized to the provided value, all non-diagonal ones to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the row or column count is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.#ctor(System.Int32,System.Int32,System.Numerics.Complex[])">
            <summary>
            Create a new diagonal matrix with the given number of rows and columns directly binding to a raw array.
            The array is assumed to contain the diagonal elements only and is used directly without copying.
            Very efficient, but changes to the array and the matrix will affect each other.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.OfMatrix(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Create a new diagonal matrix as a copy of the given other matrix.
            This new matrix will be independent from the other matrix.
            The matrix to copy from must be diagonal as well.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.OfArray(System.Numerics.Complex[0:,0:])">
            <summary>
            Create a new diagonal matrix as a copy of the given two-dimensional array.
            This new matrix will be independent from the provided array.
            The array to copy from must be diagonal as well.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.OfIndexedDiagonal(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Numerics.Complex}})">
            <summary>
            Create a new diagonal matrix and initialize each diagonal value from the provided indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.OfIndexedDiagonal(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Numerics.Complex}})">
            <summary>
            Create a new diagonal matrix and initialize each diagonal value from the provided indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.OfDiagonal(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Numerics.Complex})">
            <summary>
            Create a new diagonal matrix and initialize each diagonal value from the provided enumerable.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.Create(System.Int32,System.Int32,System.Func{System.Int32,System.Numerics.Complex})">
            <summary>
            Create a new diagonal matrix and initialize each diagonal value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.CreateIdentity(System.Int32)">
            <summary>
            Create a new square sparse identity matrix where each diagonal value is set to One.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.CreateRandom(System.Int32,System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create a new diagonal matrix with diagonal values sampled from the provided random distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.DoNegate(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Negate each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the negation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.DoConjugate(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Complex conjugates each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the conjugation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.DoAdd(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Adds another matrix to this matrix.
            </summary>
            <param name="other">The matrix to add to this matrix.</param>
            <param name="result">The matrix to store the result of the addition.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.DoSubtract(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Subtracts another matrix from this matrix.
            </summary>
            <param name="other">The matrix to subtract.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.DoMultiply(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="scalar">The scalar to multiply the matrix with.</param>
            <param name="result">The matrix to store the result of the multiplication.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Multiplies this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.DoTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies this matrix with transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.DoConjugateTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies this matrix with the conjugate transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.DoConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Multiplies the transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.DoConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Multiplies the conjugate transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.DoDivide(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Divides each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="divisor">The scalar to divide the matrix with.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.DoDivideByThis(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Divides a scalar by each element of the matrix and stores the result in the result matrix.
            </summary>
            <param name="dividend">The scalar to add.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.Determinant">
            <summary>
            Computes the determinant of this matrix.
            </summary>
            <returns>The determinant of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.Diagonal">
            <summary>
            Returns the elements of the diagonal in a <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.DenseVector"/>.
            </summary>
            <returns>The elements of the diagonal.</returns>
            <remarks>For non-square matrices, the method returns Min(Rows, Columns) elements where
            i == j (i is the row index, and j is the column index).</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.SetDiagonal(System.Numerics.Complex[])">
            <summary>
            Copies the values of the given array to the diagonal.
            </summary>
            <param name="source">The array to copy the values from. The length of the vector should be
            Min(Rows, Columns).</param>
            <exception cref="T:System.ArgumentException">If the length of <paramref name="source"/> does not
            equal Min(Rows, Columns).</exception>
            <remarks>For non-square matrices, the elements of <paramref name="source"/> are copied to
            this[i,i].</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.SetDiagonal(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Copies the values of the given <see cref="T:MathNet.Numerics.LinearAlgebra.Vector`1"/> to the diagonal.
            </summary>
            <param name="source">The vector to copy the values from. The length of the vector should be
            Min(Rows, Columns).</param>
            <exception cref="T:System.ArgumentException">If the length of <paramref name="source"/> does not
            equal Min(Rows, Columns).</exception>
            <remarks>For non-square matrices, the elements of <paramref name="source"/> are copied to
            this[i,i].</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.L1Norm">
            <summary>Calculates the induced L1 norm of this matrix.</summary>
            <returns>The maximum absolute column sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.L2Norm">
            <summary>Calculates the induced L2 norm of the matrix.</summary>
            <returns>The largest singular value of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.InfinityNorm">
            <summary>Calculates the induced infinity norm of this matrix.</summary>
            <returns>The maximum absolute row sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.FrobeniusNorm">
            <summary>Calculates the Frobenius norm of this matrix.</summary>
            <returns>The Frobenius norm of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.ConditionNumber">
            <summary>Calculates the condition number of this matrix.</summary>
            <returns>The condition number of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.Inverse">
            <summary>Computes the inverse of this matrix.</summary>
            <exception cref="T:System.ArgumentException">If <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix"/> is singular.</exception>
            <returns>The inverse of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.LowerTriangle">
            <summary>
            Returns a new matrix containing the lower triangle of this matrix.
            </summary>
            <returns>The lower triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.LowerTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Puts the lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.StrictlyLowerTriangle">
            <summary>
            Returns a new matrix containing the lower triangle of this matrix. The new matrix
            does not contain the diagonal elements of this matrix.
            </summary>
            <returns>The lower triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.StrictlyLowerTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Puts the strictly lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.UpperTriangle">
            <summary>
            Returns a new matrix containing the upper triangle of this matrix.
            </summary>
            <returns>The upper triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.UpperTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Puts the upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.StrictlyUpperTriangle">
            <summary>
            Returns a new matrix containing the upper triangle of this matrix. The new matrix
            does not contain the diagonal elements of this matrix.
            </summary>
            <returns>The upper triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.StrictlyUpperTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Puts the strictly upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.SubMatrix(System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Creates a matrix that contains the values from the requested sub-matrix.
            </summary>
            <param name="rowIndex">The row to start copying from.</param>
            <param name="rowCount">The number of rows to copy. Must be positive.</param>
            <param name="columnIndex">The column to start copying from.</param>
            <param name="columnCount">The number of columns to copy. Must be positive.</param>
            <returns>The requested sub-matrix.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If: <list><item><paramref name="rowIndex"/> is
            negative, or greater than or equal to the number of rows.</item>
            <item><paramref name="columnIndex"/> is negative, or greater than or equal to the number
            of columns.</item>
            <item><c>(columnIndex + columnLength) &gt;= Columns</c></item>
            <item><c>(rowIndex + rowLength) &gt;= Rows</c></item></list></exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="rowCount"/> or <paramref name="columnCount"/>
            is not positive.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.PermuteColumns(MathNet.Numerics.Permutation)">
            <summary>
            Permute the columns of a matrix according to a permutation.
            </summary>
            <param name="p">The column permutation to apply to this matrix.</param>
            <exception cref="T:System.InvalidOperationException">Always thrown</exception>
            <remarks>Permutation in diagonal matrix are senseless, because of matrix nature</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.PermuteRows(MathNet.Numerics.Permutation)">
            <summary>
            Permute the rows of a matrix according to a permutation.
            </summary>
            <param name="p">The row permutation to apply to this matrix.</param>
            <exception cref="T:System.InvalidOperationException">Always thrown</exception>
            <remarks>Permutation in diagonal matrix are senseless, because of matrix nature</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.IsSymmetric">
            <summary>
            Evaluates whether this matrix is symmetric.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.DiagonalMatrix.IsHermitian">
            <summary>
            Evaluates whether this matrix is Hermitian (conjugate symmetric).
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.Cholesky">
            <summary>
            <para>A class which encapsulates the functionality of a Cholesky factorization.</para>
            <para>For a symmetric, positive definite matrix A, the Cholesky factorization
            is an lower triangular matrix L so that A = L*L'.</para>
            </summary>
            <remarks>
            The computation of the Cholesky factorization is done at construction time. If the matrix is not symmetric
            or positive definite, the constructor will throw an exception.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Factorization.Cholesky.Determinant">
            <summary>
            Gets the determinant of the matrix for which the Cholesky matrix was computed.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Factorization.Cholesky.DeterminantLn">
            <summary>
            Gets the log determinant of the matrix for which the Cholesky matrix was computed.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseCholesky">
            <summary>
            <para>A class which encapsulates the functionality of a Cholesky factorization for dense matrices.</para>
            <para>For a symmetric, positive definite matrix A, the Cholesky factorization
            is an lower triangular matrix L so that A = L*L'.</para>
            </summary>
            <remarks>
            The computation of the Cholesky factorization is done at construction time. If the matrix is not symmetric
            or positive definite, the constructor will throw an exception.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseCholesky.Create(MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseCholesky"/> class. This object will compute the
            Cholesky factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not positive definite.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseCholesky.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A Cholesky factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseCholesky.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A Cholesky factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseCholesky.Factorize(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Calculates the Cholesky factorization of the input matrix.
            </summary>
            <param name="matrix">The matrix to be factorized<see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not positive definite.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="matrix"/> does not have the same dimensions as the existing factor.</exception>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseEvd">
            <summary>
            Eigenvalues and eigenvectors of a complex matrix.
            </summary>
            <remarks>
            If A is Hermitian, then A = V*D*V' where the eigenvalue matrix D is
            diagonal and the eigenvector matrix V is Hermitian.
            I.e. A = V*D*V' and V*VH=I.
            If A is not symmetric, then the eigenvalue matrix D is block diagonal
            with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues,
            lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda].  The
            columns of V represent the eigenvectors in the sense that A*V = V*D,
            i.e. A.Multiply(V) equals V.Multiply(D).  The matrix V may be badly
            conditioned, or even singular, so the validity of the equation
            A = V*D*Inverse(V) depends upon V.Condition().
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseEvd.Create(MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix,MathNet.Numerics.LinearAlgebra.Symmetricity)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseEvd"/> class. This object will compute the
            the eigenvalue decomposition when the constructor is called and cache it's decomposition.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="symmetricity">If it is known whether the matrix is symmetric or not the routine can skip checking it itself.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If EVD algorithm failed to converge with matrix <paramref name="matrix"/>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseEvd.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseEvd.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A EVD factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseGramSchmidt">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition Modified Gram-Schmidt Orthogonalization.</para>
            <para>Any complex square matrix A may be decomposed as A = QR where Q is an unitary mxn matrix and R is an nxn upper triangular matrix.</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by modified Gram-Schmidt Orthogonalization.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseGramSchmidt.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseGramSchmidt"/> class. This object creates an unitary matrix
            using the modified Gram-Schmidt method.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> row count is less then column count</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is rank deficient</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseGramSchmidt.Factorize(System.Numerics.Complex[],System.Int32,System.Int32,System.Numerics.Complex[])">
            <summary>
            Factorize matrix using the modified Gram-Schmidt method.
            </summary>
            <param name="q">Initial matrix. On exit is replaced by <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/> Q.</param>
            <param name="rowsQ">Number of rows in <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/> Q.</param>
            <param name="columnsQ">Number of columns in <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/> Q.</param>
            <param name="r">On exit is filled by <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/> R.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseGramSchmidt.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseGramSchmidt.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseLU">
            <summary>
            <para>A class which encapsulates the functionality of an LU factorization.</para>
            <para>For a matrix A, the LU factorization is a pair of lower triangular matrix L and
            upper triangular matrix U so that A = L*U.</para>
            </summary>
            <remarks>
            The computation of the LU factorization is done at construction time.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseLU.Create(MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseLU"/> class. This object will compute the
            LU factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseLU.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <c>AX = B</c>, with A LU factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>B</c>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>X</c>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseLU.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <c>Ax = b</c>, with A LU factorized.
            </summary>
            <param name="input">The right hand side vector, <c>b</c>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>x</c>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseLU.Inverse">
            <summary>
            Returns the inverse of this matrix. The inverse is calculated using LU decomposition.
            </summary>
            <returns>The inverse of this matrix.</returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseQR">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition.</para>
            <para>Any real square matrix A may be decomposed as A = QR where Q is an orthogonal matrix
            (its columns are orthogonal unit vectors meaning QTQ = I) and R is an upper triangular matrix
            (also called right triangular matrix).</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by Householder transformation.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseQR.Tau">
            <summary>
             Gets or sets Tau vector. Contains additional information on Q - used for native solver.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseQR.Create(MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix,MathNet.Numerics.LinearAlgebra.Factorization.QRMethod)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseQR"/> class. This object will compute the
            QR factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="method">The type of QR factorization to perform.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> row count is less then column count</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseQR.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseQR.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseSvd">
            <summary>
            <para>A class which encapsulates the functionality of the singular value decomposition (SVD) for <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix"/>.</para>
            <para>Suppose M is an m-by-n matrix whose entries are real numbers.
            Then there exists a factorization of the form M = UΣVT where:
            - U is an m-by-m unitary matrix;
            - Σ is m-by-n diagonal matrix with nonnegative real numbers on the diagonal;
            - VT denotes transpose of V, an n-by-n unitary matrix;
            Such a factorization is called a singular-value decomposition of M. A common convention is to order the diagonal
            entries Σ(i,i) in descending order. In this case, the diagonal matrix Σ is uniquely determined
            by M (though the matrices U and V are not). The diagonal entries of Σ are known as the singular values of M.</para>
            </summary>
            <remarks>
            The computation of the singular value decomposition is done at construction time.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseSvd.Create(MathNet.Numerics.LinearAlgebra.Complex.DenseMatrix,System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseSvd"/> class. This object will compute the
            the singular value decomposition when the constructor is called and cache it's decomposition.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="computeVectors">Compute the singular U and VT vectors or not.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If SVD algorithm failed to converge with matrix <paramref name="matrix"/>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseSvd.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.DenseSvd.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.Evd">
            <summary>
            Eigenvalues and eigenvectors of a real matrix.
            </summary>
            <remarks>
            If A is symmetric, then A = V*D*V' where the eigenvalue matrix D is
            diagonal and the eigenvector matrix V is orthogonal.
            I.e. A = V*D*V' and V*VT=I.
            If A is not symmetric, then the eigenvalue matrix D is block diagonal
            with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues,
            lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda].  The
            columns of V represent the eigenvectors in the sense that A*V = V*D,
            i.e. A.Multiply(V) equals V.Multiply(D).  The matrix V may be badly
            conditioned, or even singular, so the validity of the equation
            A = V*D*Inverse(V) depends upon V.Condition().
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Factorization.Evd.Determinant">
            <summary>
            Gets the absolute value of determinant of the square matrix for which the EVD was computed.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Factorization.Evd.Rank">
            <summary>
            Gets the effective numerical matrix rank.
            </summary>
            <value>The number of non-negligible singular values.</value>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Factorization.Evd.IsFullRank">
            <summary>
            Gets a value indicating whether the matrix is full rank or not.
            </summary>
            <value><c>true</c> if the matrix is full rank; otherwise <c>false</c>.</value>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.GramSchmidt">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition Modified Gram-Schmidt Orthogonalization.</para>
            <para>Any real square matrix A may be decomposed as A = QR where Q is an orthogonal mxn matrix and R is an nxn upper triangular matrix.</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by modified Gram-Schmidt Orthogonalization.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Factorization.GramSchmidt.Determinant">
            <summary>
            Gets the absolute determinant value of the matrix for which the QR matrix was computed.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Factorization.GramSchmidt.IsFullRank">
            <summary>
            Gets a value indicating whether the matrix is full rank or not.
            </summary>
            <value><c>true</c> if the matrix is full rank; otherwise <c>false</c>.</value>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.LU">
            <summary>
            <para>A class which encapsulates the functionality of an LU factorization.</para>
            <para>For a matrix A, the LU factorization is a pair of lower triangular matrix L and
            upper triangular matrix U so that A = L*U.</para>
            <para>In the Math.NET implementation we also store a set of pivot elements for increased
            numerical stability. The pivot elements encode a permutation matrix P such that P*A = L*U.</para>
            </summary>
            <remarks>
            The computation of the LU factorization is done at construction time.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Factorization.LU.Determinant">
            <summary>
            Gets the determinant of the matrix for which the LU factorization was computed.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.QR">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition.</para>
            <para>Any real square matrix A (m x n) may be decomposed as A = QR where Q is an orthogonal matrix
            (its columns are orthogonal unit vectors meaning QTQ = I) and R is an upper triangular matrix
            (also called right triangular matrix).</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by Householder transformation.
            If a <seealso cref="F:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod.Full"/> factorization is performed, the resulting Q matrix is an m x m matrix
            and the R matrix is an m x n matrix. If a <seealso cref="F:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod.Thin"/> factorization is performed, the
            resulting Q matrix is an m x n matrix and the R matrix is an n x n matrix.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Factorization.QR.Determinant">
            <summary>
            Gets the absolute determinant value of the matrix for which the QR matrix was computed.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Factorization.QR.IsFullRank">
            <summary>
            Gets a value indicating whether the matrix is full rank or not.
            </summary>
            <value><c>true</c> if the matrix is full rank; otherwise <c>false</c>.</value>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.Svd">
            <summary>
            <para>A class which encapsulates the functionality of the singular value decomposition (SVD).</para>
            <para>Suppose M is an m-by-n matrix whose entries are real numbers.
            Then there exists a factorization of the form M = UΣVT where:
            - U is an m-by-m unitary matrix;
            - Σ is m-by-n diagonal matrix with nonnegative real numbers on the diagonal;
            - VT denotes transpose of V, an n-by-n unitary matrix;
            Such a factorization is called a singular-value decomposition of M. A common convention is to order the diagonal
            entries Σ(i,i) in descending order. In this case, the diagonal matrix Σ is uniquely determined
            by M (though the matrices U and V are not). The diagonal entries of Σ are known as the singular values of M.</para>
            </summary>
            <remarks>
            The computation of the singular value decomposition is done at construction time.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Factorization.Svd.Rank">
            <summary>
            Gets the effective numerical matrix rank.
            </summary>
            <value>The number of non-negligible singular values.</value>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Factorization.Svd.L2Norm">
            <summary>
            Gets the two norm of the <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.
            </summary>
            <returns>The 2-norm of the <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.</returns>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Factorization.Svd.ConditionNumber">
            <summary>
            Gets the condition number <b>max(S) / min(S)</b>
            </summary>
            <returns>The condition number.</returns>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Factorization.Svd.Determinant">
            <summary>
            Gets the determinant of the square matrix for which the SVD was computed.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserCholesky">
            <summary>
            <para>A class which encapsulates the functionality of a Cholesky factorization for user matrices.</para>
            <para>For a symmetric, positive definite matrix A, the Cholesky factorization
            is an lower triangular matrix L so that A = L*L'.</para>
            </summary>
            <remarks>
            The computation of the Cholesky factorization is done at construction time. If the matrix is not symmetric
            or positive definite, the constructor will throw an exception.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserCholesky.DoCholesky(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Computes the Cholesky factorization in-place.
            </summary>
            <param name="factor">On entry, the matrix to factor. On exit, the Cholesky factor matrix</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="factor"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="factor"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="factor"/> is not positive definite.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserCholesky.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserCholesky"/> class. This object will compute the
            Cholesky factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not positive definite.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserCholesky.Factorize(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Calculates the Cholesky factorization of the input matrix.
            </summary>
            <param name="matrix">The matrix to be factorized<see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not positive definite.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="matrix"/> does not have the same dimensions as the existing factor.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserCholesky.DoCholeskyStep(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},System.Int32,System.Int32,System.Int32,System.Numerics.Complex[],System.Int32)">
            <summary>
            Calculate Cholesky step
            </summary>
            <param name="data">Factor matrix</param>
            <param name="rowDim">Number of rows</param>
            <param name="firstCol">Column start</param>
            <param name="colLimit">Total columns</param>
            <param name="multipliers">Multipliers calculated previously</param>
            <param name="availableCores">Number of available processors</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserCholesky.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A Cholesky factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserCholesky.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A Cholesky factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserEvd">
            <summary>
            Eigenvalues and eigenvectors of a complex matrix.
            </summary>
            <remarks>
            If A is Hermitian, then A = V*D*V' where the eigenvalue matrix D is
            diagonal and the eigenvector matrix V is Hermitian.
            I.e. A = V*D*V' and V*VH=I.
            If A is not symmetric, then the eigenvalue matrix D is block diagonal
            with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues,
            lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda].  The
            columns of V represent the eigenvectors in the sense that A*V = V*D,
            i.e. A.Multiply(V) equals V.Multiply(D).  The matrix V may be badly
            conditioned, or even singular, so the validity of the equation
            A = V*D*Inverse(V) depends upon V.Condition().
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserEvd.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Symmetricity)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserEvd"/> class. This object will compute the
            the eigenvalue decomposition when the constructor is called and cache it's decomposition.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="symmetricity">If it is known whether the matrix is symmetric or not the routine can skip checking it itself.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If EVD algorithm failed to converge with matrix <paramref name="matrix"/>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserEvd.SymmetricTridiagonalize(System.Numerics.Complex[0:,0:],System.Double[],System.Double[],System.Numerics.Complex[],System.Int32)">
            <summary>
            Reduces a complex Hermitian matrix to a real symmetric tridiagonal matrix using unitary similarity transformations.
            </summary>
            <param name="matrixA">Source matrix to reduce</param>
            <param name="d">Output: Arrays for internal storage of real parts of eigenvalues</param>
            <param name="e">Output: Arrays for internal storage of imaginary parts of eigenvalues</param>
            <param name="tau">Output: Arrays that contains further information about the transformations.</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures HTRIDI by
            Smith, Boyle, Dongarra, Garbow, Ikebe, Klema, Moler, and Wilkinson, Handbook for
            Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserEvd.SymmetricDiagonalize(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},System.Double[],System.Double[],System.Int32)">
            <summary>
            Symmetric tridiagonal QL algorithm.
            </summary>
            <param name="eigenVectors">The eigen vectors to work on.</param>
            <param name="d">Arrays for internal storage of real parts of eigenvalues</param>
            <param name="e">Arrays for internal storage of imaginary parts of eigenvalues</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures tql2, by
            Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
            Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserEvd.SymmetricUntridiagonalize(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},System.Numerics.Complex[0:,0:],System.Numerics.Complex[],System.Int32)">
            <summary>
            Determines eigenvectors by undoing the symmetric tridiagonalize transformation
            </summary>
            <param name="eigenVectors">The eigen vectors to work on.</param>
            <param name="matrixA">Previously tridiagonalized matrix by <see cref="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserEvd.SymmetricTridiagonalize(System.Numerics.Complex[0:,0:],System.Double[],System.Double[],System.Numerics.Complex[],System.Int32)"/>.</param>
            <param name="tau">Contains further information about the transformations</param>
            <param name="order">Input matrix order</param>
            <remarks>This is derived from the Algol procedures HTRIBK, by
            by Smith, Boyle, Dongarra, Garbow, Ikebe, Klema, Moler, and Wilkinson, Handbook for
            Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserEvd.NonsymmetricReduceToHessenberg(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},System.Numerics.Complex[0:,0:],System.Int32)">
            <summary>
            Nonsymmetric reduction to Hessenberg form.
            </summary>
            <param name="eigenVectors">The eigen vectors to work on.</param>
            <param name="matrixH">Array for internal storage of nonsymmetric Hessenberg form.</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures orthes and ortran,
            by Martin and Wilkinson, Handbook for Auto. Comp.,
            Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutines in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserEvd.NonsymmetricReduceHessenberToRealSchur(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},System.Numerics.Complex[0:,0:],System.Int32)">
            <summary>
            Nonsymmetric reduction from Hessenberg to real Schur form.
            </summary>
            <param name="eigenVectors">The eigen vectors to work on.</param>
            <param name="eigenValues">The eigen values to work on.</param>
            <param name="matrixH">Array for internal storage of nonsymmetric Hessenberg form.</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedure hqr2,
            by Martin and Wilkinson, Handbook for Auto. Comp.,
            Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserEvd.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserEvd.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A EVD factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserGramSchmidt">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition Modified Gram-Schmidt Orthogonalization.</para>
            <para>Any complex square matrix A may be decomposed as A = QR where Q is an unitary mxn matrix and R is an nxn upper triangular matrix.</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by modified Gram-Schmidt Orthogonalization.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserGramSchmidt.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserGramSchmidt"/> class. This object creates an unitary matrix
            using the modified Gram-Schmidt method.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> row count is less then column count</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is rank deficient</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserGramSchmidt.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserGramSchmidt.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserLU">
            <summary>
            <para>A class which encapsulates the functionality of an LU factorization.</para>
            <para>For a matrix A, the LU factorization is a pair of lower triangular matrix L and
            upper triangular matrix U so that A = L*U.</para>
            </summary>
            <remarks>
            The computation of the LU factorization is done at construction time.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserLU.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserLU"/> class. This object will compute the
            LU factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserLU.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <c>AX = B</c>, with A LU factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>B</c>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>X</c>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserLU.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <c>Ax = b</c>, with A LU factorized.
            </summary>
            <param name="input">The right hand side vector, <c>b</c>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>x</c>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserLU.Inverse">
            <summary>
            Returns the inverse of this matrix. The inverse is calculated using LU decomposition.
            </summary>
            <returns>The inverse of this matrix.</returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserQR">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition.</para>
            <para>Any real square matrix A may be decomposed as A = QR where Q is an orthogonal matrix
            (its columns are orthogonal unit vectors meaning QTQ = I) and R is an upper triangular matrix
            (also called right triangular matrix).</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by Householder transformation.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserQR.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Factorization.QRMethod)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserQR"/> class. This object will compute the
            QR factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="method">The QR factorization method to use.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserQR.GenerateColumn(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},System.Int32,System.Int32)">
            <summary>
            Generate column from initial matrix to work array
            </summary>
            <param name="a">Initial matrix</param>
            <param name="row">The first row</param>
            <param name="column">Column index</param>
            <returns>Generated vector</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserQR.ComputeQR(System.Numerics.Complex[],MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},System.Int32,System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Perform calculation of Q or R
            </summary>
            <param name="u">Work array</param>
            <param name="a">Q or R matrices</param>
            <param name="rowStart">The first row</param>
            <param name="rowDim">The last row</param>
            <param name="columnStart">The first column</param>
            <param name="columnDim">The last column</param>
            <param name="availableCores">Number of available CPUs</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserQR.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserQR.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserSvd">
            <summary>
            <para>A class which encapsulates the functionality of the singular value decomposition (SVD) for <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.</para>
            <para>Suppose M is an m-by-n matrix whose entries are real numbers.
            Then there exists a factorization of the form M = UΣVT where:
            - U is an m-by-m unitary matrix;
            - Σ is m-by-n diagonal matrix with nonnegative real numbers on the diagonal;
            - VT denotes transpose of V, an n-by-n unitary matrix;
            Such a factorization is called a singular-value decomposition of M. A common convention is to order the diagonal
            entries Σ(i,i) in descending order. In this case, the diagonal matrix Σ is uniquely determined
            by M (though the matrices U and V are not). The diagonal entries of Σ are known as the singular values of M.</para>
            </summary>
            <remarks>
            The computation of the singular value decomposition is done at construction time.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserSvd.Create(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserSvd"/> class. This object will compute the
            the singular value decomposition when the constructor is called and cache it's decomposition.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="computeVectors">Compute the singular U and VT vectors or not.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserSvd.Csign(System.Numerics.Complex,System.Numerics.Complex)">
            <summary>
            Calculates absolute value of <paramref name="z1"/> multiplied on signum function of <paramref name="z2"/>
            </summary>
            <param name="z1">Complex value z1</param>
            <param name="z2">Complex value z2</param>
            <returns>Result multiplication of signum function and absolute value</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserSvd.Swap(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},System.Int32,System.Int32,System.Int32)">
            <summary>
            Interchanges two vectors  <paramref name="columnA"/>  and  <paramref name="columnB"/>
            </summary>
            <param name="a">Source matrix</param>
            <param name="rowCount">The number of rows in <paramref name="a"/></param>
            <param name="columnA">Column A index to swap</param>
            <param name="columnB">Column B index to swap</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserSvd.CscalColumn(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},System.Int32,System.Int32,System.Int32,System.Numerics.Complex)">
            <summary>
            Scale column <paramref name="column"/> by <paramref name="z"/> starting from row <paramref name="rowStart"/>
            </summary>
            <param name="a">Source matrix</param>
            <param name="rowCount">The number of rows in <paramref name="a"/> </param>
            <param name="column">Column to scale</param>
            <param name="rowStart">Row to scale from</param>
            <param name="z">Scale value</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserSvd.CscalVector(System.Numerics.Complex[],System.Int32,System.Numerics.Complex)">
            <summary>
            Scale vector <paramref name="a"/> by <paramref name="z"/> starting from index <paramref name="start"/>
            </summary>
            <param name="a">Source vector</param>
            <param name="start">Row to scale from</param>
            <param name="z">Scale value</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserSvd.Srotg(System.Double@,System.Double@,System.Double@,System.Double@)">
            <summary>
            Given the Cartesian coordinates (da, db) of a point p, these function return the parameters da, db, c, and s
            associated with the Givens rotation that zeros the y-coordinate of the point.
            </summary>
            <param name="da">Provides the x-coordinate of the point p. On exit contains the parameter r associated with the Givens rotation</param>
            <param name="db">Provides the y-coordinate of the point p. On exit contains the parameter z associated with the Givens rotation</param>
            <param name="c">Contains the parameter c associated with the Givens rotation</param>
            <param name="s">Contains the parameter s associated with the Givens rotation</param>
            <remarks>This is equivalent to the DROTG LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserSvd.Cnrm2Column(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},System.Int32,System.Int32,System.Int32)">
            <summary>
            Calculate Norm 2 of the column <paramref name="column"/> in matrix <paramref name="a"/> starting from row <paramref name="rowStart"/>
            </summary>
            <param name="a">Source matrix</param>
            <param name="rowCount">The number of rows in <paramref name="a"/></param>
            <param name="column">Column index</param>
            <param name="rowStart">Start row index</param>
            <returns>Norm2 (Euclidean norm) of the column</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserSvd.Cnrm2Vector(System.Numerics.Complex[],System.Int32)">
            <summary>
            Calculate Norm 2 of the vector <paramref name="a"/> starting from index <paramref name="rowStart"/>
            </summary>
            <param name="a">Source vector</param>
            <param name="rowStart">Start index</param>
            <returns>Norm2 (Euclidean norm) of the vector</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserSvd.Cdotc(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Calculate dot product of <paramref name="columnA"/> and <paramref name="columnB"/> conjugating the first vector.
            </summary>
            <param name="a">Source matrix</param>
            <param name="rowCount">The number of rows in <paramref name="a"/></param>
            <param name="columnA">Index of column A</param>
            <param name="columnB">Index of column B</param>
            <param name="rowStart">Starting row index</param>
            <returns>Dot product value</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserSvd.Csrot(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},System.Int32,System.Int32,System.Int32,System.Double,System.Double)">
            <summary>
            Performs rotation of points in the plane. Given two vectors x <paramref name="columnA"/> and y <paramref name="columnB"/>,
            each vector element of these vectors is replaced as follows: x(i) = c*x(i) + s*y(i); y(i) = c*y(i) - s*x(i)
            </summary>
            <param name="a">Source matrix</param>
            <param name="rowCount">The number of rows in <paramref name="a"/></param>
            <param name="columnA">Index of column A</param>
            <param name="columnB">Index of column B</param>
            <param name="c">scalar cos value</param>
            <param name="s">scalar sin value</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserSvd.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Factorization.UserSvd.Solve(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Matrix">
            <summary>
            <c>Complex</c> version of the <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/> class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.#ctor(MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage{System.Numerics.Complex})">
            <summary>
            Initializes a new instance of the Matrix class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.CoerceZero(System.Double)">
            <summary>
            Set all values whose absolute value is smaller than the threshold to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.ConjugateTranspose">
            <summary>
            Returns the conjugate transpose of this matrix.
            </summary>
            <returns>The conjugate transpose of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.ConjugateTranspose(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Puts the conjugate transpose of this matrix into the result matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoConjugate(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Complex conjugates each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the conjugation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoNegate(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Negate each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the negation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoAdd(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Add a scalar to each element of the matrix and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to add.</param>
            <param name="result">The matrix to store the result of the addition.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoAdd(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Adds another matrix to this matrix.
            </summary>
            <param name="other">The matrix to add to this matrix.</param>
            <param name="result">The matrix to store the result of the addition.</param>
            <exception cref="T:System.ArgumentNullException">If the other matrix is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoSubtract(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Subtracts a scalar from each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to subtract.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoSubtract(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Subtracts another matrix from this matrix.
            </summary>
            <param name="other">The matrix to subtract to this matrix.</param>
            <param name="result">The matrix to store the result of subtraction.</param>
            <exception cref="T:System.ArgumentNullException">If the other matrix is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoMultiply(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="scalar">The scalar to multiply the matrix with.</param>
            <param name="result">The matrix to store the result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Multiplies this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoDivide(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Divides each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="divisor">The scalar to divide the matrix with.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoDivideByThis(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Divides a scalar by each element of the matrix and stores the result in the result matrix.
            </summary>
            <param name="dividend">The scalar to divide by each element of the matrix.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies this matrix with transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoConjugateTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies this matrix with the conjugate transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Multiplies the transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Multiplies the conjugate transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Pointwise multiplies this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="other">The matrix to pointwise multiply with this one.</param>
            <param name="result">The matrix to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Pointwise divide this matrix by another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The matrix to pointwise divide this one by.</param>
            <param name="result">The matrix to store the result of the pointwise division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoPointwisePower(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Pointwise raise this matrix to an exponent and store the result into the result matrix.
            </summary>
            <param name="exponent">The exponent to raise this matrix values to.</param>
            <param name="result">The matrix to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoPointwisePower(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Pointwise raise this matrix to an exponent and store the result into the result matrix.
            </summary>
            <param name="exponent">The exponent to raise this matrix values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoPointwiseModulus(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Pointwise canonical modulus, where the result has the sign of the divisor,
            of this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The pointwise denominator matrix to use</param>
            <param name="result">The result of the modulus.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoPointwiseRemainder(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Pointwise remainder (% operator), where the result has the sign of the dividend,
            of this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The pointwise denominator matrix to use</param>
            <param name="result">The result of the modulus.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoModulus(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoModulusByThis(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given dividend for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoRemainder(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoRemainderByThis(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given dividend for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoPointwiseExp(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Pointwise applies the exponential function to each value and stores the result into the result matrix.
            </summary>
            <param name="result">The matrix to store the result.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.DoPointwiseLog(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Pointwise applies the natural logarithm function to each value and stores the result into the result matrix.
            </summary>
            <param name="result">The matrix to store the result.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.PseudoInverse">
            <summary>
            Computes the Moore-Penrose Pseudo-Inverse of this matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.Trace">
            <summary>
            Computes the trace of this matrix.
            </summary>
            <returns>The trace of this matrix</returns>
            <exception cref="T:System.ArgumentException">If the matrix is not square</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.L1Norm">
            <summary>Calculates the induced L1 norm of this matrix.</summary>
            <returns>The maximum absolute column sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.InfinityNorm">
            <summary>Calculates the induced infinity norm of this matrix.</summary>
            <returns>The maximum absolute row sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.FrobeniusNorm">
            <summary>Calculates the entry-wise Frobenius norm of this matrix.</summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.RowNorms(System.Double)">
            <summary>
            Calculates the p-norms of all row vectors.
            Typical values for p are 1.0 (L1, Manhattan norm), 2.0 (L2, Euclidean norm) and positive infinity (infinity norm)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.ColumnNorms(System.Double)">
            <summary>
            Calculates the p-norms of all column vectors.
            Typical values for p are 1.0 (L1, Manhattan norm), 2.0 (L2, Euclidean norm) and positive infinity (infinity norm)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.NormalizeRows(System.Double)">
            <summary>
            Normalizes all row vectors to a unit p-norm.
            Typical values for p are 1.0 (L1, Manhattan norm), 2.0 (L2, Euclidean norm) and positive infinity (infinity norm)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.NormalizeColumns(System.Double)">
            <summary>
            Normalizes all column vectors to a unit p-norm.
            Typical values for p are 1.0 (L1, Manhattan norm), 2.0 (L2, Euclidean norm) and positive infinity (infinity norm)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.RowSums">
            <summary>
            Calculates the value sum of each row vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.RowAbsoluteSums">
            <summary>
            Calculates the absolute value sum of each row vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.ColumnSums">
            <summary>
            Calculates the value sum of each column vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.ColumnAbsoluteSums">
            <summary>
            Calculates the absolute value sum of each column vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Matrix.IsHermitian">
            <summary>
            Evaluates whether this matrix is Hermitian (conjugate symmetric).
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Solvers.BiCgStab">
            <summary>
            A Bi-Conjugate Gradient stabilized iterative matrix solver.
            </summary>
            <remarks>
            <para>
            The Bi-Conjugate Gradient Stabilized (BiCGStab) solver is an 'improvement'
            of the standard Conjugate Gradient (CG) solver. Unlike the CG solver the
            BiCGStab can be used on non-symmetric matrices. <br/>
            Note that much of the success of the solver depends on the selection of the
            proper preconditioner.
            </para>
            <para>
            The Bi-CGSTAB algorithm was taken from: <br/>
            Templates for the solution of linear systems: Building blocks
            for iterative methods
            <br/>
            Richard Barrett, Michael Berry, Tony F. Chan, James Demmel,
            June M. Donato, Jack Dongarra, Victor Eijkhout, Roldan Pozo,
            Charles Romine and Henk van der Vorst
            <br/>
            Url: <a href="http://www.netlib.org/templates/Templates.html">http://www.netlib.org/templates/Templates.html</a>
            <br/>
            Algorithm is described in Chapter 2, section 2.3.8, page 27
            </para>
            <para>
            The example code below provides an indication of the possible use of the
            solver.
            </para>
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.BiCgStab.CalculateTrueResidual(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Calculates the <c>true</c> residual of the matrix equation Ax = b according to: residual = b - Ax
            </summary>
            <param name="matrix">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Matrix"/> A.</param>
            <param name="residual">Residual values in <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Vector"/>.</param>
            <param name="x">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Vector"/> x.</param>
            <param name="b">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Vector"/> b.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.BiCgStab.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{System.Numerics.Complex})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Matrix"/>, <c>A</c>.</param>
            <param name="input">The solution <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Vector"/>, <c>b</c>.</param>
            <param name="result">The result <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Vector"/>, <c>x</c>.</param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Solvers.CompositeSolver">
            <summary>
            A composite matrix solver. The actual solver is made by a sequence of
            matrix solvers.
            </summary>
            <remarks>
            <para>
            Solver based on:<br />
            Faster PDE-based simulations using robust composite linear solvers<br />
            S. Bhowmicka, P. Raghavan a,*, L. McInnes b, B. Norris<br />
            Future Generation Computer Systems, Vol 20, 2004, pp 373�387<br />
            </para>
            <para>
            Note that if an iterator is passed to this solver it will be used for all the sub-solvers.
            </para>
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.Solvers.CompositeSolver._solvers">
            <summary>
            The collection of solvers that will be used
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.CompositeSolver.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{System.Numerics.Complex})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient matrix, <c>A</c>.</param>
            <param name="input">The solution vector, <c>b</c></param>
            <param name="result">The result vector, <c>x</c></param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Solvers.DiagonalPreconditioner">
            <summary>
            A diagonal preconditioner. The preconditioner uses the inverse
            of the matrix diagonal as preconditioning values.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.Solvers.DiagonalPreconditioner._inverseDiagonals">
            <summary>
            The inverse of the matrix diagonal.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.DiagonalPreconditioner.DiagonalEntries">
            <summary>
            Returns the decomposed matrix diagonal.
            </summary>
            <returns>The matrix diagonal.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.DiagonalPreconditioner.Initialize(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Initializes the preconditioner and loads the internal data structures.
            </summary>
            <param name="matrix">
            The <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Matrix"/> upon which this preconditioner is based.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <see langword="null" />. </exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.DiagonalPreconditioner.Approximate(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Approximates the solution to the matrix equation <b>Ax = b</b>.
            </summary>
            <param name="rhs">The right hand side vector.</param>
            <param name="lhs">The left hand side vector. Also known as the result vector.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Solvers.GpBiCg">
            <summary>
            A Generalized Product Bi-Conjugate Gradient iterative matrix solver.
            </summary>
            <remarks>
            <para>
            The Generalized Product Bi-Conjugate Gradient (GPBiCG) solver is an
            alternative version of the Bi-Conjugate Gradient stabilized (CG) solver.
            Unlike the CG solver the GPBiCG solver can be used on
            non-symmetric matrices. <br/>
            Note that much of the success of the solver depends on the selection of the
            proper preconditioner.
            </para>
            <para>
            The GPBiCG algorithm was taken from: <br/>
            GPBiCG(m,l): A hybrid of BiCGSTAB and GPBiCG methods with
            efficiency and robustness
            <br/>
            S. Fujino
            <br/>
            Applied Numerical Mathematics, Volume 41, 2002, pp 107 - 117
            <br/>
            </para>
            <para>
            The example code below provides an indication of the possible use of the
            solver.
            </para>
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.Solvers.GpBiCg._numberOfBiCgStabSteps">
            <summary>
            Indicates the number of <c>BiCGStab</c> steps should be taken
            before switching.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.Solvers.GpBiCg._numberOfGpbiCgSteps">
            <summary>
            Indicates the number of <c>GPBiCG</c> steps should be taken
            before switching.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Solvers.GpBiCg.NumberOfBiCgStabSteps">
            <summary>
            Gets or sets the number of steps taken with the <c>BiCgStab</c> algorithm
            before switching over to the <c>GPBiCG</c> algorithm.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Solvers.GpBiCg.NumberOfGpBiCgSteps">
            <summary>
            Gets or sets the number of steps taken with the <c>GPBiCG</c> algorithm
            before switching over to the <c>BiCgStab</c> algorithm.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.GpBiCg.CalculateTrueResidual(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Calculates the <c>true</c> residual of the matrix equation Ax = b according to: residual = b - Ax
            </summary>
            <param name="matrix">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Matrix"/> A.</param>
            <param name="residual">Residual values in <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Vector"/>.</param>
            <param name="x">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Vector"/> x.</param>
            <param name="b">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Vector"/> b.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.GpBiCg.ShouldRunBiCgStabSteps(System.Int32)">
            <summary>
            Decide if to do steps with BiCgStab
            </summary>
            <param name="iterationNumber">Number of iteration</param>
            <returns><c>true</c> if yes, otherwise <c>false</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.GpBiCg.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{System.Numerics.Complex})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient matrix, <c>A</c>.</param>
            <param name="input">The solution vector, <c>b</c></param>
            <param name="result">The result vector, <c>x</c></param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILU0Preconditioner">
            <summary>
            An incomplete, level 0, LU factorization preconditioner.
            </summary>
            <remarks>
            The ILU(0) algorithm was taken from: <br/>
            Iterative methods for sparse linear systems <br/>
            Yousef Saad <br/>
            Algorithm is described in Chapter 10, section 10.3.2, page 275 <br/>
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILU0Preconditioner._decompositionLU">
            <summary>
            The matrix holding the lower (L) and upper (U) matrices. The
            decomposition matrices are combined to reduce storage.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILU0Preconditioner.UpperTriangle">
            <summary>
            Returns the upper triagonal matrix that was created during the LU decomposition.
            </summary>
            <returns>A new matrix containing the upper triagonal elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILU0Preconditioner.LowerTriangle">
            <summary>
            Returns the lower triagonal matrix that was created during the LU decomposition.
            </summary>
            <returns>A new matrix containing the lower triagonal elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILU0Preconditioner.Initialize(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Initializes the preconditioner and loads the internal data structures.
            </summary>
            <param name="matrix">The matrix upon which the preconditioner is based. </param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILU0Preconditioner.Approximate(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Approximates the solution to the matrix equation <b>Ax = b</b>.
            </summary>
            <param name="rhs">The right hand side vector.</param>
            <param name="lhs">The left hand side vector. Also known as the result vector.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner">
            <summary>
            This class performs an Incomplete LU factorization with drop tolerance
            and partial pivoting. The drop tolerance indicates which additional entries
            will be dropped from the factorized LU matrices.
            </summary>
            <remarks>
            The ILUTP-Mem algorithm was taken from: <br/>
            ILUTP_Mem: a Space-Efficient Incomplete LU Preconditioner
            <br/>
            Tzu-Yi Chen, Department of Mathematics and Computer Science, <br/>
            Pomona College, Claremont CA 91711, USA <br/>
            Published in: <br/>
            Lecture Notes in Computer Science <br/>
            Volume 3046 / 2004 <br/>
            pp. 20 - 28 <br/>
            Algorithm is described in Section 2, page 22
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner.DefaultFillLevel">
            <summary>
            The default fill level.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner.DefaultDropTolerance">
            <summary>
            The default drop tolerance.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner._upper">
            <summary>
            The decomposed upper triangular matrix.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner._lower">
            <summary>
            The decomposed lower triangular matrix.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner._pivots">
            <summary>
            The array containing the pivot values.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner._fillLevel">
            <summary>
            The fill level.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner._dropTolerance">
            <summary>
            The drop tolerance.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner._pivotTolerance">
            <summary>
            The pivot tolerance.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner"/> class with the default settings.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner.#ctor(System.Double,System.Double,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner"/> class with the specified settings.
            </summary>
            <param name="fillLevel">
            The amount of fill that is allowed in the matrix. The value is a fraction of
            the number of non-zero entries in the original matrix. Values should be positive.
            </param>
            <param name="dropTolerance">
            The absolute drop tolerance which indicates below what absolute value an entry
            will be dropped from the matrix. A drop tolerance of 0.0 means that no values
            will be dropped. Values should always be positive.
            </param>
            <param name="pivotTolerance">
            The pivot tolerance which indicates at what level pivoting will take place. A
            value of 0.0 means that no pivoting will take place.
            </param>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner.FillLevel">
            <summary>
            Gets or sets the amount of fill that is allowed in the matrix. The
            value is a fraction of the number of non-zero entries in the original
            matrix. The standard value is 200.
            </summary>
            <remarks>
            <para>
            Values should always be positive and can be higher than 1.0. A value lower
            than 1.0 means that the eventual preconditioner matrix will have fewer
            non-zero entries as the original matrix. A value higher than 1.0 means that
            the eventual preconditioner can have more non-zero values than the original
            matrix.
            </para>
            <para>
            Note that any changes to the <b>FillLevel</b> after creating the preconditioner
            will invalidate the created preconditioner and will require a re-initialization of
            the preconditioner.
            </para>
            </remarks>
            <exception cref="T:System.ArgumentOutOfRangeException">Thrown if a negative value is provided.</exception>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner.DropTolerance">
            <summary>
            Gets or sets the absolute drop tolerance which indicates below what absolute value
            an entry will be dropped from the matrix. The standard value is 0.0001.
            </summary>
            <remarks>
            <para>
            The values should always be positive and can be larger than 1.0. A low value will
            keep more small numbers in the preconditioner matrix. A high value will remove
            more small numbers from the preconditioner matrix.
            </para>
            <para>
            Note that any changes to the <b>DropTolerance</b> after creating the preconditioner
            will invalidate the created preconditioner and will require a re-initialization of
            the preconditioner.
            </para>
            </remarks>
            <exception cref="T:System.ArgumentOutOfRangeException">Thrown if a negative value is provided.</exception>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner.PivotTolerance">
            <summary>
            Gets or sets the pivot tolerance which indicates at what level pivoting will
            take place. The standard value is 0.0 which means pivoting will never take place.
            </summary>
            <remarks>
            <para>
            The pivot tolerance is used to calculate if pivoting is necessary. Pivoting
            will take place if any of the values in a row is bigger than the
            diagonal value of that row divided by the pivot tolerance, i.e. pivoting
            will take place if <b>row(i,j) > row(i,i) / PivotTolerance</b> for
            any <b>j</b> that is not equal to <b>i</b>.
            </para>
            <para>
            Note that any changes to the <b>PivotTolerance</b> after creating the preconditioner
            will invalidate the created preconditioner and will require a re-initialization of
            the preconditioner.
            </para>
            </remarks>
            <exception cref="T:System.ArgumentOutOfRangeException">Thrown if a negative value is provided.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner.UpperTriangle">
            <summary>
            Returns the upper triagonal matrix that was created during the LU decomposition.
            </summary>
            <remarks>
            This method is used for debugging purposes only and should normally not be used.
            </remarks>
            <returns>A new matrix containing the upper triagonal elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner.LowerTriangle">
            <summary>
            Returns the lower triagonal matrix that was created during the LU decomposition.
            </summary>
            <remarks>
            This method is used for debugging purposes only and should normally not be used.
            </remarks>
            <returns>A new matrix containing the lower triagonal elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner.Pivots">
            <summary>
            Returns the pivot array. This array is not needed for normal use because
            the preconditioner will return the solution vector values in the proper order.
            </summary>
            <remarks>
            This method is used for debugging purposes only and should normally not be used.
            </remarks>
            <returns>The pivot array.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner.Initialize(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Initializes the preconditioner and loads the internal data structures.
            </summary>
            <param name="matrix">
            The <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Matrix"/> upon which this preconditioner is based. Note that the
            method takes a general matrix type. However internally the data is stored
            as a sparse matrix. Therefore it is not recommended to pass a dense matrix.
            </param>
            <exception cref="T:System.ArgumentNullException"> If <paramref name="matrix"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner.PivotRow(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Pivot elements in the <paramref name="row"/> according to internal pivot array
            </summary>
            <param name="row">Row <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Vector"/> to pivot in</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner.PivotMapFound(System.Collections.Generic.Dictionary{System.Int32,System.Int32},System.Int32)">
            <summary>
            Was pivoting already performed
            </summary>
            <param name="knownPivots">Pivots already done</param>
            <param name="currentItem">Current item to pivot</param>
            <returns><c>true</c> if performed, otherwise <c>false</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner.SwapColumns(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},System.Int32,System.Int32)">
            <summary>
            Swap columns in the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Matrix"/>
            </summary>
            <param name="matrix">Source <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Matrix"/>.</param>
            <param name="firstColumn">First column index to swap</param>
            <param name="secondColumn">Second column index to swap</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner.FindLargestItems(System.Int32,System.Int32,System.Int32[],MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Sort vector descending, not changing vector but placing sorted indices to <paramref name="sortedIndices"/>
            </summary>
            <param name="lowerBound">Start sort form</param>
            <param name="upperBound">Sort till upper bound</param>
            <param name="sortedIndices">Array with sorted vector indices</param>
            <param name="values">Source <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Vector"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner.Approximate(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Approximates the solution to the matrix equation <b>Ax = b</b>.
            </summary>
            <param name="rhs">The right hand side vector.</param>
            <param name="lhs">The left hand side vector. Also known as the result vector.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner.Pivot(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Pivot elements in <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Vector"/> according to internal pivot array
            </summary>
            <param name="vector">Source <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Vector"/>.</param>
            <param name="result">Result <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Vector"/> after pivoting.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPElementSorter">
            <summary>
            An element sort algorithm for the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPPreconditioner"/> class.
            </summary>
            <remarks>
            This sort algorithm is used to sort the columns in a sparse matrix based on
            the value of the element on the diagonal of the matrix.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPElementSorter.SortDoubleIndicesDecreasing(System.Int32,System.Int32,System.Int32[],MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Sorts the elements of the <paramref name="values"/> vector in decreasing
            fashion. The vector itself is not affected.
            </summary>
            <param name="lowerBound">The starting index.</param>
            <param name="upperBound">The stopping index.</param>
            <param name="sortedIndices">An array that will contain the sorted indices once the algorithm finishes.</param>
            <param name="values">The <see cref="T:MathNet.Numerics.LinearAlgebra.Vector`1"/> that contains the values that need to be sorted.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPElementSorter.HeapSortDoublesIndices(System.Int32,System.Int32,System.Int32[],MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Sorts the elements of the <paramref name="values"/> vector in decreasing
            fashion using heap sort algorithm. The vector itself is not affected.
            </summary>
            <param name="lowerBound">The starting index.</param>
            <param name="upperBound">The stopping index.</param>
            <param name="sortedIndices">An array that will contain the sorted indices once the algorithm finishes.</param>
            <param name="values">The <see cref="T:MathNet.Numerics.LinearAlgebra.Vector`1"/> that contains the values that need to be sorted.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPElementSorter.BuildDoubleIndexHeap(System.Int32,System.Int32,System.Int32[],MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Build heap for double indices
            </summary>
            <param name="start">Root position</param>
            <param name="count">Length of <paramref name="values"/></param>
            <param name="sortedIndices">Indices of <paramref name="values"/></param>
            <param name="values">Target <see cref="T:MathNet.Numerics.LinearAlgebra.Vector`1"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPElementSorter.SiftDoubleIndices(System.Int32[],MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},System.Int32,System.Int32)">
            <summary>
            Sift double indices
            </summary>
            <param name="sortedIndices">Indices of <paramref name="values"/></param>
            <param name="values">Target <see cref="T:MathNet.Numerics.LinearAlgebra.Vector`1"/></param>
            <param name="begin">Root position</param>
            <param name="count">Length of <paramref name="values"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPElementSorter.SortIntegersDecreasing(System.Int32[])">
            <summary>
            Sorts the given integers in a decreasing fashion.
            </summary>
            <param name="values">The values.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPElementSorter.HeapSortIntegers(System.Int32[],System.Int32)">
            <summary>
            Sort the given integers in a decreasing fashion using heapsort algorithm
            </summary>
            <param name="values">Array of values to sort</param>
            <param name="count">Length of <paramref name="values"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPElementSorter.BuildHeap(System.Int32[],System.Int32,System.Int32)">
            <summary>
            Build heap
            </summary>
            <param name="values">Target values array</param>
            <param name="start">Root position</param>
            <param name="count">Length of <paramref name="values"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPElementSorter.Sift(System.Int32[],System.Int32,System.Int32)">
            <summary>
            Sift values
            </summary>
            <param name="values">Target value array</param>
            <param name="start">Root position</param>
            <param name="count">Length of <paramref name="values"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.ILUTPElementSorter.Exchange(System.Int32[],System.Int32,System.Int32)">
            <summary>
            Exchange values in array
            </summary>
            <param name="values">Target values array</param>
            <param name="first">First value to exchange</param>
            <param name="second">Second value to exchange</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Solvers.MILU0Preconditioner">
            <summary>
            A simple milu(0) preconditioner.
            </summary>
            <remarks>
            Original Fortran code by Yousef Saad (07 January 2004)
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.MILU0Preconditioner.#ctor(System.Boolean)">
            <param name="modified">Use modified or standard ILU(0)</param>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Solvers.MILU0Preconditioner.UseModified">
            <summary>
            Gets or sets a value indicating whether to use modified or standard ILU(0).
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Solvers.MILU0Preconditioner.IsInitialized">
            <summary>
            Gets a value indicating whether the preconditioner is initialized.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.MILU0Preconditioner.Initialize(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Initializes the preconditioner and loads the internal data structures.
            </summary>
            <param name="matrix">The matrix upon which the preconditioner is based. </param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square or is not an
            instance of SparseCompressedRowMatrixStorage.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.MILU0Preconditioner.Approximate(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Approximates the solution to the matrix equation <b>Ax = b</b>.
            </summary>
            <param name="input">The right hand side vector b.</param>
            <param name="result">The left hand side vector x.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.MILU0Preconditioner.Compute(System.Int32,System.Numerics.Complex[],System.Int32[],System.Int32[],System.Numerics.Complex[],System.Int32[],System.Int32[],System.Boolean)">
            <summary>
            MILU0 is a simple milu(0) preconditioner.
            </summary>
            <param name="n">Order of the matrix.</param>
            <param name="a">Matrix values in CSR format (input).</param>
            <param name="ja">Column indices (input).</param>
            <param name="ia">Row pointers (input).</param>
            <param name="alu">Matrix values in MSR format (output).</param>
            <param name="jlu">Row pointers and column indices (output).</param>
            <param name="ju">Pointer to diagonal elements (output).</param>
            <param name="modified">True if the modified/MILU algorithm should be used (recommended)</param>
            <returns>Returns 0 on success or k > 0 if a zero pivot was encountered at step k.</returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Solvers.MlkBiCgStab">
            <summary>
            A Multiple-Lanczos Bi-Conjugate Gradient stabilized iterative matrix solver.
            </summary>
            <remarks>
            <para>
            The Multiple-Lanczos Bi-Conjugate Gradient stabilized (ML(k)-BiCGStab) solver is an 'improvement'
            of the standard BiCgStab solver.
            </para>
            <para>
            The algorithm was taken from: <br/>
            ML(k)BiCGSTAB: A BiCGSTAB variant based on multiple Lanczos starting vectors
            <br/>
            Man-Chung Yeung and Tony F. Chan
            <br/>
            SIAM Journal of Scientific Computing
            <br/>
            Volume 21, Number 4, pp. 1263 - 1290
            </para>
            <para>
            The example code below provides an indication of the possible use of the
            solver.
            </para>
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.Solvers.MlkBiCgStab.DefaultNumberOfStartingVectors">
            <summary>
            The default number of starting vectors.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.Solvers.MlkBiCgStab._startingVectors">
            <summary>
            The collection of starting vectors which are used as the basis for the Krylov sub-space.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex.Solvers.MlkBiCgStab._numberOfStartingVectors">
            <summary>
            The number of starting vectors used by the algorithm
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Solvers.MlkBiCgStab.NumberOfStartingVectors">
            <summary>
            Gets or sets the number of starting vectors.
            </summary>
            <remarks>
            Must be larger than 1 and smaller than the number of variables in the matrix that
            for which this solver will be used.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.MlkBiCgStab.ResetNumberOfStartingVectors">
            <summary>
            Resets the number of starting vectors to the default value.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.Solvers.MlkBiCgStab.StartingVectors">
            <summary>
            Gets or sets a series of orthonormal vectors which will be used as basis for the
            Krylov sub-space.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.MlkBiCgStab.NumberOfStartingVectorsToCreate(System.Int32,System.Int32)">
            <summary>
            Gets the number of starting vectors to create
            </summary>
            <param name="maximumNumberOfStartingVectors">Maximum number</param>
            <param name="numberOfVariables">Number of variables</param>
            <returns>Number of starting vectors to create</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.MlkBiCgStab.CreateStartingVectors(System.Int32,System.Int32)">
            <summary>
            Returns an array of starting vectors.
            </summary>
            <param name="maximumNumberOfStartingVectors">The maximum number of starting vectors that should be created.</param>
            <param name="numberOfVariables">The number of variables.</param>
            <returns>
             An array with starting vectors. The array will never be larger than the
             <paramref name="maximumNumberOfStartingVectors"/> but it may be smaller if
             the <paramref name="numberOfVariables"/> is smaller than
             the <paramref name="maximumNumberOfStartingVectors"/>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.MlkBiCgStab.CreateVectorArray(System.Int32,System.Int32)">
            <summary>
            Create random vectors array
            </summary>
            <param name="arraySize">Number of vectors</param>
            <param name="vectorSize">Size of each vector</param>
            <returns>Array of random vectors</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.MlkBiCgStab.CalculateTrueResidual(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Calculates the <c>true</c> residual of the matrix equation Ax = b according to: residual = b - Ax
            </summary>
            <param name="matrix">Source <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Matrix"/>A.</param>
            <param name="residual">Residual <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Vector"/> data.</param>
            <param name="x">x <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Vector"/> data.</param>
            <param name="b">b <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Vector"/> data.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.MlkBiCgStab.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{System.Numerics.Complex})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient matrix, <c>A</c>.</param>
            <param name="input">The solution vector, <c>b</c></param>
            <param name="result">The result vector, <c>x</c></param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Solvers.TFQMR">
            <summary>
            A Transpose Free Quasi-Minimal Residual (TFQMR) iterative matrix solver.
            </summary>
            <remarks>
            <para>
            The TFQMR algorithm was taken from: <br/>
            Iterative methods for sparse linear systems.
            <br/>
            Yousef Saad
            <br/>
            Algorithm is described in Chapter 7, section 7.4.3, page 219
            </para>
            <para>
            The example code below provides an indication of the possible use of the
            solver.
            </para>
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.TFQMR.CalculateTrueResidual(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Calculates the <c>true</c> residual of the matrix equation Ax = b according to: residual = b - Ax
            </summary>
            <param name="matrix">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Matrix"/> A.</param>
            <param name="residual">Residual values in <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Vector"/>.</param>
            <param name="x">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Vector"/> x.</param>
            <param name="b">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex.Vector"/> b.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.TFQMR.IsEven(System.Int32)">
            <summary>
            Is <paramref name="number"/> even?
            </summary>
            <param name="number">Number to check</param>
            <returns><c>true</c> if <paramref name="number"/> even, otherwise <c>false</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Solvers.TFQMR.Solve(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{System.Numerics.Complex})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient matrix, <c>A</c>.</param>
            <param name="input">The solution vector, <c>b</c></param>
            <param name="result">The result vector, <c>x</c></param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix">
            <summary>
            A Matrix with sparse storage, intended for very large matrices where most of the cells are zero.
            The underlying storage scheme is 3-array compressed-sparse-row (CSR) Format.
            <a href="http://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_.28CSR_or_CRS.29">Wikipedia - CSR</a>.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.NonZerosCount">
            <summary>
            Gets the number of non zero elements in the matrix.
            </summary>
            <value>The number of non zero elements.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.#ctor(MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage{System.Numerics.Complex})">
            <summary>
            Create a new sparse matrix straight from an initialized matrix storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.#ctor(System.Int32)">
            <summary>
            Create a new square sparse matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the order is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.#ctor(System.Int32,System.Int32)">
            <summary>
            Create a new sparse matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the row or column count is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfMatrix(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Create a new sparse matrix as a copy of the given other matrix.
            This new matrix will be independent from the other matrix.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfArray(System.Numerics.Complex[0:,0:])">
            <summary>
            Create a new sparse matrix as a copy of the given two-dimensional array.
            This new matrix will be independent from the provided array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfIndexed(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Int32,System.Numerics.Complex}})">
            <summary>
            Create a new sparse matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfIndexed(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Int32,System.Numerics.Complex}})">
            <summary>
            Create a new sparse matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfRowMajor(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Numerics.Complex})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable.
            The enumerable is assumed to be in row-major order (row by row).
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
            <seealso href="http://en.wikipedia.org/wiki/Row-major_order"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfColumnMajor(System.Int32,System.Int32,System.Collections.Generic.IList{System.Numerics.Complex})">
            <summary>
            Create a new sparse matrix with the given number of rows and columns as a copy of the given array.
            The array is assumed to be in column-major order (column by column).
            This new matrix will be independent from the provided array.
            A new memory block will be allocated for storing the matrix.
            </summary>
            <seealso href="http://en.wikipedia.org/wiki/Row-major_order"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfColumns(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Numerics.Complex}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfColumns(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Numerics.Complex}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfColumnArrays(System.Numerics.Complex[][])">
            <summary>
            Create a new sparse matrix as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfColumnArrays(System.Collections.Generic.IEnumerable{System.Numerics.Complex[]})">
            <summary>
            Create a new sparse matrix as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfColumnVectors(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex}[])">
            <summary>
            Create a new sparse matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfColumnVectors(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex}})">
            <summary>
            Create a new sparse matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfRows(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Numerics.Complex}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfRows(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{System.Numerics.Complex}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfRowArrays(System.Numerics.Complex[][])">
            <summary>
            Create a new sparse matrix as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfRowArrays(System.Collections.Generic.IEnumerable{System.Numerics.Complex[]})">
            <summary>
            Create a new sparse matrix as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfRowVectors(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex}[])">
            <summary>
            Create a new sparse matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfRowVectors(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex}})">
            <summary>
            Create a new sparse matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfDiagonalVector(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfDiagonalVector(System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfDiagonalArray(System.Numerics.Complex[])">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.OfDiagonalArray(System.Int32,System.Int32,System.Numerics.Complex[])">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.Create(System.Int32,System.Int32,System.Numerics.Complex)">
            <summary>
            Create a new sparse matrix and initialize each value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.Create(System.Int32,System.Int32,System.Func{System.Int32,System.Int32,System.Numerics.Complex})">
            <summary>
            Create a new sparse matrix and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.CreateDiagonal(System.Int32,System.Int32,System.Numerics.Complex)">
            <summary>
            Create a new diagonal sparse matrix and initialize each diagonal value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.CreateDiagonal(System.Int32,System.Int32,System.Func{System.Int32,System.Numerics.Complex})">
            <summary>
            Create a new diagonal sparse matrix and initialize each diagonal value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.CreateIdentity(System.Int32)">
            <summary>
            Create a new square sparse identity matrix where each diagonal value is set to One.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.LowerTriangle">
            <summary>
            Returns a new matrix containing the lower triangle of this matrix.
            </summary>
            <returns>The lower triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.LowerTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Puts the lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="result"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.LowerTriangleImpl(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Puts the lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.UpperTriangle">
            <summary>
            Returns a new matrix containing the upper triangle of this matrix.
            </summary>
            <returns>The upper triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.UpperTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Puts the upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="result"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.UpperTriangleImpl(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Puts the upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.StrictlyLowerTriangle">
            <summary>
            Returns a new matrix containing the lower triangle of this matrix. The new matrix
            does not contain the diagonal elements of this matrix.
            </summary>
            <returns>The lower triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.StrictlyLowerTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Puts the strictly lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="result"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.StrictlyLowerTriangleImpl(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Puts the strictly lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.StrictlyUpperTriangle">
            <summary>
            Returns a new matrix containing the upper triangle of this matrix. The new matrix
            does not contain the diagonal elements of this matrix.
            </summary>
            <returns>The upper triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.StrictlyUpperTriangle(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Puts the strictly upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="result"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.StrictlyUpperTriangleImpl(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Puts the strictly upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.DoNegate(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Negate each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the negation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.InfinityNorm">
            <summary>Calculates the induced infinity norm of this matrix.</summary>
            <returns>The maximum absolute row sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.FrobeniusNorm">
            <summary>Calculates the entry-wise Frobenius norm of this matrix.</summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.DoAdd(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Adds another matrix to this matrix.
            </summary>
            <param name="other">The matrix to add to this matrix.</param>
            <param name="result">The matrix to store the result of the addition.</param>
            <exception cref="T:System.ArgumentNullException">If the other matrix is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.DoSubtract(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Subtracts another matrix from this matrix.
            </summary>
            <param name="other">The matrix to subtract to this matrix.</param>
            <param name="result">The matrix to store the result of subtraction.</param>
            <exception cref="T:System.ArgumentNullException">If the other matrix is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.DoMultiply(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="scalar">The scalar to multiply the matrix with.</param>
            <param name="result">The matrix to store the result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Multiplies this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.DoTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Multiplies this matrix with transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Multiplies the transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Pointwise multiplies this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="other">The matrix to pointwise multiply with this one.</param>
            <param name="result">The matrix to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Pointwise divide this matrix by another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The matrix to pointwise divide this one by.</param>
            <param name="result">The matrix to store the result of the pointwise division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.IsSymmetric">
            <summary>
            Evaluates whether this matrix is symmetric.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.IsHermitian">
            <summary>
            Evaluates whether this matrix is Hermitian (conjugate symmetric).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.op_Addition(MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix,MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix)">
            <summary>
            Adds two matrices together and returns the results.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to add.</param>
            <param name="rightSide">The right matrix to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> don't have the same dimensions.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.op_UnaryPlus(MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix)">
            <summary>
            Returns a <strong>Matrix</strong> containing the same values of <paramref name="rightSide"/>.
            </summary>
            <param name="rightSide">The matrix to get the values from.</param>
            <returns>A matrix containing a the same values as <paramref name="rightSide"/>.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.op_Subtraction(MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix,MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix)">
            <summary>
            Subtracts two matrices together and returns the results.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to subtract.</param>
            <param name="rightSide">The right matrix to subtract.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> don't have the same dimensions.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.op_UnaryNegation(MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix)">
            <summary>
            Negates each element of the matrix.
            </summary>
            <param name="rightSide">The matrix to negate.</param>
            <returns>A matrix containing the negated values.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix,System.Numerics.Complex)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.op_Multiply(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix,MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix)">
            <summary>
            Multiplies two matrices.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to multiply.</param>
            <param name="rightSide">The right matrix to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the dimensions of <paramref name="leftSide"/> or <paramref name="rightSide"/> don't conform.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix,MathNet.Numerics.LinearAlgebra.Complex.SparseVector)">
            <summary>
            Multiplies a <strong>Matrix</strong> and a Vector.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The vector to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex.SparseVector,MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix)">
            <summary>
            Multiplies a Vector and a <strong>Matrix</strong>.
            </summary>
            <param name="leftSide">The vector to multiply.</param>
            <param name="rightSide">The matrix to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix.op_Modulus(MathNet.Numerics.LinearAlgebra.Complex.SparseMatrix,System.Numerics.Complex)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.SparseVector">
            <summary>
            A vector with sparse storage, intended for very large vectors where most of the cells are zero.
            </summary>
            <remarks>The sparse vector is not thread safe.</remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.NonZerosCount">
            <summary>
            Gets the number of non zero elements in the vector.
            </summary>
            <value>The number of non zero elements.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.#ctor(MathNet.Numerics.LinearAlgebra.Storage.SparseVectorStorage{System.Numerics.Complex})">
            <summary>
            Create a new sparse vector straight from an initialized vector storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.#ctor(System.Int32)">
            <summary>
            Create a new sparse vector with the given length.
            All cells of the vector will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If length is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.OfVector(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Create a new sparse vector as a copy of the given other vector.
            This new vector will be independent from the other vector.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.OfEnumerable(System.Collections.Generic.IEnumerable{System.Numerics.Complex})">
            <summary>
            Create a new sparse vector as a copy of the given enumerable.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.OfIndexedEnumerable(System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Numerics.Complex}})">
            <summary>
            Create a new sparse vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.OfIndexedEnumerable(System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Numerics.Complex}})">
            <summary>
            Create a new sparse vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.Create(System.Int32,System.Numerics.Complex)">
            <summary>
            Create a new sparse vector and initialize each value using the provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.Create(System.Int32,System.Func{System.Int32,System.Numerics.Complex})">
            <summary>
            Create a new sparse vector and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.DoAdd(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Adds a scalar to each element of the vector and stores the result in the result vector.
            Warning, the new 'sparse vector' with a non-zero scalar added to it will be a 100% filled
            sparse vector and very inefficient. Would be better to work with a dense vector instead.
            </summary>
            <param name="scalar">
            The scalar to add.
            </param>
            <param name="result">
            The vector to store the result of the addition.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.DoAdd(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Adds another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">
            The vector to add to this one.
            </param>
            <param name="result">
            The vector to store the result of the addition.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.DoSubtract(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Subtracts a scalar from each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">
            The scalar to subtract.
            </param>
            <param name="result">
            The vector to store the result of the subtraction.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.DoSubtract(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Subtracts another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">
            The vector to subtract from this one.
            </param>
            <param name="result">
            The vector to store the result of the subtraction.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.DoNegate(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Negates vector and saves result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.DoConjugate(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Conjugates vector and save result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.DoMultiply(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Multiplies a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">
            The scalar to multiply.
            </param>
            <param name="result">
            The vector to store the result of the multiplication.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.DoDotProduct(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Computes the dot product between this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of a[i]*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.DoConjugateDotProduct(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Computes the dot product between the conjugate of this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of conj(a[i])*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.op_Addition(MathNet.Numerics.LinearAlgebra.Complex.SparseVector,MathNet.Numerics.LinearAlgebra.Complex.SparseVector)">
            <summary>
            Adds two <strong>Vectors</strong> together and returns the results.
            </summary>
            <param name="leftSide">One of the vectors to add.</param>
            <param name="rightSide">The other vector to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.op_UnaryNegation(MathNet.Numerics.LinearAlgebra.Complex.SparseVector)">
            <summary>
            Returns a <strong>Vector</strong> containing the negated values of <paramref name="rightSide"/>.
            </summary>
            <param name="rightSide">The vector to get the values from.</param>
            <returns>A vector containing the negated values as <paramref name="rightSide"/>.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.op_Subtraction(MathNet.Numerics.LinearAlgebra.Complex.SparseVector,MathNet.Numerics.LinearAlgebra.Complex.SparseVector)">
            <summary>
            Subtracts two <strong>Vectors</strong> and returns the results.
            </summary>
            <param name="leftSide">The vector to subtract from.</param>
            <param name="rightSide">The vector to subtract.</param>
            <returns>The result of the subtraction.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex.SparseVector,System.Numerics.Complex)">
            <summary>
            Multiplies a vector with a complex.
            </summary>
            <param name="leftSide">The vector to scale.</param>
            <param name="rightSide">The complex value.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.op_Multiply(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Complex.SparseVector)">
            <summary>
            Multiplies a vector with a complex.
            </summary>
            <param name="leftSide">The complex value.</param>
            <param name="rightSide">The vector to scale.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex.SparseVector,MathNet.Numerics.LinearAlgebra.Complex.SparseVector)">
            <summary>
            Computes the dot product between two <strong>Vectors</strong>.
            </summary>
            <param name="leftSide">The left row vector.</param>
            <param name="rightSide">The right column vector.</param>
            <returns>The dot product between the two vectors.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.op_Division(MathNet.Numerics.LinearAlgebra.Complex.SparseVector,System.Numerics.Complex)">
            <summary>
            Divides a vector with a complex.
            </summary>
            <param name="leftSide">The vector to divide.</param>
            <param name="rightSide">The complex value.</param>
            <returns>The result of the division.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.op_Modulus(MathNet.Numerics.LinearAlgebra.Complex.SparseVector,System.Numerics.Complex)">
            <summary>
            Computes the modulus of each element of the vector of the given divisor.
            </summary>
            <param name="leftSide">The vector whose elements we want to compute the modulus of.</param>
            <param name="rightSide">The divisor to use,</param>
            <returns>The result of the calculation</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.AbsoluteMinimumIndex">
            <summary>
            Returns the index of the absolute minimum element.
            </summary>
            <returns>The index of absolute minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.AbsoluteMaximumIndex">
            <summary>
            Returns the index of the absolute maximum element.
            </summary>
            <returns>The index of absolute maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.Sum">
            <summary>
            Computes the sum of the vector's elements.
            </summary>
            <returns>The sum of the vector's elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.L1Norm">
            <summary>
            Calculates the L1 norm of the vector, also known as Manhattan norm.
            </summary>
            <returns>The sum of the absolute values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.InfinityNorm">
            <summary>
            Calculates the infinity norm of the vector.
            </summary>
            <returns>The maximum absolute value.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.Norm(System.Double)">
            <summary>
            Computes the p-Norm.
            </summary>
            <param name="p">The p value.</param>
            <returns>Scalar <c>ret = ( ∑|this[i]|^p )^(1/p)</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Pointwise multiplies this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to pointwise multiply with this one.</param>
            <param name="result">The vector to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.Parse(System.String,System.IFormatProvider)">
            <summary>
            Creates a double sparse vector based on a string. The string can be in the following formats (without the
            quotes): 'n', 'n;n;..', '(n;n;..)', '[n;n;...]', where n is a Complex.
            </summary>
            <returns>
            A double sparse vector containing the values specified by the given string.
            </returns>
            <param name="value">
            the string to parse.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific formatting information.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.TryParse(System.String,MathNet.Numerics.LinearAlgebra.Complex.SparseVector@)">
            <summary>
            Converts the string representation of a complex sparse vector to double-precision sparse vector equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a complex vector to convert.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will be <c>null</c>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.SparseVector.TryParse(System.String,System.IFormatProvider,MathNet.Numerics.LinearAlgebra.Complex.SparseVector@)">
            <summary>
            Converts the string representation of a complex sparse vector to double-precision sparse vector equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a complex vector to convert.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific formatting information about value.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will be <c>null</c>.
            </returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex.Vector">
            <summary>
            <c>Complex</c> version of the <see cref="T:MathNet.Numerics.LinearAlgebra.Vector`1"/> class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.#ctor(MathNet.Numerics.LinearAlgebra.Storage.VectorStorage{System.Numerics.Complex})">
            <summary>
            Initializes a new instance of the Vector class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.CoerceZero(System.Double)">
            <summary>
            Set all values whose absolute value is smaller than the threshold to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoConjugate(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Conjugates vector and save result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoNegate(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Negates vector and saves result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoAdd(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Adds a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">
            The scalar to add.
            </param>
            <param name="result">
            The vector to store the result of the addition.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoAdd(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Adds another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">
            The vector to add to this one.
            </param>
            <param name="result">
            The vector to store the result of the addition.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoSubtract(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Subtracts a scalar from each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">
            The scalar to subtract.
            </param>
            <param name="result">
            The vector to store the result of the subtraction.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoSubtract(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Subtracts another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">
            The vector to subtract from this one.
            </param>
            <param name="result">
            The vector to store the result of the subtraction.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoMultiply(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Multiplies a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">
            The scalar to multiply.
            </param>
            <param name="result">
            The vector to store the result of the multiplication.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoDivide(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Divides each element of the vector by a scalar and stores the result in the result vector.
            </summary>
            <param name="divisor">
            The scalar to divide with.
            </param>
            <param name="result">
            The vector to store the result of the division.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoDivideByThis(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Divides a scalar by each element of the vector and stores the result in the result vector.
            </summary>
            <param name="dividend">The scalar to divide.</param>
            <param name="result">The vector to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Pointwise multiplies this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to pointwise multiply with this one.</param>
            <param name="result">The vector to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Pointwise divide this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The vector to pointwise divide this one by.</param>
            <param name="result">The vector to store the result of the pointwise division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoPointwisePower(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Pointwise raise this vector to an exponent and store the result into the result vector.
            </summary>
            <param name="exponent">The exponent to raise this vector values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoPointwisePower(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Pointwise raise this vector to an exponent vector and store the result into the result vector.
            </summary>
            <param name="exponent">The exponent vector to raise this vector values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoPointwiseModulus(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Pointwise canonical modulus, where the result has the sign of the divisor,
            of this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The pointwise denominator vector to use.</param>
            <param name="result">The result of the modulus.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoPointwiseRemainder(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Pointwise remainder (% operator), where the result has the sign of the dividend,
            of this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The pointwise denominator vector to use.</param>
            <param name="result">The result of the modulus.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoPointwiseExp(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Pointwise applies the exponential function to each value and stores the result into the result vector.
            </summary>
            <param name="result">The vector to store the result.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoPointwiseLog(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Pointwise applies the natural logarithm function to each value and stores the result into the result vector.
            </summary>
            <param name="result">The vector to store the result.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoDotProduct(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Computes the dot product between this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of a[i]*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoConjugateDotProduct(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Computes the dot product between the conjugate of this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of conj(a[i])*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoModulus(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoModulusByThis(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given dividend for each element of the vector.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoRemainder(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.DoRemainderByThis(System.Numerics.Complex,MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given dividend for each element of the vector.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.AbsoluteMinimum">
            <summary>
            Returns the value of the absolute minimum element.
            </summary>
            <returns>The value of the absolute minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.AbsoluteMinimumIndex">
            <summary>
            Returns the index of the absolute minimum element.
            </summary>
            <returns>The index of absolute minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.AbsoluteMaximum">
            <summary>
            Returns the value of the absolute maximum element.
            </summary>
            <returns>The value of the absolute maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.AbsoluteMaximumIndex">
            <summary>
            Returns the index of the absolute maximum element.
            </summary>
            <returns>The index of absolute maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.Sum">
            <summary>
            Computes the sum of the vector's elements.
            </summary>
            <returns>The sum of the vector's elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.L1Norm">
            <summary>
            Calculates the L1 norm of the vector, also known as Manhattan norm.
            </summary>
            <returns>The sum of the absolute values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.L2Norm">
            <summary>
            Calculates the L2 norm of the vector, also known as Euclidean norm.
            </summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.InfinityNorm">
            <summary>
            Calculates the infinity norm of the vector.
            </summary>
            <returns>The maximum absolute value.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.Norm(System.Double)">
            <summary>
            Computes the p-Norm.
            </summary>
            <param name="p">
            The p value.
            </param>
            <returns>
            <c>Scalar ret = ( ∑|At(i)|^p )^(1/p)</c>
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.MaximumIndex">
            <summary>
            Returns the index of the maximum element.
            </summary>
            <returns>The index of maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.MinimumIndex">
            <summary>
            Returns the index of the minimum element.
            </summary>
            <returns>The index of minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex.Vector.Normalize(System.Double)">
            <summary>
            Normalizes this vector to a unit vector with respect to the p-norm.
            </summary>
            <param name="p">
            The p value.
            </param>
            <returns>
            This vector normalized to a unit vector with respect to the p-norm.
            </returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix">
            <summary>
            A Matrix class with dense storage. The underlying storage is a one dimensional array in column-major order (column by column).
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix._rowCount">
            <summary>
            Number of rows.
            </summary>
            <remarks>Using this instead of the RowCount property to speed up calculating
            a matrix index in the data array.</remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix._columnCount">
            <summary>
            Number of columns.
            </summary>
            <remarks>Using this instead of the ColumnCount property to speed up calculating
            a matrix index in the data array.</remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix._values">
            <summary>
            Gets the matrix's data.
            </summary>
            <value>The matrix's data.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.#ctor(MathNet.Numerics.LinearAlgebra.Storage.DenseColumnMajorMatrixStorage{MathNet.Numerics.Complex32})">
            <summary>
            Create a new dense matrix straight from an initialized matrix storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.#ctor(System.Int32)">
            <summary>
            Create a new square dense matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the order is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.#ctor(System.Int32,System.Int32)">
            <summary>
            Create a new dense matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the row or column count is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.#ctor(System.Int32,System.Int32,MathNet.Numerics.Complex32[])">
            <summary>
            Create a new dense matrix with the given number of rows and columns directly binding to a raw array.
            The array is assumed to be in column-major order (column by column) and is used directly without copying.
            Very efficient, but changes to the array and the matrix will affect each other.
            </summary>
            <seealso href="http://en.wikipedia.org/wiki/Row-major_order"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfMatrix(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Create a new dense matrix as a copy of the given other matrix.
            This new matrix will be independent from the other matrix.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfArray(MathNet.Numerics.Complex32[0:,0:])">
            <summary>
            Create a new dense matrix as a copy of the given two-dimensional array.
            This new matrix will be independent from the provided array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfIndexed(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Int32,MathNet.Numerics.Complex32}})">
            <summary>
            Create a new dense matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfIndexed(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Int32,MathNet.Numerics.Complex32}})">
            <summary>
            Create a new dense matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfColumnMajor(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable.
            The enumerable is assumed to be in column-major order (column by column).
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfColumns(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfColumns(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfColumnArrays(MathNet.Numerics.Complex32[][])">
            <summary>
            Create a new dense matrix as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfColumnArrays(System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32[]})">
            <summary>
            Create a new dense matrix as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfColumnVectors(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32}[])">
            <summary>
            Create a new dense matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfColumnVectors(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32}})">
            <summary>
            Create a new dense matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfRows(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfRows(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfRowArrays(MathNet.Numerics.Complex32[][])">
            <summary>
            Create a new dense matrix as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfRowArrays(System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32[]})">
            <summary>
            Create a new dense matrix as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfRowVectors(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32}[])">
            <summary>
            Create a new dense matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfRowVectors(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32}})">
            <summary>
            Create a new dense matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfDiagonalVector(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfDiagonalVector(System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfDiagonalArray(MathNet.Numerics.Complex32[])">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.OfDiagonalArray(System.Int32,System.Int32,MathNet.Numerics.Complex32[])">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.Create(System.Int32,System.Int32,MathNet.Numerics.Complex32)">
            <summary>
            Create a new dense matrix and initialize each value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.Create(System.Int32,System.Int32,System.Func{System.Int32,System.Int32,MathNet.Numerics.Complex32})">
            <summary>
            Create a new dense matrix and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.CreateDiagonal(System.Int32,System.Int32,MathNet.Numerics.Complex32)">
            <summary>
            Create a new diagonal dense matrix and initialize each diagonal value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.CreateDiagonal(System.Int32,System.Int32,System.Func{System.Int32,MathNet.Numerics.Complex32})">
            <summary>
            Create a new diagonal dense matrix and initialize each diagonal value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.CreateIdentity(System.Int32)">
            <summary>
            Create a new square sparse identity matrix where each diagonal value is set to One.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.CreateRandom(System.Int32,System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create a new dense matrix with values sampled from the provided random distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.Values">
            <summary>
            Gets the matrix's data.
            </summary>
            <value>The matrix's data.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.L1Norm">
            <summary>Calculates the induced L1 norm of this matrix.</summary>
            <returns>The maximum absolute column sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.InfinityNorm">
            <summary>Calculates the induced infinity norm of this matrix.</summary>
            <returns>The maximum absolute row sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.FrobeniusNorm">
            <summary>Calculates the entry-wise Frobenius norm of this matrix.</summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.DoNegate(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Negate each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the negation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.DoConjugate(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Complex conjugates each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the conjugation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.DoAdd(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Add a scalar to each element of the matrix and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to add.</param>
            <param name="result">The matrix to store the result of the addition.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.DoAdd(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Adds another matrix to this matrix.
            </summary>
            <param name="other">The matrix to add to this matrix.</param>
            <param name="result">The matrix to store the result of add</param>
            <exception cref="T:System.ArgumentNullException">If the other matrix is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.DoSubtract(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Subtracts a scalar from each element of the matrix and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to subtract.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.DoSubtract(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Subtracts another matrix from this matrix.
            </summary>
            <param name="other">The matrix to subtract.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.DoMultiply(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="scalar">The scalar to multiply the matrix with.</param>
            <param name="result">The matrix to store the result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.DoTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies this matrix with transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.DoConjugateTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies this matrix with the conjugate transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies the transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.DoConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies the conjugate transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.DoConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.DoDivide(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Divides each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="divisor">The scalar to divide the matrix with.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise multiplies this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="other">The matrix to pointwise multiply with this one.</param>
            <param name="result">The matrix to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise divide this matrix by another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The matrix to pointwise divide this one by.</param>
            <param name="result">The matrix to store the result of the pointwise division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.DoPointwisePower(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise raise this matrix to an exponent and store the result into the result matrix.
            </summary>
            <param name="exponent">The exponent to raise this matrix values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.Trace">
            <summary>
            Computes the trace of this matrix.
            </summary>
            <returns>The trace of this matrix</returns>
            <exception cref="T:System.ArgumentException">If the matrix is not square</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.op_Addition(MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix,MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix)">
            <summary>
            Adds two matrices together and returns the results.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to add.</param>
            <param name="rightSide">The right matrix to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> don't have the same dimensions.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.op_UnaryPlus(MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix)">
            <summary>
            Returns a <strong>Matrix</strong> containing the same values of <paramref name="rightSide"/>.
            </summary>
            <param name="rightSide">The matrix to get the values from.</param>
            <returns>A matrix containing a the same values as <paramref name="rightSide"/>.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.op_Subtraction(MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix,MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix)">
            <summary>
            Subtracts two matrices together and returns the results.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to subtract.</param>
            <param name="rightSide">The right matrix to subtract.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> don't have the same dimensions.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.op_UnaryNegation(MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix)">
            <summary>
            Negates each element of the matrix.
            </summary>
            <param name="rightSide">The matrix to negate.</param>
            <returns>A matrix containing the negated values.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix,MathNet.Numerics.Complex32)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.op_Multiply(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix,MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix)">
            <summary>
            Multiplies two matrices.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to multiply.</param>
            <param name="rightSide">The right matrix to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the dimensions of <paramref name="leftSide"/> or <paramref name="rightSide"/> don't conform.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix,MathNet.Numerics.LinearAlgebra.Complex32.DenseVector)">
            <summary>
            Multiplies a <strong>Matrix</strong> and a Vector.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The vector to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex32.DenseVector,MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix)">
            <summary>
            Multiplies a Vector and a <strong>Matrix</strong>.
            </summary>
            <param name="leftSide">The vector to multiply.</param>
            <param name="rightSide">The matrix to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.op_Modulus(MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix,MathNet.Numerics.Complex32)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.IsSymmetric">
            <summary>
            Evaluates whether this matrix is symmetric.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix.IsHermitian">
            <summary>
            Evaluates whether this matrix is Hermitian (conjugate symmetric).
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector">
            <summary>
            A vector using dense storage.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector._length">
            <summary>
            Number of elements
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector._values">
            <summary>
            Gets the vector's data.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.#ctor(MathNet.Numerics.LinearAlgebra.Storage.DenseVectorStorage{MathNet.Numerics.Complex32})">
            <summary>
            Create a new dense vector straight from an initialized vector storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.#ctor(System.Int32)">
            <summary>
            Create a new dense vector with the given length.
            All cells of the vector will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If length is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.#ctor(MathNet.Numerics.Complex32[])">
            <summary>
            Create a new dense vector directly binding to a raw array.
            The array is used directly without copying.
            Very efficient, but changes to the array and the vector will affect each other.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.OfVector(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Create a new dense vector as a copy of the given other vector.
            This new vector will be independent from the other vector.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.OfArray(MathNet.Numerics.Complex32[])">
            <summary>
            Create a new dense vector as a copy of the given array.
            This new vector will be independent from the array.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.OfEnumerable(System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32})">
            <summary>
            Create a new dense vector as a copy of the given enumerable.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.OfIndexedEnumerable(System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,MathNet.Numerics.Complex32}})">
            <summary>
            Create a new dense vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.OfIndexedEnumerable(System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,MathNet.Numerics.Complex32}})">
            <summary>
            Create a new dense vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.Create(System.Int32,MathNet.Numerics.Complex32)">
            <summary>
            Create a new dense vector and initialize each value using the provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.Create(System.Int32,System.Func{System.Int32,MathNet.Numerics.Complex32})">
            <summary>
            Create a new dense vector and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.CreateRandom(System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create a new dense vector with values sampled from the provided random distribution.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.Values">
            <summary>
            Gets the vector's data.
            </summary>
            <value>The vector's data.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.op_Explicit(MathNet.Numerics.LinearAlgebra.Complex32.DenseVector)~MathNet.Numerics.Complex32[]">
            <summary>
            Returns a reference to the internal data structure.
            </summary>
            <param name="vector">The <c>DenseVector</c> whose internal data we are
            returning.</param>
            <returns>
            A reference to the internal date of the given vector.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.op_Implicit(MathNet.Numerics.Complex32[])~MathNet.Numerics.LinearAlgebra.Complex32.DenseVector">
            <summary>
            Returns a vector bound directly to a reference of the provided array.
            </summary>
            <param name="array">The array to bind to the <c>DenseVector</c> object.</param>
            <returns>
            A <c>DenseVector</c> whose values are bound to the given array.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.DoAdd(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Adds a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to add.</param>
            <param name="result">The vector to store the result of the addition.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.DoAdd(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Adds another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to add to this one.</param>
            <param name="result">The vector to store the result of the addition.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.op_Addition(MathNet.Numerics.LinearAlgebra.Complex32.DenseVector,MathNet.Numerics.LinearAlgebra.Complex32.DenseVector)">
            <summary>
            Adds two <strong>Vectors</strong> together and returns the results.
            </summary>
            <param name="leftSide">One of the vectors to add.</param>
            <param name="rightSide">The other vector to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.DoSubtract(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Subtracts a scalar from each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to subtract.</param>
            <param name="result">The vector to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.DoSubtract(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Subtracts another vector from this vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to subtract from this one.</param>
            <param name="result">The vector to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.op_UnaryNegation(MathNet.Numerics.LinearAlgebra.Complex32.DenseVector)">
            <summary>
            Returns a <strong>Vector</strong> containing the negated values of <paramref name="rightSide"/>.
            </summary>
            <param name="rightSide">The vector to get the values from.</param>
            <returns>A vector containing the negated values as <paramref name="rightSide"/>.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.op_Subtraction(MathNet.Numerics.LinearAlgebra.Complex32.DenseVector,MathNet.Numerics.LinearAlgebra.Complex32.DenseVector)">
            <summary>
            Subtracts two <strong>Vectors</strong> and returns the results.
            </summary>
            <param name="leftSide">The vector to subtract from.</param>
            <param name="rightSide">The vector to subtract.</param>
            <returns>The result of the subtraction.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.DoNegate(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Negates vector and saves result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.DoConjugate(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Conjugates vector and save result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.DoMultiply(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to multiply.</param>
            <param name="result">The vector to store the result of the multiplication.</param>
            <remarks></remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.DoDotProduct(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Computes the dot product between this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of a[i]*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.DoConjugateDotProduct(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Computes the dot product between the conjugate of this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of conj(a[i])*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex32.DenseVector,MathNet.Numerics.Complex32)">
            <summary>
            Multiplies a vector with a complex.
            </summary>
            <param name="leftSide">The vector to scale.</param>
            <param name="rightSide">The Complex32 value.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.op_Multiply(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Complex32.DenseVector)">
            <summary>
            Multiplies a vector with a complex.
            </summary>
            <param name="leftSide">The Complex32 value.</param>
            <param name="rightSide">The vector to scale.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex32.DenseVector,MathNet.Numerics.LinearAlgebra.Complex32.DenseVector)">
            <summary>
            Computes the dot product between two <strong>Vectors</strong>.
            </summary>
            <param name="leftSide">The left row vector.</param>
            <param name="rightSide">The right column vector.</param>
            <returns>The dot product between the two vectors.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.op_Division(MathNet.Numerics.LinearAlgebra.Complex32.DenseVector,MathNet.Numerics.Complex32)">
            <summary>
            Divides a vector with a complex.
            </summary>
            <param name="leftSide">The vector to divide.</param>
            <param name="rightSide">The Complex32 value.</param>
            <returns>The result of the division.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.AbsoluteMinimumIndex">
            <summary>
            Returns the index of the absolute minimum element.
            </summary>
            <returns>The index of absolute minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.AbsoluteMaximumIndex">
            <summary>
            Returns the index of the absolute maximum element.
            </summary>
            <returns>The index of absolute maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.Sum">
            <summary>
            Computes the sum of the vector's elements.
            </summary>
            <returns>The sum of the vector's elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.L1Norm">
            <summary>
            Calculates the L1 norm of the vector, also known as Manhattan norm.
            </summary>
            <returns>The sum of the absolute values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.L2Norm">
            <summary>
            Calculates the L2 norm of the vector, also known as Euclidean norm.
            </summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.InfinityNorm">
            <summary>
            Calculates the infinity norm of the vector.
            </summary>
            <returns>The maximum absolute value.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.Norm(System.Double)">
            <summary>
            Computes the p-Norm.
            </summary>
            <param name="p">The p value.</param>
            <returns>Scalar <c>ret = ( ∑|this[i]|^p )^(1/p)</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise divide this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to pointwise divide this one by.</param>
            <param name="result">The vector to store the result of the pointwise division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise divide this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The vector to pointwise divide this one by.</param>
            <param name="result">The vector to store the result of the pointwise division.</param>
            <remarks></remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.DoPointwisePower(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise raise this vector to an exponent vector and store the result into the result vector.
            </summary>
            <param name="exponent">The exponent vector to raise this vector values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.Parse(System.String,System.IFormatProvider)">
            <summary>
            Creates a Complex32 dense vector based on a string. The string can be in the following formats (without the
            quotes): 'n', 'n;n;..', '(n;n;..)', '[n;n;...]', where n is a double.
            </summary>
            <returns>
            A Complex32 dense vector containing the values specified by the given string.
            </returns>
            <param name="value">
            the string to parse.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific formatting information.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.TryParse(System.String,MathNet.Numerics.LinearAlgebra.Complex32.DenseVector@)">
            <summary>
            Converts the string representation of a complex dense vector to double-precision dense vector equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a complex vector to convert.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will be <c>null</c>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector.TryParse(System.String,System.IFormatProvider,MathNet.Numerics.LinearAlgebra.Complex32.DenseVector@)">
            <summary>
            Converts the string representation of a complex dense vector to double-precision dense vector equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a complex vector to convert.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific formatting information about value.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will be <c>null</c>.
            </returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix">
            <summary>
            A matrix type for diagonal matrices.
            </summary>
            <remarks>
            Diagonal matrices can be non-square matrices but the diagonal always starts
            at element 0,0. A diagonal matrix will throw an exception if non diagonal
            entries are set. The exception to this is when the off diagonal elements are
            0.0 or NaN; these settings will cause no change to the diagonal matrix.
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix._data">
            <summary>
            Gets the matrix's data.
            </summary>
            <value>The matrix's data.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.#ctor(MathNet.Numerics.LinearAlgebra.Storage.DiagonalMatrixStorage{MathNet.Numerics.Complex32})">
            <summary>
            Create a new diagonal matrix straight from an initialized matrix storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.#ctor(System.Int32)">
            <summary>
            Create a new square diagonal matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the order is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.#ctor(System.Int32,System.Int32)">
            <summary>
            Create a new diagonal matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the row or column count is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.#ctor(System.Int32,System.Int32,MathNet.Numerics.Complex32)">
            <summary>
            Create a new diagonal matrix with the given number of rows and columns.
            All diagonal cells of the matrix will be initialized to the provided value, all non-diagonal ones to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the row or column count is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.#ctor(System.Int32,System.Int32,MathNet.Numerics.Complex32[])">
            <summary>
            Create a new diagonal matrix with the given number of rows and columns directly binding to a raw array.
            The array is assumed to contain the diagonal elements only and is used directly without copying.
            Very efficient, but changes to the array and the matrix will affect each other.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.OfMatrix(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Create a new diagonal matrix as a copy of the given other matrix.
            This new matrix will be independent from the other matrix.
            The matrix to copy from must be diagonal as well.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.OfArray(MathNet.Numerics.Complex32[0:,0:])">
            <summary>
            Create a new diagonal matrix as a copy of the given two-dimensional array.
            This new matrix will be independent from the provided array.
            The array to copy from must be diagonal as well.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.OfIndexedDiagonal(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,MathNet.Numerics.Complex32}})">
            <summary>
            Create a new diagonal matrix and initialize each diagonal value from the provided indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.OfIndexedDiagonal(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,MathNet.Numerics.Complex32}})">
            <summary>
            Create a new diagonal matrix and initialize each diagonal value from the provided indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.OfDiagonal(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32})">
            <summary>
            Create a new diagonal matrix and initialize each diagonal value from the provided enumerable.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.Create(System.Int32,System.Int32,System.Func{System.Int32,MathNet.Numerics.Complex32})">
            <summary>
            Create a new diagonal matrix and initialize each diagonal value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.CreateIdentity(System.Int32)">
            <summary>
            Create a new square sparse identity matrix where each diagonal value is set to One.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.CreateRandom(System.Int32,System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create a new diagonal matrix with diagonal values sampled from the provided random distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.DoNegate(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Negate each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the negation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.DoConjugate(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Complex conjugates each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the conjugation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.DoAdd(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Adds another matrix to this matrix.
            </summary>
            <param name="other">The matrix to add to this matrix.</param>
            <param name="result">The matrix to store the result of the addition.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.DoSubtract(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Subtracts another matrix from this matrix.
            </summary>
            <param name="other">The matrix to subtract.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.DoMultiply(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="scalar">The scalar to multiply the matrix with.</param>
            <param name="result">The matrix to store the result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.DoTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies this matrix with transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.DoConjugateTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies this matrix with the conjugate transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.DoConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies the transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.DoConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies the conjugate transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.DoDivide(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Divides each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="divisor">The scalar to divide the matrix with.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.DoDivideByThis(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Divides a scalar by each element of the matrix and stores the result in the result matrix.
            </summary>
            <param name="dividend">The scalar to add.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.Determinant">
            <summary>
            Computes the determinant of this matrix.
            </summary>
            <returns>The determinant of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.Diagonal">
            <summary>
            Returns the elements of the diagonal in a <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.DenseVector"/>.
            </summary>
            <returns>The elements of the diagonal.</returns>
            <remarks>For non-square matrices, the method returns Min(Rows, Columns) elements where
            i == j (i is the row index, and j is the column index).</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.SetDiagonal(MathNet.Numerics.Complex32[])">
            <summary>
            Copies the values of the given array to the diagonal.
            </summary>
            <param name="source">The array to copy the values from. The length of the vector should be
            Min(Rows, Columns).</param>
            <exception cref="T:System.ArgumentException">If the length of <paramref name="source"/> does not
            equal Min(Rows, Columns).</exception>
            <remarks>For non-square matrices, the elements of <paramref name="source"/> are copied to
            this[i,i].</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.SetDiagonal(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Copies the values of the given <see cref="T:MathNet.Numerics.LinearAlgebra.Vector`1"/> to the diagonal.
            </summary>
            <param name="source">The vector to copy the values from. The length of the vector should be
            Min(Rows, Columns).</param>
            <exception cref="T:System.ArgumentException">If the length of <paramref name="source"/> does not
            equal Min(Rows, Columns).</exception>
            <remarks>For non-square matrices, the elements of <paramref name="source"/> are copied to
            this[i,i].</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.L1Norm">
            <summary>Calculates the induced L1 norm of this matrix.</summary>
            <returns>The maximum absolute column sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.L2Norm">
            <summary>Calculates the induced L2 norm of the matrix.</summary>
            <returns>The largest singular value of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.InfinityNorm">
            <summary>Calculates the induced infinity norm of this matrix.</summary>
            <returns>The maximum absolute row sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.FrobeniusNorm">
            <summary>Calculates the entry-wise Frobenius norm of this matrix.</summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.ConditionNumber">
            <summary>Calculates the condition number of this matrix.</summary>
            <returns>The condition number of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.Inverse">
            <summary>Computes the inverse of this matrix.</summary>
            <exception cref="T:System.ArgumentException">If <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix"/> is singular.</exception>
            <returns>The inverse of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.LowerTriangle">
            <summary>
            Returns a new matrix containing the lower triangle of this matrix.
            </summary>
            <returns>The lower triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.LowerTriangle(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Puts the lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.StrictlyLowerTriangle">
            <summary>
            Returns a new matrix containing the lower triangle of this matrix. The new matrix
            does not contain the diagonal elements of this matrix.
            </summary>
            <returns>The lower triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.StrictlyLowerTriangle(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Puts the strictly lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.UpperTriangle">
            <summary>
            Returns a new matrix containing the upper triangle of this matrix.
            </summary>
            <returns>The upper triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.UpperTriangle(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Puts the upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.StrictlyUpperTriangle">
            <summary>
            Returns a new matrix containing the upper triangle of this matrix. The new matrix
            does not contain the diagonal elements of this matrix.
            </summary>
            <returns>The upper triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.StrictlyUpperTriangle(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Puts the strictly upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.SubMatrix(System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Creates a matrix that contains the values from the requested sub-matrix.
            </summary>
            <param name="rowIndex">The row to start copying from.</param>
            <param name="rowCount">The number of rows to copy. Must be positive.</param>
            <param name="columnIndex">The column to start copying from.</param>
            <param name="columnCount">The number of columns to copy. Must be positive.</param>
            <returns>The requested sub-matrix.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If: <list><item><paramref name="rowIndex"/> is
            negative, or greater than or equal to the number of rows.</item>
            <item><paramref name="columnIndex"/> is negative, or greater than or equal to the number
            of columns.</item>
            <item><c>(columnIndex + columnLength) &gt;= Columns</c></item>
            <item><c>(rowIndex + rowLength) &gt;= Rows</c></item></list></exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="rowCount"/> or <paramref name="columnCount"/>
            is not positive.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.PermuteColumns(MathNet.Numerics.Permutation)">
            <summary>
            Permute the columns of a matrix according to a permutation.
            </summary>
            <param name="p">The column permutation to apply to this matrix.</param>
            <exception cref="T:System.InvalidOperationException">Always thrown</exception>
            <remarks>Permutation in diagonal matrix are senseless, because of matrix nature</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.PermuteRows(MathNet.Numerics.Permutation)">
            <summary>
            Permute the rows of a matrix according to a permutation.
            </summary>
            <param name="p">The row permutation to apply to this matrix.</param>
            <exception cref="T:System.InvalidOperationException">Always thrown</exception>
            <remarks>Permutation in diagonal matrix are senseless, because of matrix nature</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.IsSymmetric">
            <summary>
            Evaluates whether this matrix is symmetric.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.DiagonalMatrix.IsHermitian">
            <summary>
            Evaluates whether this matrix is Hermitian (conjugate symmetric).
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.Cholesky">
            <summary>
            <para>A class which encapsulates the functionality of a Cholesky factorization.</para>
            <para>For a symmetric, positive definite matrix A, the Cholesky factorization
            is an lower triangular matrix L so that A = L*L'.</para>
            </summary>
            <remarks>
            The computation of the Cholesky factorization is done at construction time. If the matrix is not symmetric
            or positive definite, the constructor will throw an exception.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.Cholesky.Determinant">
            <summary>
            Gets the determinant of the matrix for which the Cholesky matrix was computed.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.Cholesky.DeterminantLn">
            <summary>
            Gets the log determinant of the matrix for which the Cholesky matrix was computed.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseCholesky">
            <summary>
            <para>A class which encapsulates the functionality of a Cholesky factorization for dense matrices.</para>
            <para>For a symmetric, positive definite matrix A, the Cholesky factorization
            is an lower triangular matrix L so that A = L*L'.</para>
            </summary>
            <remarks>
            The computation of the Cholesky factorization is done at construction time. If the matrix is not symmetric
            or positive definite, the constructor will throw an exception.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseCholesky.Create(MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseCholesky"/> class. This object will compute the
            Cholesky factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not positive definite.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseCholesky.Solve(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A Cholesky factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseCholesky.Solve(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A Cholesky factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseCholesky.Factorize(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Calculates the Cholesky factorization of the input matrix.
            </summary>
            <param name="matrix">The matrix to be factorized<see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not positive definite.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="matrix"/> does not have the same dimensions as the existing factor.</exception>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseEvd">
            <summary>
            Eigenvalues and eigenvectors of a complex matrix.
            </summary>
            <remarks>
            If A is Hermitian, then A = V*D*V' where the eigenvalue matrix D is
            diagonal and the eigenvector matrix V is Hermitian.
            I.e. A = V*D*V' and V*VH=I.
            If A is not symmetric, then the eigenvalue matrix D is block diagonal
            with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues,
            lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda].  The
            columns of V represent the eigenvectors in the sense that A*V = V*D,
            i.e. A.Multiply(V) equals V.Multiply(D).  The matrix V may be badly
            conditioned, or even singular, so the validity of the equation
            A = V*D*Inverse(V) depends upon V.Condition().
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseEvd.Create(MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix,MathNet.Numerics.LinearAlgebra.Symmetricity)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseEvd"/> class. This object will compute the
            the eigenvalue decomposition when the constructor is called and cache it's decomposition.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="symmetricity">If it is known whether the matrix is symmetric or not the routine can skip checking it itself.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If EVD algorithm failed to converge with matrix <paramref name="matrix"/>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseEvd.Solve(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseEvd.Solve(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A EVD factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseGramSchmidt">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition Modified Gram-Schmidt Orthogonalization.</para>
            <para>Any complex square matrix A may be decomposed as A = QR where Q is an unitary mxn matrix and R is an nxn upper triangular matrix.</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by modified Gram-Schmidt Orthogonalization.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseGramSchmidt.Create(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseGramSchmidt"/> class. This object creates an unitary matrix
            using the modified Gram-Schmidt method.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> row count is less then column count</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is rank deficient</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseGramSchmidt.Factorize(MathNet.Numerics.Complex32[],System.Int32,System.Int32,MathNet.Numerics.Complex32[])">
            <summary>
            Factorize matrix using the modified Gram-Schmidt method.
            </summary>
            <param name="q">Initial matrix. On exit is replaced by <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/> Q.</param>
            <param name="rowsQ">Number of rows in <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/> Q.</param>
            <param name="columnsQ">Number of columns in <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/> Q.</param>
            <param name="r">On exit is filled by <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/> R.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseGramSchmidt.Solve(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseGramSchmidt.Solve(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseLU">
            <summary>
            <para>A class which encapsulates the functionality of an LU factorization.</para>
            <para>For a matrix A, the LU factorization is a pair of lower triangular matrix L and
            upper triangular matrix U so that A = L*U.</para>
            </summary>
            <remarks>
            The computation of the LU factorization is done at construction time.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseLU.Create(MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseLU"/> class. This object will compute the
            LU factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseLU.Solve(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <c>AX = B</c>, with A LU factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>B</c>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>X</c>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseLU.Solve(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <c>Ax = b</c>, with A LU factorized.
            </summary>
            <param name="input">The right hand side vector, <c>b</c>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>x</c>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseLU.Inverse">
            <summary>
            Returns the inverse of this matrix. The inverse is calculated using LU decomposition.
            </summary>
            <returns>The inverse of this matrix.</returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseQR">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition.</para>
            <para>Any real square matrix A may be decomposed as A = QR where Q is an orthogonal matrix
            (its columns are orthogonal unit vectors meaning QTQ = I) and R is an upper triangular matrix
            (also called right triangular matrix).</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by Householder transformation.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseQR.Tau">
            <summary>
             Gets or sets Tau vector. Contains additional information on Q - used for native solver.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseQR.Create(MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix,MathNet.Numerics.LinearAlgebra.Factorization.QRMethod)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseQR"/> class. This object will compute the
            QR factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="method">The QR factorization method to use.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> row count is less then column count</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseQR.Solve(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseQR.Solve(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseSvd">
            <summary>
            <para>A class which encapsulates the functionality of the singular value decomposition (SVD) for <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix"/>.</para>
            <para>Suppose M is an m-by-n matrix whose entries are real numbers.
            Then there exists a factorization of the form M = UΣVT where:
            - U is an m-by-m unitary matrix;
            - Σ is m-by-n diagonal matrix with nonnegative real numbers on the diagonal;
            - VT denotes transpose of V, an n-by-n unitary matrix;
            Such a factorization is called a singular-value decomposition of M. A common convention is to order the diagonal
            entries Σ(i,i) in descending order. In this case, the diagonal matrix Σ is uniquely determined
            by M (though the matrices U and V are not). The diagonal entries of Σ are known as the singular values of M.</para>
            </summary>
            <remarks>
            The computation of the singular value decomposition is done at construction time.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseSvd.Create(MathNet.Numerics.LinearAlgebra.Complex32.DenseMatrix,System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseSvd"/> class. This object will compute the
            the singular value decomposition when the constructor is called and cache it's decomposition.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="computeVectors">Compute the singular U and VT vectors or not.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If SVD algorithm failed to converge with matrix <paramref name="matrix"/>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseSvd.Solve(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.DenseSvd.Solve(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.Evd">
            <summary>
            Eigenvalues and eigenvectors of a real matrix.
            </summary>
            <remarks>
            If A is symmetric, then A = V*D*V' where the eigenvalue matrix D is
            diagonal and the eigenvector matrix V is orthogonal.
            I.e. A = V*D*V' and V*VT=I.
            If A is not symmetric, then the eigenvalue matrix D is block diagonal
            with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues,
            lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda].  The
            columns of V represent the eigenvectors in the sense that A*V = V*D,
            i.e. A.Multiply(V) equals V.Multiply(D).  The matrix V may be badly
            conditioned, or even singular, so the validity of the equation
            A = V*D*Inverse(V) depends upon V.Condition().
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.Evd.Determinant">
            <summary>
            Gets the absolute value of determinant of the square matrix for which the EVD was computed.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.Evd.Rank">
            <summary>
            Gets the effective numerical matrix rank.
            </summary>
            <value>The number of non-negligible singular values.</value>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.Evd.IsFullRank">
            <summary>
            Gets a value indicating whether the matrix is full rank or not.
            </summary>
            <value><c>true</c> if the matrix is full rank; otherwise <c>false</c>.</value>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.GramSchmidt">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition Modified Gram-Schmidt Orthogonalization.</para>
            <para>Any real square matrix A may be decomposed as A = QR where Q is an orthogonal mxn matrix and R is an nxn upper triangular matrix.</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by modified Gram-Schmidt Orthogonalization.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.GramSchmidt.Determinant">
            <summary>
            Gets the absolute determinant value of the matrix for which the QR matrix was computed.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.GramSchmidt.IsFullRank">
            <summary>
            Gets a value indicating whether the matrix is full rank or not.
            </summary>
            <value><c>true</c> if the matrix is full rank; otherwise <c>false</c>.</value>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.LU">
            <summary>
            <para>A class which encapsulates the functionality of an LU factorization.</para>
            <para>For a matrix A, the LU factorization is a pair of lower triangular matrix L and
            upper triangular matrix U so that A = L*U.</para>
            <para>In the Math.NET implementation we also store a set of pivot elements for increased
            numerical stability. The pivot elements encode a permutation matrix P such that P*A = L*U.</para>
            </summary>
            <remarks>
            The computation of the LU factorization is done at construction time.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.LU.Determinant">
            <summary>
            Gets the determinant of the matrix for which the LU factorization was computed.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.QR">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition.</para>
            <para>Any real square matrix A (m x n) may be decomposed as A = QR where Q is an orthogonal matrix
            (its columns are orthogonal unit vectors meaning QTQ = I) and R is an upper triangular matrix
            (also called right triangular matrix).</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by Householder transformation.
            If a <seealso cref="F:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod.Full"/> factorization is performed, the resulting Q matrix is an m x m matrix
            and the R matrix is an m x n matrix. If a <seealso cref="F:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod.Thin"/> factorization is performed, the
            resulting Q matrix is an m x n matrix and the R matrix is an n x n matrix.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.QR.Determinant">
            <summary>
            Gets the absolute determinant value of the matrix for which the QR matrix was computed.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.QR.IsFullRank">
            <summary>
            Gets a value indicating whether the matrix is full rank or not.
            </summary>
            <value><c>true</c> if the matrix is full rank; otherwise <c>false</c>.</value>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.Svd">
            <summary>
            <para>A class which encapsulates the functionality of the singular value decomposition (SVD).</para>
            <para>Suppose M is an m-by-n matrix whose entries are real numbers.
            Then there exists a factorization of the form M = UΣVT where:
            - U is an m-by-m unitary matrix;
            - Σ is m-by-n diagonal matrix with nonnegative real numbers on the diagonal;
            - VT denotes transpose of V, an n-by-n unitary matrix;
            Such a factorization is called a singular-value decomposition of M. A common convention is to order the diagonal
            entries Σ(i,i) in descending order. In this case, the diagonal matrix Σ is uniquely determined
            by M (though the matrices U and V are not). The diagonal entries of Σ are known as the singular values of M.</para>
            </summary>
            <remarks>
            The computation of the singular value decomposition is done at construction time.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.Svd.Rank">
            <summary>
            Gets the effective numerical matrix rank.
            </summary>
            <value>The number of non-negligible singular values.</value>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.Svd.L2Norm">
            <summary>
            Gets the two norm of the <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.
            </summary>
            <returns>The 2-norm of the <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.</returns>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.Svd.ConditionNumber">
            <summary>
            Gets the condition number <b>max(S) / min(S)</b>
            </summary>
            <returns>The condition number.</returns>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.Svd.Determinant">
            <summary>
            Gets the determinant of the square matrix for which the SVD was computed.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserCholesky">
            <summary>
            <para>A class which encapsulates the functionality of a Cholesky factorization for user matrices.</para>
            <para>For a symmetric, positive definite matrix A, the Cholesky factorization
            is an lower triangular matrix L so that A = L*L'.</para>
            </summary>
            <remarks>
            The computation of the Cholesky factorization is done at construction time. If the matrix is not symmetric
            or positive definite, the constructor will throw an exception.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserCholesky.DoCholesky(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Computes the Cholesky factorization in-place.
            </summary>
            <param name="factor">On entry, the matrix to factor. On exit, the Cholesky factor matrix</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="factor"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="factor"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="factor"/> is not positive definite.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserCholesky.Create(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserCholesky"/> class. This object will compute the
            Cholesky factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not positive definite.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserCholesky.Factorize(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Calculates the Cholesky factorization of the input matrix.
            </summary>
            <param name="matrix">The matrix to be factorized<see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not positive definite.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="matrix"/> does not have the same dimensions as the existing factor.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserCholesky.DoCholeskyStep(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},System.Int32,System.Int32,System.Int32,MathNet.Numerics.Complex32[],System.Int32)">
            <summary>
            Calculate Cholesky step
            </summary>
            <param name="data">Factor matrix</param>
            <param name="rowDim">Number of rows</param>
            <param name="firstCol">Column start</param>
            <param name="colLimit">Total columns</param>
            <param name="multipliers">Multipliers calculated previously</param>
            <param name="availableCores">Number of available processors</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserCholesky.Solve(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A Cholesky factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserCholesky.Solve(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A Cholesky factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserEvd">
            <summary>
            Eigenvalues and eigenvectors of a complex matrix.
            </summary>
            <remarks>
            If A is Hermitian, then A = V*D*V' where the eigenvalue matrix D is
            diagonal and the eigenvector matrix V is Hermitian.
            I.e. A = V*D*V' and V*VH=I.
            If A is not symmetric, then the eigenvalue matrix D is block diagonal
            with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues,
            lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda].  The
            columns of V represent the eigenvectors in the sense that A*V = V*D,
            i.e. A.Multiply(V) equals V.Multiply(D).  The matrix V may be badly
            conditioned, or even singular, so the validity of the equation
            A = V*D*Inverse(V) depends upon V.Condition().
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserEvd.Create(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Symmetricity)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserEvd"/> class. This object will compute the
            the eigenvalue decomposition when the constructor is called and cache it's decomposition.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="symmetricity">If it is known whether the matrix is symmetric or not the routine can skip checking it itself.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If EVD algorithm failed to converge with matrix <paramref name="matrix"/>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserEvd.SymmetricTridiagonalize(MathNet.Numerics.Complex32[0:,0:],System.Single[],System.Single[],MathNet.Numerics.Complex32[],System.Int32)">
            <summary>
            Reduces a complex Hermitian matrix to a real symmetric tridiagonal matrix using unitary similarity transformations.
            </summary>
            <param name="matrixA">Source matrix to reduce</param>
            <param name="d">Output: Arrays for internal storage of real parts of eigenvalues</param>
            <param name="e">Output: Arrays for internal storage of imaginary parts of eigenvalues</param>
            <param name="tau">Output: Arrays that contains further information about the transformations.</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures HTRIDI by
            Smith, Boyle, Dongarra, Garbow, Ikebe, Klema, Moler, and Wilkinson, Handbook for
            Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserEvd.SymmetricDiagonalize(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},System.Single[],System.Single[],System.Int32)">
            <summary>
            Symmetric tridiagonal QL algorithm.
            </summary>
            <param name="eigenVectors">The eigen vectors to work on.</param>
            <param name="d">Arrays for internal storage of real parts of eigenvalues</param>
            <param name="e">Arrays for internal storage of imaginary parts of eigenvalues</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures tql2, by
            Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
            Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserEvd.SymmetricUntridiagonalize(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.Complex32[0:,0:],MathNet.Numerics.Complex32[],System.Int32)">
            <summary>
            Determines eigenvectors by undoing the symmetric tridiagonalize transformation
            </summary>
            <param name="eigenVectors">The eigen vectors to work on.</param>
            <param name="matrixA">Previously tridiagonalized matrix by <see cref="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserEvd.SymmetricTridiagonalize(MathNet.Numerics.Complex32[0:,0:],System.Single[],System.Single[],MathNet.Numerics.Complex32[],System.Int32)"/>.</param>
            <param name="tau">Contains further information about the transformations</param>
            <param name="order">Input matrix order</param>
            <remarks>This is derived from the Algol procedures HTRIBK, by
            by Smith, Boyle, Dongarra, Garbow, Ikebe, Klema, Moler, and Wilkinson, Handbook for
            Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserEvd.NonsymmetricReduceToHessenberg(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.Complex32[0:,0:],System.Int32)">
            <summary>
            Nonsymmetric reduction to Hessenberg form.
            </summary>
            <param name="eigenVectors">The eigen vectors to work on.</param>
            <param name="matrixH">Array for internal storage of nonsymmetric Hessenberg form.</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures orthes and ortran,
            by Martin and Wilkinson, Handbook for Auto. Comp.,
            Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutines in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserEvd.NonsymmetricReduceHessenberToRealSchur(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex},MathNet.Numerics.Complex32[0:,0:],System.Int32)">
            <summary>
            Nonsymmetric reduction from Hessenberg to real Schur form.
            </summary>
            <param name="eigenVectors">The eigen vectors to work on.</param>
            <param name="eigenValues">The eigen values to work on.</param>
            <param name="matrixH">Array for internal storage of nonsymmetric Hessenberg form.</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedure hqr2,
            by Martin and Wilkinson, Handbook for Auto. Comp.,
            Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserEvd.Solve(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserEvd.Solve(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A EVD factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserGramSchmidt">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition Modified Gram-Schmidt Orthogonalization.</para>
            <para>Any complex square matrix A may be decomposed as A = QR where Q is an unitary mxn matrix and R is an nxn upper triangular matrix.</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by modified Gram-Schmidt Orthogonalization.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserGramSchmidt.Create(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserGramSchmidt"/> class. This object creates an unitary matrix
            using the modified Gram-Schmidt method.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> row count is less then column count</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is rank deficient</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserGramSchmidt.Solve(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserGramSchmidt.Solve(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserLU">
            <summary>
            <para>A class which encapsulates the functionality of an LU factorization.</para>
            <para>For a matrix A, the LU factorization is a pair of lower triangular matrix L and
            upper triangular matrix U so that A = L*U.</para>
            </summary>
            <remarks>
            The computation of the LU factorization is done at construction time.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserLU.Create(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserLU"/> class. This object will compute the
            LU factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserLU.Solve(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <c>AX = B</c>, with A LU factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>B</c>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>X</c>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserLU.Solve(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <c>Ax = b</c>, with A LU factorized.
            </summary>
            <param name="input">The right hand side vector, <c>b</c>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <c>x</c>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserLU.Inverse">
            <summary>
            Returns the inverse of this matrix. The inverse is calculated using LU decomposition.
            </summary>
            <returns>The inverse of this matrix.</returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserQR">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition.</para>
            <para>Any real square matrix A may be decomposed as A = QR where Q is an orthogonal matrix
            (its columns are orthogonal unit vectors meaning QTQ = I) and R is an upper triangular matrix
            (also called right triangular matrix).</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by Householder transformation.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserQR.Create(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Factorization.QRMethod)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserQR"/> class. This object will compute the
            QR factorization when the constructor is called and cache it's factorization.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="method">The QR factorization method to use.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserQR.GenerateColumn(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},System.Int32,System.Int32)">
            <summary>
            Generate column from initial matrix to work array
            </summary>
            <param name="a">Initial matrix</param>
            <param name="row">The first row</param>
            <param name="column">Column index</param>
            <returns>Generated vector</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserQR.ComputeQR(MathNet.Numerics.Complex32[],MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},System.Int32,System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Perform calculation of Q or R
            </summary>
            <param name="u">Work array</param>
            <param name="a">Q or R matrices</param>
            <param name="rowStart">The first row</param>
            <param name="rowDim">The last row</param>
            <param name="columnStart">The first column</param>
            <param name="columnDim">The last column</param>
            <param name="availableCores">Number of available CPUs</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserQR.Solve(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserQR.Solve(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserSvd">
            <summary>
            <para>A class which encapsulates the functionality of the singular value decomposition (SVD) for <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.</para>
            <para>Suppose M is an m-by-n matrix whose entries are real numbers.
            Then there exists a factorization of the form M = UΣVT where:
            - U is an m-by-m unitary matrix;
            - Σ is m-by-n diagonal matrix with nonnegative real numbers on the diagonal;
            - VT denotes transpose of V, an n-by-n unitary matrix;
            Such a factorization is called a singular-value decomposition of M. A common convention is to order the diagonal
            entries Σ(i,i) in descending order. In this case, the diagonal matrix Σ is uniquely determined
            by M (though the matrices U and V are not). The diagonal entries of Σ are known as the singular values of M.</para>
            </summary>
            <remarks>
            The computation of the singular value decomposition is done at construction time.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserSvd.Create(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserSvd"/> class. This object will compute the
            the singular value decomposition when the constructor is called and cache it's decomposition.
            </summary>
            <param name="matrix">The matrix to factor.</param>
            <param name="computeVectors">Compute the singular U and VT vectors or not.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserSvd.Csign(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32)">
            <summary>
            Calculates absolute value of <paramref name="z1"/> multiplied on signum function of <paramref name="z2"/>
            </summary>
            <param name="z1">Complex32 value z1</param>
            <param name="z2">Complex32 value z2</param>
            <returns>Result multiplication of signum function and absolute value</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserSvd.Swap(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},System.Int32,System.Int32,System.Int32)">
            <summary>
            Interchanges two vectors  <paramref name="columnA"/>  and  <paramref name="columnB"/>
            </summary>
            <param name="a">Source matrix</param>
            <param name="rowCount">The number of rows in <paramref name="a"/></param>
            <param name="columnA">Column A index to swap</param>
            <param name="columnB">Column B index to swap</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserSvd.CscalColumn(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},System.Int32,System.Int32,System.Int32,MathNet.Numerics.Complex32)">
            <summary>
            Scale column <paramref name="column"/> by <paramref name="z"/> starting from row <paramref name="rowStart"/>
            </summary>
            <param name="a">Source matrix</param>
            <param name="rowCount">The number of rows in <paramref name="a"/> </param>
            <param name="column">Column to scale</param>
            <param name="rowStart">Row to scale from</param>
            <param name="z">Scale value</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserSvd.CscalVector(MathNet.Numerics.Complex32[],System.Int32,MathNet.Numerics.Complex32)">
            <summary>
            Scale vector <paramref name="a"/> by <paramref name="z"/> starting from index <paramref name="start"/>
            </summary>
            <param name="a">Source vector</param>
            <param name="start">Row to scale from</param>
            <param name="z">Scale value</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserSvd.Srotg(System.Single@,System.Single@,System.Single@,System.Single@)">
            <summary>
            Given the Cartesian coordinates (da, db) of a point p, these function return the parameters da, db, c, and s
            associated with the Givens rotation that zeros the y-coordinate of the point.
            </summary>
            <param name="da">Provides the x-coordinate of the point p. On exit contains the parameter r associated with the Givens rotation</param>
            <param name="db">Provides the y-coordinate of the point p. On exit contains the parameter z associated with the Givens rotation</param>
            <param name="c">Contains the parameter c associated with the Givens rotation</param>
            <param name="s">Contains the parameter s associated with the Givens rotation</param>
            <remarks>This is equivalent to the DROTG LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserSvd.Cnrm2Column(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},System.Int32,System.Int32,System.Int32)">
            <summary>
            Calculate Norm 2 of the column <paramref name="column"/> in matrix <paramref name="a"/> starting from row <paramref name="rowStart"/>
            </summary>
            <param name="a">Source matrix</param>
            <param name="rowCount">The number of rows in <paramref name="a"/></param>
            <param name="column">Column index</param>
            <param name="rowStart">Start row index</param>
            <returns>Norm2 (Euclidean norm) of the column</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserSvd.Cnrm2Vector(MathNet.Numerics.Complex32[],System.Int32)">
            <summary>
            Calculate Norm 2 of the vector <paramref name="a"/> starting from index <paramref name="rowStart"/>
            </summary>
            <param name="a">Source vector</param>
            <param name="rowStart">Start index</param>
            <returns>Norm2 (Euclidean norm) of the vector</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserSvd.Cdotc(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Calculate dot product of <paramref name="columnA"/> and <paramref name="columnB"/> conjugating the first vector.
            </summary>
            <param name="a">Source matrix</param>
            <param name="rowCount">The number of rows in <paramref name="a"/></param>
            <param name="columnA">Index of column A</param>
            <param name="columnB">Index of column B</param>
            <param name="rowStart">Starting row index</param>
            <returns>Dot product value</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserSvd.Csrot(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},System.Int32,System.Int32,System.Int32,System.Single,System.Single)">
            <summary>
            Performs rotation of points in the plane. Given two vectors x <paramref name="columnA"/> and y <paramref name="columnB"/>,
            each vector element of these vectors is replaced as follows: x(i) = c*x(i) + s*y(i); y(i) = c*y(i) - s*x(i)
            </summary>
            <param name="a">Source matrix</param>
            <param name="rowCount">The number of rows in <paramref name="a"/></param>
            <param name="columnA">Index of column A</param>
            <param name="columnB">Index of column B</param>
            <param name="c">scalar cos value</param>
            <param name="s">scalar sin value</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserSvd.Solve(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Factorization.UserSvd.Solve(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Matrix">
            <summary>
            <c>Complex32</c> version of the <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/> class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.#ctor(MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage{MathNet.Numerics.Complex32})">
            <summary>
            Initializes a new instance of the Matrix class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.CoerceZero(System.Double)">
            <summary>
            Set all values whose absolute value is smaller than the threshold to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.ConjugateTranspose">
            <summary>
            Returns the conjugate transpose of this matrix.
            </summary>
            <returns>The conjugate transpose of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.ConjugateTranspose(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Puts the conjugate transpose of this matrix into the result matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoConjugate(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Complex conjugates each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the conjugation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoNegate(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Negate each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the negation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoAdd(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Add a scalar to each element of the matrix and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to add.</param>
            <param name="result">The matrix to store the result of the addition.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoAdd(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Adds another matrix to this matrix.
            </summary>
            <param name="other">The matrix to add to this matrix.</param>
            <param name="result">The matrix to store the result of the addition.</param>
            <exception cref="T:System.ArgumentNullException">If the other matrix is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoSubtract(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Subtracts a scalar from each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to subtract.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoSubtract(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Subtracts another matrix from this matrix.
            </summary>
            <param name="other">The matrix to subtract to this matrix.</param>
            <param name="result">The matrix to store the result of subtraction.</param>
            <exception cref="T:System.ArgumentNullException">If the other matrix is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoMultiply(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="scalar">The scalar to multiply the matrix with.</param>
            <param name="result">The matrix to store the result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoDivide(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Divides each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="divisor">The scalar to divide the matrix with.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoDivideByThis(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Divides a scalar by each element of the matrix and stores the result in the result matrix.
            </summary>
            <param name="dividend">The scalar to divide by each element of the matrix.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies this matrix with transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoConjugateTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies this matrix with the conjugate transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies the transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies the conjugate transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise multiplies this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="other">The matrix to pointwise multiply with this one.</param>
            <param name="result">The matrix to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise divide this matrix by another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The matrix to pointwise divide this one by.</param>
            <param name="result">The matrix to store the result of the pointwise division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoPointwisePower(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise raise this matrix to an exponent and store the result into the result matrix.
            </summary>
            <param name="exponent">The exponent to raise this matrix values to.</param>
            <param name="result">The matrix to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoPointwisePower(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise raise this matrix to an exponent and store the result into the result matrix.
            </summary>
            <param name="exponent">The exponent to raise this matrix values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoPointwiseModulus(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise canonical modulus, where the result has the sign of the divisor,
            of this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The pointwise denominator matrix to use</param>
            <param name="result">The result of the modulus.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoPointwiseRemainder(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise remainder (% operator), where the result has the sign of the dividend,
            of this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The pointwise denominator matrix to use</param>
            <param name="result">The result of the modulus.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoModulus(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoModulusByThis(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given dividend for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoRemainder(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoRemainderByThis(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given dividend for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoPointwiseExp(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise applies the exponential function to each value and stores the result into the result matrix.
            </summary>
            <param name="result">The matrix to store the result.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.DoPointwiseLog(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise applies the natural logarithm function to each value and stores the result into the result matrix.
            </summary>
            <param name="result">The matrix to store the result.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.PseudoInverse">
            <summary>
            Computes the Moore-Penrose Pseudo-Inverse of this matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.Trace">
            <summary>
            Computes the trace of this matrix.
            </summary>
            <returns>The trace of this matrix</returns>
            <exception cref="T:System.ArgumentException">If the matrix is not square</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.L1Norm">
            <summary>Calculates the induced L1 norm of this matrix.</summary>
            <returns>The maximum absolute column sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.InfinityNorm">
            <summary>Calculates the induced infinity norm of this matrix.</summary>
            <returns>The maximum absolute row sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.FrobeniusNorm">
            <summary>Calculates the entry-wise Frobenius norm of this matrix.</summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.RowNorms(System.Double)">
            <summary>
            Calculates the p-norms of all row vectors.
            Typical values for p are 1.0 (L1, Manhattan norm), 2.0 (L2, Euclidean norm) and positive infinity (infinity norm)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.ColumnNorms(System.Double)">
            <summary>
            Calculates the p-norms of all column vectors.
            Typical values for p are 1.0 (L1, Manhattan norm), 2.0 (L2, Euclidean norm) and positive infinity (infinity norm)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.NormalizeRows(System.Double)">
            <summary>
            Normalizes all row vectors to a unit p-norm.
            Typical values for p are 1.0 (L1, Manhattan norm), 2.0 (L2, Euclidean norm) and positive infinity (infinity norm)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.NormalizeColumns(System.Double)">
            <summary>
            Normalizes all column vectors to a unit p-norm.
            Typical values for p are 1.0 (L1, Manhattan norm), 2.0 (L2, Euclidean norm) and positive infinity (infinity norm)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.RowSums">
            <summary>
            Calculates the value sum of each row vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.RowAbsoluteSums">
            <summary>
            Calculates the absolute value sum of each row vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.ColumnSums">
            <summary>
            Calculates the value sum of each column vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.ColumnAbsoluteSums">
            <summary>
            Calculates the absolute value sum of each column vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Matrix.IsHermitian">
            <summary>
            Evaluates whether this matrix is Hermitian (conjugate symmetric).
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.BiCgStab">
            <summary>
            A Bi-Conjugate Gradient stabilized iterative matrix solver.
            </summary>
            <remarks>
            <para>
            The Bi-Conjugate Gradient Stabilized (BiCGStab) solver is an 'improvement'
            of the standard Conjugate Gradient (CG) solver. Unlike the CG solver the
            BiCGStab can be used on non-symmetric matrices. <br/>
            Note that much of the success of the solver depends on the selection of the
            proper preconditioner.
            </para>
            <para>
            The Bi-CGSTAB algorithm was taken from: <br/>
            Templates for the solution of linear systems: Building blocks
            for iterative methods
            <br/>
            Richard Barrett, Michael Berry, Tony F. Chan, James Demmel,
            June M. Donato, Jack Dongarra, Victor Eijkhout, Roldan Pozo,
            Charles Romine and Henk van der Vorst
            <br/>
            Url: <a href="http://www.netlib.org/templates/Templates.html">http://www.netlib.org/templates/Templates.html</a>
            <br/>
            Algorithm is described in Chapter 2, section 2.3.8, page 27
            </para>
            <para>
            The example code below provides an indication of the possible use of the
            solver.
            </para>
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.BiCgStab.CalculateTrueResidual(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Calculates the <c>true</c> residual of the matrix equation Ax = b according to: residual = b - Ax
            </summary>
            <param name="matrix">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Matrix"/> A.</param>
            <param name="residual">Residual values in <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/>.</param>
            <param name="x">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/> x.</param>
            <param name="b">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/> b.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.BiCgStab.Solve(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{MathNet.Numerics.Complex32})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Matrix"/>, <c>A</c>.</param>
            <param name="input">The solution <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/>, <c>b</c>.</param>
            <param name="result">The result <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/>, <c>x</c>.</param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.CompositeSolver">
            <summary>
            A composite matrix solver. The actual solver is made by a sequence of
            matrix solvers.
            </summary>
            <remarks>
            <para>
            Solver based on:<br />
            Faster PDE-based simulations using robust composite linear solvers<br />
            S. Bhowmicka, P. Raghavan a,*, L. McInnes b, B. Norris<br />
            Future Generation Computer Systems, Vol 20, 2004, pp 373�387<br />
            </para>
            <para>
            Note that if an iterator is passed to this solver it will be used for all the sub-solvers.
            </para>
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.CompositeSolver._solvers">
            <summary>
            The collection of solvers that will be used
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.CompositeSolver.Solve(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{MathNet.Numerics.Complex32})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient matrix, <c>A</c>.</param>
            <param name="input">The solution vector, <c>b</c></param>
            <param name="result">The result vector, <c>x</c></param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.DiagonalPreconditioner">
            <summary>
            A diagonal preconditioner. The preconditioner uses the inverse
            of the matrix diagonal as preconditioning values.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.DiagonalPreconditioner._inverseDiagonals">
            <summary>
            The inverse of the matrix diagonal.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.DiagonalPreconditioner.DiagonalEntries">
            <summary>
            Returns the decomposed matrix diagonal.
            </summary>
            <returns>The matrix diagonal.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.DiagonalPreconditioner.Initialize(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Initializes the preconditioner and loads the internal data structures.
            </summary>
            <param name="matrix">
            The <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Matrix"/> upon which this preconditioner is based.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <see langword="null" />. </exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.DiagonalPreconditioner.Approximate(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Approximates the solution to the matrix equation <b>Ax = b</b>.
            </summary>
            <param name="rhs">The right hand side vector.</param>
            <param name="lhs">The left hand side vector. Also known as the result vector.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.GpBiCg">
            <summary>
            A Generalized Product Bi-Conjugate Gradient iterative matrix solver.
            </summary>
            <remarks>
            <para>
            The Generalized Product Bi-Conjugate Gradient (GPBiCG) solver is an
            alternative version of the Bi-Conjugate Gradient stabilized (CG) solver.
            Unlike the CG solver the GPBiCG solver can be used on
            non-symmetric matrices. <br/>
            Note that much of the success of the solver depends on the selection of the
            proper preconditioner.
            </para>
            <para>
            The GPBiCG algorithm was taken from: <br/>
            GPBiCG(m,l): A hybrid of BiCGSTAB and GPBiCG methods with
            efficiency and robustness
            <br/>
            S. Fujino
            <br/>
            Applied Numerical Mathematics, Volume 41, 2002, pp 107 - 117
            <br/>
            </para>
            <para>
            The example code below provides an indication of the possible use of the
            solver.
            </para>
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.GpBiCg._numberOfBiCgStabSteps">
            <summary>
            Indicates the number of <c>BiCGStab</c> steps should be taken
            before switching.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.GpBiCg._numberOfGpbiCgSteps">
            <summary>
            Indicates the number of <c>GPBiCG</c> steps should be taken
            before switching.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.GpBiCg.NumberOfBiCgStabSteps">
            <summary>
            Gets or sets the number of steps taken with the <c>BiCgStab</c> algorithm
            before switching over to the <c>GPBiCG</c> algorithm.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.GpBiCg.NumberOfGpBiCgSteps">
            <summary>
            Gets or sets the number of steps taken with the <c>GPBiCG</c> algorithm
            before switching over to the <c>BiCgStab</c> algorithm.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.GpBiCg.CalculateTrueResidual(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Calculates the <c>true</c> residual of the matrix equation Ax = b according to: residual = b - Ax
            </summary>
            <param name="matrix">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Matrix"/> A.</param>
            <param name="residual">Residual values in <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/>.</param>
            <param name="x">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/> x.</param>
            <param name="b">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/> b.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.GpBiCg.ShouldRunBiCgStabSteps(System.Int32)">
            <summary>
            Decide if to do steps with BiCgStab
            </summary>
            <param name="iterationNumber">Number of iteration</param>
            <returns><c>true</c> if yes, otherwise <c>false</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.GpBiCg.Solve(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{MathNet.Numerics.Complex32})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient matrix, <c>A</c>.</param>
            <param name="input">The solution vector, <c>b</c></param>
            <param name="result">The result vector, <c>x</c></param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILU0Preconditioner">
            <summary>
            An incomplete, level 0, LU factorization preconditioner.
            </summary>
            <remarks>
            The ILU(0) algorithm was taken from: <br/>
            Iterative methods for sparse linear systems <br/>
            Yousef Saad <br/>
            Algorithm is described in Chapter 10, section 10.3.2, page 275 <br/>
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILU0Preconditioner._decompositionLU">
            <summary>
            The matrix holding the lower (L) and upper (U) matrices. The
            decomposition matrices are combined to reduce storage.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILU0Preconditioner.UpperTriangle">
            <summary>
            Returns the upper triagonal matrix that was created during the LU decomposition.
            </summary>
            <returns>A new matrix containing the upper triagonal elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILU0Preconditioner.LowerTriangle">
            <summary>
            Returns the lower triagonal matrix that was created during the LU decomposition.
            </summary>
            <returns>A new matrix containing the lower triagonal elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILU0Preconditioner.Initialize(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Initializes the preconditioner and loads the internal data structures.
            </summary>
            <param name="matrix">The matrix upon which the preconditioner is based. </param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILU0Preconditioner.Approximate(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Approximates the solution to the matrix equation <b>Ax = b</b>.
            </summary>
            <param name="rhs">The right hand side vector.</param>
            <param name="lhs">The left hand side vector. Also known as the result vector.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner">
            <summary>
            This class performs an Incomplete LU factorization with drop tolerance
            and partial pivoting. The drop tolerance indicates which additional entries
            will be dropped from the factorized LU matrices.
            </summary>
            <remarks>
            The ILUTP-Mem algorithm was taken from: <br/>
            ILUTP_Mem: a Space-Efficient Incomplete LU Preconditioner
            <br/>
            Tzu-Yi Chen, Department of Mathematics and Computer Science, <br/>
            Pomona College, Claremont CA 91711, USA <br/>
            Published in: <br/>
            Lecture Notes in Computer Science <br/>
            Volume 3046 / 2004 <br/>
            pp. 20 - 28 <br/>
            Algorithm is described in Section 2, page 22
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner.DefaultFillLevel">
            <summary>
            The default fill level.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner.DefaultDropTolerance">
            <summary>
            The default drop tolerance.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner._upper">
            <summary>
            The decomposed upper triangular matrix.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner._lower">
            <summary>
            The decomposed lower triangular matrix.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner._pivots">
            <summary>
            The array containing the pivot values.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner._fillLevel">
            <summary>
            The fill level.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner._dropTolerance">
            <summary>
            The drop tolerance.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner._pivotTolerance">
            <summary>
            The pivot tolerance.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner"/> class with the default settings.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner.#ctor(System.Double,System.Double,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner"/> class with the specified settings.
            </summary>
            <param name="fillLevel">
            The amount of fill that is allowed in the matrix. The value is a fraction of
            the number of non-zero entries in the original matrix. Values should be positive.
            </param>
            <param name="dropTolerance">
            The absolute drop tolerance which indicates below what absolute value an entry
            will be dropped from the matrix. A drop tolerance of 0.0 means that no values
            will be dropped. Values should always be positive.
            </param>
            <param name="pivotTolerance">
            The pivot tolerance which indicates at what level pivoting will take place. A
            value of 0.0 means that no pivoting will take place.
            </param>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner.FillLevel">
            <summary>
            Gets or sets the amount of fill that is allowed in the matrix. The
            value is a fraction of the number of non-zero entries in the original
            matrix. The standard value is 200.
            </summary>
            <remarks>
            <para>
            Values should always be positive and can be higher than 1.0. A value lower
            than 1.0 means that the eventual preconditioner matrix will have fewer
            non-zero entries as the original matrix. A value higher than 1.0 means that
            the eventual preconditioner can have more non-zero values than the original
            matrix.
            </para>
            <para>
            Note that any changes to the <b>FillLevel</b> after creating the preconditioner
            will invalidate the created preconditioner and will require a re-initialization of
            the preconditioner.
            </para>
            </remarks>
            <exception cref="T:System.ArgumentOutOfRangeException">Thrown if a negative value is provided.</exception>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner.DropTolerance">
            <summary>
            Gets or sets the absolute drop tolerance which indicates below what absolute value
            an entry will be dropped from the matrix. The standard value is 0.0001.
            </summary>
            <remarks>
            <para>
            The values should always be positive and can be larger than 1.0. A low value will
            keep more small numbers in the preconditioner matrix. A high value will remove
            more small numbers from the preconditioner matrix.
            </para>
            <para>
            Note that any changes to the <b>DropTolerance</b> after creating the preconditioner
            will invalidate the created preconditioner and will require a re-initialization of
            the preconditioner.
            </para>
            </remarks>
            <exception cref="T:System.ArgumentOutOfRangeException">Thrown if a negative value is provided.</exception>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner.PivotTolerance">
            <summary>
            Gets or sets the pivot tolerance which indicates at what level pivoting will
            take place. The standard value is 0.0 which means pivoting will never take place.
            </summary>
            <remarks>
            <para>
            The pivot tolerance is used to calculate if pivoting is necessary. Pivoting
            will take place if any of the values in a row is bigger than the
            diagonal value of that row divided by the pivot tolerance, i.e. pivoting
            will take place if <b>row(i,j) > row(i,i) / PivotTolerance</b> for
            any <b>j</b> that is not equal to <b>i</b>.
            </para>
            <para>
            Note that any changes to the <b>PivotTolerance</b> after creating the preconditioner
            will invalidate the created preconditioner and will require a re-initialization of
            the preconditioner.
            </para>
            </remarks>
            <exception cref="T:System.ArgumentOutOfRangeException">Thrown if a negative value is provided.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner.UpperTriangle">
            <summary>
            Returns the upper triagonal matrix that was created during the LU decomposition.
            </summary>
            <remarks>
            This method is used for debugging purposes only and should normally not be used.
            </remarks>
            <returns>A new matrix containing the upper triagonal elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner.LowerTriangle">
            <summary>
            Returns the lower triagonal matrix that was created during the LU decomposition.
            </summary>
            <remarks>
            This method is used for debugging purposes only and should normally not be used.
            </remarks>
            <returns>A new matrix containing the lower triagonal elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner.Pivots">
            <summary>
            Returns the pivot array. This array is not needed for normal use because
            the preconditioner will return the solution vector values in the proper order.
            </summary>
            <remarks>
            This method is used for debugging purposes only and should normally not be used.
            </remarks>
            <returns>The pivot array.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner.Initialize(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Initializes the preconditioner and loads the internal data structures.
            </summary>
            <param name="matrix">
            The <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Matrix"/> upon which this preconditioner is based. Note that the
            method takes a general matrix type. However internally the data is stored
            as a sparse matrix. Therefore it is not recommended to pass a dense matrix.
            </param>
            <exception cref="T:System.ArgumentNullException"> If <paramref name="matrix"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner.PivotRow(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Pivot elements in the <paramref name="row"/> according to internal pivot array
            </summary>
            <param name="row">Row <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/> to pivot in</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner.PivotMapFound(System.Collections.Generic.Dictionary{System.Int32,System.Int32},System.Int32)">
            <summary>
            Was pivoting already performed
            </summary>
            <param name="knownPivots">Pivots already done</param>
            <param name="currentItem">Current item to pivot</param>
            <returns><c>true</c> if performed, otherwise <c>false</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner.SwapColumns(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},System.Int32,System.Int32)">
            <summary>
            Swap columns in the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Matrix"/>
            </summary>
            <param name="matrix">Source <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Matrix"/>.</param>
            <param name="firstColumn">First column index to swap</param>
            <param name="secondColumn">Second column index to swap</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner.FindLargestItems(System.Int32,System.Int32,System.Int32[],MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Sort vector descending, not changing vector but placing sorted indices to <paramref name="sortedIndices"/>
            </summary>
            <param name="lowerBound">Start sort form</param>
            <param name="upperBound">Sort till upper bound</param>
            <param name="sortedIndices">Array with sorted vector indices</param>
            <param name="values">Source <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner.Approximate(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Approximates the solution to the matrix equation <b>Ax = b</b>.
            </summary>
            <param name="rhs">The right hand side vector.</param>
            <param name="lhs">The left hand side vector. Also known as the result vector.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner.Pivot(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Pivot elements in <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/> according to internal pivot array
            </summary>
            <param name="vector">Source <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/>.</param>
            <param name="result">Result <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/> after pivoting.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPElementSorter">
            <summary>
            An element sort algorithm for the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPPreconditioner"/> class.
            </summary>
            <remarks>
            This sort algorithm is used to sort the columns in a sparse matrix based on
            the value of the element on the diagonal of the matrix.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPElementSorter.SortDoubleIndicesDecreasing(System.Int32,System.Int32,System.Int32[],MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Sorts the elements of the <paramref name="values"/> vector in decreasing
            fashion. The vector itself is not affected.
            </summary>
            <param name="lowerBound">The starting index.</param>
            <param name="upperBound">The stopping index.</param>
            <param name="sortedIndices">An array that will contain the sorted indices once the algorithm finishes.</param>
            <param name="values">The <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/> that contains the values that need to be sorted.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPElementSorter.HeapSortDoublesIndices(System.Int32,System.Int32,System.Int32[],MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Sorts the elements of the <paramref name="values"/> vector in decreasing
            fashion using heap sort algorithm. The vector itself is not affected.
            </summary>
            <param name="lowerBound">The starting index.</param>
            <param name="upperBound">The stopping index.</param>
            <param name="sortedIndices">An array that will contain the sorted indices once the algorithm finishes.</param>
            <param name="values">The <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/> that contains the values that need to be sorted.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPElementSorter.BuildDoubleIndexHeap(System.Int32,System.Int32,System.Int32[],MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Build heap for double indices
            </summary>
            <param name="start">Root position</param>
            <param name="count">Length of <paramref name="values"/></param>
            <param name="sortedIndices">Indices of <paramref name="values"/></param>
            <param name="values">Target <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPElementSorter.SiftDoubleIndices(System.Int32[],MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},System.Int32,System.Int32)">
            <summary>
            Sift double indices
            </summary>
            <param name="sortedIndices">Indices of <paramref name="values"/></param>
            <param name="values">Target <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/></param>
            <param name="begin">Root position</param>
            <param name="count">Length of <paramref name="values"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPElementSorter.SortIntegersDecreasing(System.Int32[])">
            <summary>
            Sorts the given integers in a decreasing fashion.
            </summary>
            <param name="values">The values.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPElementSorter.HeapSortIntegers(System.Int32[],System.Int32)">
            <summary>
            Sort the given integers in a decreasing fashion using heapsort algorithm
            </summary>
            <param name="values">Array of values to sort</param>
            <param name="count">Length of <paramref name="values"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPElementSorter.BuildHeap(System.Int32[],System.Int32,System.Int32)">
            <summary>
            Build heap
            </summary>
            <param name="values">Target values array</param>
            <param name="start">Root position</param>
            <param name="count">Length of <paramref name="values"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPElementSorter.Sift(System.Int32[],System.Int32,System.Int32)">
            <summary>
            Sift values
            </summary>
            <param name="values">Target value array</param>
            <param name="start">Root position</param>
            <param name="count">Length of <paramref name="values"/></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.ILUTPElementSorter.Exchange(System.Int32[],System.Int32,System.Int32)">
            <summary>
            Exchange values in array
            </summary>
            <param name="values">Target values array</param>
            <param name="first">First value to exchange</param>
            <param name="second">Second value to exchange</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.MILU0Preconditioner">
            <summary>
            A simple milu(0) preconditioner.
            </summary>
            <remarks>
            Original Fortran code by Yousef Saad (07 January 2004)
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.MILU0Preconditioner.#ctor(System.Boolean)">
            <param name="modified">Use modified or standard ILU(0)</param>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.MILU0Preconditioner.UseModified">
            <summary>
            Gets or sets a value indicating whether to use modified or standard ILU(0).
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.MILU0Preconditioner.IsInitialized">
            <summary>
            Gets a value indicating whether the preconditioner is initialized.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.MILU0Preconditioner.Initialize(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Initializes the preconditioner and loads the internal data structures.
            </summary>
            <param name="matrix">The matrix upon which the preconditioner is based. </param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="matrix"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square or is not an
            instance of SparseCompressedRowMatrixStorage.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.MILU0Preconditioner.Approximate(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Approximates the solution to the matrix equation <b>Ax = b</b>.
            </summary>
            <param name="input">The right hand side vector b.</param>
            <param name="result">The left hand side vector x.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.MILU0Preconditioner.Compute(System.Int32,MathNet.Numerics.Complex32[],System.Int32[],System.Int32[],MathNet.Numerics.Complex32[],System.Int32[],System.Int32[],System.Boolean)">
            <summary>
            MILU0 is a simple milu(0) preconditioner.
            </summary>
            <param name="n">Order of the matrix.</param>
            <param name="a">Matrix values in CSR format (input).</param>
            <param name="ja">Column indices (input).</param>
            <param name="ia">Row pointers (input).</param>
            <param name="alu">Matrix values in MSR format (output).</param>
            <param name="jlu">Row pointers and column indices (output).</param>
            <param name="ju">Pointer to diagonal elements (output).</param>
            <param name="modified">True if the modified/MILU algorithm should be used (recommended)</param>
            <returns>Returns 0 on success or k > 0 if a zero pivot was encountered at step k.</returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.MlkBiCgStab">
            <summary>
            A Multiple-Lanczos Bi-Conjugate Gradient stabilized iterative matrix solver.
            </summary>
            <remarks>
            <para>
            The Multiple-Lanczos Bi-Conjugate Gradient stabilized (ML(k)-BiCGStab) solver is an 'improvement'
            of the standard BiCgStab solver.
            </para>
            <para>
            The algorithm was taken from: <br/>
            ML(k)BiCGSTAB: A BiCGSTAB variant based on multiple Lanczos starting vectors
            <br/>
            Man-Chung Yeung and Tony F. Chan
            <br/>
            SIAM Journal of Scientific Computing
            <br/>
            Volume 21, Number 4, pp. 1263 - 1290
            </para>
            <para>
            The example code below provides an indication of the possible use of the
            solver.
            </para>
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.MlkBiCgStab.DefaultNumberOfStartingVectors">
            <summary>
            The default number of starting vectors.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.MlkBiCgStab._startingVectors">
            <summary>
            The collection of starting vectors which are used as the basis for the Krylov sub-space.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.MlkBiCgStab._numberOfStartingVectors">
            <summary>
            The number of starting vectors used by the algorithm
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.MlkBiCgStab.NumberOfStartingVectors">
            <summary>
            Gets or sets the number of starting vectors.
            </summary>
            <remarks>
            Must be larger than 1 and smaller than the number of variables in the matrix that
            for which this solver will be used.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.MlkBiCgStab.ResetNumberOfStartingVectors">
            <summary>
            Resets the number of starting vectors to the default value.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.MlkBiCgStab.StartingVectors">
            <summary>
            Gets or sets a series of orthonormal vectors which will be used as basis for the
            Krylov sub-space.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.MlkBiCgStab.NumberOfStartingVectorsToCreate(System.Int32,System.Int32)">
            <summary>
            Gets the number of starting vectors to create
            </summary>
            <param name="maximumNumberOfStartingVectors">Maximum number</param>
            <param name="numberOfVariables">Number of variables</param>
            <returns>Number of starting vectors to create</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.MlkBiCgStab.CreateStartingVectors(System.Int32,System.Int32)">
            <summary>
            Returns an array of starting vectors.
            </summary>
            <param name="maximumNumberOfStartingVectors">The maximum number of starting vectors that should be created.</param>
            <param name="numberOfVariables">The number of variables.</param>
            <returns>
             An array with starting vectors. The array will never be larger than the
             <paramref name="maximumNumberOfStartingVectors"/> but it may be smaller if
             the <paramref name="numberOfVariables"/> is smaller than
             the <paramref name="maximumNumberOfStartingVectors"/>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.MlkBiCgStab.CreateVectorArray(System.Int32,System.Int32)">
            <summary>
            Create random vectors array
            </summary>
            <param name="arraySize">Number of vectors</param>
            <param name="vectorSize">Size of each vector</param>
            <returns>Array of random vectors</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.MlkBiCgStab.CalculateTrueResidual(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Calculates the <c>true</c> residual of the matrix equation Ax = b according to: residual = b - Ax
            </summary>
            <param name="matrix">Source <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Matrix"/>A.</param>
            <param name="residual">Residual <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/> data.</param>
            <param name="x">x <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/> data.</param>
            <param name="b">b <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/> data.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.MlkBiCgStab.Solve(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{MathNet.Numerics.Complex32})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient matrix, <c>A</c>.</param>
            <param name="input">The solution vector, <c>b</c></param>
            <param name="result">The result vector, <c>x</c></param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.TFQMR">
            <summary>
            A Transpose Free Quasi-Minimal Residual (TFQMR) iterative matrix solver.
            </summary>
            <remarks>
            <para>
            The TFQMR algorithm was taken from: <br/>
            Iterative methods for sparse linear systems.
            <br/>
            Yousef Saad
            <br/>
            Algorithm is described in Chapter 7, section 7.4.3, page 219
            </para>
            <para>
            The example code below provides an indication of the possible use of the
            solver.
            </para>
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.TFQMR.CalculateTrueResidual(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Calculates the <c>true</c> residual of the matrix equation Ax = b according to: residual = b - Ax
            </summary>
            <param name="matrix">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Matrix"/> A.</param>
            <param name="residual">Residual values in <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/>.</param>
            <param name="x">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/> x.</param>
            <param name="b">Instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector"/> b.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.TFQMR.IsEven(System.Int32)">
            <summary>
            Is <paramref name="number"/> even?
            </summary>
            <param name="number">Number to check</param>
            <returns><c>true</c> if <paramref name="number"/> even, otherwise <c>false</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Solvers.TFQMR.Solve(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{MathNet.Numerics.Complex32})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient matrix, <c>A</c>.</param>
            <param name="input">The solution vector, <c>b</c></param>
            <param name="result">The result vector, <c>x</c></param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix">
            <summary>
            A Matrix with sparse storage, intended for very large matrices where most of the cells are zero.
            The underlying storage scheme is 3-array compressed-sparse-row (CSR) Format.
            <a href="http://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_.28CSR_or_CRS.29">Wikipedia - CSR</a>.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.NonZerosCount">
            <summary>
            Gets the number of non zero elements in the matrix.
            </summary>
            <value>The number of non zero elements.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.#ctor(MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage{MathNet.Numerics.Complex32})">
            <summary>
            Create a new sparse matrix straight from an initialized matrix storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.#ctor(System.Int32)">
            <summary>
            Create a new square sparse matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the order is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.#ctor(System.Int32,System.Int32)">
            <summary>
            Create a new sparse matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If the row or column count is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfMatrix(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Create a new sparse matrix as a copy of the given other matrix.
            This new matrix will be independent from the other matrix.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfArray(MathNet.Numerics.Complex32[0:,0:])">
            <summary>
            Create a new sparse matrix as a copy of the given two-dimensional array.
            This new matrix will be independent from the provided array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfIndexed(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Int32,MathNet.Numerics.Complex32}})">
            <summary>
            Create a new sparse matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfIndexed(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Int32,MathNet.Numerics.Complex32}})">
            <summary>
            Create a new sparse matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfRowMajor(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable.
            The enumerable is assumed to be in row-major order (row by row).
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
            <seealso href="http://en.wikipedia.org/wiki/Row-major_order"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfColumnMajor(System.Int32,System.Int32,System.Collections.Generic.IList{MathNet.Numerics.Complex32})">
            <summary>
            Create a new sparse matrix with the given number of rows and columns as a copy of the given array.
            The array is assumed to be in column-major order (column by column).
            This new matrix will be independent from the provided array.
            A new memory block will be allocated for storing the matrix.
            </summary>
            <seealso href="http://en.wikipedia.org/wiki/Row-major_order"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfColumns(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfColumns(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfColumnArrays(MathNet.Numerics.Complex32[][])">
            <summary>
            Create a new sparse matrix as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfColumnArrays(System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32[]})">
            <summary>
            Create a new sparse matrix as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfColumnVectors(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32}[])">
            <summary>
            Create a new sparse matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfColumnVectors(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32}})">
            <summary>
            Create a new sparse matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfRows(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfRows(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfRowArrays(MathNet.Numerics.Complex32[][])">
            <summary>
            Create a new sparse matrix as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfRowArrays(System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32[]})">
            <summary>
            Create a new sparse matrix as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfRowVectors(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32}[])">
            <summary>
            Create a new sparse matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfRowVectors(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32}})">
            <summary>
            Create a new sparse matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfDiagonalVector(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfDiagonalVector(System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfDiagonalArray(MathNet.Numerics.Complex32[])">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.OfDiagonalArray(System.Int32,System.Int32,MathNet.Numerics.Complex32[])">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.Create(System.Int32,System.Int32,MathNet.Numerics.Complex32)">
            <summary>
            Create a new sparse matrix and initialize each value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.Create(System.Int32,System.Int32,System.Func{System.Int32,System.Int32,MathNet.Numerics.Complex32})">
            <summary>
            Create a new sparse matrix and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.CreateDiagonal(System.Int32,System.Int32,MathNet.Numerics.Complex32)">
            <summary>
            Create a new diagonal sparse matrix and initialize each diagonal value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.CreateDiagonal(System.Int32,System.Int32,System.Func{System.Int32,MathNet.Numerics.Complex32})">
            <summary>
            Create a new diagonal sparse matrix and initialize each diagonal value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.CreateIdentity(System.Int32)">
            <summary>
            Create a new square sparse identity matrix where each diagonal value is set to One.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.LowerTriangle">
            <summary>
            Returns a new matrix containing the lower triangle of this matrix.
            </summary>
            <returns>The lower triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.LowerTriangle(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Puts the lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="result"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.LowerTriangleImpl(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Puts the lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.UpperTriangle">
            <summary>
            Returns a new matrix containing the upper triangle of this matrix.
            </summary>
            <returns>The upper triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.UpperTriangle(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Puts the upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="result"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.UpperTriangleImpl(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Puts the upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.StrictlyLowerTriangle">
            <summary>
            Returns a new matrix containing the lower triangle of this matrix. The new matrix
            does not contain the diagonal elements of this matrix.
            </summary>
            <returns>The lower triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.StrictlyLowerTriangle(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Puts the strictly lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="result"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.StrictlyLowerTriangleImpl(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Puts the strictly lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.StrictlyUpperTriangle">
            <summary>
            Returns a new matrix containing the upper triangle of this matrix. The new matrix
            does not contain the diagonal elements of this matrix.
            </summary>
            <returns>The upper triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.StrictlyUpperTriangle(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Puts the strictly upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="result"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.StrictlyUpperTriangleImpl(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Puts the strictly upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.DoNegate(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Negate each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the negation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.InfinityNorm">
            <summary>Calculates the induced infinity norm of this matrix.</summary>
            <returns>The maximum absolute row sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.FrobeniusNorm">
            <summary>Calculates the entry-wise Frobenius norm of this matrix.</summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.DoAdd(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Adds another matrix to this matrix.
            </summary>
            <param name="other">The matrix to add to this matrix.</param>
            <param name="result">The matrix to store the result of the addition.</param>
            <exception cref="T:System.ArgumentNullException">If the other matrix is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.DoSubtract(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Subtracts another matrix from this matrix.
            </summary>
            <param name="other">The matrix to subtract to this matrix.</param>
            <param name="result">The matrix to store the result of subtraction.</param>
            <exception cref="T:System.ArgumentNullException">If the other matrix is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.DoMultiply(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="scalar">The scalar to multiply the matrix with.</param>
            <param name="result">The matrix to store the result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.DoMultiply(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.DoTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies this matrix with transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies the transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise multiplies this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="other">The matrix to pointwise multiply with this one.</param>
            <param name="result">The matrix to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise divide this matrix by another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The matrix to pointwise divide this one by.</param>
            <param name="result">The matrix to store the result of the pointwise division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.IsSymmetric">
            <summary>
            Evaluates whether this matrix is symmetric.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.IsHermitian">
            <summary>
            Evaluates whether this matrix is Hermitian (conjugate symmetric).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.op_Addition(MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix,MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix)">
            <summary>
            Adds two matrices together and returns the results.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to add.</param>
            <param name="rightSide">The right matrix to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> don't have the same dimensions.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.op_UnaryPlus(MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix)">
            <summary>
            Returns a <strong>Matrix</strong> containing the same values of <paramref name="rightSide"/>.
            </summary>
            <param name="rightSide">The matrix to get the values from.</param>
            <returns>A matrix containing a the same values as <paramref name="rightSide"/>.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.op_Subtraction(MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix,MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix)">
            <summary>
            Subtracts two matrices together and returns the results.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to subtract.</param>
            <param name="rightSide">The right matrix to subtract.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> don't have the same dimensions.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.op_UnaryNegation(MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix)">
            <summary>
            Negates each element of the matrix.
            </summary>
            <param name="rightSide">The matrix to negate.</param>
            <returns>A matrix containing the negated values.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix,MathNet.Numerics.Complex32)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.op_Multiply(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix,MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix)">
            <summary>
            Multiplies two matrices.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to multiply.</param>
            <param name="rightSide">The right matrix to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the dimensions of <paramref name="leftSide"/> or <paramref name="rightSide"/> don't conform.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix,MathNet.Numerics.LinearAlgebra.Complex32.SparseVector)">
            <summary>
            Multiplies a <strong>Matrix</strong> and a Vector.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The vector to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex32.SparseVector,MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix)">
            <summary>
            Multiplies a Vector and a <strong>Matrix</strong>.
            </summary>
            <param name="leftSide">The vector to multiply.</param>
            <param name="rightSide">The matrix to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix.op_Modulus(MathNet.Numerics.LinearAlgebra.Complex32.SparseMatrix,MathNet.Numerics.Complex32)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector">
            <summary>
            A vector with sparse storage, intended for very large vectors where most of the cells are zero.
            </summary>
            <remarks>The sparse vector is not thread safe.</remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.NonZerosCount">
            <summary>
            Gets the number of non zero elements in the vector.
            </summary>
            <value>The number of non zero elements.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.#ctor(MathNet.Numerics.LinearAlgebra.Storage.SparseVectorStorage{MathNet.Numerics.Complex32})">
            <summary>
            Create a new sparse vector straight from an initialized vector storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.#ctor(System.Int32)">
            <summary>
            Create a new sparse vector with the given length.
            All cells of the vector will be initialized to zero.
            </summary>
            <exception cref="T:System.ArgumentException">If length is less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.OfVector(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Create a new sparse vector as a copy of the given other vector.
            This new vector will be independent from the other vector.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.OfEnumerable(System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32})">
            <summary>
            Create a new sparse vector as a copy of the given enumerable.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.OfIndexedEnumerable(System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,MathNet.Numerics.Complex32}})">
            <summary>
            Create a new sparse vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.OfIndexedEnumerable(System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,MathNet.Numerics.Complex32}})">
            <summary>
            Create a new sparse vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.Create(System.Int32,MathNet.Numerics.Complex32)">
            <summary>
            Create a new sparse vector and initialize each value using the provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.Create(System.Int32,System.Func{System.Int32,MathNet.Numerics.Complex32})">
            <summary>
            Create a new sparse vector and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.DoAdd(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Adds a scalar to each element of the vector and stores the result in the result vector.
            Warning, the new 'sparse vector' with a non-zero scalar added to it will be a 100% filled
            sparse vector and very inefficient. Would be better to work with a dense vector instead.
            </summary>
            <param name="scalar">
            The scalar to add.
            </param>
            <param name="result">
            The vector to store the result of the addition.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.DoAdd(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Adds another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">
            The vector to add to this one.
            </param>
            <param name="result">
            The vector to store the result of the addition.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.DoSubtract(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Subtracts a scalar from each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">
            The scalar to subtract.
            </param>
            <param name="result">
            The vector to store the result of the subtraction.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.DoSubtract(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Subtracts another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">
            The vector to subtract from this one.
            </param>
            <param name="result">
            The vector to store the result of the subtraction.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.DoNegate(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Negates vector and saves result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.DoConjugate(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Conjugates vector and save result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.DoMultiply(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">
            The scalar to multiply.
            </param>
            <param name="result">
            The vector to store the result of the multiplication.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.DoDotProduct(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Computes the dot product between this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of a[i]*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.DoConjugateDotProduct(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Computes the dot product between the conjugate of this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of conj(a[i])*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.op_Addition(MathNet.Numerics.LinearAlgebra.Complex32.SparseVector,MathNet.Numerics.LinearAlgebra.Complex32.SparseVector)">
            <summary>
            Adds two <strong>Vectors</strong> together and returns the results.
            </summary>
            <param name="leftSide">One of the vectors to add.</param>
            <param name="rightSide">The other vector to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.op_UnaryNegation(MathNet.Numerics.LinearAlgebra.Complex32.SparseVector)">
            <summary>
            Returns a <strong>Vector</strong> containing the negated values of <paramref name="rightSide"/>.
            </summary>
            <param name="rightSide">The vector to get the values from.</param>
            <returns>A vector containing the negated values as <paramref name="rightSide"/>.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.op_Subtraction(MathNet.Numerics.LinearAlgebra.Complex32.SparseVector,MathNet.Numerics.LinearAlgebra.Complex32.SparseVector)">
            <summary>
            Subtracts two <strong>Vectors</strong> and returns the results.
            </summary>
            <param name="leftSide">The vector to subtract from.</param>
            <param name="rightSide">The vector to subtract.</param>
            <returns>The result of the subtraction.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex32.SparseVector,MathNet.Numerics.Complex32)">
            <summary>
            Multiplies a vector with a complex.
            </summary>
            <param name="leftSide">The vector to scale.</param>
            <param name="rightSide">The complex value.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.op_Multiply(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Complex32.SparseVector)">
            <summary>
            Multiplies a vector with a complex.
            </summary>
            <param name="leftSide">The complex value.</param>
            <param name="rightSide">The vector to scale.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.op_Multiply(MathNet.Numerics.LinearAlgebra.Complex32.SparseVector,MathNet.Numerics.LinearAlgebra.Complex32.SparseVector)">
            <summary>
            Computes the dot product between two <strong>Vectors</strong>.
            </summary>
            <param name="leftSide">The left row vector.</param>
            <param name="rightSide">The right column vector.</param>
            <returns>The dot product between the two vectors.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.op_Division(MathNet.Numerics.LinearAlgebra.Complex32.SparseVector,MathNet.Numerics.Complex32)">
            <summary>
            Divides a vector with a complex.
            </summary>
            <param name="leftSide">The vector to divide.</param>
            <param name="rightSide">The complex value.</param>
            <returns>The result of the division.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.op_Modulus(MathNet.Numerics.LinearAlgebra.Complex32.SparseVector,MathNet.Numerics.Complex32)">
            <summary>
            Computes the modulus of each element of the vector of the given divisor.
            </summary>
            <param name="leftSide">The vector whose elements we want to compute the modulus of.</param>
            <param name="rightSide">The divisor to use,</param>
            <returns>The result of the calculation</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.AbsoluteMinimumIndex">
            <summary>
            Returns the index of the absolute minimum element.
            </summary>
            <returns>The index of absolute minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.AbsoluteMaximumIndex">
            <summary>
            Returns the index of the absolute maximum element.
            </summary>
            <returns>The index of absolute maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.Sum">
            <summary>
            Computes the sum of the vector's elements.
            </summary>
            <returns>The sum of the vector's elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.L1Norm">
            <summary>
            Calculates the L1 norm of the vector, also known as Manhattan norm.
            </summary>
            <returns>The sum of the absolute values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.InfinityNorm">
            <summary>
            Calculates the infinity norm of the vector.
            </summary>
            <returns>The maximum absolute value.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.Norm(System.Double)">
            <summary>
            Computes the p-Norm.
            </summary>
            <param name="p">The p value.</param>
            <returns>Scalar <c>ret = ( ∑|this[i]|^p )^(1/p)</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise multiplies this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to pointwise multiply with this one.</param>
            <param name="result">The vector to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.Parse(System.String,System.IFormatProvider)">
            <summary>
            Creates a double sparse vector based on a string. The string can be in the following formats (without the
            quotes): 'n', 'n;n;..', '(n;n;..)', '[n;n;...]', where n is a Complex32.
            </summary>
            <returns>
            A double sparse vector containing the values specified by the given string.
            </returns>
            <param name="value">
            the string to parse.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific formatting information.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.TryParse(System.String,MathNet.Numerics.LinearAlgebra.Complex32.SparseVector@)">
            <summary>
            Converts the string representation of a complex sparse vector to double-precision sparse vector equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a complex vector to convert.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will be <c>null</c>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.SparseVector.TryParse(System.String,System.IFormatProvider,MathNet.Numerics.LinearAlgebra.Complex32.SparseVector@)">
            <summary>
            Converts the string representation of a complex sparse vector to double-precision sparse vector equivalent.
            A return value indicates whether the conversion succeeded or failed.
            </summary>
            <param name="value">
            A string containing a complex vector to convert.
            </param>
            <param name="formatProvider">
            An <see cref="T:System.IFormatProvider"/> that supplies culture-specific formatting information about value.
            </param>
            <param name="result">
            The parsed value.
            </param>
            <returns>
            If the conversion succeeds, the result will contain a complex number equivalent to value.
            Otherwise the result will be <c>null</c>.
            </returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Complex32.Vector">
            <summary>
            <c>Complex32</c> version of the <see cref="T:MathNet.Numerics.LinearAlgebra.Vector`1"/> class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.#ctor(MathNet.Numerics.LinearAlgebra.Storage.VectorStorage{MathNet.Numerics.Complex32})">
            <summary>
            Initializes a new instance of the Vector class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.CoerceZero(System.Double)">
            <summary>
            Set all values whose absolute value is smaller than the threshold to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoConjugate(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Conjugates vector and save result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoNegate(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Negates vector and saves result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoAdd(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Adds a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">
            The scalar to add.
            </param>
            <param name="result">
            The vector to store the result of the addition.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoAdd(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Adds another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">
            The vector to add to this one.
            </param>
            <param name="result">
            The vector to store the result of the addition.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoSubtract(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Subtracts a scalar from each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">
            The scalar to subtract.
            </param>
            <param name="result">
            The vector to store the result of the subtraction.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoSubtract(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Subtracts another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">
            The vector to subtract from this one.
            </param>
            <param name="result">
            The vector to store the result of the subtraction.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoMultiply(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Multiplies a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">
            The scalar to multiply.
            </param>
            <param name="result">
            The vector to store the result of the multiplication.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoDivide(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Divides each element of the vector by a scalar and stores the result in the result vector.
            </summary>
            <param name="divisor">
            The scalar to divide with.
            </param>
            <param name="result">
            The vector to store the result of the division.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoDivideByThis(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Divides a scalar by each element of the vector and stores the result in the result vector.
            </summary>
            <param name="dividend">The scalar to divide.</param>
            <param name="result">The vector to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise multiplies this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to pointwise multiply with this one.</param>
            <param name="result">The vector to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise divide this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The vector to pointwise divide this one by.</param>
            <param name="result">The vector to store the result of the pointwise division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoPointwisePower(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise raise this vector to an exponent and store the result into the result vector.
            </summary>
            <param name="exponent">The exponent to raise this vector values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoPointwisePower(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise raise this vector to an exponent vector and store the result into the result vector.
            </summary>
            <param name="exponent">The exponent vector to raise this vector values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoPointwiseModulus(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise canonical modulus, where the result has the sign of the divisor,
            of this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The pointwise denominator vector to use.</param>
            <param name="result">The result of the modulus.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoPointwiseRemainder(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32},MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise remainder (% operator), where the result has the sign of the dividend,
            of this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The pointwise denominator vector to use.</param>
            <param name="result">The result of the modulus.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoPointwiseExp(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise applies the exponential function to each value and stores the result into the result vector.
            </summary>
            <param name="result">The vector to store the result.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoPointwiseLog(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Pointwise applies the natural logarithm function to each value and stores the result into the result vector.
            </summary>
            <param name="result">The vector to store the result.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoDotProduct(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Computes the dot product between this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of a[i]*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoConjugateDotProduct(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Computes the dot product between the conjugate of this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of conj(a[i])*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoModulus(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoModulusByThis(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given dividend for each element of the vector.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoRemainder(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.DoRemainderByThis(MathNet.Numerics.Complex32,MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given dividend for each element of the vector.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.AbsoluteMinimum">
            <summary>
            Returns the value of the absolute minimum element.
            </summary>
            <returns>The value of the absolute minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.AbsoluteMinimumIndex">
            <summary>
            Returns the index of the absolute minimum element.
            </summary>
            <returns>The index of absolute minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.AbsoluteMaximum">
            <summary>
            Returns the value of the absolute maximum element.
            </summary>
            <returns>The value of the absolute maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.AbsoluteMaximumIndex">
            <summary>
            Returns the index of the absolute maximum element.
            </summary>
            <returns>The index of absolute maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.Sum">
            <summary>
            Computes the sum of the vector's elements.
            </summary>
            <returns>The sum of the vector's elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.L1Norm">
            <summary>
            Calculates the L1 norm of the vector, also known as Manhattan norm.
            </summary>
            <returns>The sum of the absolute values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.L2Norm">
            <summary>
            Calculates the L2 norm of the vector, also known as Euclidean norm.
            </summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.InfinityNorm">
            <summary>
            Calculates the infinity norm of the vector.
            </summary>
            <returns>The maximum absolute value.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.Norm(System.Double)">
            <summary>
            Computes the p-Norm.
            </summary>
            <param name="p">
            The p value.
            </param>
            <returns>
            <c>Scalar ret = ( ∑|At(i)|^p )^(1/p)</c>
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.MaximumIndex">
            <summary>
            Returns the index of the maximum element.
            </summary>
            <returns>The index of maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.MinimumIndex">
            <summary>
            Returns the index of the minimum element.
            </summary>
            <returns>The index of minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Complex32.Vector.Normalize(System.Double)">
            <summary>
            Normalizes this vector to a unit vector with respect to the p-norm.
            </summary>
            <param name="p">
            The p value.
            </param>
            <returns>
            This vector normalized to a unit vector with respect to the p-norm.
            </returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1">
            <summary>
            Generic linear algebra type builder, for situations where a matrix or vector
            must be created in a generic way. Usage of generic builders should not be
            required in normal user code.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.Zero">
            <summary>
            Gets the value of <c>0.0</c> for type T.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.One">
            <summary>
            Gets the value of <c>1.0</c> for type T.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.OfStorage(MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage{`0})">
            <summary>
            Create a new matrix straight from an initialized matrix storage instance.
            If you have an instance of a discrete storage type instead, use their direct methods instead.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SameAs``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},System.Int32,System.Int32,System.Boolean)">
            <summary>
            Create a new matrix with the same kind of the provided example.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SameAs``1(MathNet.Numerics.LinearAlgebra.Matrix{``0})">
            <summary>
            Create a new matrix with the same kind and dimensions of the provided example.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SameAs(MathNet.Numerics.LinearAlgebra.Vector{`0},System.Int32,System.Int32)">
            <summary>
            Create a new matrix with the same kind of the provided example.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SameAs(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0},System.Int32,System.Int32,System.Boolean)">
            <summary>
            Create a new matrix with a type that can represent and is closest to both provided samples.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SameAs(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Create a new matrix with a type that can represent and is closest to both provided samples and the dimensions of example.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.Random(System.Int32,System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create a new dense matrix with values sampled from the provided random distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.Random(System.Int32,System.Int32)">
            <summary>
            Create a new dense matrix with values sampled from the standard distribution with a system random source.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.Random(System.Int32,System.Int32,System.Int32)">
            <summary>
            Create a new dense matrix with values sampled from the standard distribution with a system random source.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.RandomPositiveDefinite(System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create a new positive definite dense matrix where each value is the product
            of two samples from the provided random distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.RandomPositiveDefinite(System.Int32)">
            <summary>
            Create a new positive definite dense matrix where each value is the product
            of two samples from the standard distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.RandomPositiveDefinite(System.Int32,System.Int32)">
            <summary>
            Create a new positive definite dense matrix where each value is the product
            of two samples from the provided random distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.Dense(MathNet.Numerics.LinearAlgebra.Storage.DenseColumnMajorMatrixStorage{`0})">
            <summary>
            Create a new dense matrix straight from an initialized matrix storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.Dense(System.Int32,System.Int32)">
            <summary>
            Create a new dense matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.Dense(System.Int32,System.Int32,`0[])">
            <summary>
            Create a new dense matrix with the given number of rows and columns directly binding to a raw array.
            The array is assumed to be in column-major order (column by column) and is used directly without copying.
            Very efficient, but changes to the array and the matrix will affect each other.
            </summary>
            <seealso href="http://en.wikipedia.org/wiki/Row-major_order"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.Dense(System.Int32,System.Int32,`0)">
            <summary>
            Create a new dense matrix and initialize each value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.Dense(System.Int32,System.Int32,System.Func{System.Int32,System.Int32,`0})">
            <summary>
            Create a new dense matrix and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseDiagonal(System.Int32,System.Int32,`0)">
            <summary>
            Create a new diagonal dense matrix and initialize each diagonal value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseDiagonal(System.Int32,`0)">
            <summary>
            Create a new diagonal dense matrix and initialize each diagonal value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseDiagonal(System.Int32,System.Int32,System.Func{System.Int32,`0})">
            <summary>
            Create a new diagonal dense matrix and initialize each diagonal value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseIdentity(System.Int32,System.Int32)">
            <summary>
            Create a new diagonal dense identity matrix with a one-diagonal.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseIdentity(System.Int32)">
            <summary>
            Create a new diagonal dense identity matrix with a one-diagonal.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfMatrix(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Create a new dense matrix as a copy of the given other matrix.
            This new matrix will be independent from the other matrix.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfArray(`0[0:,0:])">
            <summary>
            Create a new dense matrix as a copy of the given two-dimensional array.
            This new matrix will be independent from the provided array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfIndexed(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Int32,`0}})">
            <summary>
            Create a new dense matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfIndexed(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Int32,`0}})">
            <summary>
            Create a new dense matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfColumnMajor(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{`0})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable.
            The enumerable is assumed to be in column-major order (column by column).
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfColumns(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{`0}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfColumns(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{`0}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfColumnArrays(`0[][])">
            <summary>
            Create a new dense matrix of T as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfColumnArrays(System.Collections.Generic.IEnumerable{`0[]})">
            <summary>
            Create a new dense matrix of T as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfColumnVectors(MathNet.Numerics.LinearAlgebra.Vector{`0}[])">
            <summary>
            Create a new dense matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfColumnVectors(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{`0}})">
            <summary>
            Create a new dense matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfRowMajor(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{`0})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable.
            The enumerable is assumed to be in row-major order (row by row).
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfRows(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{`0}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfRows(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{`0}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfRowArrays(`0[][])">
            <summary>
            Create a new dense matrix of T as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfRowArrays(System.Collections.Generic.IEnumerable{`0[]})">
            <summary>
            Create a new dense matrix of T as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfRowVectors(MathNet.Numerics.LinearAlgebra.Vector{`0}[])">
            <summary>
            Create a new dense matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfRowVectors(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{`0}})">
            <summary>
            Create a new dense matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfDiagonalVector(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfDiagonalVector(System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfDiagonalArray(`0[])">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfDiagonalArray(System.Int32,System.Int32,`0[])">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DenseOfMatrixArray(MathNet.Numerics.LinearAlgebra.Matrix{`0}[0:,0:])">
            <summary>
            Create a new dense matrix from a 2D array of existing matrices.
            The matrices in the array are not required to be dense already.
            If the matrices do not align properly, they are placed on the top left
            corner of their cell with the remaining fields left zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.Sparse(MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage{`0})">
            <summary>
            Create a new sparse matrix straight from an initialized matrix storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
            <param name="storage">The SparseCompressedRowMatrixStorage</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.Sparse(System.Int32,System.Int32)">
            <summary>
            Create a sparse matrix of T with the given number of rows and columns.
            </summary>
            <param name="rows">The number of rows.</param>
            <param name="columns">The number of columns.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.Sparse(System.Int32,System.Int32,`0)">
            <summary>
            Create a new sparse matrix and initialize each value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.Sparse(System.Int32,System.Int32,System.Func{System.Int32,System.Int32,`0})">
            <summary>
            Create a new sparse matrix and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseDiagonal(System.Int32,System.Int32,`0)">
            <summary>
            Create a new diagonal sparse matrix and initialize each diagonal value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseDiagonal(System.Int32,`0)">
            <summary>
            Create a new diagonal sparse matrix and initialize each diagonal value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseDiagonal(System.Int32,System.Int32,System.Func{System.Int32,`0})">
            <summary>
            Create a new diagonal sparse matrix and initialize each diagonal value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseIdentity(System.Int32,System.Int32)">
            <summary>
            Create a new diagonal dense identity matrix with a one-diagonal.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseIdentity(System.Int32)">
            <summary>
            Create a new diagonal dense identity matrix with a one-diagonal.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfMatrix(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Create a new sparse matrix as a copy of the given other matrix.
            This new matrix will be independent from the other matrix.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfArray(`0[0:,0:])">
            <summary>
            Create a new sparse matrix as a copy of the given two-dimensional array.
            This new matrix will be independent from the provided array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfIndexed(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Int32,`0}})">
            <summary>
            Create a new sparse matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfIndexed(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Int32,`0}})">
            <summary>
            Create a new sparse matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfRowMajor(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{`0})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable.
            The enumerable is assumed to be in row-major order (row by row).
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
            <seealso href="http://en.wikipedia.org/wiki/Row-major_order"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfColumnMajor(System.Int32,System.Int32,System.Collections.Generic.IList{`0})">
            <summary>
            Create a new sparse matrix with the given number of rows and columns as a copy of the given array.
            The array is assumed to be in column-major order (column by column).
            This new matrix will be independent from the provided array.
            A new memory block will be allocated for storing the matrix.
            </summary>
            <seealso href="http://en.wikipedia.org/wiki/Row-major_order"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfColumns(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{`0}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfColumns(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{`0}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfColumnArrays(`0[][])">
            <summary>
            Create a new sparse matrix as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfColumnArrays(System.Collections.Generic.IEnumerable{`0[]})">
            <summary>
            Create a new sparse matrix as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfColumnVectors(MathNet.Numerics.LinearAlgebra.Vector{`0}[])">
            <summary>
            Create a new sparse matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfColumnVectors(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{`0}})">
            <summary>
            Create a new sparse matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfRows(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{`0}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfRows(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{`0}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfRowArrays(`0[][])">
            <summary>
            Create a new sparse matrix as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfRowArrays(System.Collections.Generic.IEnumerable{`0[]})">
            <summary>
            Create a new sparse matrix as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfRowVectors(MathNet.Numerics.LinearAlgebra.Vector{`0}[])">
            <summary>
            Create a new sparse matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfRowVectors(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{`0}})">
            <summary>
            Create a new sparse matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfDiagonalVector(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfDiagonalVector(System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfDiagonalArray(`0[])">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfDiagonalArray(System.Int32,System.Int32,`0[])">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseOfMatrixArray(MathNet.Numerics.LinearAlgebra.Matrix{`0}[0:,0:])">
            <summary>
            Create a new sparse matrix from a 2D array of existing matrices.
            The matrices in the array are not required to be sparse already.
            If the matrices do not align properly, they are placed on the top left
            corner of their cell with the remaining fields left zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseFromCoordinateFormat(System.Int32,System.Int32,System.Int32,System.Int32[],System.Int32[],`0[])">
            <summary>
            Create a new sparse matrix from a coordinate format.
            This new matrix will be independent from the given arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
            <param name="rows">The number of rows.</param>
            <param name="columns">The number of columns.</param>
            <param name="valueCount">The number of stored values including explicit zeros.</param>
            <param name="rowIndices">The row index array of the coordinate format.</param>
            <param name="columnIndices">The column index array of the coordinate format.</param>
            <param name="values">The data array of the coordinate format.</param>
            <returns>The sparse matrix from the coordinate format.</returns>
            <remarks>Duplicate entries will be summed together and explicit zeros will be not intentionally removed.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseFromCompressedSparseRowFormat(System.Int32,System.Int32,System.Int32,System.Int32[],System.Int32[],`0[])">
            <summary>
            Create a new sparse matrix from a compressed sparse row format.
            This new matrix will be independent from the given arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
            <param name="rows">The number of rows.</param>
            <param name="columns">The number of columns.</param>
            <param name="valueCount">The number of stored values including explicit zeros.</param>
            <param name="rowPointers">The row pointer array of the compressed sparse row format.</param>
            <param name="columnIndices">The column index array of the compressed sparse row format.</param>
            <param name="values">The data array of the compressed sparse row format.</param>
            <returns>The sparse matrix from the compressed sparse row format.</returns>
            <remarks>Duplicate entries will be summed together and explicit zeros will be not intentionally removed.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.SparseFromCompressedSparseColumnFormat(System.Int32,System.Int32,System.Int32,System.Int32[],System.Int32[],`0[])">
            <summary>
            Create a new sparse matrix from a compressed sparse column format.
            This new matrix will be independent from the given arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
            <param name="rows">The number of rows.</param>
            <param name="columns">The number of columns.</param>
            <param name="valueCount">The number of stored values including explicit zeros.</param>
            <param name="rowIndices">The row index array of the compressed sparse column format.</param>
            <param name="columnPointers">The column pointer array of the compressed sparse column format.</param>
            <param name="values">The data array of the compressed sparse column format.</param>
            <returns>The sparse matrix from the compressed sparse column format.</returns>
            <remarks>Duplicate entries will be summed together and explicit zeros will be not intentionally removed.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.Diagonal(MathNet.Numerics.LinearAlgebra.Storage.DiagonalMatrixStorage{`0})">
            <summary>
            Create a new diagonal matrix straight from an initialized matrix storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.Diagonal(System.Int32,System.Int32)">
            <summary>
            Create a new diagonal matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.Diagonal(System.Int32,System.Int32,`0[])">
            <summary>
            Create a new diagonal matrix with the given number of rows and columns directly binding to a raw array.
            The array is assumed to represent the diagonal values and is used directly without copying.
            Very efficient, but changes to the array and the matrix will affect each other.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.Diagonal(`0[])">
            <summary>
            Create a new square diagonal matrix directly binding to a raw array.
            The array is assumed to represent the diagonal values and is used directly without copying.
            Very efficient, but changes to the array and the matrix will affect each other.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.Diagonal(System.Int32,System.Int32,`0)">
            <summary>
            Create a new diagonal matrix and initialize each diagonal value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.Diagonal(System.Int32,System.Int32,System.Func{System.Int32,`0})">
            <summary>
            Create a new diagonal matrix and initialize each diagonal value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DiagonalIdentity(System.Int32,System.Int32)">
            <summary>
            Create a new diagonal identity matrix with a one-diagonal.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DiagonalIdentity(System.Int32)">
            <summary>
            Create a new diagonal identity matrix with a one-diagonal.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DiagonalOfDiagonalVector(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Create a new diagonal matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DiagonalOfDiagonalVector(System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Create a new diagonal matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DiagonalOfDiagonalArray(`0[])">
            <summary>
            Create a new diagonal matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.DiagonalOfDiagonalArray(System.Int32,System.Int32,`0[])">
            <summary>
            Create a new diagonal matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.VectorBuilder`1">
            <summary>
            Generic linear algebra type builder, for situations where a matrix or vector
            must be created in a generic way. Usage of generic builders should not be
            required in normal user code.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.Zero">
            <summary>
            Gets the value of <c>0.0</c> for type T.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.One">
            <summary>
            Gets the value of <c>1.0</c> for type T.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.OfStorage(MathNet.Numerics.LinearAlgebra.Storage.VectorStorage{`0})">
            <summary>
            Create a new vector straight from an initialized matrix storage instance.
            If you have an instance of a discrete storage type instead, use their direct methods instead.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.SameAs``1(MathNet.Numerics.LinearAlgebra.Vector{``0},System.Int32)">
            <summary>
            Create a new vector with the same kind of the provided example.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.SameAs``1(MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Create a new vector with the same kind and dimension of the provided example.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.SameAs``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},System.Int32)">
            <summary>
            Create a new vector with the same kind of the provided example.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.SameAs(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},System.Int32)">
            <summary>
            Create a new vector with a type that can represent and is closest to both provided samples.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.SameAs(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Create a new vector with a type that can represent and is closest to both provided samples and the dimensions of example.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.SameAs(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},System.Int32)">
            <summary>
            Create a new vector with a type that can represent and is closest to both provided samples.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.Random(System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create a new dense vector with values sampled from the provided random distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.Random(System.Int32)">
            <summary>
            Create a new dense vector with values sampled from the standard distribution with a system random source.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.Random(System.Int32,System.Int32)">
            <summary>
            Create a new dense vector with values sampled from the standard distribution with a system random source.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.Dense(MathNet.Numerics.LinearAlgebra.Storage.DenseVectorStorage{`0})">
            <summary>
            Create a new dense vector straight from an initialized vector storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.Dense(System.Int32)">
            <summary>
            Create a dense vector of T with the given size.
            </summary>
            <param name="size">The size of the vector.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.Dense(`0[])">
            <summary>
            Create a dense vector of T that is directly bound to the specified array.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.Dense(System.Int32,`0)">
            <summary>
            Create a new dense vector and initialize each value using the provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.Dense(System.Int32,System.Func{System.Int32,`0})">
            <summary>
            Create a new dense vector and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.DenseOfVector(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Create a new dense vector as a copy of the given other vector.
            This new vector will be independent from the other vector.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.DenseOfArray(`0[])">
            <summary>
            Create a new dense vector as a copy of the given array.
            This new vector will be independent from the array.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.DenseOfEnumerable(System.Collections.Generic.IEnumerable{`0})">
            <summary>
            Create a new dense vector as a copy of the given enumerable.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.DenseOfIndexed(System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,`0}})">
            <summary>
            Create a new dense vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.DenseOfIndexed(System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,`0}})">
            <summary>
            Create a new dense vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.Sparse(MathNet.Numerics.LinearAlgebra.Storage.SparseVectorStorage{`0})">
            <summary>
            Create a new sparse vector straight from an initialized vector storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.Sparse(System.Int32)">
            <summary>
            Create a sparse vector of T with the given size.
            </summary>
            <param name="size">The size of the vector.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.Sparse(System.Int32,`0)">
            <summary>
            Create a new sparse vector and initialize each value using the provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.Sparse(System.Int32,System.Func{System.Int32,`0})">
            <summary>
            Create a new sparse vector and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.SparseOfVector(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Create a new sparse vector as a copy of the given other vector.
            This new vector will be independent from the other vector.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.SparseOfArray(`0[])">
            <summary>
            Create a new sparse vector as a copy of the given array.
            This new vector will be independent from the array.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.SparseOfEnumerable(System.Collections.Generic.IEnumerable{`0})">
            <summary>
            Create a new sparse vector as a copy of the given enumerable.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.SparseOfIndexed(System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,`0}})">
            <summary>
            Create a new sparse vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorBuilder`1.SparseOfIndexed(System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,`0}})">
            <summary>
            Create a new sparse vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.WithStorage``1(MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage{``0})">
            <summary>
            Create a new matrix straight from an initialized matrix storage instance.
            If you have an instance of a discrete storage type instead, use their direct methods instead.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SameAs``2(MathNet.Numerics.LinearAlgebra.Matrix{``1},System.Int32,System.Int32,System.Boolean)">
            <summary>
            Create a new matrix with the same kind of the provided example.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SameAs``2(MathNet.Numerics.LinearAlgebra.Matrix{``1})">
            <summary>
            Create a new matrix with the same kind and dimensions of the provided example.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SameAs``1(MathNet.Numerics.LinearAlgebra.Vector{``0},System.Int32,System.Int32)">
            <summary>
            Create a new matrix with the same kind of the provided example.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SameAs``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Matrix{``0},System.Int32,System.Int32,System.Boolean)">
            <summary>
            Create a new matrix with a type that can represent and is closest to both provided samples.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SameAs``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Matrix{``0})">
            <summary>
            Create a new matrix with a type that can represent and is closest to both provided samples and the dimensions of example.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.Random``1(System.Int32,System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create a new dense matrix with values sampled from the provided random distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.Random``1(System.Int32,System.Int32)">
            <summary>
            Create a new dense matrix with values sampled from the standard distribution with a system random source.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.Random``1(System.Int32,System.Int32,System.Int32)">
            <summary>
            Create a new dense matrix with values sampled from the standard distribution with a system random source.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.RandomPositiveDefinite``1(System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create a new positive definite dense matrix where each value is the product
            of two samples from the provided random distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.RandomPositiveDefinite``1(System.Int32)">
            <summary>
            Create a new positive definite dense matrix where each value is the product
            of two samples from the standard distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.RandomPositiveDefinite``1(System.Int32,System.Int32)">
            <summary>
            Create a new positive definite dense matrix where each value is the product
            of two samples from the provided random distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.Dense``1(MathNet.Numerics.LinearAlgebra.Storage.DenseColumnMajorMatrixStorage{``0})">
            <summary>
            Create a new dense matrix straight from an initialized matrix storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.Dense``1(System.Int32,System.Int32)">
            <summary>
            Create a new dense matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.Dense``1(System.Int32,System.Int32,``0[])">
            <summary>
            Create a new dense matrix with the given number of rows and columns directly binding to a raw array.
            The array is assumed to be in column-major order (column by column) and is used directly without copying.
            Very efficient, but changes to the array and the matrix will affect each other.
            </summary>
            <seealso href="http://en.wikipedia.org/wiki/Row-major_order"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.Dense``1(System.Int32,System.Int32,``0)">
            <summary>
            Create a new dense matrix and initialize each value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.Dense``1(System.Int32,System.Int32,System.Func{System.Int32,System.Int32,``0})">
            <summary>
            Create a new dense matrix and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseDiagonal``1(System.Int32,System.Int32,``0)">
            <summary>
            Create a new diagonal dense matrix and initialize each diagonal value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseDiagonal``1(System.Int32,``0)">
            <summary>
            Create a new diagonal dense matrix and initialize each diagonal value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseDiagonal``1(System.Int32,System.Int32,System.Func{System.Int32,``0})">
            <summary>
            Create a new diagonal dense matrix and initialize each diagonal value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseIdentity``1(System.Int32,System.Int32)">
            <summary>
            Create a new diagonal dense identity matrix with a one-diagonal.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseIdentity``1(System.Int32)">
            <summary>
            Create a new diagonal dense identity matrix with a one-diagonal.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfMatrix``1(MathNet.Numerics.LinearAlgebra.Matrix{``0})">
            <summary>
            Create a new dense matrix as a copy of the given other matrix.
            This new matrix will be independent from the other matrix.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfArray``1(``0[0:,0:])">
            <summary>
            Create a new dense matrix as a copy of the given two-dimensional array.
            This new matrix will be independent from the provided array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfIndexed``1(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Int32,``0}})">
            <summary>
            Create a new dense matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfIndexed``1(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Int32,``0}})">
            <summary>
            Create a new dense matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfColumnMajor``1(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{``0})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable.
            The enumerable is assumed to be in column-major order (column by column).
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfColumns``1(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{``0}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfColumns``1(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{``0}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfColumnArrays``1(``0[][])">
            <summary>
            Create a new dense matrix of T as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfColumnArrays``1(System.Collections.Generic.IEnumerable{``0[]})">
            <summary>
            Create a new dense matrix of T as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfColumnVectors``1(MathNet.Numerics.LinearAlgebra.Vector{``0}[])">
            <summary>
            Create a new dense matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfColumnVectors``1(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{``0}})">
            <summary>
            Create a new dense matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfRows``1(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{``0}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfRows``1(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{``0}})">
            <summary>
            Create a new dense matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfRowArrays``1(``0[][])">
            <summary>
            Create a new dense matrix of T as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfRowArrays``1(System.Collections.Generic.IEnumerable{``0[]})">
            <summary>
            Create a new dense matrix of T as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfRowVectors``1(MathNet.Numerics.LinearAlgebra.Vector{``0}[])">
            <summary>
            Create a new dense matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfRowVectors``1(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{``0}})">
            <summary>
            Create a new dense matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfDiagonalVector``1(MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfDiagonalVector``1(System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfDiagonalArray``1(``0[])">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfDiagonalArray``1(System.Int32,System.Int32,``0[])">
            <summary>
            Create a new dense matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DenseOfMatrixArray``1(MathNet.Numerics.LinearAlgebra.Matrix{``0}[0:,0:])">
            <summary>
            Create a new dense matrix from a 2D array of existing matrices.
            The matrices in the array are not required to be dense already.
            If the matrices do not align properly, they are placed on the top left
            corner of their cell with the remaining fields left zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.Sparse``1(MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage{``0})">
            <summary>
            Create a new sparse matrix straight from an initialized matrix storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.Sparse``1(System.Int32,System.Int32)">
            <summary>
            Create a sparse matrix of T with the given number of rows and columns.
            </summary>
            <param name="rows">The number of rows.</param>
            <param name="columns">The number of columns.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.Sparse``1(System.Int32,System.Int32,``0)">
            <summary>
            Create a new sparse matrix and initialize each value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.Sparse``1(System.Int32,System.Int32,System.Func{System.Int32,System.Int32,``0})">
            <summary>
            Create a new sparse matrix and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseDiagonal``1(System.Int32,System.Int32,``0)">
            <summary>
            Create a new diagonal sparse matrix and initialize each diagonal value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseDiagonal``1(System.Int32,``0)">
            <summary>
            Create a new diagonal sparse matrix and initialize each diagonal value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseDiagonal``1(System.Int32,System.Int32,System.Func{System.Int32,``0})">
            <summary>
            Create a new diagonal sparse matrix and initialize each diagonal value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseIdentity``1(System.Int32,System.Int32)">
            <summary>
            Create a new diagonal dense identity matrix with a one-diagonal.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseIdentity``1(System.Int32)">
            <summary>
            Create a new diagonal dense identity matrix with a one-diagonal.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfMatrix``1(MathNet.Numerics.LinearAlgebra.Matrix{``0})">
            <summary>
            Create a new sparse matrix as a copy of the given other matrix.
            This new matrix will be independent from the other matrix.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfArray``1(``0[0:,0:])">
            <summary>
            Create a new sparse matrix as a copy of the given two-dimensional array.
            This new matrix will be independent from the provided array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfIndexed``1(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,System.Int32,``0}})">
            <summary>
            Create a new sparse matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfIndexed``1(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,System.Int32,``0}})">
            <summary>
            Create a new sparse matrix as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfRowMajor``1(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{``0})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable.
            The enumerable is assumed to be in row-major order (row by row).
            This new matrix will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
            <seealso href="http://en.wikipedia.org/wiki/Row-major_order"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfColumnMajor``1(System.Int32,System.Int32,System.Collections.Generic.IList{``0})">
            <summary>
            Create a new sparse matrix with the given number of rows and columns as a copy of the given array.
            The array is assumed to be in column-major order (column by column).
            This new matrix will be independent from the provided array.
            A new memory block will be allocated for storing the matrix.
            </summary>
            <seealso href="http://en.wikipedia.org/wiki/Row-major_order"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfColumns``1(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{``0}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfColumns``1(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{``0}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable columns.
            Each enumerable in the master enumerable specifies a column.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfColumnArrays``1(``0[][])">
            <summary>
            Create a new sparse matrix as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfColumnArrays``1(System.Collections.Generic.IEnumerable{``0[]})">
            <summary>
            Create a new sparse matrix as a copy of the given column arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfColumnVectors``1(MathNet.Numerics.LinearAlgebra.Vector{``0}[])">
            <summary>
            Create a new sparse matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfColumnVectors``1(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{``0}})">
            <summary>
            Create a new sparse matrix as a copy of the given column vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfRows``1(System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{``0}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfRows``1(System.Int32,System.Int32,System.Collections.Generic.IEnumerable{System.Collections.Generic.IEnumerable{``0}})">
            <summary>
            Create a new sparse matrix as a copy of the given enumerable of enumerable rows.
            Each enumerable in the master enumerable specifies a row.
            This new matrix will be independent from the enumerables.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfRowArrays``1(``0[][])">
            <summary>
            Create a new sparse matrix as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfRowArrays``1(System.Collections.Generic.IEnumerable{``0[]})">
            <summary>
            Create a new sparse matrix as a copy of the given row arrays.
            This new matrix will be independent from the arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfRowVectors``1(MathNet.Numerics.LinearAlgebra.Vector{``0}[])">
            <summary>
            Create a new sparse matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfRowVectors``1(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Vector{``0}})">
            <summary>
            Create a new sparse matrix as a copy of the given row vectors.
            This new matrix will be independent from the vectors.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfDiagonalVector``1(MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfDiagonalVector``1(System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfDiagonalArray``1(``0[])">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfDiagonalArray``1(System.Int32,System.Int32,``0[])">
            <summary>
            Create a new sparse matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseOfMatrixArray``1(MathNet.Numerics.LinearAlgebra.Matrix{``0}[0:,0:])">
            <summary>
            Create a new sparse matrix from a 2D array of existing matrices.
            The matrices in the array are not required to be sparse already.
            If the matrices do not align properly, they are placed on the top left
            corner of their cell with the remaining fields left zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseFromCoordinateFormat``1(System.Int32,System.Int32,System.Int32,System.Int32[],System.Int32[],``0[])">
            <summary>
            Create a new sparse matrix from a coordinate format.
            This new matrix will be independent from the given arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
            <param name="rows">The number of rows.</param>
            <param name="columns">The number of columns.</param>
            <param name="valueCount">The number of stored values including explicit zeros.</param>
            <param name="rowIndices">The row index array of the coordinate format.</param>
            <param name="columnIndices">The column index array of the coordinate format.</param>
            <param name="values">The data array of the coordinate format.</param>
            <returns>The sparse matrix from the coordinate format.</returns>
            <remarks>Duplicate entries will be summed together and explicit zeros will be not intentionally removed.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseFromCompressedSparseRowFormat``1(System.Int32,System.Int32,System.Int32,System.Int32[],System.Int32[],``0[])">
            <summary>
            Create a new sparse matrix from a compressed sparse row format.
            This new matrix will be independent from the given arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
            <param name="rows">The number of rows.</param>
            <param name="columns">The number of columns.</param>
            <param name="valueCount">The number of stored values including explicit zeros.</param>
            <param name="rowPointers">The row pointer array of the compressed sparse row format.</param>
            <param name="columnIndices">The column index array of the compressed sparse row format.</param>
            <param name="values">The data array of the compressed sparse row format.</param>
            <returns>The sparse matrix from the compressed sparse row format.</returns>
            <remarks>Duplicate entries will be summed together and explicit zeros will be not intentionally removed.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.SparseFromCompressedSparseColumnFormat``1(System.Int32,System.Int32,System.Int32,System.Int32[],System.Int32[],``0[])">
            <summary>
            Create a new sparse matrix from a compressed sparse column format.
            This new matrix will be independent from the given arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
            <param name="rows">The number of rows.</param>
            <param name="columns">The number of columns.</param>
            <param name="valueCount">The number of stored values including explicit zeros.</param>
            <param name="rowIndices">The row index array of the compressed sparse column format.</param>
            <param name="columnPointers">The column pointer array of the compressed sparse column format.</param>
            <param name="values">The data array of the compressed sparse column format.</param>
            <returns>The sparse matrix from the compressed sparse column format.</returns>
            <remarks>Duplicate entries will be summed together and explicit zeros will be not intentionally removed.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.Diagonal``1(MathNet.Numerics.LinearAlgebra.Storage.DiagonalMatrixStorage{``0})">
            <summary>
            Create a new diagonal matrix straight from an initialized matrix storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.Diagonal``1(System.Int32,System.Int32)">
            <summary>
            Create a new diagonal matrix with the given number of rows and columns.
            All cells of the matrix will be initialized to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.Diagonal``1(System.Int32,System.Int32,``0[])">
            <summary>
            Create a new diagonal matrix with the given number of rows and columns directly binding to a raw array.
            The array is assumed to represent the diagonal values and is used directly without copying.
            Very efficient, but changes to the array and the matrix will affect each other.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.Diagonal``1(``0[])">
            <summary>
            Create a new square diagonal matrix directly binding to a raw array.
            The array is assumed to represent the diagonal values and is used directly without copying.
            Very efficient, but changes to the array and the matrix will affect each other.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.Diagonal``1(System.Int32,System.Int32,``0)">
            <summary>
            Create a new diagonal matrix and initialize each diagonal value to the same provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.Diagonal``1(System.Int32,System.Int32,System.Func{System.Int32,``0})">
            <summary>
            Create a new diagonal matrix and initialize each diagonal value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DiagonalIdentity``1(System.Int32,System.Int32)">
            <summary>
            Create a new diagonal identity matrix with a one-diagonal.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DiagonalIdentity``1(System.Int32)">
            <summary>
            Create a new diagonal identity matrix with a one-diagonal.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DiagonalOfDiagonalVector``1(MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Create a new diagonal matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DiagonalOfDiagonalVector``1(System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Create a new diagonal matrix with the diagonal as a copy of the given vector.
            This new matrix will be independent from the vector.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DiagonalOfDiagonalArray``1(``0[])">
            <summary>
            Create a new diagonal matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateMatrix.DiagonalOfDiagonalArray``1(System.Int32,System.Int32,``0[])">
            <summary>
            Create a new diagonal matrix with the diagonal as a copy of the given array.
            This new matrix will be independent from the array.
            A new memory block will be allocated for storing the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.WithStorage``1(MathNet.Numerics.LinearAlgebra.Storage.VectorStorage{``0})">
            <summary>
            Create a new vector straight from an initialized matrix storage instance.
            If you have an instance of a discrete storage type instead, use their direct methods instead.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.SameAs``2(MathNet.Numerics.LinearAlgebra.Vector{``1},System.Int32)">
            <summary>
            Create a new vector with the same kind of the provided example.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.SameAs``2(MathNet.Numerics.LinearAlgebra.Vector{``1})">
            <summary>
            Create a new vector with the same kind and dimension of the provided example.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.SameAs``2(MathNet.Numerics.LinearAlgebra.Matrix{``1},System.Int32)">
            <summary>
            Create a new vector with the same kind of the provided example.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.SameAs``1(MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Vector{``0},System.Int32)">
            <summary>
            Create a new vector with a type that can represent and is closest to both provided samples.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.SameAs``1(MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Create a new vector with a type that can represent and is closest to both provided samples and the dimensions of example.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.SameAs``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Vector{``0},System.Int32)">
            <summary>
            Create a new vector with a type that can represent and is closest to both provided samples.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.Random``1(System.Int32,MathNet.Numerics.Distributions.IContinuousDistribution)">
            <summary>
            Create a new dense vector with values sampled from the provided random distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.Random``1(System.Int32)">
            <summary>
            Create a new dense vector with values sampled from the standard distribution with a system random source.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.Random``1(System.Int32,System.Int32)">
            <summary>
            Create a new dense vector with values sampled from the standard distribution with a system random source.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.Dense``1(MathNet.Numerics.LinearAlgebra.Storage.DenseVectorStorage{``0})">
            <summary>
            Create a new dense vector straight from an initialized vector storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.Dense``1(System.Int32)">
            <summary>
            Create a dense vector of T with the given size.
            </summary>
            <param name="size">The size of the vector.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.Dense``1(``0[])">
            <summary>
            Create a dense vector of T that is directly bound to the specified array.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.Dense``1(System.Int32,``0)">
            <summary>
            Create a new dense vector and initialize each value using the provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.Dense``1(System.Int32,System.Func{System.Int32,``0})">
            <summary>
            Create a new dense vector and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.DenseOfVector``1(MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Create a new dense vector as a copy of the given other vector.
            This new vector will be independent from the other vector.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.DenseOfArray``1(``0[])">
            <summary>
            Create a new dense vector as a copy of the given array.
            This new vector will be independent from the array.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.DenseOfEnumerable``1(System.Collections.Generic.IEnumerable{``0})">
            <summary>
            Create a new dense vector as a copy of the given enumerable.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.DenseOfIndexed``1(System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,``0}})">
            <summary>
            Create a new dense vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.DenseOfIndexed``1(System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,``0}})">
            <summary>
            Create a new dense vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.Sparse``1(MathNet.Numerics.LinearAlgebra.Storage.SparseVectorStorage{``0})">
            <summary>
            Create a new sparse vector straight from an initialized vector storage instance.
            The storage is used directly without copying.
            Intended for advanced scenarios where you're working directly with
            storage for performance or interop reasons.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.Sparse``1(System.Int32)">
            <summary>
            Create a sparse vector of T with the given size.
            </summary>
            <param name="size">The size of the vector.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.Sparse``1(System.Int32,``0)">
            <summary>
            Create a new sparse vector and initialize each value using the provided value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.Sparse``1(System.Int32,System.Func{System.Int32,``0})">
            <summary>
            Create a new sparse vector and initialize each value using the provided init function.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.SparseOfVector``1(MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Create a new sparse vector as a copy of the given other vector.
            This new vector will be independent from the other vector.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.SparseOfArray``1(``0[])">
            <summary>
            Create a new sparse vector as a copy of the given array.
            This new vector will be independent from the array.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.SparseOfEnumerable``1(System.Collections.Generic.IEnumerable{``0})">
            <summary>
            Create a new sparse vector as a copy of the given enumerable.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.SparseOfIndexed``1(System.Int32,System.Collections.Generic.IEnumerable{System.Tuple{System.Int32,``0}})">
            <summary>
            Create a new sparse vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.CreateVector.SparseOfIndexed``1(System.Int32,System.Collections.Generic.IEnumerable{System.ValueTuple{System.Int32,``0}})">
            <summary>
            Create a new sparse vector as a copy of the given indexed enumerable.
            Keys must be provided at most once, zero is assumed if a key is omitted.
            This new vector will be independent from the enumerable.
            A new memory block will be allocated for storing the vector.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Factorization.Cholesky`1">
            <summary>
            <para>A class which encapsulates the functionality of a Cholesky factorization.</para>
            <para>For a symmetric, positive definite matrix A, the Cholesky factorization
            is an lower triangular matrix L so that A = L*L'.</para>
            </summary>
            <remarks>
            The computation of the Cholesky factorization is done at construction time. If the matrix is not symmetric
            or positive definite, the constructor will throw an exception.
            </remarks>
            <typeparam name="T">Supported data types are double, single, <see cref="N:MathNet.Numerics.LinearAlgebra.Complex"/>, and <see cref="N:MathNet.Numerics.LinearAlgebra.Complex32"/>.</typeparam>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.Cholesky`1.Factor">
            <summary>
            Gets the lower triangular form of the Cholesky matrix.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.Cholesky`1.Determinant">
            <summary>
            Gets the determinant of the matrix for which the Cholesky matrix was computed.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.Cholesky`1.DeterminantLn">
            <summary>
            Gets the log determinant of the matrix for which the Cholesky matrix was computed.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.Cholesky`1.Factorize(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Calculates the Cholesky factorization of the input matrix.
            </summary>
            <param name="matrix">The matrix to be factorized<see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.Cholesky`1.Solve(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A Cholesky factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <returns>The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.Cholesky`1.Solve(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A Cholesky factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.Cholesky`1.Solve(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A Cholesky factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <returns>The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Vector`1"/>, <b>x</b>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.Cholesky`1.Solve(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A Cholesky factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Factorization.Evd`1">
            <summary>
            Eigenvalues and eigenvectors of a real matrix.
            </summary>
            <remarks>
            If A is symmetric, then A = V*D*V' where the eigenvalue matrix D is
            diagonal and the eigenvector matrix V is orthogonal.
            I.e. A = V*D*V' and V*VT=I.
            If A is not symmetric, then the eigenvalue matrix D is block diagonal
            with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues,
            lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda].  The
            columns of V represent the eigenvectors in the sense that A*V = V*D,
            i.e. A.Multiply(V) equals V.Multiply(D).  The matrix V may be badly
            conditioned, or even singular, so the validity of the equation
            A = V*D*Inverse(V) depends upon V.Condition().
            </remarks>
            <typeparam name="T">Supported data types are double, single, <see cref="T:System.Numerics.Complex"/>, and <see cref="T:MathNet.Numerics.Complex32"/>.</typeparam>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.Evd`1.IsSymmetric">
            <summary>
            Gets or sets a value indicating whether matrix is symmetric or not
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.Evd`1.Determinant">
            <summary>
            Gets the absolute value of determinant of the square matrix for which the EVD was computed.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.Evd`1.Rank">
            <summary>
            Gets the effective numerical matrix rank.
            </summary>
            <value>The number of non-negligible singular values.</value>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.Evd`1.IsFullRank">
            <summary>
            Gets a value indicating whether the matrix is full rank or not.
            </summary>
            <value><c>true</c> if the matrix is full rank; otherwise <c>false</c>.</value>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.Evd`1.EigenValues">
            <summary>
            Gets or sets the eigen values (λ) of matrix in ascending value.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.Evd`1.EigenVectors">
            <summary>
            Gets or sets eigenvectors.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.Evd`1.D">
            <summary>
            Gets or sets the block diagonal eigenvalue matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.Evd`1.Solve(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A EVD factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <returns>The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.Evd`1.Solve(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A EVD factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.Evd`1.Solve(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A EVD factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <returns>The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Vector`1"/>, <b>x</b>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.Evd`1.Solve(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A EVD factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Factorization.GramSchmidt`1">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition Modified Gram-Schmidt Orthogonalization.</para>
            <para>Any real square matrix A may be decomposed as A = QR where Q is an orthogonal mxn matrix and R is an nxn upper triangular matrix.</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by modified Gram-Schmidt Orthogonalization.
            </remarks>
            <typeparam name="T">Supported data types are double, single, <see cref="N:MathNet.Numerics.LinearAlgebra.Complex"/>, and <see cref="N:MathNet.Numerics.LinearAlgebra.Complex32"/>.</typeparam>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Factorization.ISolver`1">
            <summary>
            Classes that solves a system of linear equations, <c>AX = B</c>.
            </summary>
            <typeparam name="T">Supported data types are double, single, <see cref="N:MathNet.Numerics.LinearAlgebra.Complex"/>, and <see cref="N:MathNet.Numerics.LinearAlgebra.Complex32"/>.</typeparam>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.ISolver`1.Solve(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Solves a system of linear equations, <c>AX = B</c>.
            </summary>
            <param name="input">The right hand side Matrix, <c>B</c>.</param>
            <returns>The left hand side Matrix, <c>X</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.ISolver`1.Solve(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Solves a system of linear equations, <c>AX = B</c>.
            </summary>
            <param name="input">The right hand side Matrix, <c>B</c>.</param>
            <param name="result">The left hand side Matrix, <c>X</c>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.ISolver`1.Solve(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Solves a system of linear equations, <c>Ax = b</c>
            </summary>
            <param name="input">The right hand side vector, <c>b</c>.</param>
            <returns>The left hand side Vector, <c>x</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.ISolver`1.Solve(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Solves a system of linear equations, <c>Ax = b</c>.
            </summary>
            <param name="input">The right hand side vector, <c>b</c>.</param>
            <param name="result">The left hand side Matrix>, <c>x</c>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Factorization.LU`1">
            <summary>
            <para>A class which encapsulates the functionality of an LU factorization.</para>
            <para>For a matrix A, the LU factorization is a pair of lower triangular matrix L and
            upper triangular matrix U so that A = L*U.</para>
            <para>In the Math.NET implementation we also store a set of pivot elements for increased
            numerical stability. The pivot elements encode a permutation matrix P such that P*A = L*U.</para>
            </summary>
            <remarks>
            The computation of the LU factorization is done at construction time.
            </remarks>
            <typeparam name="T">Supported data types are double, single, <see cref="N:MathNet.Numerics.LinearAlgebra.Complex"/>, and <see cref="N:MathNet.Numerics.LinearAlgebra.Complex32"/>.</typeparam>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.LU`1.L">
            <summary>
            Gets the lower triangular factor.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.LU`1.U">
            <summary>
            Gets the upper triangular factor.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.LU`1.P">
            <summary>
            Gets the permutation applied to LU factorization.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.LU`1.Determinant">
            <summary>
            Gets the determinant of the matrix for which the LU factorization was computed.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.LU`1.Solve(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A LU factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <returns>The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.LU`1.Solve(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A LU factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.LU`1.Solve(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A LU factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <returns>The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Vector`1"/>, <b>x</b>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.LU`1.Solve(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A LU factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.LU`1.Inverse">
            <summary>
            Returns the inverse of this matrix. The inverse is calculated using LU decomposition.
            </summary>
            <returns>The inverse of this matrix.</returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod">
            <summary>
            The type of QR factorization go perform.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod.Full">
            <summary>
            Compute the full QR factorization of a matrix.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod.Thin">
            <summary>
            Compute the thin QR factorization of a matrix.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Factorization.QR`1">
            <summary>
            <para>A class which encapsulates the functionality of the QR decomposition.</para>
            <para>Any real square matrix A (m x n) may be decomposed as A = QR where Q is an orthogonal matrix
            (its columns are orthogonal unit vectors meaning QTQ = I) and R is an upper triangular matrix
            (also called right triangular matrix).</para>
            </summary>
            <remarks>
            The computation of the QR decomposition is done at construction time by Householder transformation.
            If a <seealso cref="F:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod.Full"/> factorization is performed, the resulting Q matrix is an m x m matrix
            and the R matrix is an m x n matrix. If a <seealso cref="F:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod.Thin"/> factorization is performed, the
            resulting Q matrix is an m x n matrix and the R matrix is an n x n matrix.
            </remarks>
            <typeparam name="T">Supported data types are double, single, <see cref="N:MathNet.Numerics.LinearAlgebra.Complex"/>, and <see cref="N:MathNet.Numerics.LinearAlgebra.Complex32"/>.</typeparam>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.QR`1.Q">
            <summary>
            Gets or sets orthogonal Q matrix
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.QR`1.R">
            <summary>
            Gets the upper triangular factor R.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.QR`1.Determinant">
            <summary>
            Gets the absolute determinant value of the matrix for which the QR matrix was computed.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.QR`1.IsFullRank">
            <summary>
            Gets a value indicating whether the matrix is full rank or not.
            </summary>
            <value><c>true</c> if the matrix is full rank; otherwise <c>false</c>.</value>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.QR`1.Solve(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <returns>The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.QR`1.Solve(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.QR`1.Solve(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <returns>The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Vector`1"/>, <b>x</b>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.QR`1.Solve(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Factorization.Svd`1">
            <summary>
            <para>A class which encapsulates the functionality of the singular value decomposition (SVD).</para>
            <para>Suppose M is an m-by-n matrix whose entries are real numbers.
            Then there exists a factorization of the form M = UΣVT where:
            - U is an m-by-m unitary matrix;
            - Σ is m-by-n diagonal matrix with nonnegative real numbers on the diagonal;
            - VT denotes transpose of V, an n-by-n unitary matrix;
            Such a factorization is called a singular-value decomposition of M. A common convention is to order the diagonal
            entries Σ(i,i) in descending order. In this case, the diagonal matrix Σ is uniquely determined
            by M (though the matrices U and V are not). The diagonal entries of Σ are known as the singular values of M.</para>
            </summary>
            <remarks>
            The computation of the singular value decomposition is done at construction time.
            </remarks>
            <typeparam name="T">Supported data types are double, single, <see cref="N:MathNet.Numerics.LinearAlgebra.Complex"/>, and <see cref="N:MathNet.Numerics.LinearAlgebra.Complex32"/>.</typeparam>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Factorization.Svd`1.VectorsComputed">
            <summary>Indicating whether U and VT matrices have been computed during SVD factorization.</summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.Svd`1.S">
            <summary>
            Gets the singular values (Σ) of matrix in ascending value.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.Svd`1.U">
            <summary>
            Gets the left singular vectors (U - m-by-m unitary matrix)
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.Svd`1.VT">
            <summary>
            Gets the transpose right singular vectors (transpose of V, an n-by-n unitary matrix)
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.Svd`1.W">
            <summary>
            Returns the singular values as a diagonal <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.
            </summary>
            <returns>The singular values as a diagonal <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.</returns>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.Svd`1.Rank">
            <summary>
            Gets the effective numerical matrix rank.
            </summary>
            <value>The number of non-negligible singular values.</value>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.Svd`1.L2Norm">
            <summary>
            Gets the two norm of the <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.
            </summary>
            <returns>The 2-norm of the <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>.</returns>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.Svd`1.ConditionNumber">
            <summary>
            Gets the condition number <b>max(S) / min(S)</b>
            </summary>
            <returns>The condition number.</returns>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Factorization.Svd`1.Determinant">
            <summary>
            Gets the determinant of the square matrix for which the SVD was computed.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.Svd`1.Solve(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <returns>The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.Svd`1.Solve(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.Svd`1.Solve(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <returns>The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Vector`1"/>, <b>x</b>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Factorization.Svd`1.Solve(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A SVD factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Matrix`1">
            <summary>
            Defines the base class for <c>Matrix</c> classes.
            </summary>
            <summary>
            Defines the base class for <c>Matrix</c> classes.
            </summary>
            <typeparam name="T">Supported data types are <c>double</c>, <c>single</c>, <see cref="N:MathNet.Numerics.LinearAlgebra.Complex"/>, and <see cref="N:MathNet.Numerics.LinearAlgebra.Complex32"/>.</typeparam>
            <summary>
            Defines the base class for <c>Matrix</c> classes.
            </summary>
            <summary>
            Defines the base class for <c>Matrix</c> classes.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Matrix`1.One">
            <summary>
            The value of 1.0.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Matrix`1.Zero">
            <summary>
            The value of 0.0.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoNegate(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Negate each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the negation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoConjugate(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Complex conjugates each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the conjugation.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoAdd(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Add a scalar to each element of the matrix and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to add.</param>
            <param name="result">The matrix to store the result of the addition.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoAdd(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Adds another matrix to this matrix.
            </summary>
            <param name="other">The matrix to add to this matrix.</param>
            <param name="result">The matrix to store the result of the addition.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoSubtract(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Subtracts a scalar from each element of the matrix and stores the result in the result matrix.
            </summary>
            <param name="scalar">The scalar to subtract.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoSubtractFrom(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Subtracts each element of the matrix from a scalar and stores the result in the result matrix.
            </summary>
            <param name="scalar">The scalar to subtract from.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoSubtract(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Subtracts another matrix from this matrix.
            </summary>
            <param name="other">The matrix to subtract.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoMultiply(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Multiplies each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="scalar">The scalar to multiply the matrix with.</param>
            <param name="result">The matrix to store the result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoMultiply(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Multiplies this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoMultiply(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Multiplies this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Multiplies this matrix with the transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoConjugateTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Multiplies this matrix with the conjugate transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Multiplies the transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Multiplies the conjugate transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoDivide(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Divides each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoDivideByThis(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Divides a scalar by each element of the matrix and stores the result in the result matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">The matrix to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoModulus(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoModulusByThis(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given dividend for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoRemainder(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given divisor each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoRemainderByThis(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given dividend for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise multiplies this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="other">The matrix to pointwise multiply with this one.</param>
            <param name="result">The matrix to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise divide this matrix by another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The pointwise denominator matrix to use.</param>
            <param name="result">The matrix to store the result of the pointwise division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoPointwisePower(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise raise this matrix to an exponent and store the result into the result matrix.
            </summary>
            <param name="exponent">The exponent to raise this matrix values to.</param>
            <param name="result">The matrix to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoPointwisePower(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise raise this matrix to an exponent matrix and store the result into the result matrix.
            </summary>
            <param name="exponent">The exponent matrix to raise this matrix values to.</param>
            <param name="result">The matrix to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoPointwiseModulus(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise canonical modulus, where the result has the sign of the divisor,
            of this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The pointwise denominator matrix to use</param>
            <param name="result">The result of the modulus.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoPointwiseRemainder(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise remainder (% operator), where the result has the sign of the dividend,
            of this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The pointwise denominator matrix to use</param>
            <param name="result">The result of the modulus.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoPointwiseExp(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the exponential function to each value and stores the result into the result matrix.
            </summary>
            <param name="result">The matrix to store the result.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoPointwiseLog(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the natural logarithm function to each value and stores the result into the result matrix.
            </summary>
            <param name="result">The matrix to store the result.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Add(`0)">
            <summary>
            Adds a scalar to each element of the matrix.
            </summary>
            <param name="scalar">The scalar to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Add(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Adds a scalar to each element of the matrix and stores the result in the result matrix.
            </summary>
            <param name="scalar">The scalar to add.</param>
            <param name="result">The matrix to store the result of the addition.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Add(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Adds another matrix to this matrix.
            </summary>
            <param name="other">The matrix to add to this matrix.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Add(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Adds another matrix to this matrix.
            </summary>
            <param name="other">The matrix to add to this matrix.</param>
            <param name="result">The matrix to store the result of the addition.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Subtract(`0)">
            <summary>
            Subtracts a scalar from each element of the matrix.
            </summary>
            <param name="scalar">The scalar to subtract.</param>
            <returns>A new matrix containing the subtraction of this matrix and the scalar.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Subtract(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Subtracts a scalar from each element of the matrix and stores the result in the result matrix.
            </summary>
            <param name="scalar">The scalar to subtract.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.SubtractFrom(`0)">
            <summary>
            Subtracts each element of the matrix from a scalar.
            </summary>
            <param name="scalar">The scalar to subtract from.</param>
            <returns>A new matrix containing the subtraction of the scalar and this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.SubtractFrom(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Subtracts each element of the matrix from a scalar and stores the result in the result matrix.
            </summary>
            <param name="scalar">The scalar to subtract from.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Subtract(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Subtracts another matrix from this matrix.
            </summary>
            <param name="other">The matrix to subtract.</param>
            <returns>The result of the subtraction.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Subtract(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Subtracts another matrix from this matrix.
            </summary>
            <param name="other">The matrix to subtract.</param>
            <param name="result">The matrix to store the result of the subtraction.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Multiply(`0)">
            <summary>
            Multiplies each element of this matrix with a scalar.
            </summary>
            <param name="scalar">The scalar to multiply with.</param>
            <returns>The result of the multiplication.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Multiply(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Multiplies each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="scalar">The scalar to multiply the matrix with.</param>
            <param name="result">The matrix to store the result of the multiplication.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Divide(`0)">
            <summary>
            Divides each element of this matrix with a scalar.
            </summary>
            <param name="scalar">The scalar to divide with.</param>
            <returns>The result of the division.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Divide(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Divides each element of the matrix by a scalar and places results into the result matrix.
            </summary>
            <param name="scalar">The scalar to divide the matrix with.</param>
            <param name="result">The matrix to store the result of the division.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DivideByThis(`0)">
            <summary>
            Divides a scalar by each element of the matrix.
            </summary>
            <param name="scalar">The scalar to divide.</param>
            <returns>The result of the division.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DivideByThis(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Divides a scalar by each element of the matrix and places results into the result matrix.
            </summary>
            <param name="scalar">The scalar to divide.</param>
            <param name="result">The matrix to store the result of the division.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Multiply(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Multiplies this matrix by a vector and returns the result.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentException">If <c>this.ColumnCount != rightSide.Count</c>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Multiply(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Multiplies this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
            <exception cref="T:System.ArgumentException">If <strong>result.Count != this.RowCount</strong>.</exception>
            <exception cref="T:System.ArgumentException">If <strong>this.ColumnCount != <paramref name="rightSide"/>.Count</strong>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.LeftMultiply(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Left multiply a matrix with a vector ( = vector * matrix ).
            </summary>
            <param name="leftSide">The vector to multiply with.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentException">If <strong>this.RowCount != <paramref name="leftSide"/>.Count</strong>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.LeftMultiply(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Left multiply a matrix with a vector ( = vector * matrix ) and place the result in the result vector.
            </summary>
            <param name="leftSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
            <exception cref="T:System.ArgumentException">If <strong>result.Count != this.ColumnCount</strong>.</exception>
            <exception cref="T:System.ArgumentException">If <strong>this.RowCount != <paramref name="leftSide"/>.Count</strong>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DoLeftMultiply(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Left multiply a matrix with a vector ( = vector * matrix ) and place the result in the result vector.
            </summary>
            <param name="leftSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Multiply(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Multiplies this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
            <exception cref="T:System.ArgumentException">If <strong>this.Columns != other.Rows</strong>.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the this.Rows x other.Columns.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Multiply(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Multiplies this matrix with another matrix and returns the result.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <exception cref="T:System.ArgumentException">If <strong>this.Columns != other.Rows</strong>.</exception>
            <returns>The result of the multiplication.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.TransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Multiplies this matrix with transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
            <exception cref="T:System.ArgumentException">If <strong>this.Columns != other.ColumnCount</strong>.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the this.RowCount x other.RowCount.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.TransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Multiplies this matrix with transpose of another matrix and returns the result.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <exception cref="T:System.ArgumentException">If <strong>this.Columns != other.ColumnCount</strong>.</exception>
            <returns>The result of the multiplication.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.TransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Multiplies the transpose of this matrix by a vector and returns the result.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentException">If <c>this.RowCount != rightSide.Count</c>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.TransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Multiplies the transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
            <exception cref="T:System.ArgumentException">If <strong>result.Count != this.ColumnCount</strong>.</exception>
            <exception cref="T:System.ArgumentException">If <strong>this.RowCount != <paramref name="rightSide"/>.Count</strong>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.TransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
            <exception cref="T:System.ArgumentException">If <strong>this.Rows != other.RowCount</strong>.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the this.ColumnCount x other.ColumnCount.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.TransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Multiplies the transpose of this matrix with another matrix and returns the result.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <exception cref="T:System.ArgumentException">If <strong>this.Rows != other.RowCount</strong>.</exception>
            <returns>The result of the multiplication.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ConjugateTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Multiplies this matrix with the conjugate transpose of another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
            <exception cref="T:System.ArgumentException">If <strong>this.Columns != other.ColumnCount</strong>.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the this.RowCount x other.RowCount.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ConjugateTransposeAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Multiplies this matrix with the conjugate transpose of another matrix and returns the result.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <exception cref="T:System.ArgumentException">If <strong>this.Columns != other.ColumnCount</strong>.</exception>
            <returns>The result of the multiplication.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Multiplies the conjugate transpose of this matrix by a vector and returns the result.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentException">If <c>this.RowCount != rightSide.Count</c>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Multiplies the conjugate transpose of this matrix with a vector and places the results into the result vector.
            </summary>
            <param name="rightSide">The vector to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
            <exception cref="T:System.ArgumentException">If <strong>result.Count != this.ColumnCount</strong>.</exception>
            <exception cref="T:System.ArgumentException">If <strong>this.RowCount != <paramref name="rightSide"/>.Count</strong>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Multiplies the conjugate transpose of this matrix with another matrix and places the results into the result matrix.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <param name="result">The result of the multiplication.</param>
            <exception cref="T:System.ArgumentException">If <strong>this.Rows != other.RowCount</strong>.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the this.ColumnCount x other.ColumnCount.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ConjugateTransposeThisAndMultiply(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Multiplies the conjugate transpose of this matrix with another matrix and returns the result.
            </summary>
            <param name="other">The matrix to multiply with.</param>
            <exception cref="T:System.ArgumentException">If <strong>this.Rows != other.RowCount</strong>.</exception>
            <returns>The result of the multiplication.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Power(System.Int32,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Raises this square matrix to a positive integer exponent and places the results into the result matrix.
            </summary>
            <param name="exponent">The positive integer exponent to raise the matrix to.</param>
            <param name="result">The result of the power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Power(System.Int32)">
            <summary>
            Multiplies this square matrix with another matrix and returns the result.
            </summary>
            <param name="exponent">The positive integer exponent to raise the matrix to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Negate">
            <summary>
            Negate each element of this matrix.
            </summary>
            <returns>A matrix containing the negated values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Negate(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Negate each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the negation.</param>
            <exception cref="T:System.ArgumentException">if the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Conjugate">
            <summary>
            Complex conjugate each element of this matrix.
            </summary>
            <returns>A matrix containing the conjugated values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Conjugate(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Complex conjugate each element of this matrix and place the results into the result matrix.
            </summary>
            <param name="result">The result of the conjugation.</param>
            <exception cref="T:System.ArgumentException">if the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Modulus(`0)">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <returns>A matrix containing the results.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Modulus(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ModulusByThis(`0)">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <returns>A matrix containing the results.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ModulusByThis(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Remainder(`0)">
            <summary>
            Computes the remainder (matrix % divisor), where the result has the sign of the dividend,
            for each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <returns>A matrix containing the results.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Remainder(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the remainder (matrix % divisor), where the result has the sign of the dividend,
            for each element of the matrix.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.RemainderByThis(`0)">
            <summary>
            Computes the remainder (dividend % matrix), where the result has the sign of the dividend,
            for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <returns>A matrix containing the results.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.RemainderByThis(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the remainder (dividend % matrix), where the result has the sign of the dividend,
            for each element of the matrix.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">Matrix to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseMultiply(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise multiplies this matrix with another matrix.
            </summary>
            <param name="other">The matrix to pointwise multiply with this one.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="other"/> are not the same size.</exception>
            <returns>A new matrix that is the pointwise multiplication of this matrix and <paramref name="other"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseMultiply(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise multiplies this matrix with another matrix and stores the result into the result matrix.
            </summary>
            <param name="other">The matrix to pointwise multiply with this one.</param>
            <param name="result">The matrix to store the result of the pointwise multiplication.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="other"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseDivide(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise divide this matrix by another matrix.
            </summary>
            <param name="divisor">The pointwise denominator matrix to use.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="divisor"/> are not the same size.</exception>
            <returns>A new matrix that is the pointwise division of this matrix and <paramref name="divisor"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseDivide(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise divide this matrix by another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The pointwise denominator matrix to use.</param>
            <param name="result">The matrix to store the result of the pointwise division.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="divisor"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwisePower(`0)">
            <summary>
            Pointwise raise this matrix to an exponent and store the result into the result matrix.
            </summary>
            <param name="exponent">The exponent to raise this matrix values to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwisePower(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise raise this matrix to an exponent.
            </summary>
            <param name="exponent">The exponent to raise this matrix values to.</param>
            <param name="result">The matrix to store the result into.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwisePower(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise raise this matrix to an exponent and store the result into the result matrix.
            </summary>
            <param name="exponent">The exponent to raise this matrix values to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwisePower(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise raise this matrix to an exponent.
            </summary>
            <param name="exponent">The exponent to raise this matrix values to.</param>
            <param name="result">The matrix to store the result into.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseModulus(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise canonical modulus, where the result has the sign of the divisor,
            of this matrix by another matrix.
            </summary>
            <param name="divisor">The pointwise denominator matrix to use.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="divisor"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseModulus(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise canonical modulus, where the result has the sign of the divisor,
            of this matrix by another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The pointwise denominator matrix to use.</param>
            <param name="result">The matrix to store the result of the pointwise modulus.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="divisor"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseRemainder(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise remainder (% operator), where the result has the sign of the dividend,
            of this matrix by another matrix.
            </summary>
            <param name="divisor">The pointwise denominator matrix to use.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="divisor"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseRemainder(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise remainder (% operator), where the result has the sign of the dividend,
            of this matrix by another matrix and stores the result into the result matrix.
            </summary>
            <param name="divisor">The pointwise denominator matrix to use.</param>
            <param name="result">The matrix to store the result of the pointwise remainder.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="divisor"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseUnary(System.Action{MathNet.Numerics.LinearAlgebra.Matrix{`0}})">
            <summary>
            Helper function to apply a unary function to a matrix. The function
            f modifies the matrix given to it in place.  Before its
            called, a copy of the 'this' matrix is first created, then passed to
            f.  The copy is then returned as the result
            </summary>
            <param name="f">Function which takes a matrix, modifies it in place and returns void</param>
            <returns>New instance of matrix which is the result</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseUnary(System.Action{MathNet.Numerics.LinearAlgebra.Matrix{`0}},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Helper function to apply a unary function which modifies a matrix
            in place.
            </summary>
            <param name="f">Function which takes a matrix, modifies it in place and returns void</param>
            <param name="result">The matrix to be passed to f and where the result is to be stored</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseBinary(System.Action{MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0}},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Helper function to apply a binary function which takes two matrices
            and modifies the latter in place.  A copy of the "this" matrix is
            first made and then passed to f together with the other matrix. The
            copy is then returned as the result
            </summary>
            <param name="f">Function which takes two matrices, modifies the second in place and returns void</param>
            <param name="other">The other matrix to be passed to the function as argument. It is not modified</param>
            <returns>The resulting matrix</returns>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="other"/> are not the same dimension.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseBinary(System.Action{MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0}},MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Helper function to apply a binary function which takes two matrices
            and modifies the second one in place
            </summary>
            <param name="f">Function which takes two matrices, modifies the second in place and returns void</param>
            <param name="other">The other matrix to be passed to the function as argument. It is not modified</param>
            <param name="result">The matrix to store the result.</param>
            <returns>The resulting matrix</returns>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="other"/> are not the same dimension.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseExp">
            <summary>
            Pointwise applies the exponent function to each value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseExp(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the exponent function to each value.
            </summary>
            <param name="result">The matrix to store the result.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseLog">
            <summary>
            Pointwise applies the natural logarithm function to each value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseLog(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the natural logarithm function to each value.
            </summary>
            <param name="result">The matrix to store the result.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseAbs">
            <summary>
            Pointwise applies the abs function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseAbs(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the abs function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseAcos">
            <summary>
            Pointwise applies the acos function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseAcos(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the acos function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseAsin">
            <summary>
            Pointwise applies the asin function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseAsin(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the asin function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseAtan">
            <summary>
            Pointwise applies the atan function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseAtan(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the atan function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseAtan2(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the atan2 function to each value of the current
            matrix and a given other matrix being the 'x' of atan2 and the
            'this' matrix being the 'y'
            </summary>
            <param name="other"></param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseAtan2(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the atan2 function to each value of the current
            matrix and a given other matrix being the 'x' of atan2 and the
            'this' matrix being the 'y'
            </summary>
            <param name="other">The other matrix 'y'</param>
            <param name="result">The matrix with the result and 'x'</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseCeiling">
            <summary>
            Pointwise applies the ceiling function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseCeiling(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the ceiling function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseCos">
            <summary>
            Pointwise applies the cos function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseCos(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the cos function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseCosh">
            <summary>
            Pointwise applies the cosh function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseCosh(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the cosh function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseFloor">
            <summary>
            Pointwise applies the floor function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseFloor(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the floor function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseLog10">
            <summary>
            Pointwise applies the log10 function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseLog10(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the log10 function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseRound">
            <summary>
            Pointwise applies the round function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseRound(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the round function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseSign">
            <summary>
            Pointwise applies the sign function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseSign(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the sign function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseSin">
            <summary>
            Pointwise applies the sin function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseSin(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the sin function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseSinh">
            <summary>
            Pointwise applies the sinh function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseSinh(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the sinh function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseSqrt">
            <summary>
            Pointwise applies the sqrt function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseSqrt(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the sqrt function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseTan">
            <summary>
            Pointwise applies the tan function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseTan(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the tan function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseTanh">
            <summary>
            Pointwise applies the tanh function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseTanh(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the tanh function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Trace">
            <summary>
            Computes the trace of this matrix.
            </summary>
            <returns>The trace of this matrix</returns>
            <exception cref="T:System.ArgumentException">If the matrix is not square</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Rank">
            <summary>
            Calculates the rank of the matrix.
            </summary>
            <returns>effective numerical rank, obtained from SVD</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Nullity">
            <summary>
            Calculates the nullity of the matrix.
            </summary>
            <returns>effective numerical nullity, obtained from SVD</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ConditionNumber">
            <summary>Calculates the condition number of this matrix.</summary>
            <returns>The condition number of the matrix.</returns>
            <remarks>The condition number is calculated using singular value decomposition.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Determinant">
            <summary>Computes the determinant of this matrix.</summary>
            <returns>The determinant of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Kernel">
            <summary>
            Computes an orthonormal basis for the null space of this matrix,
            also known as the kernel of the corresponding matrix transformation.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Range">
            <summary>
            Computes an orthonormal basis for the column space of this matrix,
            also known as the range or image of the corresponding matrix transformation.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Inverse">
            <summary>Computes the inverse of this matrix.</summary>
            <returns>The inverse of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PseudoInverse">
            <summary>Computes the Moore-Penrose Pseudo-Inverse of this matrix.</summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.KroneckerProduct(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the Kronecker product of this matrix with the given matrix. The new matrix is M-by-N
            with M = this.Rows * lower.Rows and N = this.Columns * lower.Columns.
            </summary>
            <param name="other">The other matrix.</param>
            <returns>The Kronecker product of the two matrices.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.KroneckerProduct(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the Kronecker product of this matrix with the given matrix. The new matrix is M-by-N
            with M = this.Rows * lower.Rows and N = this.Columns * lower.Columns.
            </summary>
            <param name="other">The other matrix.</param>
            <param name="result">The Kronecker product of the two matrices.</param>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not (this.Rows * lower.rows) x (this.Columns * lower.Columns).</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseMinimum(`0)">
            <summary>
            Pointwise applies the minimum with a scalar to each value.
            </summary>
            <param name="scalar">The scalar value to compare to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseMinimum(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the minimum with a scalar to each value.
            </summary>
            <param name="scalar">The scalar value to compare to.</param>
            <param name="result">The vector to store the result.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseMaximum(`0)">
            <summary>
            Pointwise applies the maximum with a scalar to each value.
            </summary>
            <param name="scalar">The scalar value to compare to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseMaximum(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the maximum with a scalar to each value.
            </summary>
            <param name="scalar">The scalar value to compare to.</param>
            <param name="result">The matrix to store the result.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseAbsoluteMinimum(`0)">
            <summary>
            Pointwise applies the absolute minimum with a scalar to each value.
            </summary>
            <param name="scalar">The scalar value to compare to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseAbsoluteMinimum(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the absolute minimum with a scalar to each value.
            </summary>
            <param name="scalar">The scalar value to compare to.</param>
            <param name="result">The matrix to store the result.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseAbsoluteMaximum(`0)">
            <summary>
            Pointwise applies the absolute maximum with a scalar to each value.
            </summary>
            <param name="scalar">The scalar value to compare to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseAbsoluteMaximum(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the absolute maximum with a scalar to each value.
            </summary>
            <param name="scalar">The scalar value to compare to.</param>
            <param name="result">The matrix to store the result.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseMinimum(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the minimum with the values of another matrix to each value.
            </summary>
            <param name="other">The matrix with the values to compare to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseMinimum(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the minimum with the values of another matrix to each value.
            </summary>
            <param name="other">The matrix with the values to compare to.</param>
            <param name="result">The matrix to store the result.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseMaximum(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the maximum with the values of another matrix to each value.
            </summary>
            <param name="other">The matrix with the values to compare to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseMaximum(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the maximum with the values of another matrix to each value.
            </summary>
            <param name="other">The matrix with the values to compare to.</param>
            <param name="result">The matrix to store the result.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseAbsoluteMinimum(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the absolute minimum with the values of another matrix to each value.
            </summary>
            <param name="other">The matrix with the values to compare to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseAbsoluteMinimum(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the absolute minimum with the values of another matrix to each value.
            </summary>
            <param name="other">The matrix with the values to compare to.</param>
            <param name="result">The matrix to store the result.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseAbsoluteMaximum(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the absolute maximum with the values of another matrix to each value.
            </summary>
            <param name="other">The matrix with the values to compare to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PointwiseAbsoluteMaximum(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Pointwise applies the absolute maximum with the values of another matrix to each value.
            </summary>
            <param name="other">The matrix with the values to compare to.</param>
            <param name="result">The matrix to store the result.</param>
            <exception cref="T:System.ArgumentException">If this matrix and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.L1Norm">
            <summary>Calculates the induced L1 norm of this matrix.</summary>
            <returns>The maximum absolute column sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.L2Norm">
            <summary>Calculates the induced L2 norm of the matrix.</summary>
            <returns>The largest singular value of the matrix.</returns>
            <remarks>
            For sparse matrices, the L2 norm is computed using a dense implementation of singular value decomposition.
            In a later release, it will be replaced with a sparse implementation.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.InfinityNorm">
            <summary>Calculates the induced infinity norm of this matrix.</summary>
            <returns>The maximum absolute row sum of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.FrobeniusNorm">
            <summary>Calculates the entry-wise Frobenius norm of this matrix.</summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.RowNorms(System.Double)">
            <summary>
            Calculates the p-norms of all row vectors.
            Typical values for p are 1.0 (L1, Manhattan norm), 2.0 (L2, Euclidean norm) and positive infinity (infinity norm)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ColumnNorms(System.Double)">
            <summary>
            Calculates the p-norms of all column vectors.
            Typical values for p are 1.0 (L1, Manhattan norm), 2.0 (L2, Euclidean norm) and positive infinity (infinity norm)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.NormalizeRows(System.Double)">
            <summary>
            Normalizes all row vectors to a unit p-norm.
            Typical values for p are 1.0 (L1, Manhattan norm), 2.0 (L2, Euclidean norm) and positive infinity (infinity norm)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.NormalizeColumns(System.Double)">
            <summary>
            Normalizes all column vectors to a unit p-norm.
            Typical values for p are 1.0 (L1, Manhattan norm), 2.0 (L2, Euclidean norm) and positive infinity (infinity norm)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.RowSums">
            <summary>
            Calculates the value sum of each row vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ColumnSums">
            <summary>
            Calculates the value sum of each column vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.RowAbsoluteSums">
            <summary>
            Calculates the absolute value sum of each row vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ColumnAbsoluteSums">
            <summary>
            Calculates the absolute value sum of each column vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Equals(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Indicates whether the current object is equal to another object of the same type.
            </summary>
            <param name="other">
            An object to compare with this object.
            </param>
            <returns>
            <c>true</c> if the current object is equal to the <paramref name="other"/> parameter; otherwise, <c>false</c>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Equals(System.Object)">
            <summary>
            Determines whether the specified <see cref="T:System.Object"/> is equal to this instance.
            </summary>
            <param name="obj">The <see cref="T:System.Object"/> to compare with this instance.</param>
            <returns>
                <c>true</c> if the specified <see cref="T:System.Object"/> is equal to this instance; otherwise, <c>false</c>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.GetHashCode">
            <summary>
            Returns a hash code for this instance.
            </summary>
            <returns>
            A hash code for this instance, suitable for use in hashing algorithms and data structures like a hash table.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.System#ICloneable#Clone">
            <summary>
            Creates a new object that is a copy of the current instance.
            </summary>
            <returns>
            A new object that is a copy of this instance.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ToTypeString">
            <summary>
            Returns a string that describes the type, dimensions and shape of this matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ToMatrixStringArray(System.Int32,System.Int32,System.Int32,System.Int32,System.String,System.String,System.String,System.Func{`0,System.String})">
            <summary>
            Returns a string 2D array that summarizes the content of this matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ToMatrixStringArray(System.Int32,System.Int32,System.Int32,System.Int32,System.Int32,System.Int32,System.String,System.String,System.String,System.Func{`0,System.String})">
            <summary>
            Returns a string 2D array that summarizes the content of this matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ToMatrixString(System.Int32,System.Int32,System.String,System.IFormatProvider)">
            <summary>
            Returns a string that summarizes the content of this matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ToMatrixString(System.String,System.IFormatProvider)">
            <summary>
            Returns a string that summarizes the content of this matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ToString(System.Int32,System.Int32,System.String,System.IFormatProvider)">
            <summary>
            Returns a string that summarizes this matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ToString">
            <summary>
            Returns a string that summarizes this matrix.
            The maximum number of cells can be configured in the <see cref="T:MathNet.Numerics.Control"/> class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ToString(System.String,System.IFormatProvider)">
            <summary>
            Returns a string that summarizes this matrix.
            The maximum number of cells can be configured in the <see cref="T:MathNet.Numerics.Control"/> class.
            The format string is ignored.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.#ctor(MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage{`0})">
            <summary>
            Initializes a new instance of the Matrix class.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Matrix`1.Storage">
            <summary>
            Gets the raw matrix data storage.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Matrix`1.ColumnCount">
            <summary>
            Gets the number of columns.
            </summary>
            <value>The number of columns.</value>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Matrix`1.RowCount">
            <summary>
            Gets the number of rows.
            </summary>
            <value>The number of rows.</value>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Matrix`1.Item(System.Int32,System.Int32)">
            <summary>
            Gets or sets the value at the given row and column, with range checking.
            </summary>
            <param name="row">
            The row of the element.
            </param>
            <param name="column">
            The column of the element.
            </param>
            <value>The value to get or set.</value>
            <remarks>This method is ranged checked. <see cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.At(System.Int32,System.Int32)"/> and <see cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.At(System.Int32,System.Int32,`0)"/>
            to get and set values without range checking.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.At(System.Int32,System.Int32)">
            <summary>
            Retrieves the requested element without range checking.
            </summary>
            <param name="row">
            The row of the element.
            </param>
            <param name="column">
            The column of the element.
            </param>
            <returns>
            The requested element.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.At(System.Int32,System.Int32,`0)">
            <summary>
            Sets the value of the given element without range checking.
            </summary>
            <param name="row">
            The row of the element.
            </param>
            <param name="column">
            The column of the element.
            </param>
            <param name="value">
            The value to set the element to.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Clear">
            <summary>
            Sets all values to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ClearRow(System.Int32)">
            <summary>
            Sets all values of a row to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ClearColumn(System.Int32)">
            <summary>
            Sets all values of a column to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ClearRows(System.Int32[])">
            <summary>
            Sets all values for all of the chosen rows to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ClearColumns(System.Int32[])">
            <summary>
            Sets all values for all of the chosen columns to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ClearSubMatrix(System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Sets all values of a sub-matrix to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.CoerceZero(System.Double)">
            <summary>
            Set all values whose absolute value is smaller than the threshold to zero, in-place.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.CoerceZero(System.Func{`0,System.Boolean})">
            <summary>
            Set all values that meet the predicate to zero, in-place.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Clone">
            <summary>
            Creates a clone of this instance.
            </summary>
            <returns>
            A clone of the instance.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.CopyTo(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Copies the elements of this matrix to the given matrix.
            </summary>
            <param name="target">
            The matrix to copy values into.
            </param>
            <exception cref="T:System.ArgumentNullException">
            If target is <see langword="null"/>.
            </exception>
            <exception cref="T:System.ArgumentException">
            If this and the target matrix do not have the same dimensions..
            </exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Row(System.Int32)">
            <summary>
            Copies a row into an Vector.
            </summary>
            <param name="index">The row to copy.</param>
            <returns>A Vector containing the copied elements.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="index"/> is negative,
            or greater than or equal to the number of rows.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Row(System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Copies a row into to the given Vector.
            </summary>
            <param name="index">The row to copy.</param>
            <param name="result">The Vector to copy the row into.</param>
            <exception cref="T:System.ArgumentNullException">If the result vector is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="index"/> is negative,
            or greater than or equal to the number of rows.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <b>this.Columns != result.Count</b>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Row(System.Int32,System.Int32,System.Int32)">
            <summary>
            Copies the requested row elements into a new Vector.
            </summary>
            <param name="rowIndex">The row to copy elements from.</param>
            <param name="columnIndex">The column to start copying from.</param>
            <param name="length">The number of elements to copy.</param>
            <returns>A Vector containing the requested elements.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If:
            <list><item><paramref name="rowIndex"/> is negative,
            or greater than or equal to the number of rows.</item>
            <item><paramref name="columnIndex"/> is negative,
            or greater than or equal to the number of columns.</item>
            <item><c>(columnIndex + length) &gt;= Columns.</c></item></list></exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="length"/> is not positive.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Row(System.Int32,System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Copies the requested row elements into a new Vector.
            </summary>
            <param name="rowIndex">The row to copy elements from.</param>
            <param name="columnIndex">The column to start copying from.</param>
            <param name="length">The number of elements to copy.</param>
            <param name="result">The Vector to copy the column into.</param>
            <exception cref="T:System.ArgumentNullException">If the result Vector is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="rowIndex"/> is negative,
            or greater than or equal to the number of columns.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="columnIndex"/> is negative,
            or greater than or equal to the number of rows.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="columnIndex"/> + <paramref name="length"/>
            is greater than or equal to the number of rows.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="length"/> is not positive.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <strong>result.Count &lt; length</strong>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Column(System.Int32)">
            <summary>
            Copies a column into a new Vector>.
            </summary>
            <param name="index">The column to copy.</param>
            <returns>A Vector containing the copied elements.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="index"/> is negative,
            or greater than or equal to the number of columns.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Column(System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Copies a column into to the given Vector.
            </summary>
            <param name="index">The column to copy.</param>
            <param name="result">The Vector to copy the column into.</param>
            <exception cref="T:System.ArgumentNullException">If the result Vector is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="index"/> is negative,
            or greater than or equal to the number of columns.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <b>this.Rows != result.Count</b>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Column(System.Int32,System.Int32,System.Int32)">
            <summary>
            Copies the requested column elements into a new Vector.
            </summary>
            <param name="columnIndex">The column to copy elements from.</param>
            <param name="rowIndex">The row to start copying from.</param>
            <param name="length">The number of elements to copy.</param>
            <returns>A Vector containing the requested elements.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If:
            <list><item><paramref name="columnIndex"/> is negative,
            or greater than or equal to the number of columns.</item>
            <item><paramref name="rowIndex"/> is negative,
            or greater than or equal to the number of rows.</item>
            <item><c>(rowIndex + length) &gt;= Rows.</c></item></list>
            </exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="length"/> is not positive.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Column(System.Int32,System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Copies the requested column elements into the given vector.
            </summary>
            <param name="columnIndex">The column to copy elements from.</param>
            <param name="rowIndex">The row to start copying from.</param>
            <param name="length">The number of elements to copy.</param>
            <param name="result">The Vector to copy the column into.</param>
            <exception cref="T:System.ArgumentNullException">If the result Vector is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="columnIndex"/> is negative,
            or greater than or equal to the number of columns.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="rowIndex"/> is negative,
            or greater than or equal to the number of rows.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="rowIndex"/> + <paramref name="length"/>
            is greater than or equal to the number of rows.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="length"/> is not positive.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <strong>result.Count &lt; length</strong>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.UpperTriangle">
            <summary>
            Returns a new matrix containing the upper triangle of this matrix.
            </summary>
            <returns>The upper triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.LowerTriangle">
            <summary>
            Returns a new matrix containing the lower triangle of this matrix.
            </summary>
            <returns>The lower triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.LowerTriangle(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Puts the lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="result"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.UpperTriangle(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Puts the upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="result"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.SubMatrix(System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Creates a matrix that contains the values from the requested sub-matrix.
            </summary>
            <param name="rowIndex">The row to start copying from.</param>
            <param name="rowCount">The number of rows to copy. Must be positive.</param>
            <param name="columnIndex">The column to start copying from.</param>
            <param name="columnCount">The number of columns to copy. Must be positive.</param>
            <returns>The requested sub-matrix.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If: <list><item><paramref name="rowIndex"/> is
            negative, or greater than or equal to the number of rows.</item>
            <item><paramref name="columnIndex"/> is negative, or greater than or equal to the number
            of columns.</item>
            <item><c>(columnIndex + columnLength) &gt;= Columns</c></item>
            <item><c>(rowIndex + rowLength) &gt;= Rows</c></item></list></exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="rowCount"/> or <paramref name="columnCount"/>
            is not positive.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Diagonal">
            <summary>
            Returns the elements of the diagonal in a Vector.
            </summary>
            <returns>The elements of the diagonal.</returns>
            <remarks>For non-square matrices, the method returns Min(Rows, Columns) elements where
            i == j (i is the row index, and j is the column index).</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.StrictlyLowerTriangle">
            <summary>
            Returns a new matrix containing the lower triangle of this matrix. The new matrix
            does not contain the diagonal elements of this matrix.
            </summary>
            <returns>The lower triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.StrictlyLowerTriangle(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Puts the strictly lower triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="result"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.StrictlyUpperTriangle">
            <summary>
            Returns a new matrix containing the upper triangle of this matrix. The new matrix
            does not contain the diagonal elements of this matrix.
            </summary>
            <returns>The upper triangle of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.StrictlyUpperTriangle(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Puts the strictly upper triangle of this matrix into the result matrix.
            </summary>
            <param name="result">Where to store the lower triangle.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="result"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not the same as this matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.InsertColumn(System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Creates a new matrix and inserts the given column at the given index.
            </summary>
            <param name="columnIndex">The index of where to insert the column.</param>
            <param name="column">The column to insert.</param>
            <returns>A new matrix with the inserted column.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="column "/> is <see langword="null" />. </exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="columnIndex"/> is &lt; zero or &gt; the number of columns.</exception>
            <exception cref="T:System.ArgumentException">If the size of <paramref name="column"/> != the number of rows.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.RemoveColumn(System.Int32)">
            <summary>
            Creates a new matrix with the given column removed.
            </summary>
            <param name="columnIndex">The index of the column to remove.</param>
            <returns>A new matrix without the chosen column.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="columnIndex"/> is &lt; zero or &gt;= the number of columns.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.SetColumn(System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Copies the values of the given Vector to the specified column.
            </summary>
            <param name="columnIndex">The column to copy the values to.</param>
            <param name="column">The vector to copy the values from.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="column"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="columnIndex"/> is less than zero,
            or greater than or equal to the number of columns.</exception>
            <exception cref="T:System.ArgumentException">If the size of <paramref name="column"/> does not
            equal the number of rows of this <strong>Matrix</strong>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.SetColumn(System.Int32,System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Copies the values of the given Vector to the specified sub-column.
            </summary>
            <param name="columnIndex">The column to copy the values to.</param>
            <param name="rowIndex">The row to start copying to.</param>
            <param name="length">The number of elements to copy.</param>
            <param name="column">The vector to copy the values from.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="column"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="columnIndex"/> is less than zero,
            or greater than or equal to the number of columns.</exception>
            <exception cref="T:System.ArgumentException">If the size of <paramref name="column"/> does not
            equal the number of rows of this <strong>Matrix</strong>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.SetColumn(System.Int32,`0[])">
            <summary>
            Copies the values of the given array to the specified column.
            </summary>
            <param name="columnIndex">The column to copy the values to.</param>
            <param name="column">The array to copy the values from.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="column"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="columnIndex"/> is less than zero,
            or greater than or equal to the number of columns.</exception>
            <exception cref="T:System.ArgumentException">If the size of <paramref name="column"/> does not
            equal the number of rows of this <strong>Matrix</strong>.</exception>
            <exception cref="T:System.ArgumentException">If the size of <paramref name="column"/> does not
            equal the number of rows of this <strong>Matrix</strong>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.InsertRow(System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Creates a new matrix and inserts the given row at the given index.
            </summary>
            <param name="rowIndex">The index of where to insert the row.</param>
            <param name="row">The row to insert.</param>
            <returns>A new matrix with the inserted column.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="row"/> is <see langword="null" />. </exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="rowIndex"/> is &lt; zero or &gt; the number of rows.</exception>
            <exception cref="T:System.ArgumentException">If the size of <paramref name="row"/> != the number of columns.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.RemoveRow(System.Int32)">
            <summary>
            Creates a new matrix with the given row removed.
            </summary>
            <param name="rowIndex">The index of the row to remove.</param>
            <returns>A new matrix without the chosen row.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="rowIndex"/> is &lt; zero or &gt;= the number of rows.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.SetRow(System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Copies the values of the given Vector to the specified row.
            </summary>
            <param name="rowIndex">The row to copy the values to.</param>
            <param name="row">The vector to copy the values from.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="row"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="rowIndex"/> is less than zero,
            or greater than or equal to the number of rows.</exception>
            <exception cref="T:System.ArgumentException">If the size of <paramref name="row"/> does not
            equal the number of columns of this <strong>Matrix</strong>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.SetRow(System.Int32,System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Copies the values of the given Vector to the specified sub-row.
            </summary>
            <param name="rowIndex">The row to copy the values to.</param>
            <param name="columnIndex">The column to start copying to.</param>
            <param name="length">The number of elements to copy.</param>
            <param name="row">The vector to copy the values from.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="row"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="rowIndex"/> is less than zero,
            or greater than or equal to the number of rows.</exception>
            <exception cref="T:System.ArgumentException">If the size of <paramref name="row"/> does not
            equal the number of columns of this <strong>Matrix</strong>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.SetRow(System.Int32,`0[])">
            <summary>
            Copies the values of the given array to the specified row.
            </summary>
            <param name="rowIndex">The row to copy the values to.</param>
            <param name="row">The array to copy the values from.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="row"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="rowIndex"/> is less than zero,
            or greater than or equal to the number of rows.</exception>
            <exception cref="T:System.ArgumentException">If the size of <paramref name="row"/> does not
            equal the number of columns of this <strong>Matrix</strong>.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.SetSubMatrix(System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Copies the values of a given matrix into a region in this matrix.
            </summary>
            <param name="rowIndex">The row to start copying to.</param>
            <param name="columnIndex">The column to start copying to.</param>
            <param name="subMatrix">The sub-matrix to copy from.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If: <list><item><paramref name="rowIndex"/> is
            negative, or greater than or equal to the number of rows.</item>
            <item><paramref name="columnIndex"/> is negative, or greater than or equal to the number
            of columns.</item>
            <item><c>(columnIndex + columnLength) &gt;= Columns</c></item>
            <item><c>(rowIndex + rowLength) &gt;= Rows</c></item></list></exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.SetSubMatrix(System.Int32,System.Int32,System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Copies the values of a given matrix into a region in this matrix.
            </summary>
            <param name="rowIndex">The row to start copying to.</param>
            <param name="rowCount">The number of rows to copy. Must be positive.</param>
            <param name="columnIndex">The column to start copying to.</param>
            <param name="columnCount">The number of columns to copy. Must be positive.</param>
            <param name="subMatrix">The sub-matrix to copy from.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If: <list><item><paramref name="rowIndex"/> is
            negative, or greater than or equal to the number of rows.</item>
            <item><paramref name="columnIndex"/> is negative, or greater than or equal to the number
            of columns.</item>
            <item><c>(columnIndex + columnLength) &gt;= Columns</c></item>
            <item><c>(rowIndex + rowLength) &gt;= Rows</c></item></list></exception>
            <item>the size of <paramref name="subMatrix"/> is not at least <paramref name="rowCount"/> x <paramref name="columnCount"/>.</item>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="rowCount"/> or <paramref name="columnCount"/>
            is not positive.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.SetSubMatrix(System.Int32,System.Int32,System.Int32,System.Int32,System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Copies the values of a given matrix into a region in this matrix.
            </summary>
            <param name="rowIndex">The row to start copying to.</param>
            <param name="sorceRowIndex">The row of the sub-matrix to start copying from.</param>
            <param name="rowCount">The number of rows to copy. Must be positive.</param>
            <param name="columnIndex">The column to start copying to.</param>
            <param name="sourceColumnIndex">The column of the sub-matrix to start copying from.</param>
            <param name="columnCount">The number of columns to copy. Must be positive.</param>
            <param name="subMatrix">The sub-matrix to copy from.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">If: <list><item><paramref name="rowIndex"/> is
            negative, or greater than or equal to the number of rows.</item>
            <item><paramref name="columnIndex"/> is negative, or greater than or equal to the number
            of columns.</item>
            <item><c>(columnIndex + columnLength) &gt;= Columns</c></item>
            <item><c>(rowIndex + rowLength) &gt;= Rows</c></item></list></exception>
            <item>the size of <paramref name="subMatrix"/> is not at least <paramref name="rowCount"/> x <paramref name="columnCount"/>.</item>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="rowCount"/> or <paramref name="columnCount"/>
            is not positive.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.SetDiagonal(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Copies the values of the given Vector to the diagonal.
            </summary>
            <param name="source">The vector to copy the values from. The length of the vector should be
            Min(Rows, Columns).</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="source"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the length of <paramref name="source"/> does not
            equal Min(Rows, Columns).</exception>
            <remarks>For non-square matrices, the elements of <paramref name="source"/> are copied to
            this[i,i].</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.SetDiagonal(`0[])">
            <summary>
            Copies the values of the given array to the diagonal.
            </summary>
            <param name="source">The array to copy the values from. The length of the vector should be
            Min(Rows, Columns).</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="source"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the length of <paramref name="source"/> does not
            equal Min(Rows, Columns).</exception>
            <remarks>For non-square matrices, the elements of <paramref name="source"/> are copied to
            this[i,i].</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Resize(System.Int32,System.Int32)">
            <summary>
            Creates a new matrix with the desired size and copies this matrix to it.
            Values which no longer exist in the new matrix are ignored, new values are set to zero.
            </summary>
            <param name="rowCount">The number of rows of the new matrix.</param>
            <param name="columnCount">The number of columns of the new matrix.</param>
            <returns>A new matrix with the desired rows and columns.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Transpose">
            <summary>
            Returns the transpose of this matrix.
            </summary>
            <returns>The transpose of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Transpose(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Puts the transpose of this matrix into the result matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ConjugateTranspose">
            <summary>
            Returns the conjugate transpose of this matrix.
            </summary>
            <returns>The conjugate transpose of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ConjugateTranspose(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Puts the conjugate transpose of this matrix into the result matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PermuteRows(MathNet.Numerics.Permutation)">
            <summary>
            Permute the rows of a matrix according to a permutation.
            </summary>
            <param name="p">The row permutation to apply to this matrix.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.PermuteColumns(MathNet.Numerics.Permutation)">
            <summary>
            Permute the columns of a matrix according to a permutation.
            </summary>
            <param name="p">The column permutation to apply to this matrix.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Append(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
             Concatenates this matrix with the given matrix.
            </summary>
            <param name="right">The matrix to concatenate.</param>
            <returns>The combined matrix.</returns>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Stack(MathNet.Numerics.LinearAlgebra.Matrix{`0})"/>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DiagonalStack(MathNet.Numerics.LinearAlgebra.Matrix{`0})"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Append(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Concatenates this matrix with the given matrix and places the result into the result matrix.
            </summary>
            <param name="right">The matrix to concatenate.</param>
            <param name="result">The combined matrix.</param>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Stack(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})"/>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DiagonalStack(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Stack(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Stacks this matrix on top of the given matrix and places the result into the result matrix.
            </summary>
            <param name="lower">The matrix to stack this matrix upon.</param>
            <returns>The combined matrix.</returns>
            <exception cref="T:System.ArgumentNullException">If lower is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If <strong>upper.Columns != lower.Columns</strong>.</exception>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Append(MathNet.Numerics.LinearAlgebra.Matrix{`0})"/>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DiagonalStack(MathNet.Numerics.LinearAlgebra.Matrix{`0})"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Stack(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Stacks this matrix on top of the given matrix and places the result into the result matrix.
            </summary>
            <param name="lower">The matrix to stack this matrix upon.</param>
            <param name="result">The combined matrix.</param>
            <exception cref="T:System.ArgumentNullException">If lower is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If <strong>upper.Columns != lower.Columns</strong>.</exception>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Append(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})"/>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DiagonalStack(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DiagonalStack(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Diagonally stacks his matrix on top of the given matrix. The new matrix is a M-by-N matrix,
            where M = this.Rows + lower.Rows and N = this.Columns + lower.Columns.
            The values of off the off diagonal matrices/blocks are set to zero.
            </summary>
            <param name="lower">The lower, right matrix.</param>
            <exception cref="T:System.ArgumentNullException">If lower is <see langword="null" />.</exception>
            <returns>the combined matrix</returns>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Stack(MathNet.Numerics.LinearAlgebra.Matrix{`0})"/>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Append(MathNet.Numerics.LinearAlgebra.Matrix{`0})"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.DiagonalStack(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Diagonally stacks his matrix on top of the given matrix and places the combined matrix into the result matrix.
            </summary>
            <param name="lower">The lower, right matrix.</param>
            <param name="result">The combined matrix</param>
            <exception cref="T:System.ArgumentNullException">If lower is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentNullException">If the result matrix is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the result matrix's dimensions are not (this.Rows + lower.rows) x (this.Columns + lower.Columns).</exception>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Stack(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})"/>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Append(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.IsSymmetric">
            <summary>
            Evaluates whether this matrix is symmetric.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.IsHermitian">
            <summary>
            Evaluates whether this matrix is Hermitian (conjugate symmetric).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ToArray">
            <summary>
            Returns this matrix as a multidimensional array.
            The returned array will be independent from this matrix.
            A new memory block will be allocated for the array.
            </summary>
            <returns>A multidimensional containing the values of this matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ToColumnMajorArray">
            <summary>
            Returns the matrix's elements as an array with the data laid out column by column (column major).
            The returned array will be independent from this matrix.
            A new memory block will be allocated for the array.
            </summary>
            <example><pre>
            1, 2, 3
            4, 5, 6  will be returned as  1, 4, 7, 2, 5, 8, 3, 6, 9
            7, 8, 9
            </pre></example>
            <returns>An array containing the matrix's elements.</returns>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ToRowMajorArray"/>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Enumerate(MathNet.Numerics.LinearAlgebra.Zeros)"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ToRowMajorArray">
            <summary>
            Returns the matrix's elements as an array with the data laid row by row (row major).
            The returned array will be independent from this matrix.
            A new memory block will be allocated for the array.
            </summary>
            <example><pre>
            1, 2, 3
            4, 5, 6  will be returned as  1, 2, 3, 4, 5, 6, 7, 8, 9
            7, 8, 9
            </pre></example>
            <returns>An array containing the matrix's elements.</returns>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ToColumnMajorArray"/>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Enumerate(MathNet.Numerics.LinearAlgebra.Zeros)"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ToRowArrays">
            <summary>
            Returns this matrix as array of row arrays.
            The returned arrays will be independent from this matrix.
            A new memory block will be allocated for the arrays.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ToColumnArrays">
            <summary>
            Returns this matrix as array of column arrays.
            The returned arrays will be independent from this matrix.
            A new memory block will be allocated for the arrays.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.AsArray">
            <summary>
            Returns the internal multidimensional array of this matrix if, and only if, this matrix is stored by such an array internally.
            Otherwise returns null. Changes to the returned array and the matrix will affect each other.
            Use ToArray instead if you always need an independent array.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.AsColumnMajorArray">
            <summary>
            Returns the internal column by column (column major) array of this matrix if, and only if, this matrix is stored by such arrays internally.
            Otherwise returns null. Changes to the returned arrays and the matrix will affect each other.
            Use ToColumnMajorArray instead if you always need an independent array.
            </summary>
            <example><pre>
            1, 2, 3
            4, 5, 6  will be returned as  1, 4, 7, 2, 5, 8, 3, 6, 9
            7, 8, 9
            </pre></example>
            <returns>An array containing the matrix's elements.</returns>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ToRowMajorArray"/>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Enumerate(MathNet.Numerics.LinearAlgebra.Zeros)"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.AsRowMajorArray">
            <summary>
            Returns the internal row by row (row major) array of this matrix if, and only if, this matrix is stored by such arrays internally.
            Otherwise returns null. Changes to the returned arrays and the matrix will affect each other.
            Use ToRowMajorArray instead if you always need an independent array.
            </summary>
            <example><pre>
            1, 2, 3
            4, 5, 6  will be returned as  1, 2, 3, 4, 5, 6, 7, 8, 9
            7, 8, 9
            </pre></example>
            <returns>An array containing the matrix's elements.</returns>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ToColumnMajorArray"/>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Enumerate(MathNet.Numerics.LinearAlgebra.Zeros)"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.AsRowArrays">
            <summary>
            Returns the internal row arrays of this matrix if, and only if, this matrix is stored by such arrays internally.
            Otherwise returns null. Changes to the returned arrays and the matrix will affect each other.
            Use ToRowArrays instead if you always need an independent array.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.AsColumnArrays">
            <summary>
            Returns the internal column arrays of this matrix if, and only if, this matrix is stored by such arrays internally.
            Otherwise returns null. Changes to the returned arrays and the matrix will affect each other.
            Use ToColumnArrays instead if you always need an independent array.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Enumerate">
            <summary>
            Returns an IEnumerable that can be used to iterate through all values of the matrix.
            </summary>
            <remarks>
            The enumerator will include all values, even if they are zero.
            The ordering of the values is unspecified (not necessarily column-wise or row-wise).
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Enumerate(MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Returns an IEnumerable that can be used to iterate through all values of the matrix.
            </summary>
            <remarks>
            The enumerator will include all values, even if they are zero.
            The ordering of the values is unspecified (not necessarily column-wise or row-wise).
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.EnumerateIndexed">
            <summary>
            Returns an IEnumerable that can be used to iterate through all values of the matrix and their index.
            </summary>
            <remarks>
            The enumerator returns a Tuple with the first two values being the row and column index
            and the third value being the value of the element at that index.
            The enumerator will include all values, even if they are zero.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.EnumerateIndexed(MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Returns an IEnumerable that can be used to iterate through all values of the matrix and their index.
            </summary>
            <remarks>
            The enumerator returns a Tuple with the first two values being the row and column index
            and the third value being the value of the element at that index.
            The enumerator will include all values, even if they are zero.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.EnumerateColumns">
            <summary>
            Returns an IEnumerable that can be used to iterate through all columns of the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.EnumerateColumns(System.Int32,System.Int32)">
            <summary>
            Returns an IEnumerable that can be used to iterate through a subset of all columns of the matrix.
            </summary>
            <param name="index">The column to start enumerating over.</param>
            <param name="length">The number of columns to enumerating over.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.EnumerateColumnsIndexed">
            <summary>
            Returns an IEnumerable that can be used to iterate through all columns of the matrix and their index.
            </summary>
            <remarks>
            The enumerator returns a Tuple with the first value being the column index
            and the second value being the value of the column at that index.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.EnumerateColumnsIndexed(System.Int32,System.Int32)">
            <summary>
            Returns an IEnumerable that can be used to iterate through a subset of all columns of the matrix and their index.
            </summary>
            <param name="index">The column to start enumerating over.</param>
            <param name="length">The number of columns to enumerating over.</param>
            <remarks>
            The enumerator returns a Tuple with the first value being the column index
            and the second value being the value of the column at that index.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.EnumerateRows">
            <summary>
            Returns an IEnumerable that can be used to iterate through all rows of the matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.EnumerateRows(System.Int32,System.Int32)">
            <summary>
            Returns an IEnumerable that can be used to iterate through a subset of all rows of the matrix.
            </summary>
            <param name="index">The row to start enumerating over.</param>
            <param name="length">The number of rows to enumerating over.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.EnumerateRowsIndexed">
            <summary>
            Returns an IEnumerable that can be used to iterate through all rows of the matrix and their index.
            </summary>
            <remarks>
            The enumerator returns a Tuple with the first value being the row index
            and the second value being the value of the row at that index.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.EnumerateRowsIndexed(System.Int32,System.Int32)">
            <summary>
            Returns an IEnumerable that can be used to iterate through a subset of all rows of the matrix and their index.
            </summary>
            <param name="index">The row to start enumerating over.</param>
            <param name="length">The number of rows to enumerating over.</param>
            <remarks>
            The enumerator returns a Tuple with the first value being the row index
            and the second value being the value of the row at that index.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.MapInplace(System.Func{`0,`0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to each value of this matrix and replaces the value with its result.
            If forceMapZero is not set to true, zero values may or may not be skipped depending
            on the actual data storage implementation (relevant mostly for sparse matrices).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.MapIndexedInplace(System.Func{System.Int32,System.Int32,`0,`0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to each value of this matrix and replaces the value with its result.
            The row and column indices of each value (zero-based) are passed as first arguments to the function.
            If forceMapZero is not set to true, zero values may or may not be skipped depending
            on the actual data storage implementation (relevant mostly for sparse matrices).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Map(System.Func{`0,`0},MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to each value of this matrix and replaces the value in the result matrix.
            If forceMapZero is not set to true, zero values may or may not be skipped depending
            on the actual data storage implementation (relevant mostly for sparse matrices).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.MapIndexed(System.Func{System.Int32,System.Int32,`0,`0},MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to each value of this matrix and replaces the value in the result matrix.
            The index of each value (zero-based) is passed as first argument to the function.
            If forceMapZero is not set to true, zero values may or may not be skipped depending
            on the actual data storage implementation (relevant mostly for sparse matrices).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.MapConvert``1(System.Func{`0,``0},MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to each value of this matrix and replaces the value in the result matrix.
            If forceMapZero is not set to true, zero values may or may not be skipped depending
            on the actual data storage implementation (relevant mostly for sparse matrices).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.MapIndexedConvert``1(System.Func{System.Int32,System.Int32,`0,``0},MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to each value of this matrix and replaces the value in the result matrix.
            The index of each value (zero-based) is passed as first argument to the function.
            If forceMapZero is not set to true, zero values may or may not be skipped depending
            on the actual data storage implementation (relevant mostly for sparse matrices).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Map``1(System.Func{`0,``0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to each value of this matrix and returns the results as a new matrix.
            If forceMapZero is not set to true, zero values may or may not be skipped depending
            on the actual data storage implementation (relevant mostly for sparse matrices).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.MapIndexed``1(System.Func{System.Int32,System.Int32,`0,``0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to each value of this matrix and returns the results as a new matrix.
            The index of each value (zero-based) is passed as first argument to the function.
            If forceMapZero is not set to true, zero values may or may not be skipped depending
            on the actual data storage implementation (relevant mostly for sparse matrices).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.FoldByRow``1(System.Func{``0,`0,``0},``0,MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            For each row, applies a function f to each element of the row, threading an accumulator argument through the computation.
            Returns an array with the resulting accumulator states for each row.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.FoldByColumn``1(System.Func{``0,`0,``0},``0,MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            For each column, applies a function f to each element of the column, threading an accumulator argument through the computation.
            Returns an array with the resulting accumulator states for each column.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.FoldRows``1(System.Func{MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{``0}},MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Applies a function f to each row vector, threading an accumulator vector argument through the computation.
            Returns the resulting accumulator vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.FoldColumns``1(System.Func{MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{``0}},MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Applies a function f to each column vector, threading an accumulator vector argument through the computation.
            Returns the resulting accumulator vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ReduceRows(System.Func{MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0}})">
            <summary>
            Reduces all row vectors by applying a function between two of them, until only a single vector is left.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ReduceColumns(System.Func{MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0}})">
            <summary>
            Reduces all column vectors by applying a function between two of them, until only a single vector is left.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Map2(System.Func{`0,`0,`0},MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to each value pair of two matrices and replaces the value in the result vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Map2(System.Func{`0,`0,`0},MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to each value pair of two matrices and returns the results as a new vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Fold2``2(System.Func{``1,`0,``0,``1},``1,MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to update the status with each value pair of two matrices and returns the resulting status.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Find(System.Func{`0,System.Boolean},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Returns a tuple with the index and value of the first element satisfying a predicate, or null if none is found.
            Zero elements may be skipped on sparse data structures if allowed (default).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Find2``1(System.Func{`0,``0,System.Boolean},MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Returns a tuple with the index and values of the first element pair of two matrices of the same size satisfying a predicate, or null if none is found.
            Zero elements may be skipped on sparse data structures if allowed (default).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Exists(System.Func{`0,System.Boolean},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Returns true if at least one element satisfies a predicate.
            Zero elements may be skipped on sparse data structures if allowed (default).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Exists2``1(System.Func{`0,``0,System.Boolean},MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Returns true if at least one element pairs of two matrices of the same size satisfies a predicate.
            Zero elements may be skipped on sparse data structures if allowed (default).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ForAll(System.Func{`0,System.Boolean},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Returns true if all elements satisfy a predicate.
            Zero elements may be skipped on sparse data structures if allowed (default).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.ForAll2``1(System.Func{`0,``0,System.Boolean},MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Returns true if all element pairs of two matrices of the same size satisfy a predicate.
            Zero elements may be skipped on sparse data structures if allowed (default).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.op_UnaryPlus(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Returns a <strong>Matrix</strong> containing the same values of <paramref name="rightSide"/>.
            </summary>
            <param name="rightSide">The matrix to get the values from.</param>
            <returns>A matrix containing a the same values as <paramref name="rightSide"/>.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.op_UnaryNegation(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Negates each element of the matrix.
            </summary>
            <param name="rightSide">The matrix to negate.</param>
            <returns>A matrix containing the negated values.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.op_Addition(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Adds two matrices together and returns the results.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to add.</param>
            <param name="rightSide">The right matrix to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> don't have the same dimensions.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.op_Addition(MathNet.Numerics.LinearAlgebra.Matrix{`0},`0)">
            <summary>
            Adds a scalar to each element of the matrix.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of the provided matrix.</remarks>
            <param name="leftSide">The left matrix to add.</param>
            <param name="rightSide">The scalar value to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.op_Addition(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Adds a scalar to each element of the matrix.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of the provided matrix.</remarks>
            <param name="leftSide">The scalar value to add.</param>
            <param name="rightSide">The right matrix to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.op_Subtraction(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Subtracts two matrices together and returns the results.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to subtract.</param>
            <param name="rightSide">The right matrix to subtract.</param>
            <returns>The result of the subtraction.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> don't have the same dimensions.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.op_Subtraction(MathNet.Numerics.LinearAlgebra.Matrix{`0},`0)">
            <summary>
            Subtracts a scalar from each element of a matrix.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of the provided matrix.</remarks>
            <param name="leftSide">The left matrix to subtract.</param>
            <param name="rightSide">The scalar value to subtract.</param>
            <returns>The result of the subtraction.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> don't have the same dimensions.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.op_Subtraction(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Subtracts each element of a matrix from a scalar.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of the provided matrix.</remarks>
            <param name="leftSide">The scalar value to subtract.</param>
            <param name="rightSide">The right matrix to subtract.</param>
            <returns>The result of the subtraction.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> don't have the same dimensions.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.op_Multiply(MathNet.Numerics.LinearAlgebra.Matrix{`0},`0)">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.op_Multiply(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Multiplies a <strong>Matrix</strong> by a constant and returns the result.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The constant to multiply the matrix by.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.op_Multiply(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Multiplies two matrices.
            </summary>
            <remarks>This operator will allocate new memory for the result. It will
            choose the representation of either <paramref name="leftSide"/> or <paramref name="rightSide"/> depending on which
            is denser.</remarks>
            <param name="leftSide">The left matrix to multiply.</param>
            <param name="rightSide">The right matrix to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If the dimensions of <paramref name="leftSide"/> or <paramref name="rightSide"/> don't conform.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.op_Multiply(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Multiplies a <strong>Matrix</strong> and a Vector.
            </summary>
            <param name="leftSide">The matrix to multiply.</param>
            <param name="rightSide">The vector to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.op_Multiply(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Multiplies a Vector and a <strong>Matrix</strong>.
            </summary>
            <param name="leftSide">The vector to multiply.</param>
            <param name="rightSide">The matrix to multiply.</param>
            <returns>The result of multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.op_Division(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Divides a scalar with a matrix.
            </summary>
            <param name="dividend">The scalar to divide.</param>
            <param name="divisor">The matrix.</param>
            <returns>The result of the division.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="divisor"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.op_Division(MathNet.Numerics.LinearAlgebra.Matrix{`0},`0)">
            <summary>
            Divides a matrix with a scalar.
            </summary>
            <param name="dividend">The matrix to divide.</param>
            <param name="divisor">The scalar value.</param>
            <returns>The result of the division.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="dividend"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.op_Modulus(MathNet.Numerics.LinearAlgebra.Matrix{`0},`0)">
            <summary>
            Computes the pointwise remainder (% operator), where the result has the sign of the dividend,
            of each element of the matrix of the given divisor.
            </summary>
            <param name="dividend">The matrix whose elements we want to compute the modulus of.</param>
            <param name="divisor">The divisor to use.</param>
            <returns>The result of the calculation</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="dividend"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.op_Modulus(`0,MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the pointwise remainder (% operator), where the result has the sign of the dividend,
            of the given dividend of each element of the matrix.
            </summary>
            <param name="dividend">The dividend we want to compute the modulus of.</param>
            <param name="divisor">The matrix whose elements we want to use as divisor.</param>
            <returns>The result of the calculation</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="divisor"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.op_Modulus(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the pointwise remainder (% operator), where the result has the sign of the dividend,
            of each element of two matrices.
            </summary>
            <param name="dividend">The matrix whose elements we want to compute the remainder of.</param>
            <param name="divisor">The divisor to use.</param>
            <exception cref="T:System.ArgumentException">If <paramref name="dividend"/> and <paramref name="divisor"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="dividend"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Sqrt(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the sqrt of a matrix pointwise
            </summary>
            <param name="x">The input matrix</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Exp(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the exponential of a matrix pointwise
            </summary>
            <param name="x">The input matrix</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Log(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the log of a matrix pointwise
            </summary>
            <param name="x">The input matrix</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Log10(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the log10 of a matrix pointwise
            </summary>
            <param name="x">The input matrix</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Sin(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the sin of a matrix pointwise
            </summary>
            <param name="x">The input matrix</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Cos(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the cos of a matrix pointwise
            </summary>
            <param name="x">The input matrix</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Tan(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the tan of a matrix pointwise
            </summary>
            <param name="x">The input matrix</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Asin(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the asin of a matrix pointwise
            </summary>
            <param name="x">The input matrix</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Acos(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the acos of a matrix pointwise
            </summary>
            <param name="x">The input matrix</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Atan(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the atan of a matrix pointwise
            </summary>
            <param name="x">The input matrix</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Sinh(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the sinh of a matrix pointwise
            </summary>
            <param name="x">The input matrix</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Cosh(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the cosh of a matrix pointwise
            </summary>
            <param name="x">The input matrix</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Tanh(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the tanh of a matrix pointwise
            </summary>
            <param name="x">The input matrix</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Abs(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the absolute value of a matrix pointwise
            </summary>
            <param name="x">The input matrix</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Floor(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the floor of a matrix pointwise
            </summary>
            <param name="x">The input matrix</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Ceiling(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the ceiling of a matrix pointwise
            </summary>
            <param name="x">The input matrix</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Round(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the rounded value of a matrix pointwise
            </summary>
            <param name="x">The input matrix</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Cholesky">
            <summary>
            Computes the Cholesky decomposition for a matrix.
            </summary>
            <returns>The Cholesky decomposition object.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.LU">
            <summary>
            Computes the LU decomposition for a matrix.
            </summary>
            <returns>The LU decomposition object.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.QR(MathNet.Numerics.LinearAlgebra.Factorization.QRMethod)">
            <summary>
            Computes the QR decomposition for a matrix.
            </summary>
            <param name="method">The type of QR factorization to perform.</param>
            <returns>The QR decomposition object.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.GramSchmidt">
            <summary>
            Computes the QR decomposition for a matrix using Modified Gram-Schmidt Orthogonalization.
            </summary>
            <returns>The QR decomposition object.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Svd(System.Boolean)">
            <summary>
            Computes the SVD decomposition for a matrix.
            </summary>
            <param name="computeVectors">Compute the singular U and VT vectors or not.</param>
            <returns>The SVD decomposition object.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Evd(MathNet.Numerics.LinearAlgebra.Symmetricity)">
            <summary>
            Computes the EVD decomposition for a matrix.
            </summary>
            <returns>The EVD decomposition object.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Solve(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>x</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Solve(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <param name="result">The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Solve(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Solves a system of linear equations, <b>AX = B</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>B</b>.</param>
            <returns>The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Matrix`1"/>, <b>X</b>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.Solve(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Solves a system of linear equations, <b>Ax = b</b>, with A QR factorized.
            </summary>
            <param name="input">The right hand side vector, <b>b</b>.</param>
            <returns>The left hand side <see cref="T:MathNet.Numerics.LinearAlgebra.Vector`1"/>, <b>x</b>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.TrySolveIterative(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolver{`0},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{`0},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{`0})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix (this matrix), b is the solution vector and x is the unknown vector.
            </summary>
            <param name="input">The solution vector <c>b</c>.</param>
            <param name="result">The result vector <c>x</c>.</param>
            <param name="solver">The iterative solver to use.</param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.TrySolveIterative(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolver{`0},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{`0},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{`0})">
            <summary>
            Solves the matrix equation AX = B, where A is the coefficient matrix (this matrix), B is the solution matrix and X is the unknown matrix.
            </summary>
            <param name="input">The solution matrix <c>B</c>.</param>
            <param name="result">The result matrix <c>X</c></param>
            <param name="solver">The iterative solver to use.</param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.TrySolveIterative(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolver{`0},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{`0},MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion{`0}[])">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix (this matrix), b is the solution vector and x is the unknown vector.
            </summary>
            <param name="input">The solution vector <c>b</c>.</param>
            <param name="result">The result vector <c>x</c>.</param>
            <param name="solver">The iterative solver to use.</param>
            <param name="stopCriteria">Criteria to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.TrySolveIterative(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolver{`0},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{`0},MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion{`0}[])">
            <summary>
            Solves the matrix equation AX = B, where A is the coefficient matrix (this matrix), B is the solution matrix and X is the unknown matrix.
            </summary>
            <param name="input">The solution matrix <c>B</c>.</param>
            <param name="result">The result matrix <c>X</c></param>
            <param name="solver">The iterative solver to use.</param>
            <param name="stopCriteria">Criteria to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.TrySolveIterative(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolver{`0},MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion{`0}[])">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix (this matrix), b is the solution vector and x is the unknown vector.
            </summary>
            <param name="input">The solution vector <c>b</c>.</param>
            <param name="result">The result vector <c>x</c>.</param>
            <param name="solver">The iterative solver to use.</param>
            <param name="stopCriteria">Criteria to control when to stop iterating.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.TrySolveIterative(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolver{`0},MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion{`0}[])">
            <summary>
            Solves the matrix equation AX = B, where A is the coefficient matrix (this matrix), B is the solution matrix and X is the unknown matrix.
            </summary>
            <param name="input">The solution matrix <c>B</c>.</param>
            <param name="result">The result matrix <c>X</c></param>
            <param name="solver">The iterative solver to use.</param>
            <param name="stopCriteria">Criteria to control when to stop iterating.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.SolveIterative(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolver{`0},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{`0},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{`0})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix (this matrix), b is the solution vector and x is the unknown vector.
            </summary>
            <param name="input">The solution vector <c>b</c>.</param>
            <param name="solver">The iterative solver to use.</param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
            <returns>The result vector <c>x</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.SolveIterative(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolver{`0},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{`0},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{`0})">
            <summary>
            Solves the matrix equation AX = B, where A is the coefficient matrix (this matrix), B is the solution matrix and X is the unknown matrix.
            </summary>
            <param name="input">The solution matrix <c>B</c>.</param>
            <param name="solver">The iterative solver to use.</param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
            <returns>The result matrix <c>X</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.SolveIterative(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolver{`0},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{`0},MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion{`0}[])">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix (this matrix), b is the solution vector and x is the unknown vector.
            </summary>
            <param name="input">The solution vector <c>b</c>.</param>
            <param name="solver">The iterative solver to use.</param>
            <param name="stopCriteria">Criteria to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
            <returns>The result vector <c>x</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.SolveIterative(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolver{`0},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{`0},MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion{`0}[])">
            <summary>
            Solves the matrix equation AX = B, where A is the coefficient matrix (this matrix), B is the solution matrix and X is the unknown matrix.
            </summary>
            <param name="input">The solution matrix <c>B</c>.</param>
            <param name="solver">The iterative solver to use.</param>
            <param name="stopCriteria">Criteria to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
            <returns>The result matrix <c>X</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.SolveIterative(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolver{`0},MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion{`0}[])">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix (this matrix), b is the solution vector and x is the unknown vector.
            </summary>
            <param name="input">The solution vector <c>b</c>.</param>
            <param name="solver">The iterative solver to use.</param>
            <param name="stopCriteria">Criteria to control when to stop iterating.</param>
            <returns>The result vector <c>x</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Matrix`1.SolveIterative(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolver{`0},MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion{`0}[])">
            <summary>
            Solves the matrix equation AX = B, where A is the coefficient matrix (this matrix), B is the solution matrix and X is the unknown matrix.
            </summary>
            <param name="input">The solution matrix <c>B</c>.</param>
            <param name="solver">The iterative solver to use.</param>
            <param name="stopCriteria">Criteria to control when to stop iterating.</param>
            <returns>The result matrix <c>X</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixExtensions.ToSingle(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Converts a matrix to single precision.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixExtensions.ToDouble(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Converts a matrix to double precision.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixExtensions.ToComplex32(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Converts a matrix to single precision complex numbers.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixExtensions.ToComplex(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Converts a matrix to double precision complex numbers.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixExtensions.ToComplex32(MathNet.Numerics.LinearAlgebra.Matrix{System.Single})">
            <summary>
            Gets a single precision complex matrix with the real parts from the given matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixExtensions.ToComplex(MathNet.Numerics.LinearAlgebra.Matrix{System.Double})">
            <summary>
            Gets a double precision complex matrix with the real parts from the given matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixExtensions.Real(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Gets a real matrix representing the real parts of a complex matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixExtensions.Real(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Gets a real matrix representing the real parts of a complex matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixExtensions.Imaginary(MathNet.Numerics.LinearAlgebra.Matrix{System.Numerics.Complex})">
            <summary>
            Gets a real matrix representing the imaginary parts of a complex matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.MatrixExtensions.Imaginary(MathNet.Numerics.LinearAlgebra.Matrix{MathNet.Numerics.Complex32})">
            <summary>
            Gets a real matrix representing the imaginary parts of a complex matrix.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.ExistingData.Clear">
            <summary>
            Existing data may not be all zeros, so clearing may be necessary
            if not all of it will be overwritten anyway.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.ExistingData.AssumeZeros">
            <summary>
            If existing data is assumed to be all zeros already,
            clearing it may be skipped if applicable.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Zeros.AllowSkip">
            <summary>
            Allow skipping zero entries (without enforcing skipping them).
            When enumerating sparse matrices this can significantly speed up operations.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Zeros.Include">
            <summary>
            Force applying the operation to all fields even if they are zero.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Symmetricity.Unknown">
            <summary>
            It is not known yet whether a matrix is symmetric or not.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Symmetricity.Symmetric">
            <summary>
            A matrix is symmetric
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Symmetricity.Hermitian">
            <summary>
            A matrix is Hermitian (conjugate symmetric).
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Symmetricity.Asymmetric">
            <summary>
            A matrix is not symmetric
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Solvers.CancellationStopCriterion`1">
            <summary>
            Defines an <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion`1"/> that uses a cancellation token as stop criterion.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.CancellationStopCriterion`1.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1"/> class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.CancellationStopCriterion`1.#ctor(System.Threading.CancellationToken)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1"/> class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.CancellationStopCriterion`1.DetermineStatus(System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Determines the status of the iterative calculation based on the stop criteria stored
            by the current <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1"/>. Result is set into <c>Status</c> field.
            </summary>
            <param name="iterationNumber">The number of iterations that have passed so far.</param>
            <param name="solutionVector">The vector containing the current solution values.</param>
            <param name="sourceVector">The right hand side vector.</param>
            <param name="residualVector">The vector containing the current residual vectors.</param>
            <remarks>
            The individual stop criteria may internally track the progress of the calculation based
            on the invocation of this method. Therefore this method should only be called if the
            calculation has moved forwards at least one step.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Solvers.CancellationStopCriterion`1.Status">
            <summary>
            Gets the current calculation status.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.CancellationStopCriterion`1.Reset">
            <summary>
            Resets the <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1"/> to the pre-calculation state.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.CancellationStopCriterion`1.Clone">
            <summary>
            Clones the current <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1"/> and its settings.
            </summary>
            <returns>A new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1"/> class.</returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Solvers.DelegateStopCriterion`1">
            <summary>
            Stop criterion that delegates the status determination to a delegate.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.DelegateStopCriterion`1.#ctor(System.Func{System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Solvers.IterationStatus})">
            <summary>
            Create a new instance of this criterion with a custom implementation.
            </summary>
            <param name="determine">Custom implementation with the same signature and semantics as the DetermineStatus method.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.DelegateStopCriterion`1.DetermineStatus(System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Determines the status of the iterative calculation by delegating it to the provided delegate.
            Result is set into <c>Status</c> field.
            </summary>
            <param name="iterationNumber">The number of iterations that have passed so far.</param>
            <param name="solutionVector">The vector containing the current solution values.</param>
            <param name="sourceVector">The right hand side vector.</param>
            <param name="residualVector">The vector containing the current residual vectors.</param>
            <remarks>
            The individual stop criteria may internally track the progress of the calculation based
            on the invocation of this method. Therefore this method should only be called if the
            calculation has moved forwards at least one step.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Solvers.DelegateStopCriterion`1.Status">
            <summary>
            Gets the current calculation status.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.DelegateStopCriterion`1.Reset">
            <summary>
            Resets the IIterationStopCriterion to the pre-calculation state.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.DelegateStopCriterion`1.Clone">
            <summary>
            Clones this criterion and its settings.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Solvers.DivergenceStopCriterion`1">
            <summary>
            Monitors an iterative calculation for signs of divergence.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Solvers.DivergenceStopCriterion`1._maximumRelativeIncrease">
            <summary>
            The maximum relative increase the residual may experience without triggering a divergence warning.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Solvers.DivergenceStopCriterion`1._minimumNumberOfIterations">
            <summary>
            The number of iterations over which a residual increase should be tracked before issuing a divergence warning.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Solvers.DivergenceStopCriterion`1._status">
            <summary>
            The status of the calculation
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Solvers.DivergenceStopCriterion`1._residualHistory">
            <summary>
            The array that holds the tracking information.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Solvers.DivergenceStopCriterion`1._lastIteration">
            <summary>
            The iteration number of the last iteration.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.DivergenceStopCriterion`1.#ctor(System.Double,System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.DivergenceStopCriterion`1"/> class with the specified maximum
            relative increase and the specified minimum number of tracking iterations.
            </summary>
            <param name="maximumRelativeIncrease">The maximum relative increase that the residual may experience before a divergence warning is issued. </param>
            <param name="minimumIterations">The minimum number of iterations over which the residual must grow before a divergence warning is issued.</param>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Solvers.DivergenceStopCriterion`1.MaximumRelativeIncrease">
            <summary>
            Gets or sets the maximum relative increase that the residual may experience before a divergence warning is issued.
            </summary>
            <exception cref="T:System.ArgumentOutOfRangeException">Thrown if the <c>Maximum</c> is set to zero or below.</exception>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Solvers.DivergenceStopCriterion`1.MinimumNumberOfIterations">
            <summary>
            Gets or sets the minimum number of iterations over which the residual must grow before
            issuing a divergence warning.
            </summary>
            <exception cref="T:System.ArgumentOutOfRangeException">Thrown if the <c>value</c> is set to less than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.DivergenceStopCriterion`1.DetermineStatus(System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Determines the status of the iterative calculation based on the stop criteria stored
            by the current <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion`1"/>. Result is set into <c>Status</c> field.
            </summary>
            <param name="iterationNumber">The number of iterations that have passed so far.</param>
            <param name="solutionVector">The vector containing the current solution values.</param>
            <param name="sourceVector">The right hand side vector.</param>
            <param name="residualVector">The vector containing the current residual vectors.</param>
            <remarks>
            The individual stop criteria may internally track the progress of the calculation based
            on the invocation of this method. Therefore this method should only be called if the
            calculation has moved forwards at least one step.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.DivergenceStopCriterion`1.IsDiverging">
            <summary>
            Detect if solution is diverging
            </summary>
            <returns><c>true</c> if diverging, otherwise <c>false</c></returns>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Solvers.DivergenceStopCriterion`1.RequiredHistoryLength">
            <summary>
            Gets required history Length
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Solvers.DivergenceStopCriterion`1.Status">
            <summary>
            Gets the current calculation status.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.DivergenceStopCriterion`1.Reset">
            <summary>
            Resets the <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion`1"/> to the pre-calculation state.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.DivergenceStopCriterion`1.Clone">
            <summary>
            Clones the current <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.DivergenceStopCriterion`1"/> and its settings.
            </summary>
            <returns>A new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.DivergenceStopCriterion`1"/> class.</returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Solvers.FailureStopCriterion`1">
            <summary>
            Defines an <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion`1"/> that monitors residuals for NaN's.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Solvers.FailureStopCriterion`1._status">
            <summary>
            The status of the calculation
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Solvers.FailureStopCriterion`1._lastIteration">
            <summary>
            The iteration number of the last iteration.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.FailureStopCriterion`1.DetermineStatus(System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Determines the status of the iterative calculation based on the stop criteria stored
            by the current <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion`1"/>. Result is set into <c>Status</c> field.
            </summary>
            <param name="iterationNumber">The number of iterations that have passed so far.</param>
            <param name="solutionVector">The vector containing the current solution values.</param>
            <param name="sourceVector">The right hand side vector.</param>
            <param name="residualVector">The vector containing the current residual vectors.</param>
            <remarks>
            The individual stop criteria may internally track the progress of the calculation based
            on the invocation of this method. Therefore this method should only be called if the
            calculation has moved forwards at least one step.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Solvers.FailureStopCriterion`1.Status">
            <summary>
            Gets the current calculation status.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.FailureStopCriterion`1.Reset">
            <summary>
            Resets the <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion`1"/> to the pre-calculation state.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.FailureStopCriterion`1.Clone">
            <summary>
            Clones the current <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.FailureStopCriterion`1"/> and its settings.
            </summary>
            <returns>A new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.FailureStopCriterion`1"/> class.</returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion`1">
            <summary>
            The base interface for classes that provide stop criteria for iterative calculations.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion`1.DetermineStatus(System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Determines the status of the iterative calculation based on the stop criteria stored
            by the current IIterationStopCriterion. Status is set to <c>Status</c> field of current object.
            </summary>
            <param name="iterationNumber">The number of iterations that have passed so far.</param>
            <param name="solutionVector">The vector containing the current solution values.</param>
            <param name="sourceVector">The right hand side vector.</param>
            <param name="residualVector">The vector containing the current residual vectors.</param>
            <remarks>
            The individual stop criteria may internally track the progress of the calculation based
            on the invocation of this method. Therefore this method should only be called if the
            calculation has moved forwards at least one step.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion`1.Status">
            <summary>
            Gets the current calculation status.
            </summary>
            <remarks><see langword="null" /> is not a legal value. Status should be set in <see cref="M:MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion`1.DetermineStatus(System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})"/> implementation.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion`1.Reset">
            <summary>
            Resets the IIterationStopCriterion to the pre-calculation state.
            </summary>
            <remarks>To implementers: Invoking this method should not clear the user defined
            property values, only the state that is used to track the progress of the
            calculation.</remarks>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolver`1">
            <summary>
            Defines the interface for <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolver`1"/> classes that solve the matrix equation Ax = b in
            an iterative manner.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolver`1.Solve(MathNet.Numerics.LinearAlgebra.Matrix{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Solvers.Iterator{`0},MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner{`0})">
            <summary>
            Solves the matrix equation Ax = b, where A is the coefficient matrix, b is the
            solution vector and x is the unknown vector.
            </summary>
            <param name="matrix">The coefficient matrix, <c>A</c>.</param>
            <param name="input">The solution vector, <c>b</c></param>
            <param name="result">The result vector, <c>x</c></param>
            <param name="iterator">The iterator to use to control when to stop iterating.</param>
            <param name="preconditioner">The preconditioner to use for approximations.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolverSetup`1">
            <summary>
            Defines the interface for objects that can create an iterative solver with
            specific settings. This interface is used to pass iterative solver creation
            setup information around.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolverSetup`1.SolverType">
            <summary>
            Gets the type of the solver that will be created by this setup object.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolverSetup`1.PreconditionerType">
            <summary>
            Gets type of preconditioner, if any, that will be created by this setup object.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolverSetup`1.CreateSolver">
            <summary>
            Creates the iterative solver to be used.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolverSetup`1.CreatePreconditioner">
            <summary>
            Creates the preconditioner to be used by default (can be overwritten).
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolverSetup`1.SolutionSpeed">
            <summary>
            Gets the relative speed of the solver.
            </summary>
            <value>Returns a value between 0 and 1, inclusive.</value>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolverSetup`1.Reliability">
            <summary>
            Gets the relative reliability of the solver.
            </summary>
            <value>Returns a value between 0 and 1 inclusive.</value>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner`1">
            <summary>
            The base interface for preconditioner classes.
            </summary>
            <remarks>
            <para>
            Preconditioners are used by iterative solvers to improve the convergence
            speed of the solving process. Increase in convergence speed
            is related to the number of iterations necessary to get a converged solution.
            So while in general the use of a preconditioner means that the iterative
            solver will perform fewer iterations it does not guarantee that the actual
            solution time decreases given that some preconditioners can be expensive to
            setup and run.
            </para>
            <para>
            Note that in general changes to the matrix will invalidate the preconditioner
            if the changes occur after creating the preconditioner.
            </para>
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner`1.Initialize(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Initializes the preconditioner and loads the internal data structures.
            </summary>
            <param name="matrix">The matrix on which the preconditioner is based.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.IPreconditioner`1.Approximate(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Approximates the solution to the matrix equation <b>Mx = b</b>.
            </summary>
            <param name="rhs">The right hand side vector.</param>
            <param name="lhs">The left hand side vector. Also known as the result vector.</param>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1">
            <summary>
            Defines an <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion`1"/> that monitors the numbers of iteration
            steps as stop criterion.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1.DefaultMaximumNumberOfIterations">
            <summary>
            The default value for the maximum number of iterations the process is allowed
            to perform.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1._maximumNumberOfIterations">
            <summary>
            The maximum number of iterations the calculation is allowed to perform.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1._status">
            <summary>
            The status of the calculation
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1"/> class with the default maximum
            number of iterations.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1.#ctor(System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1"/> class with the specified maximum
            number of iterations.
            </summary>
            <param name="maximumNumberOfIterations">The maximum number of iterations the calculation is allowed to perform.</param>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1.MaximumNumberOfIterations">
            <summary>
            Gets or sets the maximum number of iterations the calculation is allowed to perform.
            </summary>
            <exception cref="T:System.ArgumentOutOfRangeException">Thrown if the <c>Maximum</c> is set to a negative value.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1.ResetMaximumNumberOfIterationsToDefault">
            <summary>
            Returns the maximum number of iterations to the default.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1.DetermineStatus(System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Determines the status of the iterative calculation based on the stop criteria stored
            by the current <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1"/>. Result is set into <c>Status</c> field.
            </summary>
            <param name="iterationNumber">The number of iterations that have passed so far.</param>
            <param name="solutionVector">The vector containing the current solution values.</param>
            <param name="sourceVector">The right hand side vector.</param>
            <param name="residualVector">The vector containing the current residual vectors.</param>
            <remarks>
            The individual stop criteria may internally track the progress of the calculation based
            on the invocation of this method. Therefore this method should only be called if the
            calculation has moved forwards at least one step.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1.Status">
            <summary>
            Gets the current calculation status.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1.Reset">
            <summary>
            Resets the <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1"/> to the pre-calculation state.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1.Clone">
            <summary>
            Clones the current <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1"/> and its settings.
            </summary>
            <returns>A new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IterationCountStopCriterion`1"/> class.</returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Solvers.IterationStatus">
            <summary>
            Iterative Calculation Status
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Solvers.Iterator`1">
            <summary>
            An iterator that is used to check if an iterative calculation should continue or stop.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Solvers.Iterator`1._stopCriteria">
            <summary>
            The collection that holds all the stop criteria and the flag indicating if they should be added
            to the child iterators.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Solvers.Iterator`1._status">
            <summary>
            The status of the iterator.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.Iterator`1.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.Iterator`1"/> class with the default stop criteria.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.Iterator`1.#ctor(MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion{`0}[])">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.Iterator`1"/> class with the specified stop criteria.
            </summary>
            <param name="stopCriteria">
            The specified stop criteria. Only one stop criterion of each type can be passed in. None
            of the stop criteria will be passed on to child iterators.
            </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.Iterator`1.#ctor(System.Collections.Generic.IEnumerable{MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion{`0}})">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.Iterator`1"/> class with the specified stop criteria.
            </summary>
            <param name="stopCriteria">
            The specified stop criteria. Only one stop criterion of each type can be passed in. None
            of the stop criteria will be passed on to child iterators.
            </param>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Solvers.Iterator`1.Status">
            <summary>
            Gets the current calculation status.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.Iterator`1.DetermineStatus(System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Determines the status of the iterative calculation based on the stop criteria stored
            by the current <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.Iterator`1"/>. Result is set into <c>Status</c> field.
            </summary>
            <param name="iterationNumber">The number of iterations that have passed so far.</param>
            <param name="solutionVector">The vector containing the current solution values.</param>
            <param name="sourceVector">The right hand side vector.</param>
            <param name="residualVector">The vector containing the current residual vectors.</param>
            <remarks>
            The individual iterators may internally track the progress of the calculation based
            on the invocation of this method. Therefore this method should only be called if the
            calculation has moved forwards at least one step.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.Iterator`1.Cancel">
            <summary>
            Indicates to the iterator that the iterative process has been cancelled.
            </summary>
            <remarks>
            Does not reset the stop-criteria.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.Iterator`1.Reset">
            <summary>
            Resets the <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.Iterator`1"/> to the pre-calculation state.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.Iterator`1.Clone">
            <summary>
            Creates a deep clone of the current iterator.
            </summary>
            <returns>The deep clone of the current iterator.</returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Solvers.ResidualStopCriterion`1">
            <summary>
            Defines an <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion`1"/> that monitors residuals as stop criterion.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Solvers.ResidualStopCriterion`1._maximum">
            <summary>
            The maximum value for the residual below which the calculation is considered converged.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Solvers.ResidualStopCriterion`1._minimumIterationsBelowMaximum">
            <summary>
            The minimum number of iterations for which the residual has to be below the maximum before
            the calculation is considered converged.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Solvers.ResidualStopCriterion`1._status">
            <summary>
            The status of the calculation
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Solvers.ResidualStopCriterion`1._iterationCount">
            <summary>
            The number of iterations since the residuals got below the maximum.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Solvers.ResidualStopCriterion`1._lastIteration">
            <summary>
            The iteration number of the last iteration.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.ResidualStopCriterion`1.#ctor(System.Double,System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.ResidualStopCriterion`1"/> class with the specified
            maximum residual and minimum number of iterations.
            </summary>
            <param name="maximum">
            The maximum value for the residual below which the calculation is considered converged.
            </param>
            <param name="minimumIterationsBelowMaximum">
            The minimum number of iterations for which the residual has to be below the maximum before
            the calculation is considered converged.
            </param>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Solvers.ResidualStopCriterion`1.Maximum">
            <summary>
            Gets or sets the maximum value for the residual below which the calculation is considered
            converged.
            </summary>
            <exception cref="T:System.ArgumentOutOfRangeException">Thrown if the <c>Maximum</c> is set to a negative value.</exception>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Solvers.ResidualStopCriterion`1.MinimumIterationsBelowMaximum">
            <summary>
            Gets or sets the minimum number of iterations for which the residual has to be
            below the maximum before the calculation is considered converged.
            </summary>
            <exception cref="T:System.ArgumentOutOfRangeException">Thrown if the <c>BelowMaximumFor</c> is set to a value less than 1.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.ResidualStopCriterion`1.DetermineStatus(System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Determines the status of the iterative calculation based on the stop criteria stored
            by the current <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion`1"/>. Result is set into <c>Status</c> field.
            </summary>
            <param name="iterationNumber">The number of iterations that have passed so far.</param>
            <param name="solutionVector">The vector containing the current solution values.</param>
            <param name="sourceVector">The right hand side vector.</param>
            <param name="residualVector">The vector containing the current residual vectors.</param>
            <remarks>
            The individual stop criteria may internally track the progress of the calculation based
            on the invocation of this method. Therefore this method should only be called if the
            calculation has moved forwards at least one step.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Solvers.ResidualStopCriterion`1.Status">
            <summary>
            Gets the current calculation status.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.ResidualStopCriterion`1.Reset">
            <summary>
            Resets the <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterationStopCriterion`1"/> to the pre-calculation state.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.ResidualStopCriterion`1.Clone">
            <summary>
            Clones the current <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.ResidualStopCriterion`1"/> and its settings.
            </summary>
            <returns>A new instance of the <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.ResidualStopCriterion`1"/> class.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.SolverSetup`1.LoadFromAssembly(System.Reflection.Assembly,System.Boolean,System.Type[])">
            <summary>
            Loads the available <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolverSetup`1"/> objects from the specified assembly.
            </summary>
            <param name="assembly">The assembly which will be searched for setup objects.</param>
            <param name="ignoreFailed">If true, types that fail to load are simply ignored. Otherwise the exception is rethrown.</param>
            <param name="typesToExclude">The <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolver`1"/> types that should not be loaded.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.SolverSetup`1.LoadFromAssembly(System.Type,System.Boolean,System.Type[])">
            <summary>
            Loads the available <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolverSetup`1"/> objects from the specified assembly.
            </summary>
            <param name="typeInAssembly">The type in the assembly which should be searched for setup objects.</param>
            <param name="ignoreFailed">If true, types that fail to load are simply ignored. Otherwise the exception is rethrown.</param>
            <param name="typesToExclude">The <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolver`1"/> types that should not be loaded.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.SolverSetup`1.LoadFromAssembly(System.Reflection.AssemblyName,System.Boolean,System.Type[])">
            <summary>
            Loads the available <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolverSetup`1"/> objects from the specified assembly.
            </summary>
            <param name="assemblyName">The <see cref="T:System.Reflection.AssemblyName"/> of the assembly that should be searched for setup objects.</param>
            <param name="ignoreFailed">If true, types that fail to load are simply ignored. Otherwise the exception is rethrown.</param>
            <param name="typesToExclude">The <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolver`1"/> types that should not be loaded.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.SolverSetup`1.Load(System.Type[])">
            <summary>
            Loads the available <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolverSetup`1"/> objects from the Math.NET Numerics assembly.
            </summary>
            <param name="typesToExclude">The <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolver`1"/> types that should not be loaded.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.SolverSetup`1.Load">
            <summary>
            Loads the available <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolverSetup`1"/> objects from the  Math.NET Numerics assembly.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Solvers.UnitPreconditioner`1">
            <summary>
            A unit preconditioner. This preconditioner does not actually do anything
            it is only used when running an <see cref="T:MathNet.Numerics.LinearAlgebra.Solvers.IIterativeSolver`1"/> without
            a preconditioner.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Solvers.UnitPreconditioner`1._size">
            <summary>
            The coefficient matrix on which this preconditioner operates.
            Is used to check dimensions on the different vectors that are processed.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.UnitPreconditioner`1.Initialize(MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Initializes the preconditioner and loads the internal data structures.
            </summary>
            <param name="matrix">
            The matrix upon which the preconditioner is based.
            </param>
            <exception cref="T:System.ArgumentException">If <paramref name="matrix"/> is not a square matrix.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Solvers.UnitPreconditioner`1.Approximate(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Approximates the solution to the matrix equation <b>Ax = b</b>.
            </summary>
            <param name="rhs">The right hand side vector.</param>
            <param name="lhs">The left hand side vector. Also known as the result vector.</param>
            <exception cref="T:System.ArgumentException">
              <para>
                If <paramref name="rhs"/> and <paramref name="lhs"/> do not have the same size.
              </para>
              <para>
                - or -
              </para>
              <para>
                If the size of <paramref name="rhs"/> is different the number of rows of the coefficient matrix.
              </para>
            </exception>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Storage.DenseColumnMajorMatrixStorage`1.IsDense">
            <summary>
            True if the matrix storage format is dense.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Storage.DenseColumnMajorMatrixStorage`1.IsFullyMutable">
            <summary>
            True if all fields of this matrix can be set to any value.
            False if some fields are fixed, like on a diagonal matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.DenseColumnMajorMatrixStorage`1.IsMutableAt(System.Int32,System.Int32)">
            <summary>
            True if the specified field can be set to any value.
            False if the field is fixed, like an off-diagonal field on a diagonal matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.DenseColumnMajorMatrixStorage`1.At(System.Int32,System.Int32)">
            <summary>
            Retrieves the requested element without range checking.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.DenseColumnMajorMatrixStorage`1.At(System.Int32,System.Int32,`0)">
            <summary>
            Sets the element without range checking.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.DenseColumnMajorMatrixStorage`1.RowColumnAtIndex(System.Int32,System.Int32@,System.Int32@)">
            <summary>
            Evaluate the row and column at a specific data index.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Storage.DenseVectorStorage`1.IsDense">
            <summary>
            True if the vector storage format is dense.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.DenseVectorStorage`1.At(System.Int32)">
            <summary>
            Retrieves the requested element without range checking.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.DenseVectorStorage`1.At(System.Int32,`0)">
            <summary>
            Sets the element without range checking.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Storage.DiagonalMatrixStorage`1.IsDense">
            <summary>
            True if the matrix storage format is dense.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Storage.DiagonalMatrixStorage`1.IsFullyMutable">
            <summary>
            True if all fields of this matrix can be set to any value.
            False if some fields are fixed, like on a diagonal matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.DiagonalMatrixStorage`1.IsMutableAt(System.Int32,System.Int32)">
            <summary>
            True if the specified field can be set to any value.
            False if the field is fixed, like an off-diagonal field on a diagonal matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.DiagonalMatrixStorage`1.At(System.Int32,System.Int32)">
            <summary>
            Retrieves the requested element without range checking.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.DiagonalMatrixStorage`1.At(System.Int32,System.Int32,`0)">
            <summary>
            Sets the element without range checking.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.DiagonalMatrixStorage`1.GetHashCode">
            <summary>
            Returns a hash code for this instance.
            </summary>
            <returns>
            A hash code for this instance, suitable for use in hashing algorithms and data structures like a hash table.
            </returns>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage`1.IsDense">
            <summary>
            True if the matrix storage format is dense.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage`1.IsFullyMutable">
            <summary>
            True if all fields of this matrix can be set to any value.
            False if some fields are fixed, like on a diagonal matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage`1.IsMutableAt(System.Int32,System.Int32)">
            <summary>
            True if the specified field can be set to any value.
            False if the field is fixed, like an off-diagonal field on a diagonal matrix.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage`1.Item(System.Int32,System.Int32)">
            <summary>
            Gets or sets the value at the given row and column, with range checking.
            </summary>
            <param name="row">
            The row of the element.
            </param>
            <param name="column">
            The column of the element.
            </param>
            <value>The value to get or set.</value>
            <remarks>This method is ranged checked. <see cref="M:MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage`1.At(System.Int32,System.Int32)"/> and <see cref="M:MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage`1.At(System.Int32,System.Int32,`0)"/>
            to get and set values without range checking.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage`1.At(System.Int32,System.Int32)">
            <summary>
            Retrieves the requested element without range checking.
            </summary>
            <param name="row">
            The row of the element.
            </param>
            <param name="column">
            The column of the element.
            </param>
            <returns>
            The requested element.
            </returns>
            <remarks>Not range-checked.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage`1.At(System.Int32,System.Int32,`0)">
            <summary>
            Sets the element without range checking.
            </summary>
            <param name="row"> The row of the element. </param>
            <param name="column"> The column of the element. </param>
            <param name="value"> The value to set the element to. </param>
            <remarks>WARNING: This method is not thread safe. Use "lock" with it and be sure to avoid deadlocks.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage`1.Equals(MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage{`0})">
            <summary>
            Indicates whether the current object is equal to another object of the same type.
            </summary>
            <param name="other">
            An object to compare with this object.
            </param>
            <returns>
            <c>true</c> if the current object is equal to the <paramref name="other"/> parameter; otherwise, <c>false</c>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage`1.Equals(System.Object)">
            <summary>
            Determines whether the specified <see cref="T:System.Object"/> is equal to the current <see cref="T:System.Object"/>.
            </summary>
            <returns>
            true if the specified <see cref="T:System.Object"/> is equal to the current <see cref="T:System.Object"/>; otherwise, false.
            </returns>
            <param name="obj">The <see cref="T:System.Object"/> to compare with the current <see cref="T:System.Object"/>. </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage`1.GetHashCode">
            <summary>
            Serves as a hash function for a particular type.
            </summary>
            <returns>
            A hash code for the current <see cref="T:System.Object"/>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage`1.FoldByRow``1(``0[],System.Func{``0,`0,``0},System.Func{``0,System.Int32,``0},``0[],MathNet.Numerics.LinearAlgebra.Zeros)">
            <remarks>The state array will not be modified, unless it is the same instance as the target array (which is allowed).</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage`1.FoldByRowUnchecked``1(``0[],System.Func{``0,`0,``0},System.Func{``0,System.Int32,``0},``0[],MathNet.Numerics.LinearAlgebra.Zeros)">
            <remarks>The state array will not be modified, unless it is the same instance as the target array (which is allowed).</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage`1.FoldByColumn``1(``0[],System.Func{``0,`0,``0},System.Func{``0,System.Int32,``0},``0[],MathNet.Numerics.LinearAlgebra.Zeros)">
            <remarks>The state array will not be modified, unless it is the same instance as the target array (which is allowed).</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage`1.FoldByColumnUnchecked``1(``0[],System.Func{``0,`0,``0},System.Func{``0,System.Int32,``0},``0[],MathNet.Numerics.LinearAlgebra.Zeros)">
            <remarks>The state array will not be modified, unless it is the same instance as the target array (which is allowed).</remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage`1.RowPointers">
            <summary>
            The array containing the row indices of the existing rows. Element "i" of the array gives the index of the
            element in the <see cref="F:MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage`1.Values"/> array that is first non-zero element in a row "i".
            The last value is equal to ValueCount, so that the number of non-zero entries in row "i" is always
            given by RowPointers[i+i] - RowPointers[i]. This array thus has length RowCount+1.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage`1.ColumnIndices">
            <summary>
            An array containing the column indices of the non-zero values. Element "j" of the array
            is the number of the column in matrix that contains the j-th value in the <see cref="F:MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage`1.Values"/> array.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage`1.Values">
            <summary>
            Array that contains the non-zero elements of matrix. Values of the non-zero elements of matrix are mapped into the values
            array using the row-major storage mapping described in a compressed sparse row (CSR) format.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage`1.ValueCount">
            <summary>
            Gets the number of non zero elements in the matrix.
            </summary>
            <value>The number of non zero elements.</value>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage`1.IsDense">
            <summary>
            True if the matrix storage format is dense.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage`1.IsFullyMutable">
            <summary>
            True if all fields of this matrix can be set to any value.
            False if some fields are fixed, like on a diagonal matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage`1.IsMutableAt(System.Int32,System.Int32)">
            <summary>
            True if the specified field can be set to any value.
            False if the field is fixed, like an off-diagonal field on a diagonal matrix.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage`1.At(System.Int32,System.Int32)">
            <summary>
            Retrieves the requested element without range checking.
            </summary>
            <param name="row">
            The row of the element.
            </param>
            <param name="column">
            The column of the element.
            </param>
            <returns>
            The requested element.
            </returns>
            <remarks>Not range-checked.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage`1.At(System.Int32,System.Int32,`0)">
            <summary>
            Sets the element without range checking.
            </summary>
            <param name="row"> The row of the element. </param>
            <param name="column"> The column of the element. </param>
            <param name="value"> The value to set the element to. </param>
            <remarks>WARNING: This method is not thread safe. Use "lock" with it and be sure to avoid deadlocks.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage`1.RemoveAtIndexUnchecked(System.Int32,System.Int32)">
            <summary>
            Delete value from internal storage
            </summary>
            <param name="itemIndex">Index of value in nonZeroValues array</param>
            <param name="row">Row number of matrix</param>
            <remarks>WARNING: This method is not thread safe. Use "lock" with it and be sure to avoid deadlocks</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage`1.FindItem(System.Int32,System.Int32)">
            <summary>
            Find item Index in nonZeroValues array
            </summary>
            <param name="row">Matrix row index</param>
            <param name="column">Matrix column index</param>
            <returns>Item index</returns>
            <remarks>WARNING: This method is not thread safe. Use "lock" with it and be sure to avoid deadlocks</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage`1.GrowthSize">
            <summary>
            Calculates the amount with which to grow the storage array's if they need to be
            increased in size.
            </summary>
            <returns>The amount grown.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage`1.NormalizeDuplicates">
            <summary>
            Eliminate duplicate entries by adding them together.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage`1.PopulateExplicitZerosOnDiagonal">
            <summary>
            Fill zeros explicitly on the diagonal entries as required by the Intel MKL direct sparse solver.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage`1.GetHashCode">
            <summary>
            Returns a hash code for this instance.
            </summary>
            <returns>
            A hash code for this instance, suitable for use in hashing algorithms and data structures like a hash table.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage`1.OfCompressedSparseRowFormat(System.Int32,System.Int32,System.Int32,System.Int32[],System.Int32[],`0[])">
            <summary>
            Create a new sparse storage from a compressed sparse row (CSR) format.
            This new storage will be independent from the given arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
            <param name="rows">The number of rows.</param>
            <param name="columns">The number of columns.</param>
            <param name="valueCount">The number of stored values including explicit zeros.</param>
            <param name="rowPointers">The row pointer array of the compressed sparse row format.</param>
            <param name="columnIndices">The column index array of the compressed sparse row format.</param>
            <param name="values">The data array of the compressed sparse row format.</param>
            <returns>The sparse storage from the compressed sparse row format.</returns>
            <remarks>Duplicate entries will be summed together and explicit zeros will be not intentionally removed.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage`1.OfCompressedSparseColumnFormat(System.Int32,System.Int32,System.Int32,System.Int32[],System.Int32[],`0[])">
            <summary>
            Create a new sparse matrix storage from a compressed sparse column (CSC) format.
            This new storage will be independent from the given arrays.
            A new memory block will be allocated.
            </summary>
            <param name="rows">The number of rows.</param>
            <param name="columns">The number of columns.</param>
            <param name="valueCount">The number of stored values including explicit zeros.</param>
            <param name="rowIndices">The row index array of the compressed sparse column format.</param>
            <param name="columnPointers">The column pointer array of the compressed sparse column format.</param>
            <param name="values">The data array of the compressed sparse column format.</param>
            <returns>The sparse storage from the compressed sparse column format.</returns>
            <remarks>Duplicate entries will be summed together and explicit zeros will be not intentionally removed.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.SparseCompressedRowMatrixStorage`1.OfCoordinateFormat(System.Int32,System.Int32,System.Int32,System.Int32[],System.Int32[],`0[])">
            <summary>
            Create a new sparse storage from a coordinate (COO) format.
            This new storage will be independent from the given arrays.
            A new memory block will be allocated for storing the matrix.
            </summary>
            <param name="rows">The number of rows.</param>
            <param name="columns">The number of columns.</param>
            <param name="valueCount">The number of stored values including explicit zeros.</param>
            <param name="rowIndices">The row index array of the coordinate format.</param>
            <param name="columnIndices">The column index array of the coordinate format.</param>
            <param name="values">The data array of the coordinate format.</param>
            <returns>The sparse storage from the coordinate format.</returns>
            <remarks>Duplicate entries will be summed together and
            explicit zeros will be not intentionally removed.</remarks>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Storage.SparseVectorStorage`1.Indices">
            <summary>
            Array that contains the indices of the non-zero values.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Storage.SparseVectorStorage`1.Values">
            <summary>
            Array that contains the non-zero elements of the vector.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Storage.SparseVectorStorage`1.ValueCount">
            <summary>
            Gets the number of non-zero elements in the vector.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Storage.SparseVectorStorage`1.IsDense">
            <summary>
            True if the vector storage format is dense.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.SparseVectorStorage`1.At(System.Int32)">
            <summary>
            Retrieves the requested element without range checking.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.SparseVectorStorage`1.At(System.Int32,`0)">
            <summary>
            Sets the element without range checking.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.SparseVectorStorage`1.GrowthSize">
            <summary>
            Calculates the amount with which to grow the storage array's if they need to be
            increased in size.
            </summary>
            <returns>The amount grown.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.SparseVectorStorage`1.GetHashCode">
            <summary>
            Returns a hash code for this instance.
            </summary>
            <returns>
            A hash code for this instance, suitable for use in hashing algorithms and data structures like a hash table.
            </returns>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Storage.VectorStorage`1.IsDense">
            <summary>
            True if the vector storage format is dense.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Storage.VectorStorage`1.Item(System.Int32)">
            <summary>
            Gets or sets the value at the given index, with range checking.
            </summary>
            <param name="index">
            The index of the element.
            </param>
            <value>The value to get or set.</value>
            <remarks>This method is ranged checked. <see cref="M:MathNet.Numerics.LinearAlgebra.Storage.VectorStorage`1.At(System.Int32)"/> and <see cref="M:MathNet.Numerics.LinearAlgebra.Storage.VectorStorage`1.At(System.Int32,`0)"/>
            to get and set values without range checking.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.VectorStorage`1.At(System.Int32)">
            <summary>
            Retrieves the requested element without range checking.
            </summary>
            <param name="index">The index of the element.</param>
            <returns>The requested element.</returns>
            <remarks>Not range-checked.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.VectorStorage`1.At(System.Int32,`0)">
            <summary>
            Sets the element without range checking.
            </summary>
            <param name="index">The index of the element.</param>
            <param name="value">The value to set the element to. </param>
            <remarks>WARNING: This method is not thread safe. Use "lock" with it and be sure to avoid deadlocks.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.VectorStorage`1.Equals(MathNet.Numerics.LinearAlgebra.Storage.VectorStorage{`0})">
            <summary>
            Indicates whether the current object is equal to another object of the same type.
            </summary>
            <param name="other">
            An object to compare with this object.
            </param>
            <returns>
            <c>true</c> if the current object is equal to the <paramref name="other"/> parameter; otherwise, <c>false</c>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.VectorStorage`1.Equals(System.Object)">
            <summary>
            Determines whether the specified <see cref="T:System.Object"/> is equal to the current <see cref="T:System.Object"/>.
            </summary>
            <returns>
            true if the specified <see cref="T:System.Object"/> is equal to the current <see cref="T:System.Object"/>; otherwise, false.
            </returns>
            <param name="obj">The <see cref="T:System.Object"/> to compare with the current <see cref="T:System.Object"/>. </param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Storage.VectorStorage`1.GetHashCode">
            <summary>
            Serves as a hash function for a particular type.
            </summary>
            <returns>
            A hash code for the current <see cref="T:System.Object"/>.
            </returns>
        </member>
        <member name="T:MathNet.Numerics.LinearAlgebra.Vector`1">
            <summary>
            Defines the generic class for <c>Vector</c> classes.
            </summary>
            <typeparam name="T">Supported data types are double, single, <see cref="N:MathNet.Numerics.LinearAlgebra.Complex"/>, and <see cref="N:MathNet.Numerics.LinearAlgebra.Complex32"/>.</typeparam>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Vector`1.Zero">
            <summary>
            The zero value for type T.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.LinearAlgebra.Vector`1.One">
            <summary>
            The value of 1.0 for type T.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoNegate(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Negates vector and save result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoConjugate(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Complex conjugates vector and save result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoAdd(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Adds a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to add.</param>
            <param name="result">The vector to store the result of the addition.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoAdd(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Adds another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to add to this one.</param>
            <param name="result">The vector to store the result of the addition.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoSubtract(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Subtracts a scalar from each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to subtract.</param>
            <param name="result">The vector to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoSubtractFrom(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Subtracts each element of the vector from a scalar and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to subtract from.</param>
            <param name="result">The vector to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoSubtract(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Subtracts another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to subtract from this one.</param>
            <param name="result">The vector to store the result of the subtraction.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoMultiply(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Multiplies a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to multiply.</param>
            <param name="result">The vector to store the result of the multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoDotProduct(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the dot product between this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of a[i]*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoConjugateDotProduct(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the dot product between the conjugate of this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of conj(a[i])*b[i] for all i.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoOuterProduct(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the outer product M[i,j] = u[i]*v[j] of this and another vector and stores the result in the result matrix.
            </summary>
            <param name="other">The other vector</param>
            <param name="result">The matrix to store the result of the product.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoDivide(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Divides each element of the vector by a scalar and stores the result in the result vector.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">The vector to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoDivideByThis(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Divides a scalar by each element of the vector and stores the result in the result vector.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">The vector to store the result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoModulus(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoModulusByThis(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given dividend for each element of the vector.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoRemainder(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoRemainderByThis(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            for the given dividend for each element of the vector.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoPointwiseMultiply(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise multiplies this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to pointwise multiply with this one.</param>
            <param name="result">The vector to store the result of the pointwise multiplication.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoPointwiseDivide(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise divide this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The pointwise denominator vector to use.</param>
            <param name="result">The result of the division.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoPointwisePower(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise raise this vector to an exponent and store the result into the result vector.
            </summary>
            <param name="exponent">The exponent to raise this vector values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoPointwisePower(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise raise this vector to an exponent vector and store the result into the result vector.
            </summary>
            <param name="exponent">The exponent vector to raise this vector values to.</param>
            <param name="result">The vector to store the result of the pointwise power.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoPointwiseModulus(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise canonical modulus, where the result has the sign of the divisor,
            of this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The pointwise denominator vector to use.</param>
            <param name="result">The result of the modulus.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoPointwiseRemainder(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise remainder (% operator), where the result has the sign of the dividend,
            of this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The pointwise denominator vector to use.</param>
            <param name="result">The result of the modulus.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoPointwiseExp(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the exponential function to each value and stores the result into the result vector.
            </summary>
            <param name="result">The vector to store the result.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DoPointwiseLog(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the natural logarithm function to each value and stores the result into the result vector.
            </summary>
            <param name="result">The vector to store the result.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Add(`0)">
            <summary>
            Adds a scalar to each element of the vector.
            </summary>
            <param name="scalar">The scalar to add.</param>
            <returns>A copy of the vector with the scalar added.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Add(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Adds a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to add.</param>
            <param name="result">The vector to store the result of the addition.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Add(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Adds another vector to this vector.
            </summary>
            <param name="other">The vector to add to this one.</param>
            <returns>A new vector containing the sum of both vectors.</returns>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="other"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Add(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Adds another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to add to this one.</param>
            <param name="result">The vector to store the result of the addition.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="other"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Subtract(`0)">
            <summary>
            Subtracts a scalar from each element of the vector.
            </summary>
            <param name="scalar">The scalar to subtract.</param>
            <returns>A new vector containing the subtraction of this vector and the scalar.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Subtract(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Subtracts a scalar from each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to subtract.</param>
            <param name="result">The vector to store the result of the subtraction.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.SubtractFrom(`0)">
            <summary>
            Subtracts each element of the vector from a scalar.
            </summary>
            <param name="scalar">The scalar to subtract from.</param>
            <returns>A new vector containing the subtraction of the scalar and this vector.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.SubtractFrom(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Subtracts each element of the vector from a scalar and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to subtract from.</param>
            <param name="result">The vector to store the result of the subtraction.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Negate">
            <summary>
            Returns a negated vector.
            </summary>
            <returns>The negated vector.</returns>
            <remarks>Added as an alternative to the unary negation operator.</remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Negate(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Negates vector and save result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Subtract(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Subtracts another vector from this vector.
            </summary>
            <param name="other">The vector to subtract from this one.</param>
            <returns>A new vector containing the subtraction of the two vectors.</returns>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="other"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Subtract(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Subtracts another vector to this vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to subtract from this one.</param>
            <param name="result">The vector to store the result of the subtraction.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="other"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Conjugate">
            <summary>
            Return vector with complex conjugate values of the source vector
            </summary>
            <returns>Conjugated vector</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Conjugate(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Complex conjugates vector and save result to <paramref name="result"/>
            </summary>
            <param name="result">Target vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Multiply(`0)">
            <summary>
            Multiplies a scalar to each element of the vector.
            </summary>
            <param name="scalar">The scalar to multiply.</param>
            <returns>A new vector that is the multiplication of the vector and the scalar.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Multiply(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Multiplies a scalar to each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to multiply.</param>
            <param name="result">The vector to store the result of the multiplication.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DotProduct(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the dot product between this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of a[i]*b[i] for all i.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="other"/> is not of the same size.</exception>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Vector`1.ConjugateDotProduct(MathNet.Numerics.LinearAlgebra.Vector{`0})"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.ConjugateDotProduct(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the dot product between the conjugate of this vector and another vector.
            </summary>
            <param name="other">The other vector.</param>
            <returns>The sum of conj(a[i])*b[i] for all i.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="other"/> is not of the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="other"/> is <see langword="null"/>.</exception>
            <seealso cref="M:MathNet.Numerics.LinearAlgebra.Vector`1.DotProduct(MathNet.Numerics.LinearAlgebra.Vector{`0})"/>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Divide(`0)">
            <summary>
            Divides each element of the vector by a scalar.
            </summary>
            <param name="scalar">The scalar to divide with.</param>
            <returns>A new vector that is the division of the vector and the scalar.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Divide(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Divides each element of the vector by a scalar and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to divide with.</param>
            <param name="result">The vector to store the result of the division.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DivideByThis(`0)">
            <summary>
            Divides a scalar by each element of the vector.
            </summary>
            <param name="scalar">The scalar to divide.</param>
            <returns>A new vector that is the division of the vector and the scalar.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.DivideByThis(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Divides a scalar by each element of the vector and stores the result in the result vector.
            </summary>
            <param name="scalar">The scalar to divide.</param>
            <param name="result">The vector to store the result of the division.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Modulus(`0)">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <returns>A vector containing the result.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Modulus(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.ModulusByThis(`0)">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given dividend for each element of the vector.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <returns>A vector containing the result.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.ModulusByThis(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the canonical modulus, where the result has the sign of the divisor,
            for the given dividend for each element of the vector.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Remainder(`0)">
            <summary>
            Computes the remainder (vector % divisor), where the result has the sign of the dividend,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <returns>A vector containing the result.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Remainder(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the remainder (vector % divisor), where the result has the sign of the dividend,
            for each element of the vector for the given divisor.
            </summary>
            <param name="divisor">The scalar denominator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.RemainderByThis(`0)">
            <summary>
            Computes the remainder (dividend % vector), where the result has the sign of the dividend,
            for the given dividend for each element of the vector.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <returns>A vector containing the result.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.RemainderByThis(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the remainder (dividend % vector), where the result has the sign of the dividend,
            for the given dividend for each element of the vector.
            </summary>
            <param name="dividend">The scalar numerator to use.</param>
            <param name="result">A vector to store the results in.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseMultiply(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise multiplies this vector with another vector.
            </summary>
            <param name="other">The vector to pointwise multiply with this one.</param>
            <returns>A new vector which is the pointwise multiplication of the two vectors.</returns>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="other"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseMultiply(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise multiplies this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="other">The vector to pointwise multiply with this one.</param>
            <param name="result">The vector to store the result of the pointwise multiplication.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="other"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseDivide(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise divide this vector with another vector.
            </summary>
            <param name="divisor">The pointwise denominator vector to use.</param>
            <returns>A new vector which is the pointwise division of the two vectors.</returns>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="divisor"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseDivide(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise divide this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The pointwise denominator vector to use.</param>
            <param name="result">The vector to store the result of the pointwise division.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="divisor"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwisePower(`0)">
            <summary>
            Pointwise raise this vector to an exponent.
            </summary>
            <param name="exponent">The exponent to raise this vector values to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwisePower(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise raise this vector to an exponent and store the result into the result vector.
            </summary>
            <param name="exponent">The exponent to raise this vector values to.</param>
            <param name="result">The matrix to store the result into.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwisePower(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise raise this vector to an exponent and store the result into the result vector.
            </summary>
            <param name="exponent">The exponent to raise this vector values to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwisePower(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise raise this vector to an exponent.
            </summary>
            <param name="exponent">The exponent to raise this vector values to.</param>
            <param name="result">The vector to store the result into.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseModulus(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise canonical modulus, where the result has the sign of the divisor,
            of this vector with another vector.
            </summary>
            <param name="divisor">The pointwise denominator vector to use.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="divisor"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseModulus(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise canonical modulus, where the result has the sign of the divisor,
            of this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The pointwise denominator vector to use.</param>
            <param name="result">The vector to store the result of the pointwise modulus.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="divisor"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseRemainder(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise remainder (% operator), where the result has the sign of the dividend,
            of this vector with another vector.
            </summary>
            <param name="divisor">The pointwise denominator vector to use.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="divisor"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseRemainder(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise remainder (% operator), where the result has the sign of the dividend,
            this vector with another vector and stores the result into the result vector.
            </summary>
            <param name="divisor">The pointwise denominator vector to use.</param>
            <param name="result">The vector to store the result of the pointwise remainder.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="divisor"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseUnary(System.Action{MathNet.Numerics.LinearAlgebra.Vector{`0}})">
            <summary>
            Helper function to apply a unary function to a vector. The function
            f modifies the vector given to it in place.  Before its
            called, a copy of the 'this' vector with the same dimension is
            first created, then passed to f.  The copy is returned as the result
            </summary>
            <param name="f">Function which takes a vector, modifies it in place and returns void</param>
            <returns>New instance of vector which is the result</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseUnary(System.Action{MathNet.Numerics.LinearAlgebra.Vector{`0}},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Helper function to apply a unary function which modifies a vector
            in place.
            </summary>
            <param name="f">Function which takes a vector, modifies it in place and returns void</param>
            <param name="result">The vector where the result is to be stored</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseBinary(System.Action{`0,MathNet.Numerics.LinearAlgebra.Vector{`0}},`0)">
            <summary>
            Helper function to apply a binary function which takes a scalar and
            a vector and modifies the latter in place. A copy of the "this"
            vector is therefore first made and then passed to f together with
            the scalar argument.  The copy is then returned as the result
            </summary>
            <param name="f">Function which takes a scalar and a vector, modifies the vector in place and returns void</param>
            <param name="other">The scalar to be passed to the function</param>
            <returns>The resulting vector</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseBinary(System.Action{`0,MathNet.Numerics.LinearAlgebra.Vector{`0}},`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Helper function to apply a binary function which takes a scalar and
            a vector, modifies the latter in place and returns void.
            </summary>
            <param name="f">Function which takes a scalar and a vector, modifies the vector in place and returns void</param>
            <param name="x">The scalar to be passed to the function</param>
            <param name="result">The vector where the result will be placed</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseBinary(System.Action{MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0}},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Helper function to apply a binary function which takes two vectors
            and modifies the latter in place.  A copy of the "this" vector is
            first made and then passed to f together with the other vector. The
            copy is then returned as the result
            </summary>
            <param name="f">Function which takes two vectors, modifies the second in place and returns void</param>
            <param name="other">The other vector to be passed to the function as argument. It is not modified</param>
            <returns>The resulting vector</returns>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="other"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseBinary(System.Action{MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0}},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Helper function to apply a binary function which takes two vectors
            and modifies the second one in place
            </summary>
            <param name="f">Function which takes two vectors, modifies the second in place and returns void</param>
            <param name="other">The other vector to be passed to the function as argument. It is not modified</param>
            <param name="result">The resulting vector</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="other"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseExp">
            <summary>
            Pointwise applies the exponent function to each value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseExp(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the exponent function to each value.
            </summary>
            <param name="result">The vector to store the result.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseLog">
            <summary>
            Pointwise applies the natural logarithm function to each value.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseLog(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the natural logarithm function to each value.
            </summary>
            <param name="result">The vector to store the result.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseAbs">
            <summary>
            Pointwise applies the abs function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseAbs(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the abs function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseAcos">
            <summary>
            Pointwise applies the acos function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseAcos(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the acos function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseAsin">
            <summary>
            Pointwise applies the asin function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseAsin(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the asin function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseAtan">
            <summary>
            Pointwise applies the atan function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseAtan(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the atan function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseAtan2(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the atan2 function to each value of the current
            vector and a given other vector being the 'x' of atan2 and the
            'this' vector being the 'y'
            </summary>
            <param name="other"></param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseAtan2(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the atan2 function to each value of the current
            vector and a given other vector being the 'x' of atan2 and the
            'this' vector being the 'y'
            </summary>
            <param name="other"></param>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseCeiling">
            <summary>
            Pointwise applies the ceiling function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseCeiling(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the ceiling function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseCos">
            <summary>
            Pointwise applies the cos function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseCos(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the cos function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseCosh">
            <summary>
            Pointwise applies the cosh function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseCosh(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the cosh function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseFloor">
            <summary>
            Pointwise applies the floor function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseFloor(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the floor function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseLog10">
            <summary>
            Pointwise applies the log10 function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseLog10(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the log10 function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseRound">
            <summary>
            Pointwise applies the round function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseRound(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the round function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseSign">
            <summary>
            Pointwise applies the sign function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseSign(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the sign function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseSin">
            <summary>
            Pointwise applies the sin function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseSin(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the sin function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseSinh">
            <summary>
            Pointwise applies the sinh function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseSinh(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the sinh function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseSqrt">
            <summary>
            Pointwise applies the sqrt function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseSqrt(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the sqrt function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseTan">
            <summary>
            Pointwise applies the tan function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseTan(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the tan function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseTanh">
            <summary>
            Pointwise applies the tanh function to each value
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseTanh(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the tanh function to each value
            </summary>
            <param name="result">The vector to store the result</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.OuterProduct(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the outer product M[i,j] = u[i]*v[j] of this and another vector.
            </summary>
            <param name="other">The other vector</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.OuterProduct(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Matrix{`0})">
            <summary>
            Computes the outer product M[i,j] = u[i]*v[j] of this and another vector and stores the result in the result matrix.
            </summary>
            <param name="other">The other vector</param>
            <param name="result">The matrix to store the result of the product.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseMinimum(`0)">
            <summary>
            Pointwise applies the minimum with a scalar to each value.
            </summary>
            <param name="scalar">The scalar value to compare to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseMinimum(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the minimum with a scalar to each value.
            </summary>
            <param name="scalar">The scalar value to compare to.</param>
            <param name="result">The vector to store the result.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseMaximum(`0)">
            <summary>
            Pointwise applies the maximum with a scalar to each value.
            </summary>
            <param name="scalar">The scalar value to compare to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseMaximum(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the maximum with a scalar to each value.
            </summary>
            <param name="scalar">The scalar value to compare to.</param>
            <param name="result">The vector to store the result.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseAbsoluteMinimum(`0)">
            <summary>
            Pointwise applies the absolute minimum with a scalar to each value.
            </summary>
            <param name="scalar">The scalar value to compare to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseAbsoluteMinimum(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the absolute minimum with a scalar to each value.
            </summary>
            <param name="scalar">The scalar value to compare to.</param>
            <param name="result">The vector to store the result.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseAbsoluteMaximum(`0)">
            <summary>
            Pointwise applies the absolute maximum with a scalar to each value.
            </summary>
            <param name="scalar">The scalar value to compare to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseAbsoluteMaximum(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the absolute maximum with a scalar to each value.
            </summary>
            <param name="scalar">The scalar value to compare to.</param>
            <param name="result">The vector to store the result.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseMinimum(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the minimum with the values of another vector to each value.
            </summary>
            <param name="other">The vector with the values to compare to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseMinimum(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the minimum with the values of another vector to each value.
            </summary>
            <param name="other">The vector with the values to compare to.</param>
            <param name="result">The vector to store the result.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseMaximum(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the maximum with the values of another vector to each value.
            </summary>
            <param name="other">The vector with the values to compare to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseMaximum(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the maximum with the values of another vector to each value.
            </summary>
            <param name="other">The vector with the values to compare to.</param>
            <param name="result">The vector to store the result.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseAbsoluteMinimum(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the absolute minimum with the values of another vector to each value.
            </summary>
            <param name="other">The vector with the values to compare to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseAbsoluteMinimum(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the absolute minimum with the values of another vector to each value.
            </summary>
            <param name="other">The vector with the values to compare to.</param>
            <param name="result">The vector to store the result.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseAbsoluteMaximum(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the absolute maximum with the values of another vector to each value.
            </summary>
            <param name="other">The vector with the values to compare to.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.PointwiseAbsoluteMaximum(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise applies the absolute maximum with the values of another vector to each value.
            </summary>
            <param name="other">The vector with the values to compare to.</param>
            <param name="result">The vector to store the result.</param>
            <exception cref="T:System.ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.L1Norm">
            <summary>
            Calculates the L1 norm of the vector, also known as Manhattan norm.
            </summary>
            <returns>The sum of the absolute values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.L2Norm">
            <summary>
            Calculates the L2 norm of the vector, also known as Euclidean norm.
            </summary>
            <returns>The square root of the sum of the squared values.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.InfinityNorm">
            <summary>
            Calculates the infinity norm of the vector.
            </summary>
            <returns>The maximum absolute value.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Norm(System.Double)">
            <summary>
            Computes the p-Norm.
            </summary>
            <param name="p">The p value.</param>
            <returns><c>Scalar ret = (sum(abs(this[i])^p))^(1/p)</c></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Normalize(System.Double)">
            <summary>
            Normalizes this vector to a unit vector with respect to the p-norm.
            </summary>
            <param name="p">The p value.</param>
            <returns>This vector normalized to a unit vector with respect to the p-norm.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.AbsoluteMinimum">
            <summary>
            Returns the value of the absolute minimum element.
            </summary>
            <returns>The value of the absolute minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.AbsoluteMinimumIndex">
            <summary>
            Returns the index of the absolute minimum element.
            </summary>
            <returns>The index of absolute minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.AbsoluteMaximum">
            <summary>
            Returns the value of the absolute maximum element.
            </summary>
            <returns>The value of the absolute maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.AbsoluteMaximumIndex">
            <summary>
            Returns the index of the absolute maximum element.
            </summary>
            <returns>The index of absolute maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Maximum">
            <summary>
            Returns the value of maximum element.
            </summary>
            <returns>The value of maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.MaximumIndex">
            <summary>
            Returns the index of the maximum element.
            </summary>
            <returns>The index of maximum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Minimum">
            <summary>
            Returns the value of the minimum element.
            </summary>
            <returns>The value of the minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.MinimumIndex">
            <summary>
            Returns the index of the minimum element.
            </summary>
            <returns>The index of minimum element.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Sum">
            <summary>
            Computes the sum of the vector's elements.
            </summary>
            <returns>The sum of the vector's elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.SumMagnitudes">
            <summary>
            Computes the sum of the absolute value of the vector's elements.
            </summary>
            <returns>The sum of the absolute value of the vector's elements.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Equals(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Indicates whether the current object is equal to another object of the same type.
            </summary>
            <param name="other">An object to compare with this object.</param>
            <returns>
               <c>true</c> if the current object is equal to the <paramref name="other"/> parameter; otherwise, <c>false</c>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Equals(System.Object)">
            <summary>
            Determines whether the specified <see cref="T:System.Object"/> is equal to this instance.
            </summary>
            <param name="obj">The <see cref="T:System.Object"/> to compare with this instance.</param>
            <returns>
                <c>true</c> if the specified <see cref="T:System.Object"/> is equal to this instance; otherwise, <c>false</c>.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.GetHashCode">
            <summary>
            Returns a hash code for this instance.
            </summary>
            <returns>
            A hash code for this instance, suitable for use in hashing algorithms and data structures like a hash table.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.System#ICloneable#Clone">
            <summary>
            Creates a new object that is a copy of the current instance.
            </summary>
            <returns>
            A new object that is a copy of this instance.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.System#Collections#Generic#IEnumerable{T}#GetEnumerator">
            <summary>
            Returns an enumerator that iterates through the collection.
            </summary>
            <returns>
            A <see cref="T:System.Collections.Generic.IEnumerator`1"/> that can be used to iterate through the collection.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.System#Collections#IEnumerable#GetEnumerator">
            <summary>
            Returns an enumerator that iterates through a collection.
            </summary>
            <returns>
            An <see cref="T:System.Collections.IEnumerator"/> object that can be used to iterate through the collection.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.ToTypeString">
            <summary>
            Returns a string that describes the type, dimensions and shape of this vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.ToVectorString(System.Int32,System.Int32,System.String,System.String,System.String,System.Func{`0,System.String})">
            <summary>
            Returns a string that represents the content of this vector, column by column.
            </summary>
            <param name="maxPerColumn">Maximum number of entries and thus lines per column. Typical value: 12; Minimum: 3.</param>
            <param name="maxCharactersWidth">Maximum number of characters per line over all columns. Typical value: 80; Minimum: 16.</param>
            <param name="ellipsis">Character to use to print if there is not enough space to print all entries. Typical value: "..".</param>
            <param name="columnSeparator">Character to use to separate two columns on a line. Typical value: "  " (2 spaces).</param>
            <param name="rowSeparator">Character to use to separate two rows/lines. Typical value: Environment.NewLine.</param>
            <param name="formatValue">Function to provide a string for any given entry value.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.ToVectorString(System.Int32,System.Int32,System.String,System.IFormatProvider)">
            <summary>
            Returns a string that represents the content of this vector, column by column.
            </summary>
            <param name="maxPerColumn">Maximum number of entries and thus lines per column. Typical value: 12; Minimum: 3.</param>
            <param name="maxCharactersWidth">Maximum number of characters per line over all columns. Typical value: 80; Minimum: 16.</param>
            <param name="format">Floating point format string. Can be null. Default value: G6.</param>
            <param name="provider">Format provider or culture. Can be null.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.ToVectorString(System.String,System.IFormatProvider)">
            <summary>
            Returns a string that represents the content of this vector, column by column.
            </summary>
            <param name="format">Floating point format string. Can be null. Default value: G6.</param>
            <param name="provider">Format provider or culture. Can be null.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.ToString(System.Int32,System.Int32,System.String,System.IFormatProvider)">
            <summary>
            Returns a string that summarizes this vector, column by column and with a type header.
            </summary>
            <param name="maxPerColumn">Maximum number of entries and thus lines per column. Typical value: 12; Minimum: 3.</param>
            <param name="maxCharactersWidth">Maximum number of characters per line over all columns. Typical value: 80; Minimum: 16.</param>
            <param name="format">Floating point format string. Can be null. Default value: G6.</param>
            <param name="provider">Format provider or culture. Can be null.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.ToString">
            <summary>
            Returns a string that summarizes this vector.
            The maximum number of cells can be configured in the <see cref="T:MathNet.Numerics.Control"/> class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.ToString(System.String,System.IFormatProvider)">
            <summary>
            Returns a string that summarizes this vector.
            The maximum number of cells can be configured in the <see cref="T:MathNet.Numerics.Control"/> class.
            The format string is ignored.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.#ctor(MathNet.Numerics.LinearAlgebra.Storage.VectorStorage{`0})">
            <summary>
            Initializes a new instance of the Vector class.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Vector`1.Storage">
            <summary>
            Gets the raw vector data storage.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Vector`1.Count">
            <summary>
            Gets the length or number of dimensions of this vector.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.LinearAlgebra.Vector`1.Item(System.Int32)">
            <summary>Gets or sets the value at the given <paramref name="index"/>.</summary>
            <param name="index">The index of the value to get or set.</param>
            <returns>The value of the vector at the given <paramref name="index"/>.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">If <paramref name="index"/> is negative or
            greater than the size of the vector.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.At(System.Int32)">
            <summary>Gets the value at the given <paramref name="index"/> without range checking..</summary>
            <param name="index">The index of the value to get or set.</param>
            <returns>The value of the vector at the given <paramref name="index"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.At(System.Int32,`0)">
            <summary>Sets the <paramref name="value"/> at the given <paramref name="index"/> without range checking..</summary>
            <param name="index">The index of the value to get or set.</param>
            <param name="value">The value to set.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Clear">
            <summary>
            Resets all values to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.ClearSubVector(System.Int32,System.Int32)">
            <summary>
            Sets all values of a subvector to zero.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.CoerceZero(System.Double)">
            <summary>
            Set all values whose absolute value is smaller than the threshold to zero, in-place.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.CoerceZero(System.Func{`0,System.Boolean})">
            <summary>
            Set all values that meet the predicate to zero, in-place.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Clone">
            <summary>
            Returns a deep-copy clone of the vector.
            </summary>
            <returns>A deep-copy clone of the vector.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.SetValues(`0[])">
            <summary>
            Set the values of this vector to the given values.
            </summary>
            <param name="values">The array containing the values to use.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="values"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="values"/> is not the same size as this vector.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.CopyTo(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Copies the values of this vector into the target vector.
            </summary>
            <param name="target">The vector to copy elements into.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="target"/> is <see langword="null"/>.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="target"/> is not the same size as this vector.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.SubVector(System.Int32,System.Int32)">
            <summary>
            Creates a vector containing specified elements.
            </summary>
            <param name="index">The first element to begin copying from.</param>
            <param name="count">The number of elements to copy.</param>
            <returns>A vector containing a copy of the specified elements.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException"><list><item>If <paramref name="index"/> is not positive or
            greater than or equal to the size of the vector.</item>
            <item>If <paramref name="index"/> + <paramref name="count"/> is greater than or equal to the size of the vector.</item>
            </list></exception>
            <exception cref="T:System.ArgumentException">If <paramref name="count"/> is not positive.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.SetSubVector(System.Int32,System.Int32,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Copies the values of a given vector into a region in this vector.
            </summary>
            <param name="index">The field to start copying to</param>
            <param name="count">The number of fields to copy. Must be positive.</param>
            <param name="subVector">The sub-vector to copy from.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="subVector"/> is <see langword="null" /></exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.CopySubVectorTo(MathNet.Numerics.LinearAlgebra.Vector{`0},System.Int32,System.Int32,System.Int32)">
            <summary>
            Copies the requested elements from this vector to another.
            </summary>
            <param name="destination">The vector to copy the elements to.</param>
            <param name="sourceIndex">The element to start copying from.</param>
            <param name="targetIndex">The element to start copying to.</param>
            <param name="count">The number of elements to copy.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.ToArray">
            <summary>
            Returns the data contained in the vector as an array.
            The returned array will be independent from this vector.
            A new memory block will be allocated for the array.
            </summary>
            <returns>The vector's data as an array.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.AsArray">
            <summary>
            Returns the internal array of this vector if, and only if, this vector is stored by such an array internally.
            Otherwise returns null. Changes to the returned array and the vector will affect each other.
            Use ToArray instead if you always need an independent array.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.ToColumnMatrix">
            <summary>
            Create a matrix based on this vector in column form (one single column).
            </summary>
            <returns>
            This vector as a column matrix.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.ToRowMatrix">
            <summary>
            Create a matrix based on this vector in row form (one single row).
            </summary>
            <returns>
            This vector as a row matrix.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Enumerate">
            <summary>
            Returns an IEnumerable that can be used to iterate through all values of the vector.
            </summary>
            <remarks>
            The enumerator will include all values, even if they are zero.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Enumerate(MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Returns an IEnumerable that can be used to iterate through all values of the vector.
            </summary>
            <remarks>
            The enumerator will include all values, even if they are zero.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.EnumerateIndexed">
            <summary>
            Returns an IEnumerable that can be used to iterate through all values of the vector and their index.
            </summary>
            <remarks>
            The enumerator returns a Tuple with the first value being the element index
            and the second value being the value of the element at that index.
            The enumerator will include all values, even if they are zero.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.EnumerateIndexed(MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Returns an IEnumerable that can be used to iterate through all values of the vector and their index.
            </summary>
            <remarks>
            The enumerator returns a Tuple with the first value being the element index
            and the second value being the value of the element at that index.
            The enumerator will include all values, even if they are zero.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.MapInplace(System.Func{`0,`0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to each value of this vector and replaces the value with its result.
            If forceMapZero is not set to true, zero values may or may not be skipped depending
            on the actual data storage implementation (relevant mostly for sparse vectors).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.MapIndexedInplace(System.Func{System.Int32,`0,`0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to each value of this vector and replaces the value with its result.
            The index of each value (zero-based) is passed as first argument to the function.
            If forceMapZero is not set to true, zero values may or may not be skipped depending
            on the actual data storage implementation (relevant mostly for sparse vectors).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Map(System.Func{`0,`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to each value of this vector and replaces the value in the result vector.
            If forceMapZero is not set to true, zero values may or may not be skipped depending
            on the actual data storage implementation (relevant mostly for sparse vectors).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.MapIndexed(System.Func{System.Int32,`0,`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to each value of this vector and replaces the value in the result vector.
            The index of each value (zero-based) is passed as first argument to the function.
            If forceMapZero is not set to true, zero values may or may not be skipped depending
            on the actual data storage implementation (relevant mostly for sparse vectors).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.MapConvert``1(System.Func{`0,``0},MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to each value of this vector and replaces the value in the result vector.
            If forceMapZero is not set to true, zero values may or may not be skipped depending
            on the actual data storage implementation (relevant mostly for sparse vectors).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.MapIndexedConvert``1(System.Func{System.Int32,`0,``0},MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to each value of this vector and replaces the value in the result vector.
            The index of each value (zero-based) is passed as first argument to the function.
            If forceMapZero is not set to true, zero values may or may not be skipped depending
            on the actual data storage implementation (relevant mostly for sparse vectors).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Map``1(System.Func{`0,``0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to each value of this vector and returns the results as a new vector.
            If forceMapZero is not set to true, zero values may or may not be skipped depending
            on the actual data storage implementation (relevant mostly for sparse vectors).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.MapIndexed``1(System.Func{System.Int32,`0,``0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to each value of this vector and returns the results as a new vector.
            The index of each value (zero-based) is passed as first argument to the function.
            If forceMapZero is not set to true, zero values may or may not be skipped depending
            on the actual data storage implementation (relevant mostly for sparse vectors).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Map2(System.Func{`0,`0,`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to each value pair of two vectors and replaces the value in the result vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Map2(System.Func{`0,`0,`0},MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to each value pair of two vectors and returns the results as a new vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Fold2``2(System.Func{``1,`0,``0,``1},``1,MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Applies a function to update the status with each value pair of two vectors and returns the resulting status.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Find(System.Func{`0,System.Boolean},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Returns a tuple with the index and value of the first element satisfying a predicate, or null if none is found.
            Zero elements may be skipped on sparse data structures if allowed (default).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Find2``1(System.Func{`0,``0,System.Boolean},MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Returns a tuple with the index and values of the first element pair of two vectors of the same size satisfying a predicate, or null if none is found.
            Zero elements may be skipped on sparse data structures if allowed (default).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Exists(System.Func{`0,System.Boolean},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Returns true if at least one element satisfies a predicate.
            Zero elements may be skipped on sparse data structures if allowed (default).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Exists2``1(System.Func{`0,``0,System.Boolean},MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Returns true if at least one element pairs of two vectors of the same size satisfies a predicate.
            Zero elements may be skipped on sparse data structures if allowed (default).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.ForAll(System.Func{`0,System.Boolean},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Returns true if all elements satisfy a predicate.
            Zero elements may be skipped on sparse data structures if allowed (default).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.ForAll2``1(System.Func{`0,``0,System.Boolean},MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Zeros)">
            <summary>
            Returns true if all element pairs of two vectors of the same size satisfy a predicate.
            Zero elements may be skipped on sparse data structures if allowed (default).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.op_UnaryPlus(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Returns a <strong>Vector</strong> containing the same values of <paramref name="rightSide"/>.
            </summary>
            <remarks>This method is included for completeness.</remarks>
            <param name="rightSide">The vector to get the values from.</param>
            <returns>A vector containing the same values as <paramref name="rightSide"/>.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.op_UnaryNegation(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Returns a <strong>Vector</strong> containing the negated values of <paramref name="rightSide"/>.
            </summary>
            <param name="rightSide">The vector to get the values from.</param>
            <returns>A vector containing the negated values as <paramref name="rightSide"/>.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.op_Addition(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Adds two <strong>Vectors</strong> together and returns the results.
            </summary>
            <param name="leftSide">One of the vectors to add.</param>
            <param name="rightSide">The other vector to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.op_Addition(MathNet.Numerics.LinearAlgebra.Vector{`0},`0)">
            <summary>
            Adds a scalar to each element of a vector.
            </summary>
            <param name="leftSide">The vector to add to.</param>
            <param name="rightSide">The scalar value to add.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.op_Addition(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Adds a scalar to each element of a vector.
            </summary>
            <param name="leftSide">The scalar value to add.</param>
            <param name="rightSide">The vector to add to.</param>
            <returns>The result of the addition.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.op_Subtraction(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Subtracts two <strong>Vectors</strong> and returns the results.
            </summary>
            <param name="leftSide">The vector to subtract from.</param>
            <param name="rightSide">The vector to subtract.</param>
            <returns>The result of the subtraction.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.op_Subtraction(MathNet.Numerics.LinearAlgebra.Vector{`0},`0)">
            <summary>
            Subtracts a scalar from each element of a vector.
            </summary>
            <param name="leftSide">The vector to subtract from.</param>
            <param name="rightSide">The scalar value to subtract.</param>
            <returns>The result of the subtraction.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.op_Subtraction(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Subtracts each element of a vector from a scalar.
            </summary>
            <param name="leftSide">The scalar value to subtract from.</param>
            <param name="rightSide">The vector to subtract.</param>
            <returns>The result of the subtraction.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.op_Multiply(MathNet.Numerics.LinearAlgebra.Vector{`0},`0)">
            <summary>
            Multiplies a vector with a scalar.
            </summary>
            <param name="leftSide">The vector to scale.</param>
            <param name="rightSide">The scalar value.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.op_Multiply(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Multiplies a vector with a scalar.
            </summary>
            <param name="leftSide">The scalar value.</param>
            <param name="rightSide">The vector to scale.</param>
            <returns>The result of the multiplication.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.op_Multiply(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the dot product between two <strong>Vectors</strong>.
            </summary>
            <param name="leftSide">The left row vector.</param>
            <param name="rightSide">The right column vector.</param>
            <returns>The dot product between the two vectors.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="leftSide"/> and <paramref name="rightSide"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="leftSide"/> or <paramref name="rightSide"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.op_Division(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Divides a scalar with a vector.
            </summary>
            <param name="dividend">The scalar to divide.</param>
            <param name="divisor">The vector.</param>
            <returns>The result of the division.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="divisor"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.op_Division(MathNet.Numerics.LinearAlgebra.Vector{`0},`0)">
            <summary>
            Divides a vector with a scalar.
            </summary>
            <param name="dividend">The vector to divide.</param>
            <param name="divisor">The scalar value.</param>
            <returns>The result of the division.</returns>
            <exception cref="T:System.ArgumentNullException">If <paramref name="dividend"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.op_Division(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Pointwise divides two <strong>Vectors</strong>.
            </summary>
            <param name="dividend">The vector to divide.</param>
            <param name="divisor">The other vector.</param>
            <returns>The result of the division.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="dividend"/> and <paramref name="divisor"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="dividend"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.op_Modulus(MathNet.Numerics.LinearAlgebra.Vector{`0},`0)">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            of each element of the vector of the given divisor.
            </summary>
            <param name="dividend">The vector whose elements we want to compute the remainder of.</param>
            <param name="divisor">The divisor to use.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="dividend"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.op_Modulus(`0,MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the remainder (% operator), where the result has the sign of the dividend,
            of the given dividend of each element of the vector.
            </summary>
            <param name="dividend">The dividend we want to compute the remainder of.</param>
            <param name="divisor">The vector whose elements we want to use as divisor.</param>
            <exception cref="T:System.ArgumentNullException">If <paramref name="dividend"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.op_Modulus(MathNet.Numerics.LinearAlgebra.Vector{`0},MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the pointwise remainder (% operator), where the result has the sign of the dividend,
            of each element of two vectors.
            </summary>
            <param name="dividend">The vector whose elements we want to compute the remainder of.</param>
            <param name="divisor">The divisor to use.</param>
            <exception cref="T:System.ArgumentException">If <paramref name="dividend"/> and <paramref name="divisor"/> are not the same size.</exception>
            <exception cref="T:System.ArgumentNullException">If <paramref name="dividend"/> is <see langword="null" />.</exception>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Sqrt(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the sqrt of a vector pointwise
            </summary>
            <param name="x">The input vector</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Exp(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the exponential of a vector pointwise
            </summary>
            <param name="x">The input vector</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Log(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the log of a vector pointwise
            </summary>
            <param name="x">The input vector</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Log10(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the log10 of a vector pointwise
            </summary>
            <param name="x">The input vector</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Sin(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the sin of a vector pointwise
            </summary>
            <param name="x">The input vector</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Cos(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the cos of a vector pointwise
            </summary>
            <param name="x">The input vector</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Tan(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the tan of a vector pointwise
            </summary>
            <param name="x">The input vector</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Asin(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the asin of a vector pointwise
            </summary>
            <param name="x">The input vector</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Acos(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the acos of a vector pointwise
            </summary>
            <param name="x">The input vector</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Atan(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the atan of a vector pointwise
            </summary>
            <param name="x">The input vector</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Sinh(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the sinh of a vector pointwise
            </summary>
            <param name="x">The input vector</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Cosh(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the cosh of a vector pointwise
            </summary>
            <param name="x">The input vector</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Tanh(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the tanh of a vector pointwise
            </summary>
            <param name="x">The input vector</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Abs(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the absolute value of a vector pointwise
            </summary>
            <param name="x">The input vector</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Floor(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the floor of a vector pointwise
            </summary>
            <param name="x">The input vector</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Ceiling(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the ceiling of a vector pointwise
            </summary>
            <param name="x">The input vector</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.Vector`1.Round(MathNet.Numerics.LinearAlgebra.Vector{`0})">
            <summary>
            Computes the rounded value of a vector pointwise
            </summary>
            <param name="x">The input vector</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorExtensions.ToSingle(MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Converts a vector to single precision.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorExtensions.ToDouble(MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Converts a vector to double precision.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorExtensions.ToComplex32(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Converts a vector to single precision complex numbers.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorExtensions.ToComplex(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Converts a vector to double precision complex numbers.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorExtensions.ToComplex32(MathNet.Numerics.LinearAlgebra.Vector{System.Single})">
            <summary>
            Gets a single precision complex vector with the real parts from the given vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorExtensions.ToComplex(MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Gets a double precision complex vector with the real parts from the given vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorExtensions.Real(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Gets a real vector representing the real parts of a complex vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorExtensions.Real(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Gets a real vector representing the real parts of a complex vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorExtensions.Imaginary(MathNet.Numerics.LinearAlgebra.Vector{System.Numerics.Complex})">
            <summary>
            Gets a real vector representing the imaginary parts of a complex vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearAlgebra.VectorExtensions.Imaginary(MathNet.Numerics.LinearAlgebra.Vector{MathNet.Numerics.Complex32})">
            <summary>
            Gets a real vector representing the imaginary parts of a complex vector.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.MultipleRegression.DirectMethod``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearRegression.DirectRegressionMethod)">
            <summary>
            Find the model parameters β such that X*β with predictor X becomes as close to response Y as possible, with least squares residuals.
            </summary>
            <param name="x">Predictor matrix X</param>
            <param name="y">Response vector Y</param>
            <param name="method">The direct method to be used to compute the regression.</param>
            <returns>Best fitting vector for model parameters β</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.MultipleRegression.DirectMethod``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearRegression.DirectRegressionMethod)">
            <summary>
            Find the model parameters β such that X*β with predictor X becomes as close to response Y as possible, with least squares residuals.
            </summary>
            <param name="x">Predictor matrix X</param>
            <param name="y">Response matrix Y</param>
            <param name="method">The direct method to be used to compute the regression.</param>
            <returns>Best fitting vector for model parameters β</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.MultipleRegression.DirectMethod``1(``0[][],``0[],System.Boolean,MathNet.Numerics.LinearRegression.DirectRegressionMethod)">
            <summary>
            Find the model parameters β such that their linear combination with all predictor-arrays in X become as close to their response in Y as possible, with least squares residuals.
            </summary>
            <param name="x">List of predictor-arrays.</param>
            <param name="y">List of responses</param>
            <param name="intercept">True if an intercept should be added as first artificial predictor value. Default = false.</param>
            <param name="method">The direct method to be used to compute the regression.</param>
            <returns>Best fitting list of model parameters β for each element in the predictor-arrays.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.MultipleRegression.DirectMethod``1(System.Collections.Generic.IEnumerable{System.Tuple{``0[],``0}},System.Boolean,MathNet.Numerics.LinearRegression.DirectRegressionMethod)">
            <summary>
            Find the model parameters β such that their linear combination with all predictor-arrays in X become as close to their response in Y as possible, with least squares residuals.
            Uses the cholesky decomposition of the normal equations.
            </summary>
            <param name="samples">Sequence of predictor-arrays and their response.</param>
            <param name="intercept">True if an intercept should be added as first artificial predictor value. Default = false.</param>
            <param name="method">The direct method to be used to compute the regression.</param>
            <returns>Best fitting list of model parameters β for each element in the predictor-arrays.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.MultipleRegression.NormalEquations``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Find the model parameters β such that X*β with predictor X becomes as close to response Y as possible, with least squares residuals.
            Uses the cholesky decomposition of the normal equations.
            </summary>
            <param name="x">Predictor matrix X</param>
            <param name="y">Response vector Y</param>
            <returns>Best fitting vector for model parameters β</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.MultipleRegression.NormalEquations``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Matrix{``0})">
            <summary>
            Find the model parameters β such that X*β with predictor X becomes as close to response Y as possible, with least squares residuals.
            Uses the cholesky decomposition of the normal equations.
            </summary>
            <param name="x">Predictor matrix X</param>
            <param name="y">Response matrix Y</param>
            <returns>Best fitting vector for model parameters β</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.MultipleRegression.NormalEquations``1(``0[][],``0[],System.Boolean)">
            <summary>
            Find the model parameters β such that their linear combination with all predictor-arrays in X become as close to their response in Y as possible, with least squares residuals.
            Uses the cholesky decomposition of the normal equations.
            </summary>
            <param name="x">List of predictor-arrays.</param>
            <param name="y">List of responses</param>
            <param name="intercept">True if an intercept should be added as first artificial predictor value. Default = false.</param>
            <returns>Best fitting list of model parameters β for each element in the predictor-arrays.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.MultipleRegression.NormalEquations``1(System.Collections.Generic.IEnumerable{System.Tuple{``0[],``0}},System.Boolean)">
            <summary>
            Find the model parameters β such that their linear combination with all predictor-arrays in X become as close to their response in Y as possible, with least squares residuals.
            Uses the cholesky decomposition of the normal equations.
            </summary>
            <param name="samples">Sequence of predictor-arrays and their response.</param>
            <param name="intercept">True if an intercept should be added as first artificial predictor value. Default = false.</param>
            <returns>Best fitting list of model parameters β for each element in the predictor-arrays.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.MultipleRegression.NormalEquations``1(System.Collections.Generic.IEnumerable{System.ValueTuple{``0[],``0}},System.Boolean)">
            <summary>
            Find the model parameters β such that their linear combination with all predictor-arrays in X become as close to their response in Y as possible, with least squares residuals.
            Uses the cholesky decomposition of the normal equations.
            </summary>
            <param name="samples">Sequence of predictor-arrays and their response.</param>
            <param name="intercept">True if an intercept should be added as first artificial predictor value. Default = false.</param>
            <returns>Best fitting list of model parameters β for each element in the predictor-arrays.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.MultipleRegression.QR``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Find the model parameters β such that X*β with predictor X becomes as close to response Y as possible, with least squares residuals.
            Uses an orthogonal decomposition and is therefore more numerically stable than the normal equations but also slower.
            </summary>
            <param name="x">Predictor matrix X</param>
            <param name="y">Response vector Y</param>
            <returns>Best fitting vector for model parameters β</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.MultipleRegression.QR``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Matrix{``0})">
            <summary>
            Find the model parameters β such that X*β with predictor X becomes as close to response Y as possible, with least squares residuals.
            Uses an orthogonal decomposition and is therefore more numerically stable than the normal equations but also slower.
            </summary>
            <param name="x">Predictor matrix X</param>
            <param name="y">Response matrix Y</param>
            <returns>Best fitting vector for model parameters β</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.MultipleRegression.QR``1(``0[][],``0[],System.Boolean)">
            <summary>
            Find the model parameters β such that their linear combination with all predictor-arrays in X become as close to their response in Y as possible, with least squares residuals.
            Uses an orthogonal decomposition and is therefore more numerically stable than the normal equations but also slower.
            </summary>
            <param name="x">List of predictor-arrays.</param>
            <param name="y">List of responses</param>
            <param name="intercept">True if an intercept should be added as first artificial predictor value. Default = false.</param>
            <returns>Best fitting list of model parameters β for each element in the predictor-arrays.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.MultipleRegression.QR``1(System.Collections.Generic.IEnumerable{System.Tuple{``0[],``0}},System.Boolean)">
            <summary>
            Find the model parameters β such that their linear combination with all predictor-arrays in X become as close to their response in Y as possible, with least squares residuals.
            Uses an orthogonal decomposition and is therefore more numerically stable than the normal equations but also slower.
            </summary>
            <param name="samples">Sequence of predictor-arrays and their response.</param>
            <param name="intercept">True if an intercept should be added as first artificial predictor value. Default = false.</param>
            <returns>Best fitting list of model parameters β for each element in the predictor-arrays.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.MultipleRegression.QR``1(System.Collections.Generic.IEnumerable{System.ValueTuple{``0[],``0}},System.Boolean)">
            <summary>
            Find the model parameters β such that their linear combination with all predictor-arrays in X become as close to their response in Y as possible, with least squares residuals.
            Uses an orthogonal decomposition and is therefore more numerically stable than the normal equations but also slower.
            </summary>
            <param name="samples">Sequence of predictor-arrays and their response.</param>
            <param name="intercept">True if an intercept should be added as first artificial predictor value. Default = false.</param>
            <returns>Best fitting list of model parameters β for each element in the predictor-arrays.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.MultipleRegression.Svd``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Vector{``0})">
            <summary>
            Find the model parameters β such that X*β with predictor X becomes as close to response Y as possible, with least squares residuals.
            Uses a singular value decomposition and is therefore more numerically stable (especially if ill-conditioned) than the normal equations or QR but also slower.
            </summary>
            <param name="x">Predictor matrix X</param>
            <param name="y">Response vector Y</param>
            <returns>Best fitting vector for model parameters β</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.MultipleRegression.Svd``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Matrix{``0})">
            <summary>
            Find the model parameters β such that X*β with predictor X becomes as close to response Y as possible, with least squares residuals.
            Uses a singular value decomposition and is therefore more numerically stable (especially if ill-conditioned) than the normal equations or QR but also slower.
            </summary>
            <param name="x">Predictor matrix X</param>
            <param name="y">Response matrix Y</param>
            <returns>Best fitting vector for model parameters β</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.MultipleRegression.Svd``1(``0[][],``0[],System.Boolean)">
            <summary>
            Find the model parameters β such that their linear combination with all predictor-arrays in X become as close to their response in Y as possible, with least squares residuals.
            Uses a singular value decomposition and is therefore more numerically stable (especially if ill-conditioned) than the normal equations or QR but also slower.
            </summary>
            <param name="x">List of predictor-arrays.</param>
            <param name="y">List of responses</param>
            <param name="intercept">True if an intercept should be added as first artificial predictor value. Default = false.</param>
            <returns>Best fitting list of model parameters β for each element in the predictor-arrays.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.MultipleRegression.Svd``1(System.Collections.Generic.IEnumerable{System.Tuple{``0[],``0}},System.Boolean)">
            <summary>
            Find the model parameters β such that their linear combination with all predictor-arrays in X become as close to their response in Y as possible, with least squares residuals.
            Uses a singular value decomposition and is therefore more numerically stable (especially if ill-conditioned) than the normal equations or QR but also slower.
            </summary>
            <param name="samples">Sequence of predictor-arrays and their response.</param>
            <param name="intercept">True if an intercept should be added as first artificial predictor value. Default = false.</param>
            <returns>Best fitting list of model parameters β for each element in the predictor-arrays.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.MultipleRegression.Svd``1(System.Collections.Generic.IEnumerable{System.ValueTuple{``0[],``0}},System.Boolean)">
            <summary>
            Find the model parameters β such that their linear combination with all predictor-arrays in X become as close to their response in Y as possible, with least squares residuals.
            Uses a singular value decomposition and is therefore more numerically stable (especially if ill-conditioned) than the normal equations or QR but also slower.
            </summary>
            <param name="samples">Sequence of predictor-arrays and their response.</param>
            <param name="intercept">True if an intercept should be added as first artificial predictor value. Default = false.</param>
            <returns>Best fitting list of model parameters β for each element in the predictor-arrays.</returns>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.SimpleRegression.Fit(System.Double[],System.Double[])">
            <summary>
            Least-Squares fitting the points (x,y) to a line y : x -> a+b*x,
            returning its best fitting parameters as (a, b) tuple,
            where a is the intercept and b the slope.
            </summary>
            <param name="x">Predictor (independent)</param>
            <param name="y">Response (dependent)</param>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.SimpleRegression.Fit(System.Collections.Generic.IEnumerable{System.Tuple{System.Double,System.Double}})">
            <summary>
            Least-Squares fitting the points (x,y) to a line y : x -> a+b*x,
            returning its best fitting parameters as (a, b) tuple,
            where a is the intercept and b the slope.
            </summary>
            <param name="samples">Predictor-Response samples as tuples</param>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.SimpleRegression.Fit(System.Collections.Generic.IEnumerable{System.ValueTuple{System.Double,System.Double}})">
            <summary>
            Least-Squares fitting the points (x,y) to a line y : x -> a+b*x,
            returning its best fitting parameters as (a, b) tuple,
            where a is the intercept and b the slope.
            </summary>
            <param name="samples">Predictor-Response samples as tuples</param>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.SimpleRegression.FitThroughOrigin(System.Double[],System.Double[])">
            <summary>
            Least-Squares fitting the points (x,y) to a line y : x -> b*x,
            returning its best fitting parameter b,
            where the intercept is zero and b the slope.
            </summary>
            <param name="x">Predictor (independent)</param>
            <param name="y">Response (dependent)</param>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.SimpleRegression.FitThroughOrigin(System.Collections.Generic.IEnumerable{System.Tuple{System.Double,System.Double}})">
            <summary>
            Least-Squares fitting the points (x,y) to a line y : x -> b*x,
            returning its best fitting parameter b,
            where the intercept is zero and b the slope.
            </summary>
            <param name="samples">Predictor-Response samples as tuples</param>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.WeightedRegression.Weighted``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Matrix{``0})">
            <summary>
            Weighted Linear Regression using normal equations.
            </summary>
            <param name="x">Predictor matrix X</param>
            <param name="y">Response vector Y</param>
            <param name="w">Weight matrix W, usually diagonal with an entry for each predictor (row).</param>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.WeightedRegression.Weighted``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Matrix{``0})">
            <summary>
            Weighted Linear Regression using normal equations.
            </summary>
            <param name="x">Predictor matrix X</param>
            <param name="y">Response matrix Y</param>
            <param name="w">Weight matrix W, usually diagonal with an entry for each predictor (row).</param>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.WeightedRegression.Weighted``1(``0[][],``0[],``0[],System.Boolean)">
            <summary>
            Weighted Linear Regression using normal equations.
            </summary>
            <param name="x">Predictor matrix X</param>
            <param name="y">Response vector Y</param>
            <param name="w">Weight matrix W, usually diagonal with an entry for each predictor (row).</param>
            <param name="intercept">True if an intercept should be added as first artificial predictor value. Default = false.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.WeightedRegression.Weighted``1(System.Collections.Generic.IEnumerable{System.Tuple{``0[],``0}},``0[],System.Boolean)">
            <summary>
            Weighted Linear Regression using normal equations.
            </summary>
            <param name="samples">List of sample vectors (predictor) together with their response.</param>
            <param name="weights">List of weights, one for each sample.</param>
            <param name="intercept">True if an intercept should be added as first artificial predictor value. Default = false.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.WeightedRegression.Weighted``1(System.Collections.Generic.IEnumerable{System.ValueTuple{``0[],``0}},``0[],System.Boolean)">
            <summary>
            Weighted Linear Regression using normal equations.
            </summary>
            <param name="samples">List of sample vectors (predictor) together with their response.</param>
            <param name="weights">List of weights, one for each sample.</param>
            <param name="intercept">True if an intercept should be added as first artificial predictor value. Default = false.</param>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.WeightedRegression.Local``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Vector{``0},System.Double,System.Func{System.Double,``0})">
            <summary>
            Locally-Weighted Linear Regression using normal equations.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.LinearRegression.WeightedRegression.Local``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Vector{``0},System.Double,System.Func{System.Double,``0})">
            <summary>
            Locally-Weighted Linear Regression using normal equations.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.OdeSolvers.AdamsBashforth.FirstOrder(System.Double,System.Double,System.Double,System.Int32,System.Func{System.Double,System.Double,System.Double})">
            <summary>
            First Order AB method(same as Forward Euler)
            </summary>
            <param name="y0">Initial value</param>
            <param name="start">Start Time</param>
            <param name="end">End Time</param>
            <param name="N">Size of output array(the larger, the finer)</param>
            <param name="f">ode model</param>
            <returns>approximation with size N</returns>
        </member>
        <member name="M:MathNet.Numerics.OdeSolvers.AdamsBashforth.SecondOrder(System.Double,System.Double,System.Double,System.Int32,System.Func{System.Double,System.Double,System.Double})">
            <summary>
            Second Order AB Method
            </summary>
            <param name="y0">Initial value 1</param>
            <param name="start">Start Time</param>
            <param name="end">End Time</param>
            <param name="N">Size of output array(the larger, the finer)</param>
            <param name="f">ode model</param>
            <returns>approximation with size N</returns>
        </member>
        <member name="M:MathNet.Numerics.OdeSolvers.AdamsBashforth.ThirdOrder(System.Double,System.Double,System.Double,System.Int32,System.Func{System.Double,System.Double,System.Double})">
            <summary>
            Third Order AB Method
            </summary>
            <param name="y0">Initial value 1</param>
            <param name="start">Start Time</param>
            <param name="end">End Time</param>
            <param name="N">Size of output array(the larger, the finer)</param>
            <param name="f">ode model</param>
            <returns>approximation with size N</returns>
        </member>
        <member name="M:MathNet.Numerics.OdeSolvers.AdamsBashforth.FourthOrder(System.Double,System.Double,System.Double,System.Int32,System.Func{System.Double,System.Double,System.Double})">
            <summary>
            Fourth Order AB Method
            </summary>
            <param name="y0">Initial value 1</param>
            <param name="start">Start Time</param>
            <param name="end">End Time</param>
            <param name="N">Size of output array(the larger, the finer)</param>
            <param name="f">ode model</param>
            <returns>approximation with size N</returns>
        </member>
        <member name="T:MathNet.Numerics.OdeSolvers.RungeKutta">
            <summary>
            ODE Solver Algorithms
            </summary>
        </member>
        <member name="M:MathNet.Numerics.OdeSolvers.RungeKutta.SecondOrder(System.Double,System.Double,System.Double,System.Int32,System.Func{System.Double,System.Double,System.Double})">
            <summary>
            Second Order Runge-Kutta method
            </summary>
            <param name="y0">initial value</param>
            <param name="start">start time</param>
            <param name="end">end time</param>
            <param name="N">Size of output array(the larger, the finer)</param>
            <param name="f">ode function</param>
            <returns>approximations</returns>
        </member>
        <member name="M:MathNet.Numerics.OdeSolvers.RungeKutta.FourthOrder(System.Double,System.Double,System.Double,System.Int32,System.Func{System.Double,System.Double,System.Double})">
            <summary>
            Fourth Order Runge-Kutta method
            </summary>
            <param name="y0">initial value</param>
            <param name="start">start time</param>
            <param name="end">end time</param>
            <param name="N">Size of output array(the larger, the finer)</param>
            <param name="f">ode function</param>
            <returns>approximations</returns>
        </member>
        <member name="M:MathNet.Numerics.OdeSolvers.RungeKutta.SecondOrder(MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double,System.Double,System.Int32,System.Func{System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double}})">
            <summary>
            Second Order Runge-Kutta to solve ODE SYSTEM
            </summary>
            <param name="y0">initial vector</param>
            <param name="start">start time</param>
            <param name="end">end time</param>
            <param name="N">Size of output array(the larger, the finer)</param>
            <param name="f">ode function</param>
            <returns>approximations</returns>
        </member>
        <member name="M:MathNet.Numerics.OdeSolvers.RungeKutta.FourthOrder(MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double,System.Double,System.Int32,System.Func{System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double}})">
            <summary>
            Fourth Order Runge-Kutta to solve ODE SYSTEM
            </summary>
            <param name="y0">initial vector</param>
            <param name="start">start time</param>
            <param name="end">end time</param>
            <param name="N">Size of output array(the larger, the finer)</param>
            <param name="f">ode function</param>
            <returns>approximations</returns>
        </member>
        <member name="T:MathNet.Numerics.Optimization.BfgsBMinimizer">
            <summary>
            Broyden–Fletcher–Goldfarb–Shanno Bounded (BFGS-B) algorithm is an iterative method for solving box-constrained nonlinear optimization problems
            http://www.ece.northwestern.edu/~nocedal/PSfiles/limited.ps.gz
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.BfgsBMinimizer.FindMinimum(MathNet.Numerics.Optimization.IObjectiveFunction,MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Find the minimum of the objective function given lower and upper bounds
            </summary>
            <param name="objective">The objective function, must support a gradient</param>
            <param name="lowerBound">The lower bound</param>
            <param name="upperBound">The upper bound</param>
            <param name="initialGuess">The initial guess</param>
            <returns>The MinimizationResult which contains the minimum and the ExitCondition</returns>
        </member>
        <member name="T:MathNet.Numerics.Optimization.BfgsMinimizer">
            <summary>
            Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is an iterative method for solving unconstrained nonlinear optimization problems
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.BfgsMinimizer.#ctor(System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Creates BFGS minimizer
            </summary>
            <param name="gradientTolerance">The gradient tolerance</param>
            <param name="parameterTolerance">The parameter tolerance</param>
            <param name="functionProgressTolerance">The function progress tolerance</param>
            <param name="maximumIterations">The maximum number of iterations</param>
        </member>
        <member name="M:MathNet.Numerics.Optimization.BfgsMinimizer.FindMinimum(MathNet.Numerics.Optimization.IObjectiveFunction,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Find the minimum of the objective function given lower and upper bounds
            </summary>
            <param name="objective">The objective function, must support a gradient</param>
            <param name="initialGuess">The initial guess</param>
            <returns>The MinimizationResult which contains the minimum and the ExitCondition</returns>
        </member>
        <member name="M:MathNet.Numerics.Optimization.BfgsMinimizerBase.#ctor(System.Double,System.Double,System.Double,System.Int32)">
            <inheritdoc />
            <summary>
            Creates a base class for BFGS minimization
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Optimization.BfgsSolver">
            <summary>
            Broyden-Fletcher-Goldfarb-Shanno solver for finding function minima
            See http://en.wikipedia.org/wiki/Broyden%E2%80%93Fletcher%E2%80%93Goldfarb%E2%80%93Shanno_algorithm
            Inspired by implementation: https://github.com/PatWie/CppNumericalSolvers/blob/master/src/BfgsSolver.cpp
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.BfgsSolver.Solve(MathNet.Numerics.LinearAlgebra.Double.Vector,System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double},System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double}})">
            <summary>
            Finds a minimum of a function by the BFGS quasi-Newton method
            This uses the function and it's gradient (partial derivatives in each direction) and approximates the Hessian
            </summary>
            <param name="initialGuess">An initial guess</param>
            <param name="functionValue">Evaluates the function at a point</param>
            <param name="functionGradient">Evaluates the gradient of the function at a point</param>
            <returns>The minimum found</returns>
        </member>
        <member name="T:MathNet.Numerics.Optimization.IObjectiveFunctionEvaluation">
            <summary>
            Objective function with a frozen evaluation that must not be changed from the outside.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.IObjectiveFunctionEvaluation.CreateNew">
            <summary>Create a new unevaluated and independent copy of this objective function</summary>
        </member>
        <member name="T:MathNet.Numerics.Optimization.IObjectiveFunction">
            <summary>
            Objective function with a mutable evaluation.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.IObjectiveFunction.Fork">
            <summary>Create a new independent copy of this objective function, evaluated at the same point.</summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.IObjectiveModelEvaluation.ObservedY">
            <summary>
            Get the y-values of the observations.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.IObjectiveModelEvaluation.Weights">
            <summary>
            Get the values of the weights for the observations.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.IObjectiveModelEvaluation.ModelValues">
            <summary>
            Get the y-values of the fitted model that correspond to the independent values.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.IObjectiveModelEvaluation.Point">
            <summary>
            Get the values of the parameters.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.IObjectiveModelEvaluation.Value">
            <summary>
            Get the residual sum of squares.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.IObjectiveModelEvaluation.Gradient">
            <summary>
            Get the Gradient vector. G = J'(y - f(x; p))
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.IObjectiveModelEvaluation.Hessian">
            <summary>
            Get the approximated Hessian matrix. H = J'J
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.IObjectiveModelEvaluation.FunctionEvaluations">
            <summary>
            Get the number of calls to function.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.IObjectiveModelEvaluation.JacobianEvaluations">
            <summary>
            Get the number of calls to jacobian.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.IObjectiveModelEvaluation.DegreeOfFreedom">
            <summary>
            Get the degree of freedom.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.LevenbergMarquardtMinimizer.InitialMu">
            <summary>
            The scale factor for initial mu
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.LevenbergMarquardtMinimizer.Minimum(MathNet.Numerics.Optimization.IObjectiveModel,MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Collections.Generic.List{System.Boolean},System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Non-linear least square fitting by the Levenberg-Marduardt algorithm.
            </summary>
            <param name="objective">The objective function, including model, observations, and parameter bounds.</param>
            <param name="initialGuess">The initial guess values.</param>
            <param name="initialMu">The initial damping parameter of mu.</param>
            <param name="gradientTolerance">The stopping threshold for infinity norm of the gradient vector.</param>
            <param name="stepTolerance">The stopping threshold for L2 norm of the change of parameters.</param>
            <param name="functionTolerance">The stopping threshold for L2 norm of the residuals.</param>
            <param name="maximumIterations">The max iterations.</param>
            <returns>The result of the Levenberg-Marquardt minimization</returns>
        </member>
        <member name="T:MathNet.Numerics.Optimization.LimitedMemoryBfgsMinimizer">
            <summary>
            Limited Memory version of Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.LimitedMemoryBfgsMinimizer.#ctor(System.Double,System.Double,System.Double,System.Int32,System.Int32)">
            <inheritdoc />
            <summary>
            Creates L-BFGS minimizer
            </summary>
            <param name="memory">Numbers of gradients and steps to store.</param>
        </member>
        <member name="M:MathNet.Numerics.Optimization.LimitedMemoryBfgsMinimizer.FindMinimum(MathNet.Numerics.Optimization.IObjectiveFunction,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Find the minimum of the objective function given lower and upper bounds
            </summary>
            <param name="objective">The objective function, must support a gradient</param>
            <param name="initialGuess">The initial guess</param>
            <returns>The MinimizationResult which contains the minimum and the ExitCondition</returns>
        </member>
        <member name="T:MathNet.Numerics.Optimization.LineSearch.WeakWolfeLineSearch">
             <summary>
             Search for a step size alpha that satisfies the weak Wolfe conditions. The weak Wolfe
             Conditions are
             i)  Armijo Rule:         f(x_k + alpha_k p_k) &lt;= f(x_k) + c1 alpha_k p_k^T g(x_k)
             ii) Curvature Condition: p_k^T g(x_k + alpha_k p_k) &gt;= c2 p_k^T g(x_k)
             where g(x) is the gradient of f(x), 0 &lt; c1 &lt; c2 &lt; 1.
            
             Implementation is based on http://www.math.washington.edu/~burke/crs/408/lectures/L9-weak-Wolfe.pdf
            
             references:
             http://en.wikipedia.org/wiki/Wolfe_conditions
             http://www.math.washington.edu/~burke/crs/408/lectures/L9-weak-Wolfe.pdf
             </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.LineSearch.WolfeLineSearch.FindConformingStep(MathNet.Numerics.Optimization.IObjectiveFunctionEvaluation,MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double)">
            <summary>Implemented following http://www.math.washington.edu/~burke/crs/408/lectures/L9-weak-Wolfe.pdf</summary>
            <param name="startingPoint">The objective function being optimized, evaluated at the starting point of the search</param>
            <param name="searchDirection">Search direction</param>
            <param name="initialStep">Initial size of the step in the search direction</param>
        </member>
        <member name="M:MathNet.Numerics.Optimization.LineSearch.WolfeLineSearch.FindConformingStep(MathNet.Numerics.Optimization.IObjectiveFunctionEvaluation,MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double,System.Double)">
            <summary></summary>
            <param name="startingPoint">The objective function being optimized, evaluated at the starting point of the search</param>
            <param name="searchDirection">Search direction</param>
            <param name="initialStep">Initial size of the step in the search direction</param>
            <param name="upperBound">The upper bound</param>
        </member>
        <member name="M:MathNet.Numerics.Optimization.MinimizerBase.#ctor(System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Creates a base class for minimization
            </summary>
            <param name="gradientTolerance">The gradient tolerance</param>
            <param name="parameterTolerance">The parameter tolerance</param>
            <param name="functionProgressTolerance">The function progress tolerance</param>
            <param name="maximumIterations">The maximum number of iterations</param>
        </member>
        <member name="T:MathNet.Numerics.Optimization.NelderMeadSimplex">
            <summary>
            Class implementing the Nelder-Mead simplex algorithm, used to find a minima when no gradient is available.
            Called fminsearch() in Matlab. A description of the algorithm can be found at
            http://se.mathworks.com/help/matlab/math/optimizing-nonlinear-functions.html#bsgpq6p-11
            or
            https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.NelderMeadSimplex.FindMinimum(MathNet.Numerics.Optimization.IObjectiveFunction,MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Finds the minimum of the objective function without an initial perturbation, the default values used
            by fminsearch() in Matlab are used instead
            http://se.mathworks.com/help/matlab/math/optimizing-nonlinear-functions.html#bsgpq6p-11
            </summary>
            <param name="objectiveFunction">The objective function, no gradient or hessian needed</param>
            <param name="initialGuess">The initial guess</param>
            <returns>The minimum point</returns>
        </member>
        <member name="M:MathNet.Numerics.Optimization.NelderMeadSimplex.FindMinimum(MathNet.Numerics.Optimization.IObjectiveFunction,MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Finds the minimum of the objective function with an initial perturbation
            </summary>
            <param name="objectiveFunction">The objective function, no gradient or hessian needed</param>
            <param name="initialGuess">The initial guess</param>
            <param name="initalPertubation">The initial perturbation</param>
            <returns>The minimum point</returns>
        </member>
        <member name="M:MathNet.Numerics.Optimization.NelderMeadSimplex.Minimum(MathNet.Numerics.Optimization.IObjectiveFunction,MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double,System.Int32)">
            <summary>
            Finds the minimum of the objective function without an initial perturbation, the default values used
            by fminsearch() in Matlab are used instead
            http://se.mathworks.com/help/matlab/math/optimizing-nonlinear-functions.html#bsgpq6p-11
            </summary>
            <param name="objectiveFunction">The objective function, no gradient or hessian needed</param>
            <param name="initialGuess">The initial guess</param>
            <returns>The minimum point</returns>
        </member>
        <member name="M:MathNet.Numerics.Optimization.NelderMeadSimplex.Minimum(MathNet.Numerics.Optimization.IObjectiveFunction,MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double,System.Int32)">
            <summary>
            Finds the minimum of the objective function with an initial perturbation
            </summary>
            <param name="objectiveFunction">The objective function, no gradient or hessian needed</param>
            <param name="initialGuess">The initial guess</param>
            <param name="initalPertubation">The initial perturbation</param>
            <returns>The minimum point</returns>
        </member>
        <member name="M:MathNet.Numerics.Optimization.NelderMeadSimplex.InitializeErrorValues(MathNet.Numerics.LinearAlgebra.Vector{System.Double}[],MathNet.Numerics.Optimization.IObjectiveFunction)">
            <summary>
            Evaluate the objective function at each vertex to create a corresponding
            list of error values for each vertex
            </summary>
            <param name="vertices"></param>
            <param name="objectiveFunction"></param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.Optimization.NelderMeadSimplex.HasConverged(System.Double,MathNet.Numerics.Optimization.NelderMeadSimplex.ErrorProfile,System.Double[])">
            <summary>
            Check whether the points in the error profile have so little range that we
            consider ourselves to have converged
            </summary>
            <param name="convergenceTolerance"></param>
            <param name="errorProfile"></param>
            <param name="errorValues"></param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.Optimization.NelderMeadSimplex.EvaluateSimplex(System.Double[])">
            <summary>
            Examine all error values to determine the ErrorProfile
            </summary>
            <param name="errorValues"></param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.Optimization.NelderMeadSimplex.InitializeVertices(MathNet.Numerics.Optimization.NelderMeadSimplex.SimplexConstant[])">
            <summary>
            Construct an initial simplex, given starting guesses for the constants, and
            initial step sizes for each dimension
            </summary>
            <param name="simplexConstants"></param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.Optimization.NelderMeadSimplex.TryToScaleSimplex(System.Double,MathNet.Numerics.Optimization.NelderMeadSimplex.ErrorProfile@,MathNet.Numerics.LinearAlgebra.Vector{System.Double}[],System.Double[],MathNet.Numerics.Optimization.IObjectiveFunction)">
            <summary>
            Test a scaling operation of the high point, and replace it if it is an improvement
            </summary>
            <param name="scaleFactor"></param>
            <param name="errorProfile"></param>
            <param name="vertices"></param>
            <param name="errorValues"></param>
            <param name="objectiveFunction"></param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.Optimization.NelderMeadSimplex.ShrinkSimplex(MathNet.Numerics.Optimization.NelderMeadSimplex.ErrorProfile,MathNet.Numerics.LinearAlgebra.Vector{System.Double}[],System.Double[],MathNet.Numerics.Optimization.IObjectiveFunction)">
            <summary>
            Contract the simplex uniformly around the lowest point
            </summary>
            <param name="errorProfile"></param>
            <param name="vertices"></param>
            <param name="errorValues"></param>
            <param name="objectiveFunction"></param>
        </member>
        <member name="M:MathNet.Numerics.Optimization.NelderMeadSimplex.ComputeCentroid(MathNet.Numerics.LinearAlgebra.Vector{System.Double}[],MathNet.Numerics.Optimization.NelderMeadSimplex.ErrorProfile)">
            <summary>
            Compute the centroid of all points except the worst
            </summary>
            <param name="vertices"></param>
            <param name="errorProfile"></param>
            <returns></returns>
        </member>
        <member name="P:MathNet.Numerics.Optimization.NelderMeadSimplex.SimplexConstant.Value">
            <summary>
            The value of the constant
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.NonlinearMinimizationResult.MinimizingPoint">
            <summary>
            Returns the best fit parameters.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.NonlinearMinimizationResult.StandardErrors">
            <summary>
            Returns the standard errors of the corresponding parameters
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.NonlinearMinimizationResult.MinimizedValues">
            <summary>
            Returns the y-values of the fitted model that correspond to the independent values.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.NonlinearMinimizationResult.Covariance">
            <summary>
            Returns the covariance matrix at minimizing point.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.NonlinearMinimizationResult.Correlation">
            <summary>
             Returns the correlation matrix at minimizing point.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.NonlinearMinimizerBase.FunctionTolerance">
            <summary>
            The stopping threshold for the function value or L2 norm of the residuals.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.NonlinearMinimizerBase.StepTolerance">
            <summary>
            The stopping threshold for L2 norm of the change of the parameters.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.NonlinearMinimizerBase.GradientTolerance">
            <summary>
            The stopping threshold for infinity norm of the gradient.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.NonlinearMinimizerBase.MaximumIterations">
            <summary>
            The maximum number of iterations.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.NonlinearMinimizerBase.LowerBound">
            <summary>
            The lower bound of the parameters.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.NonlinearMinimizerBase.UpperBound">
            <summary>
            The upper bound of the parameters.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.NonlinearMinimizerBase.Scales">
            <summary>
            The scale factors for the parameters.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.ObjectiveFunction.Value(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double})">
            <summary>
            Objective function where neither Gradient nor Hessian is available.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.ObjectiveFunction.Gradient(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.ValueTuple{System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double}}})">
            <summary>
            Objective function where the Gradient is available. Greedy evaluation.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.ObjectiveFunction.Gradient(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double},System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double}})">
            <summary>
            Objective function where the Gradient is available. Lazy evaluation.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.ObjectiveFunction.Hessian(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.ValueTuple{System.Double,MathNet.Numerics.LinearAlgebra.Matrix{System.Double}}})">
            <summary>
            Objective function where the Hessian is available. Greedy evaluation.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.ObjectiveFunction.Hessian(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double},System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double}})">
            <summary>
            Objective function where the Hessian is available. Lazy evaluation.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.ObjectiveFunction.GradientHessian(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.ValueTuple{System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double}}})">
            <summary>
            Objective function where both Gradient and Hessian are available. Greedy evaluation.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.ObjectiveFunction.GradientHessian(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double},System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double}},System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double}})">
            <summary>
            Objective function where both Gradient and Hessian are available. Lazy evaluation.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.ObjectiveFunction.ScalarValue(System.Func{System.Double,System.Double})">
            <summary>
            Objective function where neither first nor second derivative is available.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.ObjectiveFunction.ScalarDerivative(System.Func{System.Double,System.Double},System.Func{System.Double,System.Double})">
            <summary>
            Objective function where the first derivative is available.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.ObjectiveFunction.ScalarSecondDerivative(System.Func{System.Double,System.Double},System.Func{System.Double,System.Double},System.Func{System.Double,System.Double})">
            <summary>
            Objective function where the first and second derivatives are available.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.ObjectiveFunction.NonlinearModel(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double}},System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double}},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            objective model with a user supplied jacobian for non-linear least squares regression.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.ObjectiveFunction.NonlinearModel(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double}},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Int32)">
            <summary>
            Objective model for non-linear least squares regression.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.ObjectiveFunction.NonlinearModel(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double,System.Double},System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double,MathNet.Numerics.LinearAlgebra.Vector{System.Double}},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Objective model with a user supplied jacobian for non-linear least squares regression.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.ObjectiveFunction.NonlinearModel(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Double,System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Int32)">
            <summary>
            Objective model for non-linear least squares regression.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.ObjectiveFunction.NonlinearFunction(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double}},System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Matrix{System.Double}},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Objective function with a user supplied jacobian for nonlinear least squares regression.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.ObjectiveFunction.NonlinearFunction(System.Func{MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double}},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Int32)">
            <summary>
            Objective function for nonlinear least squares regression.
            The numerical jacobian with accuracy order is used.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Optimization.ObjectiveFunctions.ForwardDifferenceGradientObjectiveFunction">
             <summary>
             Adapts an objective function with only value implemented
             to provide a gradient as well. Gradient calculation is
             done using the finite difference method, specifically
             forward differences.
            
             For each gradient computed, the algorithm requires an
             additional number of function evaluations equal to the
             functions's number of input parameters.
             </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.ObjectiveFunctions.NonlinearObjectiveFunction.ObservedX">
            <summary>
            Set or get the values of the independent variable.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.ObjectiveFunctions.NonlinearObjectiveFunction.ObservedY">
            <summary>
            Set or get the values of the observations.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.ObjectiveFunctions.NonlinearObjectiveFunction.Weights">
            <summary>
            Set or get the values of the weights for the observations.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.ObjectiveFunctions.NonlinearObjectiveFunction.IsFixed">
            <summary>
            Get whether parameters are fixed or free.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.ObjectiveFunctions.NonlinearObjectiveFunction.NumberOfObservations">
            <summary>
            Get the number of observations.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.ObjectiveFunctions.NonlinearObjectiveFunction.NumberOfParameters">
            <summary>
            Get the number of unknown parameters.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.ObjectiveFunctions.NonlinearObjectiveFunction.DegreeOfFreedom">
            <summary>
            Get the degree of freedom
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.ObjectiveFunctions.NonlinearObjectiveFunction.FunctionEvaluations">
            <summary>
            Get the number of calls to function.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.ObjectiveFunctions.NonlinearObjectiveFunction.JacobianEvaluations">
            <summary>
            Get the number of calls to jacobian.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.ObjectiveFunctions.NonlinearObjectiveFunction.Point">
            <summary>
            Set or get the values of the parameters.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.ObjectiveFunctions.NonlinearObjectiveFunction.ModelValues">
            <summary>
            Get the y-values of the fitted model that correspond to the independent values.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.ObjectiveFunctions.NonlinearObjectiveFunction.Value">
            <summary>
            Get the residual sum of squares.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.ObjectiveFunctions.NonlinearObjectiveFunction.Gradient">
            <summary>
            Get the Gradient vector of x and p.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.ObjectiveFunctions.NonlinearObjectiveFunction.Hessian">
            <summary>
            Get the Hessian matrix of x and p, J'WJ
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.ObjectiveFunctions.NonlinearObjectiveFunction.SetObserved(MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double})">
            <summary>
            Set observed data to fit.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.ObjectiveFunctions.NonlinearObjectiveFunction.SetParameters(MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Collections.Generic.List{System.Boolean})">
            <summary>
            Set parameters and bounds.
            </summary>
            <param name="initialGuess">The initial values of parameters.</param>
            <param name="isFixed">The list to the parameters fix or free.</param>
        </member>
        <member name="M:MathNet.Numerics.Optimization.TrustRegion.TrustRegionDogLegMinimizer.#ctor(System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Non-linear least square fitting by the trust region dogleg algorithm.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Optimization.TrustRegion.TrustRegionMinimizerBase.Subproblem">
            <summary>
            The trust region subproblem.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Optimization.TrustRegion.TrustRegionMinimizerBase.RadiusTolerance">
            <summary>
            The stopping threshold for the trust region radius.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Optimization.TrustRegion.TrustRegionMinimizerBase.Minimum(MathNet.Numerics.Optimization.TrustRegion.ITrustRegionSubproblem,MathNet.Numerics.Optimization.IObjectiveModel,MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},MathNet.Numerics.LinearAlgebra.Vector{System.Double},System.Collections.Generic.List{System.Boolean},System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Non-linear least square fitting by the trust-region algorithm.
            </summary>
            <param name="objective">The objective model, including function, jacobian, observations, and parameter bounds.</param>
            <param name="subproblem">The subproblem</param>
            <param name="initialGuess">The initial guess values.</param>
            <param name="functionTolerance">The stopping threshold for L2 norm of the residuals.</param>
            <param name="gradientTolerance">The stopping threshold for infinity norm of the gradient vector.</param>
            <param name="stepTolerance">The stopping threshold for L2 norm of the change of parameters.</param>
            <param name="radiusTolerance">The stopping threshold for trust region radius</param>
            <param name="maximumIterations">The max iterations.</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.Optimization.TrustRegion.TrustRegionNewtonCGMinimizer.#ctor(System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Non-linear least square fitting by the trust region Newton-Conjugate-Gradient algorithm.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Permutation">
            <summary>
            Class to represent a permutation for a subset of the natural numbers.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Permutation._indices">
            <summary>
            Entry _indices[i] represents the location to which i is permuted to.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Permutation.#ctor(System.Int32[])">
            <summary>
            Initializes a new instance of the Permutation class.
            </summary>
            <param name="indices">An array which represents where each integer is permuted too: indices[i] represents that integer i
            is permuted to location indices[i].</param>
        </member>
        <member name="P:MathNet.Numerics.Permutation.Dimension">
            <summary>
            Gets the number of elements this permutation is over.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Permutation.Item(System.Int32)">
            <summary>
            Computes where <paramref name="idx"/> permutes too.
            </summary>
            <param name="idx">The index to permute from.</param>
            <returns>The index which is permuted to.</returns>
        </member>
        <member name="M:MathNet.Numerics.Permutation.Inverse">
            <summary>
            Computes the inverse of the permutation.
            </summary>
            <returns>The inverse of the permutation.</returns>
        </member>
        <member name="M:MathNet.Numerics.Permutation.FromInversions(System.Int32[])">
            <summary>
            Construct an array from a sequence of inversions.
            </summary>
            <example>
            From wikipedia: the permutation 12043 has the inversions (0,2), (1,2) and (3,4). This would be
            encoded using the array [22244].
            </example>
            <param name="inv">The set of inversions to construct the permutation from.</param>
            <returns>A permutation generated from a sequence of inversions.</returns>
        </member>
        <member name="M:MathNet.Numerics.Permutation.ToInversions">
            <summary>
            Construct a sequence of inversions from the permutation.
            </summary>
            <example>
            From wikipedia: the permutation 12043 has the inversions (0,2), (1,2) and (3,4). This would be
            encoded using the array [22244].
            </example>
            <returns>A sequence of inversions.</returns>
        </member>
        <member name="M:MathNet.Numerics.Permutation.CheckForProperPermutation(System.Int32[])">
            <summary>
            Checks whether the <paramref name="indices"/> array represents a proper permutation.
            </summary>
            <param name="indices">An array which represents where each integer is permuted too: indices[i] represents that integer i
            is permuted to location indices[i].</param>
            <returns>True if <paramref name="indices"/> represents a proper permutation, <c>false</c> otherwise.</returns>
        </member>
        <member name="T:MathNet.Numerics.Polynomial">
            <summary>
            A single-variable polynomial with real-valued coefficients and non-negative exponents.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Polynomial.Coefficients">
            <summary>
            The coefficients of the polynomial in a
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Polynomial.VariableName">
            <summary>
            Only needed for the ToString method
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Polynomial.Degree">
            <summary>
            Degree of the polynomial, i.e. the largest monomial exponent. For example, the degree of y=x^2+x^5 is 5, for y=3 it is 0.
            The null-polynomial returns degree -1 because the correct degree, negative infinity, cannot be represented by integers.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.#ctor(System.Int32)">
            <summary>
            Create a zero-polynomial with a coefficient array of the given length.
            An array of length N can support polynomials of a degree of at most N-1.
            </summary>
            <param name="n">Length of the coefficient array</param>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.#ctor">
            <summary>
            Create a zero-polynomial
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.#ctor(System.Double)">
            <summary>
            Create a constant polynomial.
            Example: 3.0 -> "p : x -> 3.0"
            </summary>
            <param name="coefficient">The coefficient of the "x^0" monomial.</param>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.#ctor(System.Double[])">
            <summary>
            Create a polynomial with the provided coefficients (in ascending order, where the index matches the exponent).
            Example: {5, 0, 2} -> "p : x -> 5 + 0 x^1 + 2 x^2".
            </summary>
            <param name="coefficients">Polynomial coefficients as array</param>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.#ctor(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Create a polynomial with the provided coefficients (in ascending order, where the index matches the exponent).
            Example: {5, 0, 2} -> "p : x -> 5 + 0 x^1 + 2 x^2".
            </summary>
            <param name="coefficients">Polynomial coefficients as enumerable</param>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.Fit(System.Double[],System.Double[],System.Int32,MathNet.Numerics.LinearRegression.DirectRegressionMethod)">
            <summary>
            Least-Squares fitting the points (x,y) to a k-order polynomial y : x -> p0 + p1*x + p2*x^2 + ... + pk*x^k
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.Evaluate(System.Double,System.Double[])">
            <summary>
            Evaluate a polynomial at point x.
            Coefficients are ordered ascending by power with power k at index k.
            Example: coefficients [3,-1,2] represent y=2x^2-x+3.
            </summary>
            <param name="z">The location where to evaluate the polynomial at.</param>
            <param name="coefficients">The coefficients of the polynomial, coefficient for power k at index k.</param>
            <exception cref="T:System.ArgumentNullException">
            <paramref name="coefficients"/> is a null reference.
            </exception>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.Evaluate(System.Numerics.Complex,System.Double[])">
            <summary>
            Evaluate a polynomial at point x.
            Coefficients are ordered ascending by power with power k at index k.
            Example: coefficients [3,-1,2] represent y=2x^2-x+3.
            </summary>
            <param name="z">The location where to evaluate the polynomial at.</param>
            <param name="coefficients">The coefficients of the polynomial, coefficient for power k at index k.</param>
            <exception cref="T:System.ArgumentNullException">
            <paramref name="coefficients"/> is a null reference.
            </exception>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.Evaluate(System.Numerics.Complex,System.Numerics.Complex[])">
            <summary>
            Evaluate a polynomial at point x.
            Coefficients are ordered ascending by power with power k at index k.
            Example: coefficients [3,-1,2] represent y=2x^2-x+3.
            </summary>
            <param name="z">The location where to evaluate the polynomial at.</param>
            <param name="coefficients">The coefficients of the polynomial, coefficient for power k at index k.</param>
            <exception cref="T:System.ArgumentNullException">
            <paramref name="coefficients"/> is a null reference.
            </exception>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.Evaluate(System.Double)">
            <summary>
            Evaluate a polynomial at point x.
            </summary>
            <param name="z">The location where to evaluate the polynomial at.</param>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.Evaluate(System.Numerics.Complex)">
            <summary>
            Evaluate a polynomial at point x.
            </summary>
            <param name="z">The location where to evaluate the polynomial at.</param>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.Evaluate(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Evaluate a polynomial at points z.
            </summary>
            <param name="z">The locations where to evaluate the polynomial at.</param>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.Evaluate(System.Collections.Generic.IEnumerable{System.Numerics.Complex})">
            <summary>
            Evaluate a polynomial at points z.
            </summary>
            <param name="z">The locations where to evaluate the polynomial at.</param>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.Roots">
            <summary>
            Calculates the complex roots of the Polynomial by eigenvalue decomposition
            </summary>
            <returns>a vector of complex numbers with the roots</returns>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.EigenvalueMatrix">
            <summary>
            Get the eigenvalue matrix A of this polynomial such that eig(A) = roots of this polynomial.
            </summary>
            <returns>Eigenvalue matrix A</returns>
            <note>This matrix is similar to the companion matrix of this polynomial, in such a way, that it's transpose is the columnflip of the companion matrix</note>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.Add(MathNet.Numerics.Polynomial,MathNet.Numerics.Polynomial)">
            <summary>
            Addition of two Polynomials (point-wise).
            </summary>
            <param name="a">Left Polynomial</param>
            <param name="b">Right Polynomial</param>
            <returns>Resulting Polynomial</returns>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.Add(MathNet.Numerics.Polynomial,System.Double)">
            <summary>
            Addition of a polynomial and a scalar.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.Subtract(MathNet.Numerics.Polynomial,MathNet.Numerics.Polynomial)">
            <summary>
            Subtraction of two Polynomials (point-wise).
            </summary>
            <param name="a">Left Polynomial</param>
            <param name="b">Right Polynomial</param>
            <returns>Resulting Polynomial</returns>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.Subtract(MathNet.Numerics.Polynomial,System.Double)">
            <summary>
            Addition of a scalar from a polynomial.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.Subtract(System.Double,MathNet.Numerics.Polynomial)">
            <summary>
            Addition of a polynomial from a scalar.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.Negate(MathNet.Numerics.Polynomial)">
            <summary>
            Negation of a polynomial.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.Multiply(MathNet.Numerics.Polynomial,MathNet.Numerics.Polynomial)">
            <summary>
            Multiplies a polynomial by a polynomial (convolution)
            </summary>
            <param name="a">Left polynomial</param>
            <param name="b">Right polynomial</param>
            <returns>Resulting Polynomial</returns>
            <exception cref="T:System.ArgumentNullException">
            <paramref name="a"/> or <paramref name="b"/> is a null reference.
            </exception>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.Multiply(MathNet.Numerics.Polynomial,System.Double)">
            <summary>
            Scales a polynomial by a scalar
            </summary>
            <param name="a">Polynomial</param>
            <param name="k">Scalar value</param>
            <returns>Resulting Polynomial</returns>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.Divide(MathNet.Numerics.Polynomial,System.Double)">
            <summary>
            Scales a polynomial by division by a scalar
            </summary>
            <param name="a">Polynomial</param>
            <param name="k">Scalar value</param>
            <returns>Resulting Polynomial</returns>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.DivideRemainder(MathNet.Numerics.Polynomial,MathNet.Numerics.Polynomial)">
            <summary>
            Euclidean long division of two polynomials, returning the quotient q and remainder r of the two polynomials a and b such that a = q*b + r
            </summary>
            <param name="a">Left polynomial</param>
            <param name="b">Right polynomial</param>
            <returns>A tuple holding quotient in first and remainder in second</returns>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.PointwiseDivide(MathNet.Numerics.Polynomial,MathNet.Numerics.Polynomial)">
            <summary>
            Point-wise division of two Polynomials
            </summary>
            <param name="a">Left Polynomial</param>
            <param name="b">Right Polynomial</param>
            <returns>Resulting Polynomial</returns>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.PointwiseMultiply(MathNet.Numerics.Polynomial,MathNet.Numerics.Polynomial)">
            <summary>
            Point-wise multiplication of two Polynomials
            </summary>
            <param name="a">Left Polynomial</param>
            <param name="b">Right Polynomial</param>
            <returns>Resulting Polynomial</returns>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.DivideRemainder(MathNet.Numerics.Polynomial)">
            <summary>
            Division of two polynomials returning the quotient-with-remainder of the two polynomials given
            </summary>
            <param name="b">Right polynomial</param>
            <returns>A tuple holding quotient in first and remainder in second</returns>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.op_Addition(MathNet.Numerics.Polynomial,MathNet.Numerics.Polynomial)">
            <summary>
            Addition of two Polynomials (piecewise)
            </summary>
            <param name="a">Left polynomial</param>
            <param name="b">Right polynomial</param>
            <returns>Resulting Polynomial</returns>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.op_Addition(MathNet.Numerics.Polynomial,System.Double)">
            <summary>
            adds a scalar to a polynomial.
            </summary>
            <param name="a">Polynomial</param>
            <param name="k">Scalar value</param>
            <returns>Resulting Polynomial</returns>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.op_Addition(System.Double,MathNet.Numerics.Polynomial)">
            <summary>
            adds a scalar to a polynomial.
            </summary>
            <param name="k">Scalar value</param>
            <param name="a">Polynomial</param>
            <returns>Resulting Polynomial</returns>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.op_Subtraction(MathNet.Numerics.Polynomial,MathNet.Numerics.Polynomial)">
            <summary>
            Subtraction of two polynomial.
            </summary>
            <param name="a">Left polynomial</param>
            <param name="b">Right polynomial</param>
            <returns>Resulting Polynomial</returns>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.op_Subtraction(MathNet.Numerics.Polynomial,System.Double)">
            <summary>
            Subtracts a scalar from a polynomial.
            </summary>
            <param name="a">Polynomial</param>
            <param name="k">Scalar value</param>
            <returns>Resulting Polynomial</returns>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.op_Subtraction(System.Double,MathNet.Numerics.Polynomial)">
            <summary>
            Subtracts a polynomial from a scalar.
            </summary>
            <param name="k">Scalar value</param>
            <param name="a">Polynomial</param>
            <returns>Resulting Polynomial</returns>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.op_UnaryNegation(MathNet.Numerics.Polynomial)">
            <summary>
            Negates a polynomial.
            </summary>
            <param name="a">Polynomial</param>
            <returns>Resulting Polynomial</returns>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.op_Multiply(MathNet.Numerics.Polynomial,MathNet.Numerics.Polynomial)">
            <summary>
            Multiplies a polynomial by a polynomial (convolution).
            </summary>
            <param name="a">Left polynomial</param>
            <param name="b">Right polynomial</param>
            <returns>resulting Polynomial</returns>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.op_Multiply(MathNet.Numerics.Polynomial,System.Double)">
            <summary>
            Multiplies a polynomial by a scalar.
            </summary>
            <param name="a">Polynomial</param>
            <param name="k">Scalar value</param>
            <returns>Resulting Polynomial</returns>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.op_Multiply(System.Double,MathNet.Numerics.Polynomial)">
            <summary>
            Multiplies a polynomial by a scalar.
            </summary>
            <param name="k">Scalar value</param>
            <param name="a">Polynomial</param>
            <returns>Resulting Polynomial</returns>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.op_Division(MathNet.Numerics.Polynomial,System.Double)">
            <summary>
            Divides a polynomial by scalar value.
            </summary>
            <param name="a">Polynomial</param>
            <param name="k">Scalar value</param>
            <returns>Resulting Polynomial</returns>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.ToString">
            <summary>
            Format the polynomial in ascending order, e.g. "4.3 + 2.0x^2 - x^3".
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.ToStringDescending">
            <summary>
            Format the polynomial in descending order, e.g. "x^3 + 2.0x^2 - 4.3".
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.ToString(System.String)">
            <summary>
            Format the polynomial in ascending order, e.g. "4.3 + 2.0x^2 - x^3".
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.ToStringDescending(System.String)">
            <summary>
            Format the polynomial in descending order, e.g. "x^3 + 2.0x^2 - 4.3".
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.ToString(System.IFormatProvider)">
            <summary>
            Format the polynomial in ascending order, e.g. "4.3 + 2.0x^2 - x^3".
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.ToStringDescending(System.IFormatProvider)">
            <summary>
            Format the polynomial in descending order, e.g. "x^3 + 2.0x^2 - 4.3".
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.ToString(System.String,System.IFormatProvider)">
            <summary>
            Format the polynomial in ascending order, e.g. "4.3 + 2.0x^2 - x^3".
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.ToStringDescending(System.String,System.IFormatProvider)">
            <summary>
            Format the polynomial in descending order, e.g. "x^3 + 2.0x^2 - 4.3".
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Polynomial.System#ICloneable#Clone">
            <summary>
            Creates a new object that is a copy of the current instance.
            </summary>
            <returns>
            A new object that is a copy of this instance.
            </returns>
        </member>
        <member name="T:MathNet.Numerics.Precision">
            <summary>
            Utilities for working with floating point numbers.
            </summary>
            <remarks>
            <para>
            Useful links:
            <list type="bullet">
            <item>
            http://docs.sun.com/source/806-3568/ncg_goldberg.html#689 - What every computer scientist should know about floating-point arithmetic
            </item>
            <item>
            http://en.wikipedia.org/wiki/Machine_epsilon - Gives the definition of machine epsilon
            </item>
            </list>
            </para>
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Precision.CompareTo(System.Double,System.Double,System.Double)">
            <summary>
            Compares two doubles and determines which double is bigger.
            a &lt; b -> -1; a ~= b (almost equal according to parameter) -> 0; a &gt; b -> +1.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumAbsoluteError">The absolute accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.CompareTo(System.Double,System.Double,System.Int32)">
            <summary>
            Compares two doubles and determines which double is bigger.
            a &lt; b -> -1; a ~= b (almost equal according to parameter) -> 0; a &gt; b -> +1.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places on which the values must be compared. Must be 1 or larger.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.CompareToRelative(System.Double,System.Double,System.Double)">
            <summary>
            Compares two doubles and determines which double is bigger.
            a &lt; b -> -1; a ~= b (almost equal according to parameter) -> 0; a &gt; b -> +1.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumError">The relative accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.CompareToRelative(System.Double,System.Double,System.Int32)">
            <summary>
            Compares two doubles and determines which double is bigger.
            a &lt; b -> -1; a ~= b (almost equal according to parameter) -> 0; a &gt; b -> +1.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places on which the values must be compared. Must be 1 or larger.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.CompareToNumbersBetween(System.Double,System.Double,System.Int64)">
            <summary>
            Compares two doubles and determines which double is bigger.
            a &lt; b -> -1; a ~= b (almost equal according to parameter) -> 0; a &gt; b -> +1.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maxNumbersBetween">The maximum error in terms of Units in Last Place (<c>ulps</c>), i.e. the maximum number of decimals that may be different. Must be 1 or larger.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsLarger(System.Double,System.Double,System.Int32)">
            <summary>
            Compares two doubles and determines if the <c>first</c> value is larger than the <c>second</c>
            value to within the specified number of decimal places or not.
            </summary>
            <remarks>
            <para>
            The values are equal if the difference between the two numbers is smaller than 10^(-numberOfDecimalPlaces). We divide by
            two so that we have half the range on each side of the numbers, e.g. if <paramref name="decimalPlaces"/> == 2, then 0.01 will equal between
            0.005 and 0.015, but not 0.02 and not 0.00
            </para>
            </remarks>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
            <returns><c>true</c> if the first value is larger than the second value; otherwise <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsLarger(System.Single,System.Single,System.Int32)">
            <summary>
            Compares two doubles and determines if the <c>first</c> value is larger than the <c>second</c>
            value to within the specified number of decimal places or not.
            </summary>
            <remarks>
            <para>
            The values are equal if the difference between the two numbers is smaller than 10^(-numberOfDecimalPlaces). We divide by
            two so that we have half the range on each side of the numbers, e.g. if <paramref name="decimalPlaces"/> == 2, then 0.01 will equal between
            0.005 and 0.015, but not 0.02 and not 0.00
            </para>
            </remarks>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
            <returns><c>true</c> if the first value is larger than the second value; otherwise <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsLarger(System.Double,System.Double,System.Double)">
            <summary>
            Compares two doubles and determines if the <c>first</c> value is larger than the <c>second</c>
            value to within the specified number of decimal places or not.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumAbsoluteError">The absolute accuracy required for being almost equal.</param>
            <returns><c>true</c> if the first value is larger than the second value; otherwise <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsLarger(System.Single,System.Single,System.Double)">
            <summary>
            Compares two doubles and determines if the <c>first</c> value is larger than the <c>second</c>
            value to within the specified number of decimal places or not.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumAbsoluteError">The absolute accuracy required for being almost equal.</param>
            <returns><c>true</c> if the first value is larger than the second value; otherwise <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsLargerRelative(System.Double,System.Double,System.Int32)">
            <summary>
            Compares two doubles and determines if the <c>first</c> value is larger than the <c>second</c>
            value to within the specified number of decimal places or not.
            </summary>
            <remarks>
            <para>
            The values are equal if the difference between the two numbers is smaller than 10^(-numberOfDecimalPlaces). We divide by
            two so that we have half the range on each side of the numbers, e.g. if <paramref name="decimalPlaces"/> == 2, then 0.01 will equal between
            0.005 and 0.015, but not 0.02 and not 0.00
            </para>
            </remarks>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
            <returns><c>true</c> if the first value is larger than the second value; otherwise <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsLargerRelative(System.Single,System.Single,System.Int32)">
            <summary>
            Compares two doubles and determines if the <c>first</c> value is larger than the <c>second</c>
            value to within the specified number of decimal places or not.
            </summary>
            <remarks>
            <para>
            The values are equal if the difference between the two numbers is smaller than 10^(-numberOfDecimalPlaces). We divide by
            two so that we have half the range on each side of the numbers, e.g. if <paramref name="decimalPlaces"/> == 2, then 0.01 will equal between
            0.005 and 0.015, but not 0.02 and not 0.00
            </para>
            </remarks>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
            <returns><c>true</c> if the first value is larger than the second value; otherwise <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsLargerRelative(System.Double,System.Double,System.Double)">
            <summary>
            Compares two doubles and determines if the <c>first</c> value is larger than the <c>second</c>
            value to within the specified number of decimal places or not.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumError">The relative accuracy required for being almost equal.</param>
            <returns><c>true</c> if the first value is larger than the second value; otherwise <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsLargerRelative(System.Single,System.Single,System.Double)">
            <summary>
            Compares two doubles and determines if the <c>first</c> value is larger than the <c>second</c>
            value to within the specified number of decimal places or not.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumError">The relative accuracy required for being almost equal.</param>
            <returns><c>true</c> if the first value is larger than the second value; otherwise <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsLargerNumbersBetween(System.Double,System.Double,System.Int64)">
            <summary>
            Compares two doubles and determines if the <c>first</c> value is larger than the <c>second</c>
            value to within the tolerance or not. Equality comparison is based on the binary representation.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maxNumbersBetween">The maximum number of floating point values for which the two values are considered equal. Must be 1 or larger.</param>
            <returns><c>true</c> if the first value is larger than the second value; otherwise <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsLargerNumbersBetween(System.Single,System.Single,System.Int64)">
            <summary>
            Compares two doubles and determines if the <c>first</c> value is larger than the <c>second</c>
            value to within the tolerance or not. Equality comparison is based on the binary representation.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maxNumbersBetween">The maximum number of floating point values for which the two values are considered equal. Must be 1 or larger.</param>
            <returns><c>true</c> if the first value is larger than the second value; otherwise <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsSmaller(System.Double,System.Double,System.Int32)">
            <summary>
            Compares two doubles and determines if the <c>first</c> value is smaller than the <c>second</c>
            value to within the specified number of decimal places or not.
            </summary>
            <remarks>
            <para>
            The values are equal if the difference between the two numbers is smaller than 10^(-numberOfDecimalPlaces). We divide by
            two so that we have half the range on each side of th<paramref name="decimalPlaces"/>g. if <paramref name="decimalPlaces"/> == 2, then 0.01 will equal between
            0.005 and 0.015, but not 0.02 and not 0.00
            </para>
            </remarks>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
            <returns><c>true</c> if the first value is smaller than the second value; otherwise <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsSmaller(System.Single,System.Single,System.Int32)">
            <summary>
            Compares two doubles and determines if the <c>first</c> value is smaller than the <c>second</c>
            value to within the specified number of decimal places or not.
            </summary>
            <remarks>
            <para>
            The values are equal if the difference between the two numbers is smaller than 10^(-numberOfDecimalPlaces). We divide by
            two so that we have half the range on each side of th<paramref name="decimalPlaces"/>g. if <paramref name="decimalPlaces"/> == 2, then 0.01 will equal between
            0.005 and 0.015, but not 0.02 and not 0.00
            </para>
            </remarks>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
            <returns><c>true</c> if the first value is smaller than the second value; otherwise <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsSmaller(System.Double,System.Double,System.Double)">
            <summary>
            Compares two doubles and determines if the <c>first</c> value is smaller than the <c>second</c>
            value to within the specified number of decimal places or not.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumAbsoluteError">The absolute accuracy required for being almost equal.</param>
            <returns><c>true</c> if the first value is smaller than the second value; otherwise <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsSmaller(System.Single,System.Single,System.Double)">
            <summary>
            Compares two doubles and determines if the <c>first</c> value is smaller than the <c>second</c>
            value to within the specified number of decimal places or not.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumAbsoluteError">The absolute accuracy required for being almost equal.</param>
            <returns><c>true</c> if the first value is smaller than the second value; otherwise <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsSmallerRelative(System.Double,System.Double,System.Int32)">
            <summary>
            Compares two doubles and determines if the <c>first</c> value is smaller than the <c>second</c>
            value to within the specified number of decimal places or not.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
            <returns><c>true</c> if the first value is smaller than the second value; otherwise <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsSmallerRelative(System.Single,System.Single,System.Int32)">
            <summary>
            Compares two doubles and determines if the <c>first</c> value is smaller than the <c>second</c>
            value to within the specified number of decimal places or not.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
            <returns><c>true</c> if the first value is smaller than the second value; otherwise <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsSmallerRelative(System.Double,System.Double,System.Double)">
            <summary>
            Compares two doubles and determines if the <c>first</c> value is smaller than the <c>second</c>
            value to within the specified number of decimal places or not.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumError">The relative accuracy required for being almost equal.</param>
            <returns><c>true</c> if the first value is smaller than the second value; otherwise <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsSmallerRelative(System.Single,System.Single,System.Double)">
            <summary>
            Compares two doubles and determines if the <c>first</c> value is smaller than the <c>second</c>
            value to within the specified number of decimal places or not.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumError">The relative accuracy required for being almost equal.</param>
            <returns><c>true</c> if the first value is smaller than the second value; otherwise <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsSmallerNumbersBetween(System.Double,System.Double,System.Int64)">
            <summary>
            Compares two doubles and determines if the <c>first</c> value is smaller than the <c>second</c>
            value to within the tolerance or not. Equality comparison is based on the binary representation.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maxNumbersBetween">The maximum number of floating point values for which the two values are considered equal. Must be 1 or larger.</param>
            <returns><c>true</c> if the first value is smaller than the second value; otherwise <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsSmallerNumbersBetween(System.Single,System.Single,System.Int64)">
            <summary>
            Compares two doubles and determines if the <c>first</c> value is smaller than the <c>second</c>
            value to within the tolerance or not. Equality comparison is based on the binary representation.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maxNumbersBetween">The maximum number of floating point values for which the two values are considered equal. Must be 1 or larger.</param>
            <returns><c>true</c> if the first value is smaller than the second value; otherwise <c>false</c>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.IsFinite(System.Double)">
            <summary>
            Checks if a given double values is finite, i.e. neither NaN nor inifnity
            </summary>
            <param name="value">The value to be checked fo finitenes.</param>
        </member>
        <member name="F:MathNet.Numerics.Precision.DoubleWidth">
            <summary>
            The number of binary digits used to represent the binary number for a double precision floating
            point value. i.e. there are this many digits used to represent the
            actual number, where in a number as: 0.134556 * 10^5 the digits are 0.134556 and the exponent is 5.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Precision.SingleWidth">
            <summary>
            The number of binary digits used to represent the binary number for a single precision floating
            point value. i.e. there are this many digits used to represent the
            actual number, where in a number as: 0.134556 * 10^5 the digits are 0.134556 and the exponent is 5.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Precision.DoublePrecision">
            <summary>
            Standard epsilon, the maximum relative precision of IEEE 754 double-precision floating numbers (64 bit).
            According to the definition of Prof. Demmel and used in LAPACK and Scilab.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Precision.PositiveDoublePrecision">
            <summary>
            Standard epsilon, the maximum relative precision of IEEE 754 double-precision floating numbers (64 bit).
            According to the definition of Prof. Higham and used in the ISO C standard and MATLAB.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Precision.SinglePrecision">
            <summary>
            Standard epsilon, the maximum relative precision of IEEE 754 single-precision floating numbers (32 bit).
            According to the definition of Prof. Demmel and used in LAPACK and Scilab.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Precision.PositiveSinglePrecision">
            <summary>
            Standard epsilon, the maximum relative precision of IEEE 754 single-precision floating numbers (32 bit).
            According to the definition of Prof. Higham and used in the ISO C standard and MATLAB.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Precision.MachineEpsilon">
            <summary>
            Actual double precision machine epsilon, the smallest number that can be subtracted from 1, yielding a results different than 1.
            This is also known as unit roundoff error. According to the definition of Prof. Demmel.
            On a standard machine this is equivalent to `DoublePrecision`.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Precision.PositiveMachineEpsilon">
            <summary>
            Actual double precision machine epsilon, the smallest number that can be added to 1, yielding a results different than 1.
            This is also known as unit roundoff error. According to the definition of Prof. Higham.
            On a standard machine this is equivalent to `PositiveDoublePrecision`.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Precision.DoubleDecimalPlaces">
            <summary>
            The number of significant decimal places of double-precision floating numbers (64 bit).
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Precision.SingleDecimalPlaces">
            <summary>
            The number of significant decimal places of single-precision floating numbers (32 bit).
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Precision.DefaultDoubleAccuracy">
            <summary>
            Value representing 10 * 2^(-53) = 1.11022302462516E-15
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Precision.DefaultSingleAccuracy">
            <summary>
            Value representing 10 * 2^(-24) = 5.96046447753906E-07
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Precision.Magnitude(System.Double)">
            <summary>
            Returns the magnitude of the number.
            </summary>
            <param name="value">The value.</param>
            <returns>The magnitude of the number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.Magnitude(System.Single)">
            <summary>
            Returns the magnitude of the number.
            </summary>
            <param name="value">The value.</param>
            <returns>The magnitude of the number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.ScaleUnitMagnitude(System.Double)">
            <summary>
            Returns the number divided by it's magnitude, effectively returning a number between -10 and 10.
            </summary>
            <param name="value">The value.</param>
            <returns>The value of the number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.AsDirectionalInt64(System.Double)">
            <summary>
            Returns a 'directional' long value. This is a long value which acts the same as a double,
            e.g. a negative double value will return a negative double value starting at 0 and going
            more negative as the double value gets more negative.
            </summary>
            <param name="value">The input double value.</param>
            <returns>A long value which is roughly the equivalent of the double value.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.AsDirectionalInt32(System.Single)">
            <summary>
            Returns a 'directional' int value. This is a int value which acts the same as a float,
            e.g. a negative float value will return a negative int value starting at 0 and going
            more negative as the float value gets more negative.
            </summary>
            <param name="value">The input float value.</param>
            <returns>An int value which is roughly the equivalent of the double value.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.Increment(System.Double,System.Int32)">
            <summary>
            Increments a floating point number to the next bigger number representable by the data type.
            </summary>
            <param name="value">The value which needs to be incremented.</param>
            <param name="count">How many times the number should be incremented.</param>
            <remarks>
            The incrementation step length depends on the provided value.
            Increment(double.MaxValue) will return positive infinity.
            </remarks>
            <returns>The next larger floating point value.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.Decrement(System.Double,System.Int32)">
            <summary>
            Decrements a floating point number to the next smaller number representable by the data type.
            </summary>
            <param name="value">The value which should be decremented.</param>
            <param name="count">How many times the number should be decremented.</param>
            <remarks>
            The decrementation step length depends on the provided value.
            Decrement(double.MinValue) will return negative infinity.
            </remarks>
            <returns>The next smaller floating point value.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.CoerceZero(System.Double,System.Int32)">
            <summary>
            Forces small numbers near zero to zero, according to the specified absolute accuracy.
            </summary>
            <param name="a">The real number to coerce to zero, if it is almost zero.</param>
            <param name="maxNumbersBetween">The maximum count of numbers between the zero and the number <paramref name="a"/>.</param>
            <returns>
                Zero if |<paramref name="a"/>| is fewer than <paramref name="maxNumbersBetween"/> numbers from zero, <paramref name="a"/> otherwise.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.CoerceZero(System.Double,System.Int64)">
            <summary>
            Forces small numbers near zero to zero, according to the specified absolute accuracy.
            </summary>
            <param name="a">The real number to coerce to zero, if it is almost zero.</param>
            <param name="maxNumbersBetween">The maximum count of numbers between the zero and the number <paramref name="a"/>.</param>
            <returns>
                Zero if |<paramref name="a"/>| is fewer than <paramref name="maxNumbersBetween"/> numbers from zero, <paramref name="a"/> otherwise.
            </returns>
            <exception cref="T:System.ArgumentOutOfRangeException">
                Thrown if <paramref name="maxNumbersBetween"/> is smaller than zero.
            </exception>
        </member>
        <member name="M:MathNet.Numerics.Precision.CoerceZero(System.Double,System.Double)">
            <summary>
            Forces small numbers near zero to zero, according to the specified absolute accuracy.
            </summary>
            <param name="a">The real number to coerce to zero, if it is almost zero.</param>
            <param name="maximumAbsoluteError">The absolute threshold for <paramref name="a"/> to consider it as zero.</param>
            <returns>Zero if |<paramref name="a"/>| is smaller than <paramref name="maximumAbsoluteError"/>, <paramref name="a"/> otherwise.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">
                Thrown if <paramref name="maximumAbsoluteError"/> is smaller than zero.
            </exception>
        </member>
        <member name="M:MathNet.Numerics.Precision.CoerceZero(System.Double)">
            <summary>
            Forces small numbers near zero to zero.
            </summary>
            <param name="a">The real number to coerce to zero, if it is almost zero.</param>
            <returns>Zero if |<paramref name="a"/>| is smaller than 2^(-53) = 1.11e-16, <paramref name="a"/> otherwise.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.RangeOfMatchingFloatingPointNumbers(System.Double,System.Int64)">
            <summary>
            Determines the range of floating point numbers that will match the specified value with the given tolerance.
            </summary>
            <param name="value">The value.</param>
            <param name="maxNumbersBetween">The <c>ulps</c> difference.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">
                Thrown if <paramref name="maxNumbersBetween"/> is smaller than zero.
            </exception>
            <returns>Tuple of the bottom and top range ends.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.MaximumMatchingFloatingPointNumber(System.Double,System.Int64)">
            <summary>
            Returns the floating point number that will match the value with the tolerance on the maximum size (i.e. the result is
            always bigger than the value)
            </summary>
            <param name="value">The value.</param>
            <param name="maxNumbersBetween">The <c>ulps</c> difference.</param>
            <returns>The maximum floating point number which is <paramref name="maxNumbersBetween"/> larger than the given <paramref name="value"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.MinimumMatchingFloatingPointNumber(System.Double,System.Int64)">
            <summary>
            Returns the floating point number that will match the value with the tolerance on the minimum size (i.e. the result is
            always smaller than the value)
            </summary>
            <param name="value">The value.</param>
            <param name="maxNumbersBetween">The <c>ulps</c> difference.</param>
            <returns>The minimum floating point number which is <paramref name="maxNumbersBetween"/> smaller than the given <paramref name="value"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.RangeOfMatchingNumbers(System.Double,System.Double)">
            <summary>
            Determines the range of <c>ulps</c> that will match the specified value with the given tolerance.
            </summary>
            <param name="value">The value.</param>
            <param name="relativeDifference">The relative difference.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">
                Thrown if <paramref name="relativeDifference"/> is smaller than zero.
            </exception>
            <exception cref="T:System.ArgumentOutOfRangeException">
                Thrown if <paramref name="value"/> is <c>double.PositiveInfinity</c> or <c>double.NegativeInfinity</c>.
            </exception>
            <exception cref="T:System.ArgumentOutOfRangeException">
                Thrown if <paramref name="value"/> is <c>double.NaN</c>.
            </exception>
            <returns>
            Tuple with the number of ULPS between the <c>value</c> and the <c>value - relativeDifference</c> as first,
            and the number of ULPS between the <c>value</c> and the <c>value + relativeDifference</c> as second value.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.NumbersBetween(System.Double,System.Double)">
            <summary>
            Evaluates the count of numbers between two double numbers
            </summary>
            <param name="a">The first parameter.</param>
            <param name="b">The second parameter.</param>
            <remarks>The second number is included in the number, thus two equal numbers evaluate to zero and two neighbor numbers evaluate to one. Therefore, what is returned is actually the count of numbers between plus 1.</remarks>
            <returns>The number of floating point values between <paramref name="a"/> and <paramref name="b"/>.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">
                Thrown if <paramref name="a"/> is <c>double.PositiveInfinity</c> or <c>double.NegativeInfinity</c>.
            </exception>
            <exception cref="T:System.ArgumentOutOfRangeException">
                Thrown if <paramref name="a"/> is <c>double.NaN</c>.
            </exception>
            <exception cref="T:System.ArgumentOutOfRangeException">
                Thrown if <paramref name="b"/> is <c>double.PositiveInfinity</c> or <c>double.NegativeInfinity</c>.
            </exception>
            <exception cref="T:System.ArgumentOutOfRangeException">
                Thrown if <paramref name="b"/> is <c>double.NaN</c>.
            </exception>
        </member>
        <member name="M:MathNet.Numerics.Precision.EpsilonOf(System.Double)">
            <summary>
            Evaluates the minimum distance to the next distinguishable number near the argument value.
            </summary>
            <param name="value">The value used to determine the minimum distance.</param>
            <returns>
            Relative Epsilon (positive double or NaN).
            </returns>
            <remarks>Evaluates the <b>negative</b> epsilon. The more common positive epsilon is equal to two times this negative epsilon.</remarks>
            <seealso cref="M:MathNet.Numerics.Precision.PositiveEpsilonOf(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Precision.EpsilonOf(System.Single)">
            <summary>
            Evaluates the minimum distance to the next distinguishable number near the argument value.
            </summary>
            <param name="value">The value used to determine the minimum distance.</param>
            <returns>
            Relative Epsilon (positive float or NaN).
            </returns>
            <remarks>Evaluates the <b>negative</b> epsilon. The more common positive epsilon is equal to two times this negative epsilon.</remarks>
            <seealso cref="M:MathNet.Numerics.Precision.PositiveEpsilonOf(System.Single)"/>
        </member>
        <member name="M:MathNet.Numerics.Precision.PositiveEpsilonOf(System.Double)">
            <summary>
            Evaluates the minimum distance to the next distinguishable number near the argument value.
            </summary>
            <param name="value">The value used to determine the minimum distance.</param>
            <returns>Relative Epsilon (positive double or NaN)</returns>
            <remarks>Evaluates the <b>positive</b> epsilon. See also <see cref="M:MathNet.Numerics.Precision.EpsilonOf(System.Double)"/></remarks>
            <seealso cref="M:MathNet.Numerics.Precision.EpsilonOf(System.Double)"/>
        </member>
        <member name="M:MathNet.Numerics.Precision.PositiveEpsilonOf(System.Single)">
            <summary>
            Evaluates the minimum distance to the next distinguishable number near the argument value.
            </summary>
            <param name="value">The value used to determine the minimum distance.</param>
            <returns>Relative Epsilon (positive float or NaN)</returns>
            <remarks>Evaluates the <b>positive</b> epsilon. See also <see cref="M:MathNet.Numerics.Precision.EpsilonOf(System.Single)"/></remarks>
            <seealso cref="M:MathNet.Numerics.Precision.EpsilonOf(System.Single)"/>
        </member>
        <member name="M:MathNet.Numerics.Precision.MeasureMachineEpsilon">
            <summary>
            Calculates the actual (negative) double precision machine epsilon - the smallest number that can be subtracted from 1, yielding a results different than 1.
            This is also known as unit roundoff error. According to the definition of Prof. Demmel.
            </summary>
            <returns>Positive Machine epsilon</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.MeasurePositiveMachineEpsilon">
            <summary>
            Calculates the actual positive double precision machine epsilon - the smallest number that can be added to 1, yielding a results different than 1.
            This is also known as unit roundoff error. According to the definition of Prof. Higham.
            </summary>
            <returns>Machine epsilon</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.RoundToMultiple(System.Double,System.Double)">
            <summary>
            Round to a multiple of the provided positive basis.
            </summary>
            <param name="number">Number to be rounded.</param>
            <param name="basis">The basis to whose multiples to round to. Must be positive.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.RoundToMultiple(System.Single,System.Single)">
            <summary>
            Round to a multiple of the provided positive basis.
            </summary>
            <param name="number">Number to be rounded.</param>
            <param name="basis">The basis to whose multiples to round to. Must be positive.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.RoundToMultiple(System.Decimal,System.Decimal)">
            <summary>
            Round to a multiple of the provided positive basis.
            </summary>
            <param name="number">Number to be rounded.</param>
            <param name="basis">The basis to whose multiples to round to. Must be positive.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.RoundToPower(System.Double,System.Double)">
            <summary>
            Round to a multiple of the provided positive basis.
            </summary>
            <param name="number">Number to be rounded.</param>
            <param name="basis">The basis to whose powers to round to. Must be positive.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.RoundToPower(System.Single,System.Single)">
            <summary>
            Round to a multiple of the provided positive basis.
            </summary>
            <param name="number">Number to be rounded.</param>
            <param name="basis">The basis to whose powers to round to. Must be positive.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.Round(System.Double,System.Int32)">
            <summary>
            Round to the number closest to 10^(-decimals). Negative decimals to round within the integer part.
            </summary>
            <param name="number">Number to be rounded</param>
            <param name="digits">If positive the number of decimals to round to. If negative the number of digits within the integer part to round, e.g. -3 will wound to the closes 1000.</param>
            <example>To round 123456789 to hundreds Round(123456789, -2) = 123456800 </example>
            <returns>Rounded number</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.Round(System.Single,System.Int32)">
            <summary>
            Round to the number closest to 10^(-decimals). Negative decimals to round within the integer part.
            </summary>
            <param name="number">Number to be rounded</param>
            <param name="digits">If positive the number of decimals to round to. If negative the number of digits within the integer part to round, e.g. -3 will wound to the closes 1000.</param>
            <example>To round 123456789 to hundreds Round(123456789, -2) = 123456800 </example>
            <returns>Rounded number</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.Round(System.Decimal,System.Int32)">
            <summary>
            Round to the number closest to 10^(-decimals). Negative decimals to round within the integer part.
            </summary>
            <param name="number">Number to be rounded</param>
            <param name="digits">If positive the number of decimals to round to. If negative the number of digits within the integer part to round, e.g. -3 will wound to the closes 1000.</param>
            <example>To round 123456789 to hundreds Round(123456789, -2) = 123456800 </example>
            <returns>Rounded number</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.Round(System.Int32,System.Int32)">
            <summary>
            Round to the number closest to 10^(-decimals). Negative decimals to round within the integer part.
            </summary>
            <param name="number">Number to be rounded</param>
            <param name="digits">If positive the number of decimals to round to. If negative the number of digits within the integer part to round, e.g. -3 will wound to the closes 1000.</param>
            <example>To round 123456789 to hundreds Round(123456789, -2) = 123456800 </example>
            <returns>Rounded number</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.Round(System.UInt32,System.Int32)">
            <summary>
            Round to the number closest to 10^(-decimals). Negative decimals to round within the integer part.
            </summary>
            <param name="number">Number to be rounded</param>
            <param name="digits">If positive the number of decimals to round to. If negative the number of digits within the integer part to round, e.g. -3 will wound to the closes 1000.</param>
            <example>To round 123456789 to hundreds Round(123456789, -2) = 123456800 </example>
            <returns>Rounded number</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.Round(System.Int64,System.Int32)">
            <summary>
            Round to the number closest to 10^(-decimals). Negative decimals to round within the integer part.
            </summary>
            <param name="number">Number to be rounded</param>
            <param name="digits">If positive the number of decimals to round to. If negative the number of digits within the integer part to round, e.g. -3 will wound to the closes 1000.</param>
            <example>To round 123456789 to hundreds Round(123456789, -2) = 123456800 </example>
            <returns>Rounded number</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.Round(System.UInt64,System.Int32)">
            <summary>
            Round to the number closest to 10^(-decimals). Negative decimals to round within the integer part.
            </summary>
            <param name="number">Number to be rounded</param>
            <param name="digits">If positive the number of decimals to round to. If negative the number of digits within the integer part to round, e.g. -3 will wound to the closes 1000.</param>
            <example>To round 123456789 to hundreds Round(123456789, -2) = 123456800 </example>
            <returns>Rounded number</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.Round(System.Int16,System.Int32)">
            <summary>
            Round to the number closest to 10^(-decimals). Negative decimals to round within the integer part.
            </summary>
            <param name="number">Number to be rounded</param>
            <param name="digits">If positive the number of decimals to round to. If negative the number of digits within the integer part to round, e.g. -3 will wound to the closes 1000.</param>
            <example>To round 123456789 to hundreds Round(123456789, -2) = 123456800 </example>
            <returns>Rounded number</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.Round(System.UInt16,System.Int32)">
            <summary>
            Round to the number closest to 10^(-decimals). Negative decimals to round within the integer part.
            </summary>
            <param name="number">Number to be rounded</param>
            <param name="digits">If positive the number of decimals to round to. If negative the number of digits within the integer part to round, e.g. -3 will wound to the closes 1000.</param>
            <example>To round 123456789 to hundreds Round(123456789, -2) = 123456800 </example>
            <returns>Rounded number</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.Round(System.Numerics.BigInteger,System.Int32)">
            <summary>
            Round to the number closest to 10^(-decimals). Negative decimals to round within the integer part.
            </summary>
            <param name="number">Number to be rounded</param>
            <param name="digits">If positive the number of decimals to round to. If negative the number of digits within the integer part to round, e.g. -3 will wound to the closes 1000.</param>
            <example>To round 123456789 to hundreds Round(123456789, -2) = 123456800 </example>
            <returns>Rounded number</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualNorm(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Compares two doubles and determines if they are equal
            within the specified maximum absolute error.
            </summary>
            <param name="a">The norm of the first value (can be negative).</param>
            <param name="b">The norm of the second value (can be negative).</param>
            <param name="diff">The norm of the difference of the two values (can be negative).</param>
            <param name="maximumAbsoluteError">The absolute accuracy required for being almost equal.</param>
            <returns>True if both doubles are almost equal up to the specified maximum absolute error, false otherwise.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualNorm``1(``0,``0,System.Double)">
            <summary>
            Compares two doubles and determines if they are equal
            within the specified maximum absolute error.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumAbsoluteError">The absolute accuracy required for being almost equal.</param>
            <returns>True if both doubles are almost equal up to the specified maximum absolute error, false otherwise.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualNormRelative(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Compares two doubles and determines if they are equal
            within the specified maximum error.
            </summary>
            <param name="a">The norm of the first value (can be negative).</param>
            <param name="b">The norm of the second value (can be negative).</param>
            <param name="diff">The norm of the difference of the two values (can be negative).</param>
            <param name="maximumError">The accuracy required for being almost equal.</param>
            <returns>True if both doubles are almost equal up to the specified maximum error, false otherwise.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualNormRelative``1(``0,``0,System.Double)">
            <summary>
            Compares two doubles and determines if they are equal
            within the specified maximum error.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumError">The accuracy required for being almost equal.</param>
            <returns>True if both doubles are almost equal up to the specified maximum error, false otherwise.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqual(System.Double,System.Double,System.Double)">
            <summary>
            Compares two doubles and determines if they are equal within
            the specified maximum error.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumAbsoluteError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqual(System.Single,System.Single,System.Double)">
            <summary>
            Compares two complex and determines if they are equal within
            the specified maximum error.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumAbsoluteError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqual(System.Numerics.Complex,System.Numerics.Complex,System.Double)">
            <summary>
            Compares two complex and determines if they are equal within
            the specified maximum error.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumAbsoluteError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqual(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32,System.Double)">
            <summary>
            Compares two complex and determines if they are equal within
            the specified maximum error.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumAbsoluteError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualRelative(System.Double,System.Double,System.Double)">
            <summary>
            Compares two doubles and determines if they are equal within
            the specified maximum error.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualRelative(System.Single,System.Single,System.Double)">
            <summary>
            Compares two complex and determines if they are equal within
            the specified maximum error.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualRelative(System.Numerics.Complex,System.Numerics.Complex,System.Double)">
            <summary>
            Compares two complex and determines if they are equal within
            the specified maximum error.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualRelative(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32,System.Double)">
            <summary>
            Compares two complex and determines if they are equal within
            the specified maximum error.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqual(System.Double,System.Double)">
            <summary>
            Checks whether two real numbers are almost equal.
            </summary>
            <param name="a">The first number</param>
            <param name="b">The second number</param>
            <returns>true if the two values differ by no more than 10 * 2^(-52); false otherwise.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqual(System.Single,System.Single)">
            <summary>
            Checks whether two real numbers are almost equal.
            </summary>
            <param name="a">The first number</param>
            <param name="b">The second number</param>
            <returns>true if the two values differ by no more than 10 * 2^(-52); false otherwise.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqual(System.Numerics.Complex,System.Numerics.Complex)">
            <summary>
            Checks whether two Complex numbers are almost equal.
            </summary>
            <param name="a">The first number</param>
            <param name="b">The second number</param>
            <returns>true if the two values differ by no more than 10 * 2^(-52); false otherwise.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqual(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32)">
            <summary>
            Checks whether two Complex numbers are almost equal.
            </summary>
            <param name="a">The first number</param>
            <param name="b">The second number</param>
            <returns>true if the two values differ by no more than 10 * 2^(-52); false otherwise.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualRelative(System.Double,System.Double)">
            <summary>
            Checks whether two real numbers are almost equal.
            </summary>
            <param name="a">The first number</param>
            <param name="b">The second number</param>
            <returns>true if the two values differ by no more than 10 * 2^(-52); false otherwise.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualRelative(System.Single,System.Single)">
            <summary>
            Checks whether two real numbers are almost equal.
            </summary>
            <param name="a">The first number</param>
            <param name="b">The second number</param>
            <returns>true if the two values differ by no more than 10 * 2^(-52); false otherwise.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualRelative(System.Numerics.Complex,System.Numerics.Complex)">
            <summary>
            Checks whether two Complex numbers are almost equal.
            </summary>
            <param name="a">The first number</param>
            <param name="b">The second number</param>
            <returns>true if the two values differ by no more than 10 * 2^(-52); false otherwise.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualRelative(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32)">
            <summary>
            Checks whether two Complex numbers are almost equal.
            </summary>
            <param name="a">The first number</param>
            <param name="b">The second number</param>
            <returns>true if the two values differ by no more than 10 * 2^(-52); false otherwise.</returns>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualNorm(System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Compares two doubles and determines if they are equal to within the specified number of decimal places or not, using the
            number of decimal places as an absolute measure.
            </summary>
            <remarks>
            <para>
            The values are equal if the difference between the two numbers is smaller than 0.5e-decimalPlaces. We divide by
            two so that we have half the range on each side of the numbers, e.g. if <paramref name="decimalPlaces"/> == 2, then 0.01 will equal between
            0.005 and 0.015, but not 0.02 and not 0.00
            </para>
            </remarks>
            <param name="a">The norm of the first value (can be negative).</param>
            <param name="b">The norm of the second value (can be negative).</param>
            <param name="diff">The norm of the difference of the two values (can be negative).</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualNorm``1(``0,``0,System.Int32)">
            <summary>
            Compares two doubles and determines if they are equal to within the specified number of decimal places or not, using the
            number of decimal places as an absolute measure.
            </summary>
            <remarks>
            <para>
            The values are equal if the difference between the two numbers is smaller than 0.5e-decimalPlaces. We divide by
            two so that we have half the range on each side of the numbers, e.g. if <paramref name="decimalPlaces"/> == 2, then 0.01 will equal between
            0.005 and 0.015, but not 0.02 and not 0.00
            </para>
            </remarks>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualNormRelative(System.Double,System.Double,System.Double,System.Int32)">
            <summary>
            Compares two doubles and determines if they are equal to within the specified number of decimal places or not. If the numbers
            are very close to zero an absolute difference is compared, otherwise the relative difference is compared.
            </summary>
            <remarks>
            <para>
            The values are equal if the difference between the two numbers is smaller than 10^(-numberOfDecimalPlaces). We divide by
            two so that we have half the range on each side of the numbers, e.g. if <paramref name="decimalPlaces"/> == 2, then 0.01 will equal between
            0.005 and 0.015, but not 0.02 and not 0.00
            </para>
            </remarks>
            <param name="a">The norm of the first value (can be negative).</param>
            <param name="b">The norm of the second value (can be negative).</param>
            <param name="diff">The norm of the difference of the two values (can be negative).</param>
            <param name="decimalPlaces">The number of decimal places.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">Thrown if <paramref name="decimalPlaces"/> is smaller than zero.</exception>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualNormRelative``1(``0,``0,System.Int32)">
            <summary>
            Compares two doubles and determines if they are equal to within the specified number of decimal places or not. If the numbers
            are very close to zero an absolute difference is compared, otherwise the relative difference is compared.
            </summary>
            <remarks>
            <para>
            The values are equal if the difference between the two numbers is smaller than 10^(-numberOfDecimalPlaces). We divide by
            two so that we have half the range on each side of the numbers, e.g. if <paramref name="decimalPlaces"/> == 2, then 0.01 will equal between
            0.005 and 0.015, but not 0.02 and not 0.00
            </para>
            </remarks>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqual(System.Double,System.Double,System.Int32)">
            <summary>
            Compares two doubles and determines if they are equal to within the specified number of decimal places or not, using the
            number of decimal places as an absolute measure.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqual(System.Single,System.Single,System.Int32)">
            <summary>
            Compares two doubles and determines if they are equal to within the specified number of decimal places or not, using the
            number of decimal places as an absolute measure.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqual(System.Numerics.Complex,System.Numerics.Complex,System.Int32)">
            <summary>
            Compares two doubles and determines if they are equal to within the specified number of decimal places or not, using the
            number of decimal places as an absolute measure.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqual(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32,System.Int32)">
            <summary>
            Compares two doubles and determines if they are equal to within the specified number of decimal places or not, using the
            number of decimal places as an absolute measure.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualRelative(System.Double,System.Double,System.Int32)">
            <summary>
            Compares two doubles and determines if they are equal to within the specified number of decimal places or not. If the numbers
            are very close to zero an absolute difference is compared, otherwise the relative difference is compared.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualRelative(System.Single,System.Single,System.Int32)">
            <summary>
            Compares two doubles and determines if they are equal to within the specified number of decimal places or not. If the numbers
            are very close to zero an absolute difference is compared, otherwise the relative difference is compared.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualRelative(System.Numerics.Complex,System.Numerics.Complex,System.Int32)">
            <summary>
            Compares two doubles and determines if they are equal to within the specified number of decimal places or not. If the numbers
            are very close to zero an absolute difference is compared, otherwise the relative difference is compared.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualRelative(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32,System.Int32)">
            <summary>
            Compares two doubles and determines if they are equal to within the specified number of decimal places or not. If the numbers
            are very close to zero an absolute difference is compared, otherwise the relative difference is compared.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualNumbersBetween(System.Double,System.Double,System.Int64)">
            <summary>
            Compares two doubles and determines if they are equal to within the tolerance or not. Equality comparison is based on the binary representation.
            </summary>
            <remarks>
            <para>
            Determines the 'number' of floating point numbers between two values (i.e. the number of discrete steps
            between the two numbers) and then checks if that is within the specified tolerance. So if a tolerance
            of 1 is passed then the result will be true only if the two numbers have the same binary representation
            OR if they are two adjacent numbers that only differ by one step.
            </para>
            <para>
            The comparison method used is explained in http://www.cygnus-software.com/papers/comparingfloats/comparingfloats.htm . The article
            at http://www.extremeoptimization.com/resources/Articles/FPDotNetConceptsAndFormats.aspx explains how to transform the C code to
            .NET enabled code without using pointers and unsafe code.
            </para>
            </remarks>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maxNumbersBetween">The maximum number of floating point values between the two values. Must be 1 or larger.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">Thrown if <paramref name="maxNumbersBetween"/> is smaller than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualNumbersBetween(System.Single,System.Single,System.Int32)">
            <summary>
            Compares two floats and determines if they are equal to within the tolerance or not. Equality comparison is based on the binary representation.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maxNumbersBetween">The maximum number of floating point values between the two values. Must be 1 or larger.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">Thrown if <paramref name="maxNumbersBetween"/> is smaller than one.</exception>
        </member>
        <member name="M:MathNet.Numerics.Precision.ListAlmostEqual(System.Collections.Generic.IList{System.Double},System.Collections.Generic.IList{System.Double},System.Double)">
            <summary>
            Compares two lists of doubles and determines if they are equal within the
            specified maximum error.
            </summary>
            <param name="a">The first value list.</param>
            <param name="b">The second value list.</param>
            <param name="maximumAbsoluteError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.ListAlmostEqual(System.Collections.Generic.IList{System.Single},System.Collections.Generic.IList{System.Single},System.Double)">
            <summary>
            Compares two lists of doubles and determines if they are equal within the
            specified maximum error.
            </summary>
            <param name="a">The first value list.</param>
            <param name="b">The second value list.</param>
            <param name="maximumAbsoluteError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.ListAlmostEqual(System.Collections.Generic.IList{System.Numerics.Complex},System.Collections.Generic.IList{System.Numerics.Complex},System.Double)">
            <summary>
            Compares two lists of doubles and determines if they are equal within the
            specified maximum error.
            </summary>
            <param name="a">The first value list.</param>
            <param name="b">The second value list.</param>
            <param name="maximumAbsoluteError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.ListAlmostEqual(System.Collections.Generic.IList{MathNet.Numerics.Complex32},System.Collections.Generic.IList{MathNet.Numerics.Complex32},System.Double)">
            <summary>
            Compares two lists of doubles and determines if they are equal within the
            specified maximum error.
            </summary>
            <param name="a">The first value list.</param>
            <param name="b">The second value list.</param>
            <param name="maximumAbsoluteError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.ListAlmostEqualRelative(System.Collections.Generic.IList{System.Double},System.Collections.Generic.IList{System.Double},System.Double)">
            <summary>
            Compares two lists of doubles and determines if they are equal within the
            specified maximum error.
            </summary>
            <param name="a">The first value list.</param>
            <param name="b">The second value list.</param>
            <param name="maximumError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.ListAlmostEqualRelative(System.Collections.Generic.IList{System.Single},System.Collections.Generic.IList{System.Single},System.Double)">
            <summary>
            Compares two lists of doubles and determines if they are equal within the
            specified maximum error.
            </summary>
            <param name="a">The first value list.</param>
            <param name="b">The second value list.</param>
            <param name="maximumError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.ListAlmostEqualRelative(System.Collections.Generic.IList{System.Numerics.Complex},System.Collections.Generic.IList{System.Numerics.Complex},System.Double)">
            <summary>
            Compares two lists of doubles and determines if they are equal within the
            specified maximum error.
            </summary>
            <param name="a">The first value list.</param>
            <param name="b">The second value list.</param>
            <param name="maximumError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.ListAlmostEqualRelative(System.Collections.Generic.IList{MathNet.Numerics.Complex32},System.Collections.Generic.IList{MathNet.Numerics.Complex32},System.Double)">
            <summary>
            Compares two lists of doubles and determines if they are equal within the
            specified maximum error.
            </summary>
            <param name="a">The first value list.</param>
            <param name="b">The second value list.</param>
            <param name="maximumError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.ListAlmostEqual(System.Collections.Generic.IList{System.Double},System.Collections.Generic.IList{System.Double},System.Int32)">
            <summary>
            Compares two lists of doubles and determines if they are equal within the
            specified maximum error.
            </summary>
            <param name="a">The first value list.</param>
            <param name="b">The second value list.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.ListAlmostEqual(System.Collections.Generic.IList{System.Single},System.Collections.Generic.IList{System.Single},System.Int32)">
            <summary>
            Compares two lists of doubles and determines if they are equal within the
            specified maximum error.
            </summary>
            <param name="a">The first value list.</param>
            <param name="b">The second value list.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.ListAlmostEqual(System.Collections.Generic.IList{System.Numerics.Complex},System.Collections.Generic.IList{System.Numerics.Complex},System.Int32)">
            <summary>
            Compares two lists of doubles and determines if they are equal within the
            specified maximum error.
            </summary>
            <param name="a">The first value list.</param>
            <param name="b">The second value list.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.ListAlmostEqual(System.Collections.Generic.IList{MathNet.Numerics.Complex32},System.Collections.Generic.IList{MathNet.Numerics.Complex32},System.Int32)">
            <summary>
            Compares two lists of doubles and determines if they are equal within the
            specified maximum error.
            </summary>
            <param name="a">The first value list.</param>
            <param name="b">The second value list.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.ListAlmostEqualRelative(System.Collections.Generic.IList{System.Double},System.Collections.Generic.IList{System.Double},System.Int32)">
            <summary>
            Compares two lists of doubles and determines if they are equal within the
            specified maximum error.
            </summary>
            <param name="a">The first value list.</param>
            <param name="b">The second value list.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.ListAlmostEqualRelative(System.Collections.Generic.IList{System.Single},System.Collections.Generic.IList{System.Single},System.Int32)">
            <summary>
            Compares two lists of doubles and determines if they are equal within the
            specified maximum error.
            </summary>
            <param name="a">The first value list.</param>
            <param name="b">The second value list.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.ListAlmostEqualRelative(System.Collections.Generic.IList{System.Numerics.Complex},System.Collections.Generic.IList{System.Numerics.Complex},System.Int32)">
            <summary>
            Compares two lists of doubles and determines if they are equal within the
            specified maximum error.
            </summary>
            <param name="a">The first value list.</param>
            <param name="b">The second value list.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.ListAlmostEqualRelative(System.Collections.Generic.IList{MathNet.Numerics.Complex32},System.Collections.Generic.IList{MathNet.Numerics.Complex32},System.Int32)">
            <summary>
            Compares two lists of doubles and determines if they are equal within the
            specified maximum error.
            </summary>
            <param name="a">The first value list.</param>
            <param name="b">The second value list.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.ListAlmostEqualNorm``1(System.Collections.Generic.IList{``0},System.Collections.Generic.IList{``0},System.Double)">
            <summary>
            Compares two lists of doubles and determines if they are equal within the
            specified maximum error.
            </summary>
            <param name="a">The first value list.</param>
            <param name="b">The second value list.</param>
            <param name="maximumAbsoluteError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.ListAlmostEqualNormRelative``1(System.Collections.Generic.IList{``0},System.Collections.Generic.IList{``0},System.Double)">
            <summary>
            Compares two lists of doubles and determines if they are equal within the
            specified maximum error.
            </summary>
            <param name="a">The first value list.</param>
            <param name="b">The second value list.</param>
            <param name="maximumError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqual``1(MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Vector{``0},System.Double)">
            <summary>
            Compares two vectors and determines if they are equal within the specified maximum error.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumAbsoluteError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualRelative``1(MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Vector{``0},System.Double)">
            <summary>
            Compares two vectors and determines if they are equal within the specified maximum error.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqual``1(MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Vector{``0},System.Int32)">
            <summary>
            Compares two vectors and determines if they are equal to within the specified number
            of decimal places or not, using the number of decimal places as an absolute measure.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualRelative``1(MathNet.Numerics.LinearAlgebra.Vector{``0},MathNet.Numerics.LinearAlgebra.Vector{``0},System.Int32)">
            <summary>
            Compares two vectors and determines if they are equal to within the specified number of decimal places or not.
            If the numbers are very close to zero an absolute difference is compared, otherwise the relative difference is compared.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqual``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Matrix{``0},System.Double)">
            <summary>
            Compares two matrices and determines if they are equal within the specified maximum error.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumAbsoluteError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualRelative``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Matrix{``0},System.Double)">
            <summary>
            Compares two matrices and determines if they are equal within the specified maximum error.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="maximumError">The accuracy required for being almost equal.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqual``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Matrix{``0},System.Int32)">
            <summary>
            Compares two matrices and determines if they are equal to within the specified number
            of decimal places or not, using the number of decimal places as an absolute measure.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="M:MathNet.Numerics.Precision.AlmostEqualRelative``1(MathNet.Numerics.LinearAlgebra.Matrix{``0},MathNet.Numerics.LinearAlgebra.Matrix{``0},System.Int32)">
            <summary>
            Compares two matrices and determines if they are equal to within the specified number of decimal places or not.
            If the numbers are very close to zero an absolute difference is compared, otherwise the relative difference is compared.
            </summary>
            <param name="a">The first value.</param>
            <param name="b">The second value.</param>
            <param name="decimalPlaces">The number of decimal places.</param>
        </member>
        <member name="T:MathNet.Numerics.IPrecisionSupport`1">
            <summary>
            Support Interface for Precision Operations (like AlmostEquals).
            </summary>
            <typeparam name="T">Type of the implementing class.</typeparam>
        </member>
        <member name="M:MathNet.Numerics.IPrecisionSupport`1.Norm">
            <summary>
            Returns a Norm of a value of this type, which is appropriate for measuring how
            close this value is to zero.
            </summary>
            <returns>A norm of this value.</returns>
        </member>
        <member name="M:MathNet.Numerics.IPrecisionSupport`1.NormOfDifference(`0)">
            <summary>
            Returns a Norm of the difference of two values of this type, which is
            appropriate for measuring how close together these two values are.
            </summary>
            <param name="otherValue">The value to compare with.</param>
            <returns>A norm of the difference between this and the other value.</returns>
        </member>
        <member name="P:MathNet.Numerics.Providers.FourierTransform.FourierTransformControl.HintPath">
            <summary>
            Optional path to try to load native provider binaries from,
            if the provider specific hint path is not set.
            If neither is set, Numerics falls back to the provider specific
            environment variables, or the default probing paths.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Providers.FourierTransform.FourierTransformControl.Provider">
            <summary>
            Gets or sets the Fourier transform provider. Consider to use UseNativeMKL or UseManaged instead.
            </summary>
            <value>The linear algebra provider.</value>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.FourierTransformControl.TryUseNative">
            <summary>
            Try to use a native provider, if available.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.FourierTransformControl.UseBest">
            <summary>
            Use the best provider available.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.FourierTransformControl.UseDefault">
            <summary>
            Use a specific provider if configured, e.g. using the
            "MathNetNumericsFFTProvider" environment variable,
            or fall back to the best provider.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.IFourierTransformProvider.IsAvailable">
            <summary>
            Try to find out whether the provider is available, at least in principle.
            Verification may still fail if available, but it will certainly fail if unavailable.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.IFourierTransformProvider.InitializeVerify">
            <summary>
            Initialize and verify that the provided is indeed available. If not, fall back to alternatives like the managed provider
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.IFourierTransformProvider.FreeResources">
            <summary>
            Frees memory buffers, caches and handles allocated in or to the provider.
            Does not unload the provider itself, it is still usable afterwards.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.BluesteinSequenceLengthThreshold">
            <summary>
            Sequences with length greater than Math.Sqrt(Int32.MaxValue) + 1
            will cause k*k in the Bluestein sequence to overflow (GH-286).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.BluesteinSequence32(System.Int32)">
            <summary>
            Generate the bluestein sequence for the provided problem size.
            </summary>
            <param name="n">Number of samples.</param>
            <returns>Bluestein sequence exp(I*Pi*k^2/N)</returns>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.BluesteinSequence(System.Int32)">
            <summary>
            Generate the bluestein sequence for the provided problem size.
            </summary>
            <param name="n">Number of samples.</param>
            <returns>Bluestein sequence exp(I*Pi*k^2/N)</returns>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.BluesteinConvolutionParallel(MathNet.Numerics.Complex32[])">
            <summary>
            Convolution with the bluestein sequence (Parallel Version).
            </summary>
            <param name="samples">Sample Vector.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.BluesteinConvolutionParallel(System.Numerics.Complex[])">
            <summary>
            Convolution with the bluestein sequence (Parallel Version).
            </summary>
            <param name="samples">Sample Vector.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.SwapRealImaginary(MathNet.Numerics.Complex32[])">
            <summary>
            Swap the real and imaginary parts of each sample.
            </summary>
            <param name="samples">Sample Vector.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.SwapRealImaginary(System.Numerics.Complex[])">
            <summary>
            Swap the real and imaginary parts of each sample.
            </summary>
            <param name="samples">Sample Vector.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.BluesteinForward(System.Numerics.Complex[])">
            <summary>
            Bluestein generic FFT for arbitrary sized sample vectors.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.BluesteinInverse(System.Numerics.Complex[])">
            <summary>
            Bluestein generic FFT for arbitrary sized sample vectors.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.BluesteinForward(MathNet.Numerics.Complex32[])">
            <summary>
            Bluestein generic FFT for arbitrary sized sample vectors.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.BluesteinInverse(MathNet.Numerics.Complex32[])">
            <summary>
            Bluestein generic FFT for arbitrary sized sample vectors.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.IsAvailable">
            <summary>
            Try to find out whether the provider is available, at least in principle.
            Verification may still fail if available, but it will certainly fail if unavailable.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.InitializeVerify">
            <summary>
            Initialize and verify that the provided is indeed available. If not, fall back to alternatives like the managed provider
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.FreeResources">
            <summary>
            Frees memory buffers, caches and handles allocated in or to the provider.
            Does not unload the provider itself, it is still usable afterwards.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.Radix2Reorder``1(``0[])">
            <summary>
            Radix-2 Reorder Helper Method
            </summary>
            <typeparam name="T">Sample type</typeparam>
            <param name="samples">Sample vector</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.Radix2Step(MathNet.Numerics.Complex32[],System.Int32,System.Int32,System.Int32)">
            <summary>
            Radix-2 Step Helper Method
            </summary>
            <param name="samples">Sample vector.</param>
            <param name="exponentSign">Fourier series exponent sign.</param>
            <param name="levelSize">Level Group Size.</param>
            <param name="k">Index inside of the level.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.Radix2Step(System.Numerics.Complex[],System.Int32,System.Int32,System.Int32)">
            <summary>
            Radix-2 Step Helper Method
            </summary>
            <param name="samples">Sample vector.</param>
            <param name="exponentSign">Fourier series exponent sign.</param>
            <param name="levelSize">Level Group Size.</param>
            <param name="k">Index inside of the level.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.Radix2Forward(MathNet.Numerics.Complex32[])">
            <summary>
            Radix-2 generic FFT for power-of-two sized sample vectors.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.Radix2Forward(System.Numerics.Complex[])">
            <summary>
            Radix-2 generic FFT for power-of-two sized sample vectors.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.Radix2Inverse(MathNet.Numerics.Complex32[])">
            <summary>
            Radix-2 generic FFT for power-of-two sized sample vectors.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.Radix2Inverse(System.Numerics.Complex[])">
            <summary>
            Radix-2 generic FFT for power-of-two sized sample vectors.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.Radix2ForwardParallel(MathNet.Numerics.Complex32[])">
            <summary>
            Radix-2 generic FFT for power-of-two sample vectors (Parallel Version).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.Radix2ForwardParallel(System.Numerics.Complex[])">
            <summary>
            Radix-2 generic FFT for power-of-two sample vectors (Parallel Version).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.Radix2InverseParallel(MathNet.Numerics.Complex32[])">
            <summary>
            Radix-2 generic FFT for power-of-two sample vectors (Parallel Version).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.Radix2InverseParallel(System.Numerics.Complex[])">
            <summary>
            Radix-2 generic FFT for power-of-two sample vectors (Parallel Version).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.FullRescale(MathNet.Numerics.Complex32[])">
            <summary>
            Fully rescale the FFT result.
            </summary>
            <param name="samples">Sample Vector.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.FullRescale(System.Numerics.Complex[])">
            <summary>
            Fully rescale the FFT result.
            </summary>
            <param name="samples">Sample Vector.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.HalfRescale(MathNet.Numerics.Complex32[])">
            <summary>
            Half rescale the FFT result (e.g. for symmetric transforms).
            </summary>
            <param name="samples">Sample Vector.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.FourierTransform.ManagedFourierTransformProvider.HalfRescale(System.Numerics.Complex[])">
            <summary>
            Fully rescale the FFT result (e.g. for symmetric transforms).
            </summary>
            <param name="samples">Sample Vector.</param>
        </member>
        <member name="T:MathNet.Numerics.Providers.LinearAlgebra.Transpose">
            <summary>
            How to transpose a matrix.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Providers.LinearAlgebra.Transpose.DontTranspose">
            <summary>
            Don't transpose a matrix.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Providers.LinearAlgebra.Transpose.Transpose">
            <summary>
            Transpose a matrix.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Providers.LinearAlgebra.Transpose.ConjugateTranspose">
            <summary>
            Conjugate transpose a complex matrix.
            </summary>
            <remarks>If a conjugate transpose is used with a real matrix, then the matrix is just transposed.</remarks>
        </member>
        <member name="T:MathNet.Numerics.Providers.LinearAlgebra.Norm">
            <summary>
            Types of matrix norms.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Providers.LinearAlgebra.Norm.OneNorm">
            <summary>
            The 1-norm.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Providers.LinearAlgebra.Norm.FrobeniusNorm">
            <summary>
            The Frobenius norm.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Providers.LinearAlgebra.Norm.InfinityNorm">
            <summary>
            The infinity norm.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Providers.LinearAlgebra.Norm.LargestAbsoluteValue">
            <summary>
            The largest absolute value norm.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider">
            <summary>
            Interface to linear algebra algorithms that work off 1-D arrays.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider.IsAvailable">
            <summary>
            Try to find out whether the provider is available, at least in principle.
            Verification may still fail if available, but it will certainly fail if unavailable.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider.InitializeVerify">
            <summary>
            Initialize and verify that the provided is indeed available. If not, fall back to alternatives like the managed provider
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider.FreeResources">
            <summary>
            Frees memory buffers, caches and handles allocated in or to the provider.
            Does not unload the provider itself, it is still usable afterwards.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1">
            <summary>
            Interface to linear algebra algorithms that work off 1-D arrays.
            </summary>
            <typeparam name="T">Supported data types are Double, Single, Complex, and Complex32.</typeparam>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.AddVectorToScaledVector(`0[],`0,`0[],`0[])">
            <summary>
            Adds a scaled vector to another: <c>result = y + alpha*x</c>.
            </summary>
            <param name="y">The vector to update.</param>
            <param name="alpha">The value to scale <paramref name="x"/> by.</param>
            <param name="x">The vector to add to <paramref name="y"/>.</param>
            <param name="result">The result of the addition.</param>
            <remarks>This is similar to the AXPY BLAS routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.ScaleArray(`0,`0[],`0[])">
            <summary>
            Scales an array. Can be used to scale a vector and a matrix.
            </summary>
            <param name="alpha">The scalar.</param>
            <param name="x">The values to scale.</param>
            <param name="result">This result of the scaling.</param>
            <remarks>This is similar to the SCAL BLAS routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.ConjugateArray(`0[],`0[])">
            <summary>
            Conjugates an array. Can be used to conjugate a vector and a matrix.
            </summary>
            <param name="x">The values to conjugate.</param>
            <param name="result">This result of the conjugation.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.DotProduct(`0[],`0[])">
            <summary>
            Computes the dot product of x and y.
            </summary>
            <param name="x">The vector x.</param>
            <param name="y">The vector y.</param>
            <returns>The dot product of x and y.</returns>
            <remarks>This is equivalent to the DOT BLAS routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.AddArrays(`0[],`0[],`0[])">
            <summary>
            Does a point wise add of two arrays <c>z = x + y</c>. This can be used
            to add vectors or matrices.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the addition.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.SubtractArrays(`0[],`0[],`0[])">
            <summary>
            Does a point wise subtraction of two arrays <c>z = x - y</c>. This can be used
            to subtract vectors or matrices.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the subtraction.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.PointWiseMultiplyArrays(`0[],`0[],`0[])">
            <summary>
            Does a point wise multiplication of two arrays <c>z = x * y</c>. This can be used
            to multiply elements of vectors or matrices.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the point wise multiplication.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.PointWiseDivideArrays(`0[],`0[],`0[])">
            <summary>
            Does a point wise division of two arrays <c>z = x / y</c>. This can be used
            to divide elements of vectors or matrices.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the point wise division.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.PointWisePowerArrays(`0[],`0[],`0[])">
            <summary>
            Does a point wise power of two arrays <c>z = x ^ y</c>. This can be used
            to raise elements of vectors or matrices to the powers of another vector or matrix.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the point wise power.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.MatrixNorm(MathNet.Numerics.Providers.LinearAlgebra.Norm,System.Int32,System.Int32,`0[])">
            <summary>
            Computes the requested <see cref="T:MathNet.Numerics.Providers.LinearAlgebra.Norm"/> of the matrix.
            </summary>
            <param name="norm">The type of norm to compute.</param>
            <param name="rows">The number of rows.</param>
            <param name="columns">The number of columns.</param>
            <param name="matrix">The matrix to compute the norm from.</param>
            <returns>
            The requested <see cref="T:MathNet.Numerics.Providers.LinearAlgebra.Norm"/> of the matrix.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.MatrixMultiply(`0[],System.Int32,System.Int32,`0[],System.Int32,System.Int32,`0[])">
            <summary>
            Multiples two matrices. <c>result = x * y</c>
            </summary>
            <param name="x">The x matrix.</param>
            <param name="rowsX">The number of rows in the x matrix.</param>
            <param name="columnsX">The number of columns in the x matrix.</param>
            <param name="y">The y matrix.</param>
            <param name="rowsY">The number of rows in the y matrix.</param>
            <param name="columnsY">The number of columns in the y matrix.</param>
            <param name="result">Where to store the result of the multiplication.</param>
            <remarks>This is a simplified version of the BLAS GEMM routine with alpha
            set to 1.0 and beta set to 0.0, and x and y are not transposed.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.MatrixMultiplyWithUpdate(MathNet.Numerics.Providers.LinearAlgebra.Transpose,MathNet.Numerics.Providers.LinearAlgebra.Transpose,`0,`0[],System.Int32,System.Int32,`0[],System.Int32,System.Int32,`0,`0[])">
            <summary>
            Multiplies two matrices and updates another with the result. <c>c = alpha*op(a)*op(b) + beta*c</c>
            </summary>
            <param name="transposeA">How to transpose the <paramref name="a"/> matrix.</param>
            <param name="transposeB">How to transpose the <paramref name="b"/> matrix.</param>
            <param name="alpha">The value to scale <paramref name="a"/> matrix.</param>
            <param name="a">The a matrix.</param>
            <param name="rowsA">The number of rows in the <paramref name="a"/> matrix.</param>
            <param name="columnsA">The number of columns in the <paramref name="a"/> matrix.</param>
            <param name="b">The b matrix</param>
            <param name="rowsB">The number of rows in the <paramref name="b"/> matrix.</param>
            <param name="columnsB">The number of columns in the <paramref name="b"/> matrix.</param>
            <param name="beta">The value to scale the <paramref name="c"/> matrix.</param>
            <param name="c">The c matrix.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.LUFactor(`0[],System.Int32,System.Int32[])">
            <summary>
            Computes the LUP factorization of A. P*A = L*U.
            </summary>
            <param name="data">An <paramref name="order"/> by <paramref name="order"/> matrix. The matrix is overwritten with the
            the LU factorization on exit. The lower triangular factor L is stored in under the diagonal of <paramref name="data"/> (the diagonal is always 1.0
            for the L factor). The upper triangular factor U is stored on and above the diagonal of <paramref name="data"/>.</param>
            <param name="order">The order of the square matrix <paramref name="data"/>.</param>
            <param name="ipiv">On exit, it contains the pivot indices. The size of the array must be <paramref name="order"/>.</param>
            <remarks>This is equivalent to the GETRF LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.LUInverse(`0[],System.Int32)">
            <summary>
            Computes the inverse of matrix using LU factorization.
            </summary>
            <param name="a">The N by N matrix to invert. Contains the inverse On exit.</param>
            <param name="order">The order of the square matrix <paramref name="a"/>.</param>
            <remarks>This is equivalent to the GETRF and GETRI LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.LUInverseFactored(`0[],System.Int32,System.Int32[])">
            <summary>
            Computes the inverse of a previously factored matrix.
            </summary>
            <param name="a">The LU factored N by N matrix.  Contains the inverse On exit.</param>
            <param name="order">The order of the square matrix <paramref name="a"/>.</param>
            <param name="ipiv">The pivot indices of <paramref name="a"/>.</param>
            <remarks>This is equivalent to the GETRI LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.LUSolve(System.Int32,`0[],System.Int32,`0[])">
            <summary>
            Solves A*X=B for X using LU factorization.
            </summary>
            <param name="columnsOfB">The number of columns of B.</param>
            <param name="a">The square matrix A.</param>
            <param name="order">The order of the square matrix <paramref name="a"/>.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <remarks>This is equivalent to the GETRF and GETRS LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.LUSolveFactored(System.Int32,`0[],System.Int32,System.Int32[],`0[])">
            <summary>
            Solves A*X=B for X using a previously factored A matrix.
            </summary>
            <param name="columnsOfB">The number of columns of B.</param>
            <param name="a">The factored A matrix.</param>
            <param name="order">The order of the square matrix <paramref name="a"/>.</param>
            <param name="ipiv">The pivot indices of <paramref name="a"/>.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <remarks>This is equivalent to the GETRS LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.CholeskyFactor(`0[],System.Int32)">
            <summary>
            Computes the Cholesky factorization of A.
            </summary>
            <param name="a">On entry, a square, positive definite matrix. On exit, the matrix is overwritten with the
            the Cholesky factorization.</param>
            <param name="order">The number of rows or columns in the matrix.</param>
            <remarks>This is equivalent to the POTRF LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.CholeskySolve(`0[],System.Int32,`0[],System.Int32)">
            <summary>
            Solves A*X=B for X using Cholesky factorization.
            </summary>
            <param name="a">The square, positive definite matrix A.</param>
            <param name="orderA">The number of rows and columns in A.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <param name="columnsB">The number of columns in the B matrix.</param>
            <remarks>This is equivalent to the POTRF add POTRS LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.CholeskySolveFactored(`0[],System.Int32,`0[],System.Int32)">
            <summary>
            Solves A*X=B for X using a previously factored A matrix.
            </summary>
            <param name="a">The square, positive definite matrix A.</param>
            <param name="orderA">The number of rows and columns in A.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <param name="columnsB">The number of columns in the B matrix.</param>
            <remarks>This is equivalent to the POTRS LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.QRFactor(`0[],System.Int32,System.Int32,`0[],`0[])">
            <summary>
            Computes the full QR factorization of A.
            </summary>
            <param name="a">On entry, it is the M by N A matrix to factor. On exit,
            it is overwritten with the R matrix of the QR factorization.</param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="q">On exit, A M by M matrix that holds the Q matrix of the
            QR factorization.</param>
            <param name="tau">A min(m,n) vector. On exit, contains additional information
            to be used by the QR solve routine.</param>
            <remarks>This is similar to the GEQRF and ORGQR LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.ThinQRFactor(`0[],System.Int32,System.Int32,`0[],`0[])">
            <summary>
            Computes the thin QR factorization of A where M &gt; N.
            </summary>
            <param name="a">On entry, it is the M by N A matrix to factor. On exit,
            it is overwritten with the Q matrix of the QR factorization.</param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="r">On exit, A N by N matrix that holds the R matrix of the
            QR factorization.</param>
            <param name="tau">A min(m,n) vector. On exit, contains additional information
            to be used by the QR solve routine.</param>
            <remarks>This is similar to the GEQRF and ORGQR LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.QRSolve(`0[],System.Int32,System.Int32,`0[],System.Int32,`0[],MathNet.Numerics.LinearAlgebra.Factorization.QRMethod)">
            <summary>
            Solves A*X=B for X using QR factorization of A.
            </summary>
            <param name="a">The A matrix.</param>
            <param name="rows">The number of rows in the A matrix.</param>
            <param name="columns">The number of columns in the A matrix.</param>
            <param name="b">The B matrix.</param>
            <param name="columnsB">The number of columns of B.</param>
            <param name="x">On exit, the solution matrix.</param>
            <param name="method">The type of QR factorization to perform. <seealso cref="T:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod"/></param>
            <remarks>Rows must be greater or equal to columns.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.QRSolveFactored(`0[],`0[],System.Int32,System.Int32,`0[],`0[],System.Int32,`0[],MathNet.Numerics.LinearAlgebra.Factorization.QRMethod)">
            <summary>
            Solves A*X=B for X using a previously QR factored matrix.
            </summary>
            <param name="q">The Q matrix obtained by QR factor. This is only used for the managed provider and can be
            <c>null</c> for the native provider. The native provider uses the Q portion stored in the R matrix.</param>
            <param name="r">The R matrix obtained by calling <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.QRFactor(`0[],System.Int32,System.Int32,`0[],`0[])"/>. </param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="tau">Contains additional information on Q. Only used for the native solver
            and can be <c>null</c> for the managed provider.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <param name="columnsB">The number of columns of B.</param>
            <param name="x">On exit, the solution matrix.</param>
            <remarks>Rows must be greater or equal to columns.</remarks>
            <param name="method">The type of QR factorization to perform. <seealso cref="T:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod"/></param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.SingularValueDecomposition(System.Boolean,`0[],System.Int32,System.Int32,`0[],`0[],`0[])">
            <summary>
            Computes the singular value decomposition of A.
            </summary>
            <param name="computeVectors">Compute the singular U and VT vectors or not.</param>
            <param name="a">On entry, the M by N matrix to decompose. On exit, A may be overwritten.</param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="s">The singular values of A in ascending value. </param>
            <param name="u">If <paramref name="computeVectors"/> is <c>true</c>, on exit U contains the left
            singular vectors.</param>
            <param name="vt">If <paramref name="computeVectors"/> is <c>true</c>, on exit VT contains the transposed
            right singular vectors.</param>
            <remarks>This is equivalent to the GESVD LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.SvdSolve(`0[],System.Int32,System.Int32,`0[],System.Int32,`0[])">
            <summary>
            Solves A*X=B for X using the singular value decomposition of A.
            </summary>
            <param name="a">On entry, the M by N matrix to decompose.</param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="b">The B matrix.</param>
            <param name="columnsB">The number of columns of B.</param>
            <param name="x">On exit, the solution matrix.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.SvdSolveFactored(System.Int32,System.Int32,`0[],`0[],`0[],`0[],System.Int32,`0[])">
            <summary>
            Solves A*X=B for X using a previously SVD decomposed matrix.
            </summary>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="s">The s values returned by <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.SingularValueDecomposition(System.Boolean,`0[],System.Int32,System.Int32,`0[],`0[],`0[])"/>.</param>
            <param name="u">The left singular vectors returned by  <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.SingularValueDecomposition(System.Boolean,`0[],System.Int32,System.Int32,`0[],`0[],`0[])"/>.</param>
            <param name="vt">The right singular  vectors returned by  <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.SingularValueDecomposition(System.Boolean,`0[],System.Int32,System.Int32,`0[],`0[],`0[])"/>.</param>
            <param name="b">The B matrix</param>
            <param name="columnsB">The number of columns of B.</param>
            <param name="x">On exit, the solution matrix.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ILinearAlgebraProvider`1.EigenDecomp(System.Boolean,System.Int32,`0[],`0[],System.Numerics.Complex[],`0[])">
            <summary>
            Computes the eigenvalues and eigenvectors of a matrix.
            </summary>
            <param name="isSymmetric">Whether the matrix is symmetric or not.</param>
            <param name="order">The order of the matrix.</param>
            <param name="matrix">The matrix to decompose. The length of the array must be order * order.</param>
            <param name="matrixEv">On output, the matrix contains the eigen vectors. The length of the array must be order * order.</param>
            <param name="vectorEv">On output, the eigen values (λ) of matrix in ascending value. The length of the array must <paramref name="order"/>.</param>
            <param name="matrixD">On output, the block diagonal eigenvalue matrix. The length of the array must be order * order.</param>
        </member>
        <member name="P:MathNet.Numerics.Providers.LinearAlgebra.LinearAlgebraControl.HintPath">
            <summary>
            Optional path to try to load native provider binaries from,
            if the provider specific hint path is not set.
            If neither is set, Numerics falls back to the provider specific
            environment variables, or the default probing paths.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Providers.LinearAlgebra.LinearAlgebraControl.Provider">
            <summary>
            Gets or sets the linear algebra provider.
            Consider to use UseNativeMKL or UseManaged instead.
            </summary>
            <value>The linear algebra provider.</value>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.LinearAlgebraControl.TryUseNative">
            <summary>
            Try to use a native provider, if available.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.LinearAlgebraControl.UseBest">
            <summary>
            Use the best provider available.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.LinearAlgebraControl.UseDefault">
            <summary>
            Use a specific provider if configured, e.g. using the
            "MathNetNumericsLAProvider" environment variable,
            or fall back to the best provider.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider">
            <summary>
            The managed linear algebra provider.
            </summary>
            <summary>
            The managed linear algebra provider.
            </summary>
            <summary>
            The managed linear algebra provider.
            </summary>
            <summary>
            The managed linear algebra provider.
            </summary>
            <summary>
            The managed linear algebra provider.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.AddVectorToScaledVector(System.Numerics.Complex[],System.Numerics.Complex,System.Numerics.Complex[],System.Numerics.Complex[])">
            <summary>
            Adds a scaled vector to another: <c>result = y + alpha*x</c>.
            </summary>
            <param name="y">The vector to update.</param>
            <param name="alpha">The value to scale <paramref name="x"/> by.</param>
            <param name="x">The vector to add to <paramref name="y"/>.</param>
            <param name="result">The result of the addition.</param>
            <remarks>This is similar to the AXPY BLAS routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.ScaleArray(System.Numerics.Complex,System.Numerics.Complex[],System.Numerics.Complex[])">
            <summary>
            Scales an array. Can be used to scale a vector and a matrix.
            </summary>
            <param name="alpha">The scalar.</param>
            <param name="x">The values to scale.</param>
            <param name="result">This result of the scaling.</param>
            <remarks>This is similar to the SCAL BLAS routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.ConjugateArray(System.Numerics.Complex[],System.Numerics.Complex[])">
            <summary>
            Conjugates an array. Can be used to conjugate a vector and a matrix.
            </summary>
            <param name="x">The values to conjugate.</param>
            <param name="result">This result of the conjugation.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.DotProduct(System.Numerics.Complex[],System.Numerics.Complex[])">
            <summary>
            Computes the dot product of x and y.
            </summary>
            <param name="x">The vector x.</param>
            <param name="y">The vector y.</param>
            <returns>The dot product of x and y.</returns>
            <remarks>This is equivalent to the DOT BLAS routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.AddArrays(System.Numerics.Complex[],System.Numerics.Complex[],System.Numerics.Complex[])">
            <summary>
            Does a point wise add of two arrays <c>z = x + y</c>. This can be used
            to add vectors or matrices.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the addition.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SubtractArrays(System.Numerics.Complex[],System.Numerics.Complex[],System.Numerics.Complex[])">
            <summary>
            Does a point wise subtraction of two arrays <c>z = x - y</c>. This can be used
            to subtract vectors or matrices.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the subtraction.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.PointWiseMultiplyArrays(System.Numerics.Complex[],System.Numerics.Complex[],System.Numerics.Complex[])">
            <summary>
            Does a point wise multiplication of two arrays <c>z = x * y</c>. This can be used
            to multiple elements of vectors or matrices.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the point wise multiplication.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.PointWiseDivideArrays(System.Numerics.Complex[],System.Numerics.Complex[],System.Numerics.Complex[])">
            <summary>
            Does a point wise division of two arrays <c>z = x / y</c>. This can be used
            to divide elements of vectors or matrices.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the point wise division.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.PointWisePowerArrays(System.Numerics.Complex[],System.Numerics.Complex[],System.Numerics.Complex[])">
            <summary>
            Does a point wise power of two arrays <c>z = x ^ y</c>. This can be used
            to raise elements of vectors or matrices to the powers of another vector or matrix.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the point wise power.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.MatrixNorm(MathNet.Numerics.Providers.LinearAlgebra.Norm,System.Int32,System.Int32,System.Numerics.Complex[])">
            <summary>
            Computes the requested <see cref="T:MathNet.Numerics.Providers.LinearAlgebra.Norm"/> of the matrix.
            </summary>
            <param name="norm">The type of norm to compute.</param>
            <param name="rows">The number of rows.</param>
            <param name="columns">The number of columns.</param>
            <param name="matrix">The matrix to compute the norm from.</param>
            <returns>
            The requested <see cref="T:MathNet.Numerics.Providers.LinearAlgebra.Norm"/> of the matrix.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.MatrixMultiply(System.Numerics.Complex[],System.Int32,System.Int32,System.Numerics.Complex[],System.Int32,System.Int32,System.Numerics.Complex[])">
            <summary>
            Multiples two matrices. <c>result = x * y</c>
            </summary>
            <param name="x">The x matrix.</param>
            <param name="rowsX">The number of rows in the x matrix.</param>
            <param name="columnsX">The number of columns in the x matrix.</param>
            <param name="y">The y matrix.</param>
            <param name="rowsY">The number of rows in the y matrix.</param>
            <param name="columnsY">The number of columns in the y matrix.</param>
            <param name="result">Where to store the result of the multiplication.</param>
            <remarks>This is a simplified version of the BLAS GEMM routine with alpha
            set to 1.0 and beta set to 0.0, and x and y are not transposed.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.MatrixMultiplyWithUpdate(MathNet.Numerics.Providers.LinearAlgebra.Transpose,MathNet.Numerics.Providers.LinearAlgebra.Transpose,System.Numerics.Complex,System.Numerics.Complex[],System.Int32,System.Int32,System.Numerics.Complex[],System.Int32,System.Int32,System.Numerics.Complex,System.Numerics.Complex[])">
            <summary>
            Multiplies two matrices and updates another with the result. <c>c = alpha*op(a)*op(b) + beta*c</c>
            </summary>
            <param name="transposeA">How to transpose the <paramref name="a"/> matrix.</param>
            <param name="transposeB">How to transpose the <paramref name="b"/> matrix.</param>
            <param name="alpha">The value to scale <paramref name="a"/> matrix.</param>
            <param name="a">The a matrix.</param>
            <param name="rowsA">The number of rows in the <paramref name="a"/> matrix.</param>
            <param name="columnsA">The number of columns in the <paramref name="a"/> matrix.</param>
            <param name="b">The b matrix</param>
            <param name="rowsB">The number of rows in the <paramref name="b"/> matrix.</param>
            <param name="columnsB">The number of columns in the <paramref name="b"/> matrix.</param>
            <param name="beta">The value to scale the <paramref name="c"/> matrix.</param>
            <param name="c">The c matrix.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.LUFactor(System.Numerics.Complex[],System.Int32,System.Int32[])">
            <summary>
            Computes the LUP factorization of A. P*A = L*U.
            </summary>
            <param name="data">An <paramref name="order"/> by <paramref name="order"/> matrix. The matrix is overwritten with the
            the LU factorization on exit. The lower triangular factor L is stored in under the diagonal of <paramref name="data"/> (the diagonal is always 1.0
            for the L factor). The upper triangular factor U is stored on and above the diagonal of <paramref name="data"/>.</param>
            <param name="order">The order of the square matrix <paramref name="data"/>.</param>
            <param name="ipiv">On exit, it contains the pivot indices. The size of the array must be <paramref name="order"/>.</param>
            <remarks>This is equivalent to the GETRF LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.LUInverse(System.Numerics.Complex[],System.Int32)">
            <summary>
            Computes the inverse of matrix using LU factorization.
            </summary>
            <param name="a">The N by N matrix to invert. Contains the inverse On exit.</param>
            <param name="order">The order of the square matrix <paramref name="a"/>.</param>
            <remarks>This is equivalent to the GETRF and GETRI LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.LUInverseFactored(System.Numerics.Complex[],System.Int32,System.Int32[])">
            <summary>
            Computes the inverse of a previously factored matrix.
            </summary>
            <param name="a">The LU factored N by N matrix.  Contains the inverse On exit.</param>
            <param name="order">The order of the square matrix <paramref name="a"/>.</param>
            <param name="ipiv">The pivot indices of <paramref name="a"/>.</param>
            <remarks>This is equivalent to the GETRI LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.LUSolve(System.Int32,System.Numerics.Complex[],System.Int32,System.Numerics.Complex[])">
            <summary>
            Solves A*X=B for X using LU factorization.
            </summary>
            <param name="columnsOfB">The number of columns of B.</param>
            <param name="a">The square matrix A.</param>
            <param name="order">The order of the square matrix <paramref name="a"/>.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <remarks>This is equivalent to the GETRF and GETRS LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.LUSolveFactored(System.Int32,System.Numerics.Complex[],System.Int32,System.Int32[],System.Numerics.Complex[])">
            <summary>
            Solves A*X=B for X using a previously factored A matrix.
            </summary>
            <param name="columnsOfB">The number of columns of B.</param>
            <param name="a">The factored A matrix.</param>
            <param name="order">The order of the square matrix <paramref name="a"/>.</param>
            <param name="ipiv">The pivot indices of <paramref name="a"/>.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <remarks>This is equivalent to the GETRS LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.CholeskyFactor(System.Numerics.Complex[],System.Int32)">
            <summary>
            Computes the Cholesky factorization of A.
            </summary>
            <param name="a">On entry, a square, positive definite matrix. On exit, the matrix is overwritten with the
            the Cholesky factorization.</param>
            <param name="order">The number of rows or columns in the matrix.</param>
            <remarks>This is equivalent to the POTRF LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.DoCholeskyStep(System.Numerics.Complex[],System.Int32,System.Int32,System.Int32,System.Numerics.Complex[],System.Int32)">
            <summary>
            Calculate Cholesky step
            </summary>
            <param name="data">Factor matrix</param>
            <param name="rowDim">Number of rows</param>
            <param name="firstCol">Column start</param>
            <param name="colLimit">Total columns</param>
            <param name="multipliers">Multipliers calculated previously</param>
            <param name="availableCores">Number of available processors</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.CholeskySolve(System.Numerics.Complex[],System.Int32,System.Numerics.Complex[],System.Int32)">
            <summary>
            Solves A*X=B for X using Cholesky factorization.
            </summary>
            <param name="a">The square, positive definite matrix A.</param>
            <param name="orderA">The number of rows and columns in A.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <param name="columnsB">The number of columns in the B matrix.</param>
            <remarks>This is equivalent to the POTRF add POTRS LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.CholeskySolveFactored(System.Numerics.Complex[],System.Int32,System.Numerics.Complex[],System.Int32)">
            <summary>
            Solves A*X=B for X using a previously factored A matrix.
            </summary>
            <param name="a">The square, positive definite matrix A.</param>
            <param name="orderA">The number of rows and columns in A.</param>
            <param name="b">The B matrix.</param>
            <param name="columnsB">The number of columns in the B matrix.</param>
            <remarks>This is equivalent to the POTRS LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.DoCholeskySolve(System.Numerics.Complex[],System.Int32,System.Numerics.Complex[],System.Int32)">
            <summary>
            Solves A*X=B for X using a previously factored A matrix.
            </summary>
            <param name="a">The square, positive definite matrix A. Has to be different than <paramref name="b"/>.</param>
            <param name="orderA">The number of rows and columns in A.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <param name="index">The column to solve for.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.QRFactor(System.Numerics.Complex[],System.Int32,System.Int32,System.Numerics.Complex[],System.Numerics.Complex[])">
            <summary>
            Computes the QR factorization of A.
            </summary>
            <param name="r">On entry, it is the M by N A matrix to factor. On exit,
            it is overwritten with the R matrix of the QR factorization. </param>
            <param name="rowsR">The number of rows in the A matrix.</param>
            <param name="columnsR">The number of columns in the A matrix.</param>
            <param name="q">On exit, A M by M matrix that holds the Q matrix of the
            QR factorization.</param>
            <param name="tau">A min(m,n) vector. On exit, contains additional information
            to be used by the QR solve routine.</param>
            <remarks>This is similar to the GEQRF and ORGQR LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.ThinQRFactor(System.Numerics.Complex[],System.Int32,System.Int32,System.Numerics.Complex[],System.Numerics.Complex[])">
            <summary>
            Computes the QR factorization of A.
            </summary>
            <param name="a">On entry, it is the M by N A matrix to factor. On exit,
            it is overwritten with the Q matrix of the QR factorization.</param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="r">On exit, A N by N matrix that holds the R matrix of the
            QR factorization.</param>
            <param name="tau">A min(m,n) vector. On exit, contains additional information
            to be used by the QR solve routine.</param>
            <remarks>This is similar to the GEQRF and ORGQR LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.ComputeQR(System.Numerics.Complex[],System.Int32,System.Numerics.Complex[],System.Int32,System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Perform calculation of Q or R
            </summary>
            <param name="work">Work array</param>
            <param name="workIndex">Index of column in work array</param>
            <param name="a">Q or R matrices</param>
            <param name="rowStart">The first row in </param>
            <param name="rowCount">The last row</param>
            <param name="columnStart">The first column</param>
            <param name="columnCount">The last column</param>
            <param name="availableCores">Number of available CPUs</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.GenerateColumn(System.Numerics.Complex[],System.Numerics.Complex[],System.Int32,System.Int32,System.Int32)">
            <summary>
            Generate column from initial matrix to work array
            </summary>
            <param name="work">Work array</param>
            <param name="a">Initial matrix</param>
            <param name="rowCount">The number of rows in matrix</param>
            <param name="row">The first row</param>
            <param name="column">Column index</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.QRSolve(System.Numerics.Complex[],System.Int32,System.Int32,System.Numerics.Complex[],System.Int32,System.Numerics.Complex[],MathNet.Numerics.LinearAlgebra.Factorization.QRMethod)">
            <summary>
            Solves A*X=B for X using QR factorization of A.
            </summary>
            <param name="a">The A matrix.</param>
            <param name="rows">The number of rows in the A matrix.</param>
            <param name="columns">The number of columns in the A matrix.</param>
            <param name="b">The B matrix.</param>
            <param name="columnsB">The number of columns of B.</param>
            <param name="x">On exit, the solution matrix.</param>
            <param name="method">The type of QR factorization to perform. <seealso cref="T:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod"/></param>
            <remarks>Rows must be greater or equal to columns.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.QRSolveFactored(System.Numerics.Complex[],System.Numerics.Complex[],System.Int32,System.Int32,System.Numerics.Complex[],System.Numerics.Complex[],System.Int32,System.Numerics.Complex[],MathNet.Numerics.LinearAlgebra.Factorization.QRMethod)">
            <summary>
            Solves A*X=B for X using a previously QR factored matrix.
            </summary>
            <param name="q">The Q matrix obtained by calling <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.QRFactor(System.Numerics.Complex[],System.Int32,System.Int32,System.Numerics.Complex[],System.Numerics.Complex[])"/>.</param>
            <param name="r">The R matrix obtained by calling <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.QRFactor(System.Numerics.Complex[],System.Int32,System.Int32,System.Numerics.Complex[],System.Numerics.Complex[])"/>. </param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="tau">Contains additional information on Q. Only used for the native solver
            and can be <c>null</c> for the managed provider.</param>
            <param name="b">The B matrix.</param>
            <param name="columnsB">The number of columns of B.</param>
            <param name="x">On exit, the solution matrix.</param>
            <param name="method">The type of QR factorization to perform. <seealso cref="T:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod"/></param>
            <remarks>Rows must be greater or equal to columns.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SingularValueDecomposition(System.Boolean,System.Numerics.Complex[],System.Int32,System.Int32,System.Numerics.Complex[],System.Numerics.Complex[],System.Numerics.Complex[])">
            <summary>
            Computes the singular value decomposition of A.
            </summary>
            <param name="computeVectors">Compute the singular U and VT vectors or not.</param>
            <param name="a">On entry, the M by N matrix to decompose. On exit, A may be overwritten.</param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="s">The singular values of A in ascending value.</param>
            <param name="u">If <paramref name="computeVectors"/> is <c>true</c>, on exit U contains the left
            singular vectors.</param>
            <param name="vt">If <paramref name="computeVectors"/> is <c>true</c>, on exit VT contains the transposed
            right singular vectors.</param>
            <remarks>This is equivalent to the GESVD LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SvdSolve(System.Numerics.Complex[],System.Int32,System.Int32,System.Numerics.Complex[],System.Int32,System.Numerics.Complex[])">
            <summary>
            Solves A*X=B for X using the singular value decomposition of A.
            </summary>
            <param name="a">On entry, the M by N matrix to decompose.</param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="b">The B matrix.</param>
            <param name="columnsB">The number of columns of B.</param>
            <param name="x">On exit, the solution matrix.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SvdSolveFactored(System.Int32,System.Int32,System.Numerics.Complex[],System.Numerics.Complex[],System.Numerics.Complex[],System.Numerics.Complex[],System.Int32,System.Numerics.Complex[])">
            <summary>
            Solves A*X=B for X using a previously SVD decomposed matrix.
            </summary>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="s">The s values returned by <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SingularValueDecomposition(System.Boolean,System.Numerics.Complex[],System.Int32,System.Int32,System.Numerics.Complex[],System.Numerics.Complex[],System.Numerics.Complex[])"/>.</param>
            <param name="u">The left singular vectors returned by  <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SingularValueDecomposition(System.Boolean,System.Numerics.Complex[],System.Int32,System.Int32,System.Numerics.Complex[],System.Numerics.Complex[],System.Numerics.Complex[])"/>.</param>
            <param name="vt">The right singular  vectors returned by  <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SingularValueDecomposition(System.Boolean,System.Numerics.Complex[],System.Int32,System.Int32,System.Numerics.Complex[],System.Numerics.Complex[],System.Numerics.Complex[])"/>.</param>
            <param name="b">The B matrix.</param>
            <param name="columnsB">The number of columns of B.</param>
            <param name="x">On exit, the solution matrix.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.EigenDecomp(System.Boolean,System.Int32,System.Numerics.Complex[],System.Numerics.Complex[],System.Numerics.Complex[],System.Numerics.Complex[])">
            <summary>
            Computes the eigenvalues and eigenvectors of a matrix.
            </summary>
            <param name="isSymmetric">Whether the matrix is symmetric or not.</param>
            <param name="order">The order of the matrix.</param>
            <param name="matrix">The matrix to decompose. The length of the array must be order * order.</param>
            <param name="matrixEv">On output, the matrix contains the eigen vectors. The length of the array must be order * order.</param>
            <param name="vectorEv">On output, the eigen values (λ) of matrix in ascending value. The length of the array must <paramref name="order"/>.</param>
            <param name="matrixD">On output, the block diagonal eigenvalue matrix. The length of the array must be order * order.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SymmetricTridiagonalize(System.Numerics.Complex[],System.Double[],System.Double[],System.Numerics.Complex[],System.Int32)">
            <summary>
            Reduces a complex Hermitian matrix to a real symmetric tridiagonal matrix using unitary similarity transformations.
            </summary>
            <param name="matrixA">Source matrix to reduce</param>
            <param name="d">Output: Arrays for internal storage of real parts of eigenvalues</param>
            <param name="e">Output: Arrays for internal storage of imaginary parts of eigenvalues</param>
            <param name="tau">Output: Arrays that contains further information about the transformations.</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures HTRIDI by
            Smith, Boyle, Dongarra, Garbow, Ikebe, Klema, Moler, and Wilkinson, Handbook for
            Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SymmetricDiagonalize(System.Numerics.Complex[],System.Double[],System.Double[],System.Int32)">
            <summary>
            Symmetric tridiagonal QL algorithm.
            </summary>
            <param name="dataEv">Data array of matrix V (eigenvectors)</param>
            <param name="d">Arrays for internal storage of real parts of eigenvalues</param>
            <param name="e">Arrays for internal storage of imaginary parts of eigenvalues</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures tql2, by
            Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
            Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SymmetricUntridiagonalize(System.Numerics.Complex[],System.Numerics.Complex[],System.Numerics.Complex[],System.Int32)">
            <summary>
            Determines eigenvectors by undoing the symmetric tridiagonalize transformation
            </summary>
            <param name="dataEv">Data array of matrix V (eigenvectors)</param>
            <param name="matrixA">Previously tridiagonalized matrix by SymmetricTridiagonalize.</param>
            <param name="tau">Contains further information about the transformations</param>
            <param name="order">Input matrix order</param>
            <remarks>This is derived from the Algol procedures HTRIBK, by
            by Smith, Boyle, Dongarra, Garbow, Ikebe, Klema, Moler, and Wilkinson, Handbook for
            Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.NonsymmetricReduceToHessenberg(System.Numerics.Complex[],System.Numerics.Complex[],System.Int32)">
            <summary>
            Nonsymmetric reduction to Hessenberg form.
            </summary>
            <param name="dataEv">Data array of matrix V (eigenvectors)</param>
            <param name="matrixH">Array for internal storage of nonsymmetric Hessenberg form.</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures orthes and ortran,
            by Martin and Wilkinson, Handbook for Auto. Comp.,
            Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutines in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.NonsymmetricReduceHessenberToRealSchur(System.Numerics.Complex[],System.Numerics.Complex[],System.Numerics.Complex[],System.Int32)">
            <summary>
            Nonsymmetric reduction from Hessenberg to real Schur form.
            </summary>
            <param name="vectorV">Data array of the eigenvectors</param>
            <param name="dataEv">Data array of matrix V (eigenvectors)</param>
            <param name="matrixH">Array for internal storage of nonsymmetric Hessenberg form.</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedure hqr2,
            by Martin and Wilkinson, Handbook for Auto. Comp.,
            Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.GetRow(MathNet.Numerics.Providers.LinearAlgebra.Transpose,System.Int32,System.Int32,System.Int32,System.Numerics.Complex[],System.Numerics.Complex[])">
            <summary>
            Assumes that <paramref name="numRows"/> and <paramref name="numCols"/> have already been transposed.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.GetColumn(MathNet.Numerics.Providers.LinearAlgebra.Transpose,System.Int32,System.Int32,System.Int32,System.Numerics.Complex[],System.Numerics.Complex[])">
            <summary>
            Assumes that <paramref name="numRows"/> and <paramref name="numCols"/> have already been transposed.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.AddVectorToScaledVector(MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32,MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[])">
            <summary>
            Adds a scaled vector to another: <c>result = y + alpha*x</c>.
            </summary>
            <param name="y">The vector to update.</param>
            <param name="alpha">The value to scale <paramref name="x"/> by.</param>
            <param name="x">The vector to add to <paramref name="y"/>.</param>
            <param name="result">The result of the addition.</param>
            <remarks>This is similar to the AXPY BLAS routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.ScaleArray(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[])">
            <summary>
            Scales an array. Can be used to scale a vector and a matrix.
            </summary>
            <param name="alpha">The scalar.</param>
            <param name="x">The values to scale.</param>
            <param name="result">This result of the scaling.</param>
            <remarks>This is similar to the SCAL BLAS routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.ConjugateArray(MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[])">
            <summary>
            Conjugates an array. Can be used to conjugate a vector and a matrix.
            </summary>
            <param name="x">The values to conjugate.</param>
            <param name="result">This result of the conjugation.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.DotProduct(MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[])">
            <summary>
            Computes the dot product of x and y.
            </summary>
            <param name="x">The vector x.</param>
            <param name="y">The vector y.</param>
            <returns>The dot product of x and y.</returns>
            <remarks>This is equivalent to the DOT BLAS routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.AddArrays(MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[])">
            <summary>
            Does a point wise add of two arrays <c>z = x + y</c>. This can be used
            to add vectors or matrices.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the addition.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SubtractArrays(MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[])">
            <summary>
            Does a point wise subtraction of two arrays <c>z = x - y</c>. This can be used
            to subtract vectors or matrices.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the subtraction.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.PointWiseMultiplyArrays(MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[])">
            <summary>
            Does a point wise multiplication of two arrays <c>z = x * y</c>. This can be used
            to multiple elements of vectors or matrices.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the point wise multiplication.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.PointWiseDivideArrays(MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[])">
            <summary>
            Does a point wise division of two arrays <c>z = x / y</c>. This can be used
            to divide elements of vectors or matrices.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the point wise division.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.PointWisePowerArrays(MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[])">
            <summary>
            Does a point wise power of two arrays <c>z = x ^ y</c>. This can be used
            to raise elements of vectors or matrices to the powers of another vector or matrix.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the point wise power.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.MatrixNorm(MathNet.Numerics.Providers.LinearAlgebra.Norm,System.Int32,System.Int32,MathNet.Numerics.Complex32[])">
            <summary>
            Computes the requested <see cref="T:MathNet.Numerics.Providers.LinearAlgebra.Norm"/> of the matrix.
            </summary>
            <param name="norm">The type of norm to compute.</param>
            <param name="rows">The number of rows.</param>
            <param name="columns">The number of columns.</param>
            <param name="matrix">The matrix to compute the norm from.</param>
            <returns>The requested <see cref="T:MathNet.Numerics.Providers.LinearAlgebra.Norm"/> of the matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.MatrixMultiply(MathNet.Numerics.Complex32[],System.Int32,System.Int32,MathNet.Numerics.Complex32[],System.Int32,System.Int32,MathNet.Numerics.Complex32[])">
            <summary>
            Multiples two matrices. <c>result = x * y</c>
            </summary>
            <param name="x">The x matrix.</param>
            <param name="rowsX">The number of rows in the x matrix.</param>
            <param name="columnsX">The number of columns in the x matrix.</param>
            <param name="y">The y matrix.</param>
            <param name="rowsY">The number of rows in the y matrix.</param>
            <param name="columnsY">The number of columns in the y matrix.</param>
            <param name="result">Where to store the result of the multiplication.</param>
            <remarks>This is a simplified version of the BLAS GEMM routine with alpha
            set to 1.0 and beta set to 0.0, and x and y are not transposed.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.MatrixMultiplyWithUpdate(MathNet.Numerics.Providers.LinearAlgebra.Transpose,MathNet.Numerics.Providers.LinearAlgebra.Transpose,MathNet.Numerics.Complex32,MathNet.Numerics.Complex32[],System.Int32,System.Int32,MathNet.Numerics.Complex32[],System.Int32,System.Int32,MathNet.Numerics.Complex32,MathNet.Numerics.Complex32[])">
            <summary>
            Multiplies two matrices and updates another with the result. <c>c = alpha*op(a)*op(b) + beta*c</c>
            </summary>
            <param name="transposeA">How to transpose the <paramref name="a"/> matrix.</param>
            <param name="transposeB">How to transpose the <paramref name="b"/> matrix.</param>
            <param name="alpha">The value to scale <paramref name="a"/> matrix.</param>
            <param name="a">The a matrix.</param>
            <param name="rowsA">The number of rows in the <paramref name="a"/> matrix.</param>
            <param name="columnsA">The number of columns in the <paramref name="a"/> matrix.</param>
            <param name="b">The b matrix</param>
            <param name="rowsB">The number of rows in the <paramref name="b"/> matrix.</param>
            <param name="columnsB">The number of columns in the <paramref name="b"/> matrix.</param>
            <param name="beta">The value to scale the <paramref name="c"/> matrix.</param>
            <param name="c">The c matrix.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.LUFactor(MathNet.Numerics.Complex32[],System.Int32,System.Int32[])">
            <summary>
            Computes the LUP factorization of A. P*A = L*U.
            </summary>
            <param name="data">An <paramref name="order"/> by <paramref name="order"/> matrix. The matrix is overwritten with the
            the LU factorization on exit. The lower triangular factor L is stored in under the diagonal of <paramref name="data"/> (the diagonal is always 1.0
            for the L factor). The upper triangular factor U is stored on and above the diagonal of <paramref name="data"/>.</param>
            <param name="order">The order of the square matrix <paramref name="data"/>.</param>
            <param name="ipiv">On exit, it contains the pivot indices. The size of the array must be <paramref name="order"/>.</param>
            <remarks>This is equivalent to the GETRF LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.LUInverse(MathNet.Numerics.Complex32[],System.Int32)">
            <summary>
            Computes the inverse of matrix using LU factorization.
            </summary>
            <param name="a">The N by N matrix to invert. Contains the inverse On exit.</param>
            <param name="order">The order of the square matrix <paramref name="a"/>.</param>
            <remarks>This is equivalent to the GETRF and GETRI LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.LUInverseFactored(MathNet.Numerics.Complex32[],System.Int32,System.Int32[])">
            <summary>
            Computes the inverse of a previously factored matrix.
            </summary>
            <param name="a">The LU factored N by N matrix.  Contains the inverse On exit.</param>
            <param name="order">The order of the square matrix <paramref name="a"/>.</param>
            <param name="ipiv">The pivot indices of <paramref name="a"/>.</param>
            <remarks>This is equivalent to the GETRI LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.LUSolve(System.Int32,MathNet.Numerics.Complex32[],System.Int32,MathNet.Numerics.Complex32[])">
            <summary>
            Solves A*X=B for X using LU factorization.
            </summary>
            <param name="columnsOfB">The number of columns of B.</param>
            <param name="a">The square matrix A.</param>
            <param name="order">The order of the square matrix <paramref name="a"/>.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <remarks>This is equivalent to the GETRF and GETRS LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.LUSolveFactored(System.Int32,MathNet.Numerics.Complex32[],System.Int32,System.Int32[],MathNet.Numerics.Complex32[])">
            <summary>
            Solves A*X=B for X using a previously factored A matrix.
            </summary>
            <param name="columnsOfB">The number of columns of B.</param>
            <param name="a">The factored A matrix.</param>
            <param name="order">The order of the square matrix <paramref name="a"/>.</param>
            <param name="ipiv">The pivot indices of <paramref name="a"/>.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <remarks>This is equivalent to the GETRS LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.CholeskyFactor(MathNet.Numerics.Complex32[],System.Int32)">
            <summary>
            Computes the Cholesky factorization of A.
            </summary>
            <param name="a">On entry, a square, positive definite matrix. On exit, the matrix is overwritten with the
            the Cholesky factorization.</param>
            <param name="order">The number of rows or columns in the matrix.</param>
            <remarks>This is equivalent to the POTRF LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.DoCholeskyStep(MathNet.Numerics.Complex32[],System.Int32,System.Int32,System.Int32,MathNet.Numerics.Complex32[],System.Int32)">
            <summary>
            Calculate Cholesky step
            </summary>
            <param name="data">Factor matrix</param>
            <param name="rowDim">Number of rows</param>
            <param name="firstCol">Column start</param>
            <param name="colLimit">Total columns</param>
            <param name="multipliers">Multipliers calculated previously</param>
            <param name="availableCores">Number of available processors</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.CholeskySolve(MathNet.Numerics.Complex32[],System.Int32,MathNet.Numerics.Complex32[],System.Int32)">
            <summary>
            Solves A*X=B for X using Cholesky factorization.
            </summary>
            <param name="a">The square, positive definite matrix A.</param>
            <param name="orderA">The number of rows and columns in A.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <param name="columnsB">The number of columns in the B matrix.</param>
            <remarks>This is equivalent to the POTRF add POTRS LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.CholeskySolveFactored(MathNet.Numerics.Complex32[],System.Int32,MathNet.Numerics.Complex32[],System.Int32)">
            <summary>
            Solves A*X=B for X using a previously factored A matrix.
            </summary>
            <param name="a">The square, positive definite matrix A.</param>
            <param name="orderA">The number of rows and columns in A.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <param name="columnsB">The number of columns in the B matrix.</param>
            <remarks>This is equivalent to the POTRS LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.DoCholeskySolve(MathNet.Numerics.Complex32[],System.Int32,MathNet.Numerics.Complex32[],System.Int32)">
            <summary>
            Solves A*X=B for X using a previously factored A matrix.
            </summary>
            <param name="a">The square, positive definite matrix A. Has to be different than <paramref name="b"/>.</param>
            <param name="orderA">The number of rows and columns in A.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <param name="index">The column to solve for.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.QRFactor(MathNet.Numerics.Complex32[],System.Int32,System.Int32,MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[])">
            <summary>
            Computes the QR factorization of A.
            </summary>
            <param name="r">On entry, it is the M by N A matrix to factor. On exit,
            it is overwritten with the R matrix of the QR factorization. </param>
            <param name="rowsR">The number of rows in the A matrix.</param>
            <param name="columnsR">The number of columns in the A matrix.</param>
            <param name="q">On exit, A M by M matrix that holds the Q matrix of the
            QR factorization.</param>
            <param name="tau">A min(m,n) vector. On exit, contains additional information
            to be used by the QR solve routine.</param>
            <remarks>This is similar to the GEQRF and ORGQR LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.ThinQRFactor(MathNet.Numerics.Complex32[],System.Int32,System.Int32,MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[])">
            <summary>
            Computes the QR factorization of A.
            </summary>
            <param name="a">On entry, it is the M by N A matrix to factor. On exit,
            it is overwritten with the Q matrix of the QR factorization.</param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="r">On exit, A N by N matrix that holds the R matrix of the
            QR factorization.</param>
            <param name="tau">A min(m,n) vector. On exit, contains additional information
            to be used by the QR solve routine.</param>
            <remarks>This is similar to the GEQRF and ORGQR LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.ComputeQR(MathNet.Numerics.Complex32[],System.Int32,MathNet.Numerics.Complex32[],System.Int32,System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Perform calculation of Q or R
            </summary>
            <param name="work">Work array</param>
            <param name="workIndex">Index of column in work array</param>
            <param name="a">Q or R matrices</param>
            <param name="rowStart">The first row in </param>
            <param name="rowCount">The last row</param>
            <param name="columnStart">The first column</param>
            <param name="columnCount">The last column</param>
            <param name="availableCores">Number of available CPUs</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.GenerateColumn(MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],System.Int32,System.Int32,System.Int32)">
            <summary>
            Generate column from initial matrix to work array
            </summary>
            <param name="work">Work array</param>
            <param name="a">Initial matrix</param>
            <param name="rowCount">The number of rows in matrix</param>
            <param name="row">The first row</param>
            <param name="column">Column index</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.QRSolve(MathNet.Numerics.Complex32[],System.Int32,System.Int32,MathNet.Numerics.Complex32[],System.Int32,MathNet.Numerics.Complex32[],MathNet.Numerics.LinearAlgebra.Factorization.QRMethod)">
            <summary>
            Solves A*X=B for X using QR factorization of A.
            </summary>
            <param name="a">The A matrix.</param>
            <param name="rows">The number of rows in the A matrix.</param>
            <param name="columns">The number of columns in the A matrix.</param>
            <param name="b">The B matrix.</param>
            <param name="columnsB">The number of columns of B.</param>
            <param name="x">On exit, the solution matrix.</param>
            <param name="method">The type of QR factorization to perform. <seealso cref="T:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod"/></param>
            <remarks>Rows must be greater or equal to columns.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.QRSolveFactored(MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],System.Int32,System.Int32,MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],System.Int32,MathNet.Numerics.Complex32[],MathNet.Numerics.LinearAlgebra.Factorization.QRMethod)">
            <summary>
            Solves A*X=B for X using a previously QR factored matrix.
            </summary>
            <param name="q">The Q matrix obtained by calling <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.QRFactor(MathNet.Numerics.Complex32[],System.Int32,System.Int32,MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[])"/>.</param>
            <param name="r">The R matrix obtained by calling <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.QRFactor(MathNet.Numerics.Complex32[],System.Int32,System.Int32,MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[])"/>. </param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="tau">Contains additional information on Q. Only used for the native solver
            and can be <c>null</c> for the managed provider.</param>
            <param name="b">The B matrix.</param>
            <param name="columnsB">The number of columns of B.</param>
            <param name="x">On exit, the solution matrix.</param>
            <param name="method">The type of QR factorization to perform. <seealso cref="T:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod"/></param>
            <remarks>Rows must be greater or equal to columns.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SingularValueDecomposition(System.Boolean,MathNet.Numerics.Complex32[],System.Int32,System.Int32,MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[])">
            <summary>
            Computes the singular value decomposition of A.
            </summary>
            <param name="computeVectors">Compute the singular U and VT vectors or not.</param>
            <param name="a">On entry, the M by N matrix to decompose. On exit, A may be overwritten.</param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="s">The singular values of A in ascending value.</param>
            <param name="u">If <paramref name="computeVectors"/> is <c>true</c>, on exit U contains the left
            singular vectors.</param>
            <param name="vt">If <paramref name="computeVectors"/> is <c>true</c>, on exit VT contains the transposed
            right singular vectors.</param>
            <remarks>This is equivalent to the GESVD LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SvdSolve(MathNet.Numerics.Complex32[],System.Int32,System.Int32,MathNet.Numerics.Complex32[],System.Int32,MathNet.Numerics.Complex32[])">
            <summary>
            Solves A*X=B for X using the singular value decomposition of A.
            </summary>
            <param name="a">On entry, the M by N matrix to decompose.</param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="b">The B matrix.</param>
            <param name="columnsB">The number of columns of B.</param>
            <param name="x">On exit, the solution matrix.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SvdSolveFactored(System.Int32,System.Int32,MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],System.Int32,MathNet.Numerics.Complex32[])">
            <summary>
            Solves A*X=B for X using a previously SVD decomposed matrix.
            </summary>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="s">The s values returned by <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SingularValueDecomposition(System.Boolean,MathNet.Numerics.Complex32[],System.Int32,System.Int32,MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[])"/>.</param>
            <param name="u">The left singular vectors returned by  <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SingularValueDecomposition(System.Boolean,MathNet.Numerics.Complex32[],System.Int32,System.Int32,MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[])"/>.</param>
            <param name="vt">The right singular  vectors returned by  <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SingularValueDecomposition(System.Boolean,MathNet.Numerics.Complex32[],System.Int32,System.Int32,MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[])"/>.</param>
            <param name="b">The B matrix.</param>
            <param name="columnsB">The number of columns of B.</param>
            <param name="x">On exit, the solution matrix.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.EigenDecomp(System.Boolean,System.Int32,MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],System.Numerics.Complex[],MathNet.Numerics.Complex32[])">
            <summary>
            Computes the eigenvalues and eigenvectors of a matrix.
            </summary>
            <param name="isSymmetric">Whether the matrix is symmetric or not.</param>
            <param name="order">The order of the matrix.</param>
            <param name="matrix">The matrix to decompose. The length of the array must be order * order.</param>
            <param name="matrixEv">On output, the matrix contains the eigen vectors. The length of the array must be order * order.</param>
            <param name="vectorEv">On output, the eigen values (λ) of matrix in ascending value. The length of the array must <paramref name="order"/>.</param>
            <param name="matrixD">On output, the block diagonal eigenvalue matrix. The length of the array must be order * order.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SymmetricTridiagonalize(MathNet.Numerics.Complex32[],System.Single[],System.Single[],MathNet.Numerics.Complex32[],System.Int32)">
            <summary>
            Reduces a complex Hermitian matrix to a real symmetric tridiagonal matrix using unitary similarity transformations.
            </summary>
            <param name="matrixA">Source matrix to reduce</param>
            <param name="d">Output: Arrays for internal storage of real parts of eigenvalues</param>
            <param name="e">Output: Arrays for internal storage of imaginary parts of eigenvalues</param>
            <param name="tau">Output: Arrays that contains further information about the transformations.</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures HTRIDI by
            Smith, Boyle, Dongarra, Garbow, Ikebe, Klema, Moler, and Wilkinson, Handbook for
            Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SymmetricDiagonalize(MathNet.Numerics.Complex32[],System.Single[],System.Single[],System.Int32)">
            <summary>
            Symmetric tridiagonal QL algorithm.
            </summary>
            <param name="dataEv">Data array of matrix V (eigenvectors)</param>
            <param name="d">Arrays for internal storage of real parts of eigenvalues</param>
            <param name="e">Arrays for internal storage of imaginary parts of eigenvalues</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures tql2, by
            Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
            Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SymmetricUntridiagonalize(MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],System.Int32)">
            <summary>
            Determines eigenvectors by undoing the symmetric tridiagonalize transformation
            </summary>
            <param name="dataEv">Data array of matrix V (eigenvectors)</param>
            <param name="matrixA">Previously tridiagonalized matrix by SymmetricTridiagonalize.</param>
            <param name="tau">Contains further information about the transformations</param>
            <param name="order">Input matrix order</param>
            <remarks>This is derived from the Algol procedures HTRIBK, by
            by Smith, Boyle, Dongarra, Garbow, Ikebe, Klema, Moler, and Wilkinson, Handbook for
            Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.NonsymmetricReduceToHessenberg(MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],System.Int32)">
            <summary>
            Nonsymmetric reduction to Hessenberg form.
            </summary>
            <param name="dataEv">Data array of matrix V (eigenvectors)</param>
            <param name="matrixH">Array for internal storage of nonsymmetric Hessenberg form.</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures orthes and ortran,
            by Martin and Wilkinson, Handbook for Auto. Comp.,
            Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutines in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.NonsymmetricReduceHessenberToRealSchur(MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[],System.Int32)">
            <summary>
            Nonsymmetric reduction from Hessenberg to real Schur form.
            </summary>
            <param name="vectorV">Data array of the eigenvectors</param>
            <param name="dataEv">Data array of matrix V (eigenvectors)</param>
            <param name="matrixH">Array for internal storage of nonsymmetric Hessenberg form.</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedure hqr2,
            by Martin and Wilkinson, Handbook for Auto. Comp.,
            Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.GetRow(MathNet.Numerics.Providers.LinearAlgebra.Transpose,System.Int32,System.Int32,System.Int32,MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[])">
            <summary>
            Assumes that <paramref name="numRows"/> and <paramref name="numCols"/> have already been transposed.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.GetColumn(MathNet.Numerics.Providers.LinearAlgebra.Transpose,System.Int32,System.Int32,System.Int32,MathNet.Numerics.Complex32[],MathNet.Numerics.Complex32[])">
            <summary>
            Assumes that <paramref name="numRows"/> and <paramref name="numCols"/> have already been transposed.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.IsAvailable">
            <summary>
            Try to find out whether the provider is available, at least in principle.
            Verification may still fail if available, but it will certainly fail if unavailable.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.InitializeVerify">
            <summary>
            Initialize and verify that the provided is indeed available. If not, fall back to alternatives like the managed provider
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.FreeResources">
            <summary>
            Frees memory buffers, caches and handles allocated in or to the provider.
            Does not unload the provider itself, it is still usable afterwards.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.GetRow``1(MathNet.Numerics.Providers.LinearAlgebra.Transpose,System.Int32,System.Int32,System.Int32,``0[],``0[])">
            <summary>
            Assumes that <paramref name="numRows"/> and <paramref name="numCols"/> have already been transposed.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.GetColumn``1(MathNet.Numerics.Providers.LinearAlgebra.Transpose,System.Int32,System.Int32,System.Int32,``0[],``0[])">
            <summary>
            Assumes that <paramref name="numRows"/> and <paramref name="numCols"/> have already been transposed.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.AddVectorToScaledVector(System.Double[],System.Double,System.Double[],System.Double[])">
            <summary>
            Adds a scaled vector to another: <c>result = y + alpha*x</c>.
            </summary>
            <param name="y">The vector to update.</param>
            <param name="alpha">The value to scale <paramref name="x"/> by.</param>
            <param name="x">The vector to add to <paramref name="y"/>.</param>
            <param name="result">The result of the addition.</param>
            <remarks>This is similar to the AXPY BLAS routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.ScaleArray(System.Double,System.Double[],System.Double[])">
            <summary>
            Scales an array. Can be used to scale a vector and a matrix.
            </summary>
            <param name="alpha">The scalar.</param>
            <param name="x">The values to scale.</param>
            <param name="result">This result of the scaling.</param>
            <remarks>This is similar to the SCAL BLAS routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.ConjugateArray(System.Double[],System.Double[])">
            <summary>
            Conjugates an array. Can be used to conjugate a vector and a matrix.
            </summary>
            <param name="x">The values to conjugate.</param>
            <param name="result">This result of the conjugation.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.DotProduct(System.Double[],System.Double[])">
            <summary>
            Computes the dot product of x and y.
            </summary>
            <param name="x">The vector x.</param>
            <param name="y">The vector y.</param>
            <returns>The dot product of x and y.</returns>
            <remarks>This is equivalent to the DOT BLAS routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.AddArrays(System.Double[],System.Double[],System.Double[])">
            <summary>
            Does a point wise add of two arrays <c>z = x + y</c>. This can be used
            to add vectors or matrices.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the addition.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SubtractArrays(System.Double[],System.Double[],System.Double[])">
            <summary>
            Does a point wise subtraction of two arrays <c>z = x - y</c>. This can be used
            to subtract vectors or matrices.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the subtraction.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.PointWiseMultiplyArrays(System.Double[],System.Double[],System.Double[])">
            <summary>
            Does a point wise multiplication of two arrays <c>z = x * y</c>. This can be used
            to multiple elements of vectors or matrices.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the point wise multiplication.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.PointWiseDivideArrays(System.Double[],System.Double[],System.Double[])">
            <summary>
            Does a point wise division of two arrays <c>z = x / y</c>. This can be used
            to divide elements of vectors or matrices.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the point wise division.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.PointWisePowerArrays(System.Double[],System.Double[],System.Double[])">
            <summary>
            Does a point wise power of two arrays <c>z = x ^ y</c>. This can be used
            to raise elements of vectors or matrices to the powers of another vector or matrix.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the point wise power.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.MatrixNorm(MathNet.Numerics.Providers.LinearAlgebra.Norm,System.Int32,System.Int32,System.Double[])">
            <summary>
            Computes the requested <see cref="T:MathNet.Numerics.Providers.LinearAlgebra.Norm"/> of the matrix.
            </summary>
            <param name="norm">The type of norm to compute.</param>
            <param name="rows">The number of rows.</param>
            <param name="columns">The number of columns.</param>
            <param name="matrix">The matrix to compute the norm from.</param>
            <returns>
            The requested <see cref="T:MathNet.Numerics.Providers.LinearAlgebra.Norm"/> of the matrix.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.MatrixMultiply(System.Double[],System.Int32,System.Int32,System.Double[],System.Int32,System.Int32,System.Double[])">
            <summary>
            Multiples two matrices. <c>result = x * y</c>
            </summary>
            <param name="x">The x matrix.</param>
            <param name="rowsX">The number of rows in the x matrix.</param>
            <param name="columnsX">The number of columns in the x matrix.</param>
            <param name="y">The y matrix.</param>
            <param name="rowsY">The number of rows in the y matrix.</param>
            <param name="columnsY">The number of columns in the y matrix.</param>
            <param name="result">Where to store the result of the multiplication.</param>
            <remarks>This is a simplified version of the BLAS GEMM routine with alpha
            set to 1.0 and beta set to 0.0, and x and y are not transposed.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.MatrixMultiplyWithUpdate(MathNet.Numerics.Providers.LinearAlgebra.Transpose,MathNet.Numerics.Providers.LinearAlgebra.Transpose,System.Double,System.Double[],System.Int32,System.Int32,System.Double[],System.Int32,System.Int32,System.Double,System.Double[])">
            <summary>
            Multiplies two matrices and updates another with the result. <c>c = alpha*op(a)*op(b) + beta*c</c>
            </summary>
            <param name="transposeA">How to transpose the <paramref name="a"/> matrix.</param>
            <param name="transposeB">How to transpose the <paramref name="b"/> matrix.</param>
            <param name="alpha">The value to scale <paramref name="a"/> matrix.</param>
            <param name="a">The a matrix.</param>
            <param name="rowsA">The number of rows in the <paramref name="a"/> matrix.</param>
            <param name="columnsA">The number of columns in the <paramref name="a"/> matrix.</param>
            <param name="b">The b matrix</param>
            <param name="rowsB">The number of rows in the <paramref name="b"/> matrix.</param>
            <param name="columnsB">The number of columns in the <paramref name="b"/> matrix.</param>
            <param name="beta">The value to scale the <paramref name="c"/> matrix.</param>
            <param name="c">The c matrix.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.LUFactor(System.Double[],System.Int32,System.Int32[])">
            <summary>
            Computes the LUP factorization of A. P*A = L*U.
            </summary>
            <param name="data">An <paramref name="order"/> by <paramref name="order"/> matrix. The matrix is overwritten with the
            the LU factorization on exit. The lower triangular factor L is stored in under the diagonal of <paramref name="data"/> (the diagonal is always 1.0
            for the L factor). The upper triangular factor U is stored on and above the diagonal of <paramref name="data"/>.</param>
            <param name="order">The order of the square matrix <paramref name="data"/>.</param>
            <param name="ipiv">On exit, it contains the pivot indices. The size of the array must be <paramref name="order"/>.</param>
            <remarks>This is equivalent to the GETRF LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.LUInverse(System.Double[],System.Int32)">
            <summary>
            Computes the inverse of matrix using LU factorization.
            </summary>
            <param name="a">The N by N matrix to invert. Contains the inverse On exit.</param>
            <param name="order">The order of the square matrix <paramref name="a"/>.</param>
            <remarks>This is equivalent to the GETRF and GETRI LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.LUInverseFactored(System.Double[],System.Int32,System.Int32[])">
            <summary>
            Computes the inverse of a previously factored matrix.
            </summary>
            <param name="a">The LU factored N by N matrix.  Contains the inverse On exit.</param>
            <param name="order">The order of the square matrix <paramref name="a"/>.</param>
            <param name="ipiv">The pivot indices of <paramref name="a"/>.</param>
            <remarks>This is equivalent to the GETRI LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.LUSolve(System.Int32,System.Double[],System.Int32,System.Double[])">
            <summary>
            Solves A*X=B for X using LU factorization.
            </summary>
            <param name="columnsOfB">The number of columns of B.</param>
            <param name="a">The square matrix A.</param>
            <param name="order">The order of the square matrix <paramref name="a"/>.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <remarks>This is equivalent to the GETRF and GETRS LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.LUSolveFactored(System.Int32,System.Double[],System.Int32,System.Int32[],System.Double[])">
            <summary>
            Solves A*X=B for X using a previously factored A matrix.
            </summary>
            <param name="columnsOfB">The number of columns of B.</param>
            <param name="a">The factored A matrix.</param>
            <param name="order">The order of the square matrix <paramref name="a"/>.</param>
            <param name="ipiv">The pivot indices of <paramref name="a"/>.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <remarks>This is equivalent to the GETRS LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.CholeskyFactor(System.Double[],System.Int32)">
            <summary>
            Computes the Cholesky factorization of A.
            </summary>
            <param name="a">On entry, a square, positive definite matrix. On exit, the matrix is overwritten with the
            the Cholesky factorization.</param>
            <param name="order">The number of rows or columns in the matrix.</param>
            <remarks>This is equivalent to the POTRF LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.DoCholeskyStep(System.Double[],System.Int32,System.Int32,System.Int32,System.Double[],System.Int32)">
            <summary>
            Calculate Cholesky step
            </summary>
            <param name="data">Factor matrix</param>
            <param name="rowDim">Number of rows</param>
            <param name="firstCol">Column start</param>
            <param name="colLimit">Total columns</param>
            <param name="multipliers">Multipliers calculated previously</param>
            <param name="availableCores">Number of available processors</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.CholeskySolve(System.Double[],System.Int32,System.Double[],System.Int32)">
            <summary>
            Solves A*X=B for X using Cholesky factorization.
            </summary>
            <param name="a">The square, positive definite matrix A.</param>
            <param name="orderA">The number of rows and columns in A.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <param name="columnsB">The number of columns in the B matrix.</param>
            <remarks>This is equivalent to the POTRF add POTRS LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.CholeskySolveFactored(System.Double[],System.Int32,System.Double[],System.Int32)">
            <summary>
            Solves A*X=B for X using a previously factored A matrix.
            </summary>
            <param name="a">The square, positive definite matrix A. Has to be different than <paramref name="b"/>.</param>
            <param name="orderA">The number of rows and columns in A.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <param name="columnsB">The number of columns in the B matrix.</param>
            <remarks>This is equivalent to the POTRS LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.DoCholeskySolve(System.Double[],System.Int32,System.Double[],System.Int32)">
            <summary>
            Solves A*X=B for X using a previously factored A matrix.
            </summary>
            <param name="a">The square, positive definite matrix A. Has to be different than <paramref name="b"/>.</param>
            <param name="orderA">The number of rows and columns in A.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <param name="index">The column to solve for.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.QRFactor(System.Double[],System.Int32,System.Int32,System.Double[],System.Double[])">
            <summary>
            Computes the QR factorization of A.
            </summary>
            <param name="r">On entry, it is the M by N A matrix to factor. On exit,
            it is overwritten with the R matrix of the QR factorization. </param>
            <param name="rowsR">The number of rows in the A matrix.</param>
            <param name="columnsR">The number of columns in the A matrix.</param>
            <param name="q">On exit, A M by M matrix that holds the Q matrix of the
            QR factorization.</param>
            <param name="tau">A min(m,n) vector. On exit, contains additional information
            to be used by the QR solve routine.</param>
            <remarks>This is similar to the GEQRF and ORGQR LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.ThinQRFactor(System.Double[],System.Int32,System.Int32,System.Double[],System.Double[])">
            <summary>
            Computes the QR factorization of A.
            </summary>
            <param name="a">On entry, it is the M by N A matrix to factor. On exit,
            it is overwritten with the Q matrix of the QR factorization.</param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="r">On exit, A N by N matrix that holds the R matrix of the
            QR factorization.</param>
            <param name="tau">A min(m,n) vector. On exit, contains additional information
            to be used by the QR solve routine.</param>
            <remarks>This is similar to the GEQRF and ORGQR LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.ComputeQR(System.Double[],System.Int32,System.Double[],System.Int32,System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Perform calculation of Q or R
            </summary>
            <param name="work">Work array</param>
            <param name="workIndex">Index of column in work array</param>
            <param name="a">Q or R matrices</param>
            <param name="rowStart">The first row in </param>
            <param name="rowCount">The last row</param>
            <param name="columnStart">The first column</param>
            <param name="columnCount">The last column</param>
            <param name="availableCores">Number of available CPUs</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.GenerateColumn(System.Double[],System.Double[],System.Int32,System.Int32,System.Int32)">
            <summary>
            Generate column from initial matrix to work array
            </summary>
            <param name="work">Work array</param>
            <param name="a">Initial matrix</param>
            <param name="rowCount">The number of rows in matrix</param>
            <param name="row">The first row</param>
            <param name="column">Column index</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.QRSolve(System.Double[],System.Int32,System.Int32,System.Double[],System.Int32,System.Double[],MathNet.Numerics.LinearAlgebra.Factorization.QRMethod)">
            <summary>
            Solves A*X=B for X using QR factorization of A.
            </summary>
            <param name="a">The A matrix.</param>
            <param name="rows">The number of rows in the A matrix.</param>
            <param name="columns">The number of columns in the A matrix.</param>
            <param name="b">The B matrix.</param>
            <param name="columnsB">The number of columns of B.</param>
            <param name="x">On exit, the solution matrix.</param>
            <param name="method">The type of QR factorization to perform. <seealso cref="T:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod"/></param>
            <remarks>Rows must be greater or equal to columns.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.QRSolveFactored(System.Double[],System.Double[],System.Int32,System.Int32,System.Double[],System.Double[],System.Int32,System.Double[],MathNet.Numerics.LinearAlgebra.Factorization.QRMethod)">
            <summary>
            Solves A*X=B for X using a previously QR factored matrix.
            </summary>
            <param name="q">The Q matrix obtained by calling <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.QRFactor(System.Double[],System.Int32,System.Int32,System.Double[],System.Double[])"/>.</param>
            <param name="r">The R matrix obtained by calling <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.QRFactor(System.Double[],System.Int32,System.Int32,System.Double[],System.Double[])"/>. </param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="tau">Contains additional information on Q. Only used for the native solver
            and can be <c>null</c> for the managed provider.</param>
            <param name="b">The B matrix.</param>
            <param name="columnsB">The number of columns of B.</param>
            <param name="x">On exit, the solution matrix.</param>
            <param name="method">The type of QR factorization to perform. <seealso cref="T:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod"/></param>
            <remarks>Rows must be greater or equal to columns.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SingularValueDecomposition(System.Boolean,System.Double[],System.Int32,System.Int32,System.Double[],System.Double[],System.Double[])">
            <summary>
            Computes the singular value decomposition of A.
            </summary>
            <param name="computeVectors">Compute the singular U and VT vectors or not.</param>
            <param name="a">On entry, the M by N matrix to decompose. On exit, A may be overwritten.</param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="s">The singular values of A in ascending value.</param>
            <param name="u">If <paramref name="computeVectors"/> is <c>true</c>, on exit U contains the left
            singular vectors.</param>
            <param name="vt">If <paramref name="computeVectors"/> is <c>true</c>, on exit VT contains the transposed
            right singular vectors.</param>
            <remarks>This is equivalent to the GESVD LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.Drotg(System.Double@,System.Double@,System.Double@,System.Double@)">
            <summary>
            Given the Cartesian coordinates (da, db) of a point p, these function return the parameters da, db, c, and s
            associated with the Givens rotation that zeros the y-coordinate of the point.
            </summary>
            <param name="da">Provides the x-coordinate of the point p. On exit contains the parameter r associated with the Givens rotation</param>
            <param name="db">Provides the y-coordinate of the point p. On exit contains the parameter z associated with the Givens rotation</param>
            <param name="c">Contains the parameter c associated with the Givens rotation</param>
            <param name="s">Contains the parameter s associated with the Givens rotation</param>
            <remarks>This is equivalent to the DROTG LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SvdSolve(System.Double[],System.Int32,System.Int32,System.Double[],System.Int32,System.Double[])">
            <summary>
            Solves A*X=B for X using the singular value decomposition of A.
            </summary>
            <param name="a">On entry, the M by N matrix to decompose.</param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="b">The B matrix.</param>
            <param name="columnsB">The number of columns of B.</param>
            <param name="x">On exit, the solution matrix.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SvdSolveFactored(System.Int32,System.Int32,System.Double[],System.Double[],System.Double[],System.Double[],System.Int32,System.Double[])">
            <summary>
            Solves A*X=B for X using a previously SVD decomposed matrix.
            </summary>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="s">The s values returned by <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SingularValueDecomposition(System.Boolean,System.Double[],System.Int32,System.Int32,System.Double[],System.Double[],System.Double[])"/>.</param>
            <param name="u">The left singular vectors returned by  <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SingularValueDecomposition(System.Boolean,System.Double[],System.Int32,System.Int32,System.Double[],System.Double[],System.Double[])"/>.</param>
            <param name="vt">The right singular  vectors returned by  <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SingularValueDecomposition(System.Boolean,System.Double[],System.Int32,System.Int32,System.Double[],System.Double[],System.Double[])"/>.</param>
            <param name="b">The B matrix.</param>
            <param name="columnsB">The number of columns of B.</param>
            <param name="x">On exit, the solution matrix.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.EigenDecomp(System.Boolean,System.Int32,System.Double[],System.Double[],System.Numerics.Complex[],System.Double[])">
            <summary>
            Computes the eigenvalues and eigenvectors of a matrix.
            </summary>
            <param name="isSymmetric">Whether the matrix is symmetric or not.</param>
            <param name="order">The order of the matrix.</param>
            <param name="matrix">The matrix to decompose. The length of the array must be order * order.</param>
            <param name="matrixEv">On output, the matrix contains the eigen vectors. The length of the array must be order * order.</param>
            <param name="vectorEv">On output, the eigen values (λ) of matrix in ascending value. The length of the array must <paramref name="order"/>.</param>
            <param name="matrixD">On output, the block diagonal eigenvalue matrix. The length of the array must be order * order.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SymmetricTridiagonalize(System.Double[],System.Double[],System.Double[],System.Int32)">
            <summary>
            Symmetric Householder reduction to tridiagonal form.
            </summary>
            <param name="a">Data array of matrix V (eigenvectors)</param>
            <param name="d">Arrays for internal storage of real parts of eigenvalues</param>
            <param name="e">Arrays for internal storage of imaginary parts of eigenvalues</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures tred2 by
            Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
            Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SymmetricDiagonalize(System.Double[],System.Double[],System.Double[],System.Int32)">
            <summary>
            Symmetric tridiagonal QL algorithm.
            </summary>
            <param name="a">Data array of matrix V (eigenvectors)</param>
            <param name="d">Arrays for internal storage of real parts of eigenvalues</param>
            <param name="e">Arrays for internal storage of imaginary parts of eigenvalues</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures tql2, by
            Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
            Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.NonsymmetricReduceToHessenberg(System.Double[],System.Double[],System.Int32)">
            <summary>
            Nonsymmetric reduction to Hessenberg form.
            </summary>
            <param name="a">Data array of matrix V (eigenvectors)</param>
            <param name="matrixH">Array for internal storage of nonsymmetric Hessenberg form.</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures orthes and ortran,
            by Martin and Wilkinson, Handbook for Auto. Comp.,
            Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutines in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.NonsymmetricReduceHessenberToRealSchur(System.Double[],System.Double[],System.Double[],System.Double[],System.Int32)">
            <summary>
            Nonsymmetric reduction from Hessenberg to real Schur form.
            </summary>
            <param name="a">Data array of matrix V (eigenvectors)</param>
            <param name="matrixH">Array for internal storage of nonsymmetric Hessenberg form.</param>
            <param name="d">Arrays for internal storage of real parts of eigenvalues</param>
            <param name="e">Arrays for internal storage of imaginary parts of eigenvalues</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedure hqr2,
            by Martin and Wilkinson, Handbook for Auto. Comp.,
            Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.Cdiv(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Complex scalar division X/Y.
            </summary>
            <param name="xreal">Real part of X</param>
            <param name="ximag">Imaginary part of X</param>
            <param name="yreal">Real part of Y</param>
            <param name="yimag">Imaginary part of Y</param>
            <returns>Division result as a <see cref="T:System.Numerics.Complex"/> number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.AddVectorToScaledVector(System.Single[],System.Single,System.Single[],System.Single[])">
            <summary>
            Adds a scaled vector to another: <c>result = y + alpha*x</c>.
            </summary>
            <param name="y">The vector to update.</param>
            <param name="alpha">The value to scale <paramref name="x"/> by.</param>
            <param name="x">The vector to add to <paramref name="y"/>.</param>
            <param name="result">The result of the addition.</param>
            <remarks>This is similar to the AXPY BLAS routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.ScaleArray(System.Single,System.Single[],System.Single[])">
            <summary>
            Scales an array. Can be used to scale a vector and a matrix.
            </summary>
            <param name="alpha">The scalar.</param>
            <param name="x">The values to scale.</param>
            <param name="result">This result of the scaling.</param>
            <remarks>This is similar to the SCAL BLAS routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.ConjugateArray(System.Single[],System.Single[])">
            <summary>
            Conjugates an array. Can be used to conjugate a vector and a matrix.
            </summary>
            <param name="x">The values to conjugate.</param>
            <param name="result">This result of the conjugation.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.DotProduct(System.Single[],System.Single[])">
            <summary>
            Computes the dot product of x and y.
            </summary>
            <param name="x">The vector x.</param>
            <param name="y">The vector y.</param>
            <returns>The dot product of x and y.</returns>
            <remarks>This is equivalent to the DOT BLAS routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.AddArrays(System.Single[],System.Single[],System.Single[])">
            <summary>
            Does a point wise add of two arrays <c>z = x + y</c>. This can be used
            to add vectors or matrices.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the addition.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SubtractArrays(System.Single[],System.Single[],System.Single[])">
            <summary>
            Does a point wise subtraction of two arrays <c>z = x - y</c>. This can be used
            to subtract vectors or matrices.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the subtraction.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.PointWiseMultiplyArrays(System.Single[],System.Single[],System.Single[])">
            <summary>
            Does a point wise multiplication of two arrays <c>z = x * y</c>. This can be used
            to multiple elements of vectors or matrices.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the point wise multiplication.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.PointWiseDivideArrays(System.Single[],System.Single[],System.Single[])">
            <summary>
            Does a point wise division of two arrays <c>z = x / y</c>. This can be used
            to divide elements of vectors or matrices.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the point wise division.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.PointWisePowerArrays(System.Single[],System.Single[],System.Single[])">
            <summary>
            Does a point wise power of two arrays <c>z = x ^ y</c>. This can be used
            to raise elements of vectors or matrices to the powers of another vector or matrix.
            </summary>
            <param name="x">The array x.</param>
            <param name="y">The array y.</param>
            <param name="result">The result of the point wise power.</param>
            <remarks>There is no equivalent BLAS routine, but many libraries
            provide optimized (parallel and/or vectorized) versions of this
            routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.MatrixNorm(MathNet.Numerics.Providers.LinearAlgebra.Norm,System.Int32,System.Int32,System.Single[])">
            <summary>
            Computes the requested <see cref="T:MathNet.Numerics.Providers.LinearAlgebra.Norm"/> of the matrix.
            </summary>
            <param name="norm">The type of norm to compute.</param>
            <param name="rows">The number of rows.</param>
            <param name="columns">The number of columns.</param>
            <param name="matrix">The matrix to compute the norm from.</param>
            <returns>
            The requested <see cref="T:MathNet.Numerics.Providers.LinearAlgebra.Norm"/> of the matrix.
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.MatrixMultiply(System.Single[],System.Int32,System.Int32,System.Single[],System.Int32,System.Int32,System.Single[])">
            <summary>
            Multiples two matrices. <c>result = x * y</c>
            </summary>
            <param name="x">The x matrix.</param>
            <param name="rowsX">The number of rows in the x matrix.</param>
            <param name="columnsX">The number of columns in the x matrix.</param>
            <param name="y">The y matrix.</param>
            <param name="rowsY">The number of rows in the y matrix.</param>
            <param name="columnsY">The number of columns in the y matrix.</param>
            <param name="result">Where to store the result of the multiplication.</param>
            <remarks>This is a simplified version of the BLAS GEMM routine with alpha
            set to 1.0 and beta set to 0.0, and x and y are not transposed.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.MatrixMultiplyWithUpdate(MathNet.Numerics.Providers.LinearAlgebra.Transpose,MathNet.Numerics.Providers.LinearAlgebra.Transpose,System.Single,System.Single[],System.Int32,System.Int32,System.Single[],System.Int32,System.Int32,System.Single,System.Single[])">
            <summary>
            Multiplies two matrices and updates another with the result. <c>c = alpha*op(a)*op(b) + beta*c</c>
            </summary>
            <param name="transposeA">How to transpose the <paramref name="a"/> matrix.</param>
            <param name="transposeB">How to transpose the <paramref name="b"/> matrix.</param>
            <param name="alpha">The value to scale <paramref name="a"/> matrix.</param>
            <param name="a">The a matrix.</param>
            <param name="rowsA">The number of rows in the <paramref name="a"/> matrix.</param>
            <param name="columnsA">The number of columns in the <paramref name="a"/> matrix.</param>
            <param name="b">The b matrix</param>
            <param name="rowsB">The number of rows in the <paramref name="b"/> matrix.</param>
            <param name="columnsB">The number of columns in the <paramref name="b"/> matrix.</param>
            <param name="beta">The value to scale the <paramref name="c"/> matrix.</param>
            <param name="c">The c matrix.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.LUFactor(System.Single[],System.Int32,System.Int32[])">
            <summary>
            Computes the LUP factorization of A. P*A = L*U.
            </summary>
            <param name="data">An <paramref name="order"/> by <paramref name="order"/> matrix. The matrix is overwritten with the
            the LU factorization on exit. The lower triangular factor L is stored in under the diagonal of <paramref name="data"/> (the diagonal is always 1.0
            for the L factor). The upper triangular factor U is stored on and above the diagonal of <paramref name="data"/>.</param>
            <param name="order">The order of the square matrix <paramref name="data"/>.</param>
            <param name="ipiv">On exit, it contains the pivot indices. The size of the array must be <paramref name="order"/>.</param>
            <remarks>This is equivalent to the GETRF LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.LUInverse(System.Single[],System.Int32)">
            <summary>
            Computes the inverse of matrix using LU factorization.
            </summary>
            <param name="a">The N by N matrix to invert. Contains the inverse On exit.</param>
            <param name="order">The order of the square matrix <paramref name="a"/>.</param>
            <remarks>This is equivalent to the GETRF and GETRI LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.LUInverseFactored(System.Single[],System.Int32,System.Int32[])">
            <summary>
            Computes the inverse of a previously factored matrix.
            </summary>
            <param name="a">The LU factored N by N matrix.  Contains the inverse On exit.</param>
            <param name="order">The order of the square matrix <paramref name="a"/>.</param>
            <param name="ipiv">The pivot indices of <paramref name="a"/>.</param>
            <remarks>This is equivalent to the GETRI LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.LUSolve(System.Int32,System.Single[],System.Int32,System.Single[])">
            <summary>
            Solves A*X=B for X using LU factorization.
            </summary>
            <param name="columnsOfB">The number of columns of B.</param>
            <param name="a">The square matrix A.</param>
            <param name="order">The order of the square matrix <paramref name="a"/>.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <remarks>This is equivalent to the GETRF and GETRS LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.LUSolveFactored(System.Int32,System.Single[],System.Int32,System.Int32[],System.Single[])">
            <summary>
            Solves A*X=B for X using a previously factored A matrix.
            </summary>
            <param name="columnsOfB">The number of columns of B.</param>
            <param name="a">The factored A matrix.</param>
            <param name="order">The order of the square matrix <paramref name="a"/>.</param>
            <param name="ipiv">The pivot indices of <paramref name="a"/>.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <remarks>This is equivalent to the GETRS LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.CholeskyFactor(System.Single[],System.Int32)">
            <summary>
            Computes the Cholesky factorization of A.
            </summary>
            <param name="a">On entry, a square, positive definite matrix. On exit, the matrix is overwritten with the
            the Cholesky factorization.</param>
            <param name="order">The number of rows or columns in the matrix.</param>
            <remarks>This is equivalent to the POTRF LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.DoCholeskyStep(System.Single[],System.Int32,System.Int32,System.Int32,System.Single[],System.Int32)">
            <summary>
            Calculate Cholesky step
            </summary>
            <param name="data">Factor matrix</param>
            <param name="rowDim">Number of rows</param>
            <param name="firstCol">Column start</param>
            <param name="colLimit">Total columns</param>
            <param name="multipliers">Multipliers calculated previously</param>
            <param name="availableCores">Number of available processors</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.CholeskySolve(System.Single[],System.Int32,System.Single[],System.Int32)">
            <summary>
            Solves A*X=B for X using Cholesky factorization.
            </summary>
            <param name="a">The square, positive definite matrix A.</param>
            <param name="orderA">The number of rows and columns in A.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <param name="columnsB">The number of columns in the B matrix.</param>
            <remarks>This is equivalent to the POTRF add POTRS LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.CholeskySolveFactored(System.Single[],System.Int32,System.Single[],System.Int32)">
            <summary>
            Solves A*X=B for X using a previously factored A matrix.
            </summary>
            <param name="a">The square, positive definite matrix A.</param>
            <param name="orderA">The number of rows and columns in A.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <param name="columnsB">The number of columns in the B matrix.</param>
            <remarks>This is equivalent to the POTRS LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.DoCholeskySolve(System.Single[],System.Int32,System.Single[],System.Int32)">
            <summary>
            Solves A*X=B for X using a previously factored A matrix.
            </summary>
            <param name="a">The square, positive definite matrix A. Has to be different than <paramref name="b"/>.</param>
            <param name="orderA">The number of rows and columns in A.</param>
            <param name="b">On entry the B matrix; on exit the X matrix.</param>
            <param name="index">The column to solve for.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.QRFactor(System.Single[],System.Int32,System.Int32,System.Single[],System.Single[])">
            <summary>
            Computes the QR factorization of A.
            </summary>
            <param name="r">On entry, it is the M by N A matrix to factor. On exit,
            it is overwritten with the R matrix of the QR factorization. </param>
            <param name="rowsR">The number of rows in the A matrix.</param>
            <param name="columnsR">The number of columns in the A matrix.</param>
            <param name="q">On exit, A M by M matrix that holds the Q matrix of the
            QR factorization.</param>
            <param name="tau">A min(m,n) vector. On exit, contains additional information
            to be used by the QR solve routine.</param>
            <remarks>This is similar to the GEQRF and ORGQR LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.ThinQRFactor(System.Single[],System.Int32,System.Int32,System.Single[],System.Single[])">
            <summary>
            Computes the QR factorization of A.
            </summary>
            <param name="a">On entry, it is the M by N A matrix to factor. On exit,
            it is overwritten with the Q matrix of the QR factorization.</param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="r">On exit, A N by N matrix that holds the R matrix of the
            QR factorization.</param>
            <param name="tau">A min(m,n) vector. On exit, contains additional information
            to be used by the QR solve routine.</param>
            <remarks>This is similar to the GEQRF and ORGQR LAPACK routines.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.ComputeQR(System.Single[],System.Int32,System.Single[],System.Int32,System.Int32,System.Int32,System.Int32,System.Int32)">
            <summary>
            Perform calculation of Q or R
            </summary>
            <param name="work">Work array</param>
            <param name="workIndex">Index of column in work array</param>
            <param name="a">Q or R matrices</param>
            <param name="rowStart">The first row in </param>
            <param name="rowCount">The last row</param>
            <param name="columnStart">The first column</param>
            <param name="columnCount">The last column</param>
            <param name="availableCores">Number of available CPUs</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.GenerateColumn(System.Single[],System.Single[],System.Int32,System.Int32,System.Int32)">
            <summary>
            Generate column from initial matrix to work array
            </summary>
            <param name="work">Work array</param>
            <param name="a">Initial matrix</param>
            <param name="rowCount">The number of rows in matrix</param>
            <param name="row">The first row</param>
            <param name="column">Column index</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.QRSolve(System.Single[],System.Int32,System.Int32,System.Single[],System.Int32,System.Single[],MathNet.Numerics.LinearAlgebra.Factorization.QRMethod)">
            <summary>
            Solves A*X=B for X using QR factorization of A.
            </summary>
            <param name="a">The A matrix.</param>
            <param name="rows">The number of rows in the A matrix.</param>
            <param name="columns">The number of columns in the A matrix.</param>
            <param name="b">The B matrix.</param>
            <param name="columnsB">The number of columns of B.</param>
            <param name="x">On exit, the solution matrix.</param>
            <param name="method">The type of QR factorization to perform. <seealso cref="T:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod"/></param>
            <remarks>Rows must be greater or equal to columns.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.QRSolveFactored(System.Single[],System.Single[],System.Int32,System.Int32,System.Single[],System.Single[],System.Int32,System.Single[],MathNet.Numerics.LinearAlgebra.Factorization.QRMethod)">
            <summary>
            Solves A*X=B for X using a previously QR factored matrix.
            </summary>
            <param name="q">The Q matrix obtained by calling <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.QRFactor(System.Single[],System.Int32,System.Int32,System.Single[],System.Single[])"/>.</param>
            <param name="r">The R matrix obtained by calling <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.QRFactor(System.Single[],System.Int32,System.Int32,System.Single[],System.Single[])"/>. </param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="tau">Contains additional information on Q. Only used for the native solver
            and can be <c>null</c> for the managed provider.</param>
            <param name="b">The B matrix.</param>
            <param name="columnsB">The number of columns of B.</param>
            <param name="x">On exit, the solution matrix.</param>
            <param name="method">The type of QR factorization to perform. <seealso cref="T:MathNet.Numerics.LinearAlgebra.Factorization.QRMethod"/></param>
            <remarks>Rows must be greater or equal to columns.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SingularValueDecomposition(System.Boolean,System.Single[],System.Int32,System.Int32,System.Single[],System.Single[],System.Single[])">
            <summary>
            Computes the singular value decomposition of A.
            </summary>
            <param name="computeVectors">Compute the singular U and VT vectors or not.</param>
            <param name="a">On entry, the M by N matrix to decompose. On exit, A may be overwritten.</param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="s">The singular values of A in ascending value.</param>
            <param name="u">If <paramref name="computeVectors"/> is <c>true</c>, on exit U contains the left
            singular vectors.</param>
            <param name="vt">If <paramref name="computeVectors"/> is <c>true</c>, on exit VT contains the transposed
            right singular vectors.</param>
            <remarks>This is equivalent to the GESVD LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.Drotg(System.Single@,System.Single@,System.Single@,System.Single@)">
            <summary>
            Given the Cartesian coordinates (da, db) of a point p, these function return the parameters da, db, c, and s
            associated with the Givens rotation that zeros the y-coordinate of the point.
            </summary>
            <param name="da">Provides the x-coordinate of the point p. On exit contains the parameter r associated with the Givens rotation</param>
            <param name="db">Provides the y-coordinate of the point p. On exit contains the parameter z associated with the Givens rotation</param>
            <param name="c">Contains the parameter c associated with the Givens rotation</param>
            <param name="s">Contains the parameter s associated with the Givens rotation</param>
            <remarks>This is equivalent to the DROTG LAPACK routine.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SvdSolve(System.Single[],System.Int32,System.Int32,System.Single[],System.Int32,System.Single[])">
            <summary>
            Solves A*X=B for X using the singular value decomposition of A.
            </summary>
            <param name="a">On entry, the M by N matrix to decompose.</param>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="b">The B matrix.</param>
            <param name="columnsB">The number of columns of B.</param>
            <param name="x">On exit, the solution matrix.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SvdSolveFactored(System.Int32,System.Int32,System.Single[],System.Single[],System.Single[],System.Single[],System.Int32,System.Single[])">
            <summary>
            Solves A*X=B for X using a previously SVD decomposed matrix.
            </summary>
            <param name="rowsA">The number of rows in the A matrix.</param>
            <param name="columnsA">The number of columns in the A matrix.</param>
            <param name="s">The s values returned by <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SingularValueDecomposition(System.Boolean,System.Single[],System.Int32,System.Int32,System.Single[],System.Single[],System.Single[])"/>.</param>
            <param name="u">The left singular vectors returned by  <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SingularValueDecomposition(System.Boolean,System.Single[],System.Int32,System.Int32,System.Single[],System.Single[],System.Single[])"/>.</param>
            <param name="vt">The right singular  vectors returned by  <see cref="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SingularValueDecomposition(System.Boolean,System.Single[],System.Int32,System.Int32,System.Single[],System.Single[],System.Single[])"/>.</param>
            <param name="b">The B matrix.</param>
            <param name="columnsB">The number of columns of B.</param>
            <param name="x">On exit, the solution matrix.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.EigenDecomp(System.Boolean,System.Int32,System.Single[],System.Single[],System.Numerics.Complex[],System.Single[])">
            <summary>
            Computes the eigenvalues and eigenvectors of a matrix.
            </summary>
            <param name="isSymmetric">Whether the matrix is symmetric or not.</param>
            <param name="order">The order of the matrix.</param>
            <param name="matrix">The matrix to decompose. The length of the array must be order * order.</param>
            <param name="matrixEv">On output, the matrix contains the eigen vectors. The length of the array must be order * order.</param>
            <param name="vectorEv">On output, the eigen values (λ) of matrix in ascending value. The length of the array must <paramref name="order"/>.</param>
            <param name="matrixD">On output, the block diagonal eigenvalue matrix. The length of the array must be order * order.</param>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SymmetricTridiagonalize(System.Single[],System.Single[],System.Single[],System.Int32)">
            <summary>
            Symmetric Householder reduction to tridiagonal form.
            </summary>
            <param name="a">Data array of matrix V (eigenvectors)</param>
            <param name="d">Arrays for internal storage of real parts of eigenvalues</param>
            <param name="e">Arrays for internal storage of imaginary parts of eigenvalues</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures tred2 by
            Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
            Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.SymmetricDiagonalize(System.Single[],System.Single[],System.Single[],System.Int32)">
            <summary>
            Symmetric tridiagonal QL algorithm.
            </summary>
            <param name="a">Data array of matrix V (eigenvectors)</param>
            <param name="d">Arrays for internal storage of real parts of eigenvalues</param>
            <param name="e">Arrays for internal storage of imaginary parts of eigenvalues</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures tql2, by
            Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
            Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.NonsymmetricReduceToHessenberg(System.Single[],System.Single[],System.Int32)">
            <summary>
            Nonsymmetric reduction to Hessenberg form.
            </summary>
            <param name="a">Data array of matrix V (eigenvectors)</param>
            <param name="matrixH">Array for internal storage of nonsymmetric Hessenberg form.</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedures orthes and ortran,
            by Martin and Wilkinson, Handbook for Auto. Comp.,
            Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutines in EISPACK.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.NonsymmetricReduceHessenberToRealSchur(System.Single[],System.Single[],System.Single[],System.Single[],System.Int32)">
            <summary>
            Nonsymmetric reduction from Hessenberg to real Schur form.
            </summary>
            <param name="a">Data array of matrix V (eigenvectors)</param>
            <param name="matrixH">Array for internal storage of nonsymmetric Hessenberg form.</param>
            <param name="d">Arrays for internal storage of real parts of eigenvalues</param>
            <param name="e">Arrays for internal storage of imaginary parts of eigenvalues</param>
            <param name="order">Order of initial matrix</param>
            <remarks>This is derived from the Algol procedure hqr2,
            by Martin and Wilkinson, Handbook for Auto. Comp.,
            Vol.ii-Linear Algebra, and the corresponding
            Fortran subroutine in EISPACK.</remarks>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.Providers.LinearAlgebra.ManagedLinearAlgebraProvider.Cdiv(System.Single,System.Single,System.Single,System.Single)">
            <summary>
            Complex scalar division X/Y.
            </summary>
            <param name="xreal">Real part of X</param>
            <param name="ximag">Imaginary part of X</param>
            <param name="yreal">Real part of Y</param>
            <param name="yimag">Imaginary part of Y</param>
            <returns>Division result as a <see cref="T:System.Numerics.Complex"/> number.</returns>
        </member>
        <member name="T:MathNet.Numerics.Providers.SparseSolver.DssMatrixStructure">
            <summary>
            Structure option.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Providers.SparseSolver.DssMatrixType">
            <summary>
            Factorization option.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Providers.SparseSolver.DssSystemType">
            <summary>
            Solver step's substitution.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Providers.SparseSolver.DssSystemType.DontTranspose">
            <summary>
            Solve a system, Ax = b.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Providers.SparseSolver.DssSystemType.Transpose">
            <summary>
            Solve a transposed system, A'x = b
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Providers.SparseSolver.DssSystemType.ConjugateTranspose">
            <summary>
            Solve a conjugate transposed system, A†x = b
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Providers.SparseSolver.DssStatus">
            <summary>
            Status values
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Providers.SparseSolver.DssStatus.MKL_DSS_SUCCESS">
            <summary>
            The operation was successful.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.SparseSolver.ISparseSolverProvider.IsAvailable">
            <summary>
            Try to find out whether the provider is available, at least in principle.
            Verification may still fail if available, but it will certainly fail if unavailable.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.SparseSolver.ISparseSolverProvider.InitializeVerify">
            <summary>
            Initialize and verify that the provided is indeed available. If not, fall back to alternatives like the managed provider
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.SparseSolver.ISparseSolverProvider.FreeResources">
            <summary>
            Frees memory buffers, caches and handles allocated in or to the provider.
            Does not unload the provider itself, it is still usable afterwards.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Providers.SparseSolver.ManagedSparseSolverProvider">
            <summary>
            The managed sparse solver provider
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.SparseSolver.ManagedSparseSolverProvider.IsAvailable">
            <summary>
            Try to find out whether the provider is available, at least in principle.
            Verification may still fail if available, but it will certainly fail if unavailable.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.SparseSolver.ManagedSparseSolverProvider.InitializeVerify">
            <summary>
            Initialize and verify that the provided is indeed available. If not, fall back to alternatives like the managed provider
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.SparseSolver.ManagedSparseSolverProvider.FreeResources">
            <summary>
            Frees memory buffers, caches and handles allocated in or to the provider.
            Does not unload the provider itself, it is still usable afterwards.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Providers.SparseSolver.SparseSolverControl.HintPath">
            <summary>
            Optional path to try to load native provider binaries from,
            if the provider specific hint path is not set.
            If neither is set, Numerics falls back to the provider specific
            environment variables, or the default probing paths.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Providers.SparseSolver.SparseSolverControl.Provider">
            <summary>
            Gets or sets the sparse solver provider. Consider to use UseNativeMKL or UseManaged instead.
            </summary>
            <value>The linear algebra provider.</value>
        </member>
        <member name="M:MathNet.Numerics.Providers.SparseSolver.SparseSolverControl.TryUseNative">
            <summary>
            Try to use a native provider, if available.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.SparseSolver.SparseSolverControl.UseBest">
            <summary>
            Use the best provider available.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Providers.SparseSolver.SparseSolverControl.UseDefault">
            <summary>
            Use a specific provider if configured, e.g. using the
            "MathNetNumericsDSSProvider" environment variable,
            or fall back to the best provider.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Random.CryptoRandomSource">
            <summary>
            A random number generator based on the <see cref="T:System.Security.Cryptography.RandomNumberGenerator"/> class in the .NET library.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.CryptoRandomSource.#ctor">
            <summary>
            Construct a new random number generator with a random seed.
            </summary>
            <remarks>Uses <see cref="T:System.Security.Cryptography.RandomNumberGenerator"/> and uses the value of
            <see cref="P:MathNet.Numerics.Control.ThreadSafeRandomNumberGenerators"/> to set whether the instance is thread safe.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.CryptoRandomSource.#ctor(System.Security.Cryptography.RandomNumberGenerator)">
            <summary>
            Construct a new random number generator with random seed.
            </summary>
            <param name="rng">The <see cref="T:System.Security.Cryptography.RandomNumberGenerator"/> to use.</param>
            <remarks>Uses the value of  <see cref="P:MathNet.Numerics.Control.ThreadSafeRandomNumberGenerators"/> to set whether the instance is thread safe.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.CryptoRandomSource.#ctor(System.Boolean)">
            <summary>
            Construct a new random number generator with random seed.
            </summary>
            <remarks>Uses <see cref="T:System.Security.Cryptography.RandomNumberGenerator"/></remarks>
            <param name="threadSafe">if set to <c>true</c> , the class is thread safe.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.CryptoRandomSource.#ctor(System.Security.Cryptography.RandomNumberGenerator,System.Boolean)">
            <summary>
            Construct a new random number generator with random seed.
            </summary>
            <param name="rng">The <see cref="T:System.Security.Cryptography.RandomNumberGenerator"/> to use.</param>
            <param name="threadSafe">if set to <c>true</c> , the class is thread safe.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.CryptoRandomSource.DoSampleBytes(System.Byte[])">
            <summary>
            Fills the elements of a specified array of bytes with random numbers in full range, including zero and 255 (<see cref="F:System.Byte.MaxValue"/>).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.CryptoRandomSource.DoSample">
            <summary>
            Returns a random double-precision floating point number greater than or equal to 0.0, and less than 1.0.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.CryptoRandomSource.DoSampleInteger">
            <summary>
            Returns a random 32-bit signed integer greater than or equal to zero and less than <see cref="F:System.Int32.MaxValue"/>
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.CryptoRandomSource.Doubles(System.Double[])">
            <summary>
            Fills an array with random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.CryptoRandomSource.Doubles(System.Int32)">
            <summary>
            Returns an array of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.CryptoRandomSource.DoubleSequence">
            <summary>
            Returns an infinite sequence of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="T:MathNet.Numerics.Random.Mcg31m1">
            <summary>
            Multiplicative congruential generator using a modulus of 2^31-1 and a multiplier of 1132489760.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Mcg31m1.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Mcg31m1"/> class using
            a seed based on time and unique GUIDs.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Mcg31m1.#ctor(System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Mcg31m1"/> class using
            a seed based on time and unique GUIDs.
            </summary>
            <param name="threadSafe">if set to <c>true</c> , the class is thread safe.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.Mcg31m1.#ctor(System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Mcg31m1"/> class.
            </summary>
            <param name="seed">The seed value.</param>
            <remarks>If the seed value is zero, it is set to one. Uses the
            value of <see cref="P:MathNet.Numerics.Control.ThreadSafeRandomNumberGenerators"/> to
            set whether the instance is thread safe.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Mcg31m1.#ctor(System.Int32,System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Mcg31m1"/> class.
            </summary>
            <param name="seed">The seed value.</param>
            <param name="threadSafe">if set to <c>true</c>, the class is thread safe.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.Mcg31m1.DoSample">
            <summary>
            Returns a random double-precision floating point number greater than or equal to 0.0, and less than 1.0.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Mcg31m1.Doubles(System.Double[],System.Int32)">
            <summary>
            Fills an array with random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Mcg31m1.Doubles(System.Int32,System.Int32)">
            <summary>
            Returns an array of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Mcg31m1.DoubleSequence(System.Int32)">
            <summary>
            Returns an infinite sequence of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads, but the result must be enumerated from a single thread each.</remarks>
        </member>
        <member name="T:MathNet.Numerics.Random.Mcg59">
            <summary>
            Multiplicative congruential generator using a modulus of 2^59 and a multiplier of 13^13.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Mcg59.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Mcg59"/> class using
            a seed based on time and unique GUIDs.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Mcg59.#ctor(System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Mcg59"/> class using
            a seed based on time and unique GUIDs.
            </summary>
            <param name="threadSafe">if set to <c>true</c> , the class is thread safe.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.Mcg59.#ctor(System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Mcg59"/> class.
            </summary>
            <param name="seed">The seed value.</param>
            <remarks>If the seed value is zero, it is set to one. Uses the
            value of <see cref="P:MathNet.Numerics.Control.ThreadSafeRandomNumberGenerators"/> to
            set whether the instance is thread safe.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Mcg59.#ctor(System.Int32,System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Mcg59"/> class.
            </summary>
            <param name="seed">The seed value.</param>
            <remarks>The seed is set to 1, if the zero is used as the seed.</remarks>
            <param name="threadSafe">if set to <c>true</c> , the class is thread safe.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.Mcg59.DoSample">
            <summary>
            Returns a random double-precision floating point number greater than or equal to 0.0, and less than 1.0.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Mcg59.Doubles(System.Double[],System.Int32)">
            <summary>
            Fills an array with random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Mcg59.Doubles(System.Int32,System.Int32)">
            <summary>
            Returns an array of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Mcg59.DoubleSequence(System.Int32)">
            <summary>
            Returns an infinite sequence of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads, but the result must be enumerated from a single thread each.</remarks>
        </member>
        <member name="T:MathNet.Numerics.Random.MersenneTwister">
            <summary>
            Random number generator using Mersenne Twister 19937 algorithm.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.MersenneTwister.LowerMask">
            <summary>
            Mersenne twister constant.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.MersenneTwister.M">
            <summary>
            Mersenne twister constant.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.MersenneTwister.MatrixA">
            <summary>
            Mersenne twister constant.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.MersenneTwister.N">
            <summary>
            Mersenne twister constant.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.MersenneTwister.Reciprocal">
            <summary>
            Mersenne twister constant.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.MersenneTwister.UpperMask">
            <summary>
            Mersenne twister constant.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.MersenneTwister.Mag01">
            <summary>
            Mersenne twister constant.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.MersenneTwister._mt">
            <summary>
            Mersenne twister constant.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.MersenneTwister._mti">
            <summary>
            Mersenne twister constant.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.MersenneTwister.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.MersenneTwister"/> class using
            a seed based on time and unique GUIDs.
            </summary>
            <remarks>If the seed value is zero, it is set to one. Uses the
            value of <see cref="P:MathNet.Numerics.Control.ThreadSafeRandomNumberGenerators"/> to
            set whether the instance is thread safe.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.MersenneTwister.#ctor(System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.MersenneTwister"/> class using
            a seed based on time and unique GUIDs.
            </summary>
            <param name="threadSafe">if set to <c>true</c> , the class is thread safe.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.MersenneTwister.#ctor(System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.MersenneTwister"/> class.
            </summary>
            <param name="seed">The seed value.</param>
            <remarks>Uses the value of <see cref="P:MathNet.Numerics.Control.ThreadSafeRandomNumberGenerators"/> to
            set whether the instance is thread safe.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.MersenneTwister.#ctor(System.Int32,System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.MersenneTwister"/> class.
            </summary>
            <param name="seed">The seed value.</param>
            <param name="threadSafe">if set to <c>true</c>, the class is thread safe.</param>
        </member>
        <member name="P:MathNet.Numerics.Random.MersenneTwister.Default">
            <summary>
            Default instance, thread-safe.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.MersenneTwister.DoSample">
            <summary>
            Returns a random double-precision floating point number greater than or equal to 0.0, and less than 1.0.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.MersenneTwister.DoSampleInteger">
            <summary>
            Returns a random 32-bit signed integer greater than or equal to zero and less than <see cref="F:System.Int32.MaxValue"/>
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.MersenneTwister.DoSampleBytes(System.Byte[])">
            <summary>
            Fills the elements of a specified array of bytes with random numbers in full range, including zero and 255 (<see cref="F:System.Byte.MaxValue"/>).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.MersenneTwister.Doubles(System.Double[],System.Int32)">
            <summary>
            Fills an array with random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.MersenneTwister.Doubles(System.Int32,System.Int32)">
            <summary>
            Returns an array of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.MersenneTwister.DoubleSequence(System.Int32)">
            <summary>
            Returns an infinite sequence of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads, but the result must be enumerated from a single thread each.</remarks>
        </member>
        <member name="T:MathNet.Numerics.Random.Mrg32k3a">
             <summary>
             A 32-bit combined multiple recursive generator with 2 components of order 3.
             </summary>
             <remarks>Based off of P. L'Ecuyer, "Combined Multiple Recursive Random Number Generators," Operations Research, 44, 5 (1996), 816--822. </remarks>
            
        </member>
        <member name="M:MathNet.Numerics.Random.Mrg32k3a.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Mcg31m1"/> class using
            a seed based on time and unique GUIDs.
            </summary>
            <remarks>If the seed value is zero, it is set to one. Uses the
            value of <see cref="P:MathNet.Numerics.Control.ThreadSafeRandomNumberGenerators"/> to
            set whether the instance is thread safe.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Mrg32k3a.#ctor(System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Mcg31m1"/> class using
            a seed based on time and unique GUIDs.
            </summary>
            <param name="threadSafe">if set to <c>true</c> , the class is thread safe.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.Mrg32k3a.#ctor(System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Mrg32k3a"/> class.
            </summary>
            <param name="seed">The seed value.</param>
            <remarks>If the seed value is zero, it is set to one. Uses the
            value of <see cref="P:MathNet.Numerics.Control.ThreadSafeRandomNumberGenerators"/> to
            set whether the instance is thread safe.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Mrg32k3a.#ctor(System.Int32,System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Mrg32k3a"/> class.
            </summary>
            <param name="seed">The seed value.</param>
            <param name="threadSafe">if set to <c>true</c>, the class is thread safe.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.Mrg32k3a.DoSample">
            <summary>
            Returns a random double-precision floating point number greater than or equal to 0.0, and less than 1.0.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Mrg32k3a.Doubles(System.Double[],System.Int32)">
            <summary>
            Fills an array with random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Mrg32k3a.Doubles(System.Int32,System.Int32)">
            <summary>
            Returns an array of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Mrg32k3a.DoubleSequence(System.Int32)">
            <summary>
            Returns an infinite sequence of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads, but the result must be enumerated from a single thread each.</remarks>
        </member>
        <member name="T:MathNet.Numerics.Random.Palf">
            <summary>
            Represents a Parallel Additive Lagged Fibonacci pseudo-random number generator.
            </summary>
            <remarks>
            The <see cref="T:MathNet.Numerics.Random.Palf"/> type bases upon the implementation in the
            <a href="http://www.boost.org/libs/random/index.html">Boost Random Number Library</a>.
            It uses the modulus 2<sup>32</sup> and by default the "lags" 418 and 1279. Some popular pairs are presented on
            <a href="http://en.wikipedia.org/wiki/Lagged_Fibonacci_generator">Wikipedia - Lagged Fibonacci generator</a>.
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.Random.Palf.DefaultShortLag">
            <summary>
            Default value for the ShortLag
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.Palf.DefaultLongLag">
            <summary>
            Default value for the LongLag
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.Palf.Reciprocal">
            <summary>
            The multiplier to compute a double-precision floating point number [0, 1)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Palf.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Palf"/> class using
            a seed based on time and unique GUIDs.
            </summary>
            <remarks>If the seed value is zero, it is set to one. Uses the
            value of <see cref="P:MathNet.Numerics.Control.ThreadSafeRandomNumberGenerators"/> to
            set whether the instance is thread safe.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Palf.#ctor(System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Palf"/> class using
            a seed based on time and unique GUIDs.
            </summary>
            <param name="threadSafe">if set to <c>true</c> , the class is thread safe.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.Palf.#ctor(System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Palf"/> class.
            </summary>
            <param name="seed">The seed value.</param>
            <remarks>If the seed value is zero, it is set to one. Uses the
            value of <see cref="P:MathNet.Numerics.Control.ThreadSafeRandomNumberGenerators"/> to
            set whether the instance is thread safe.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Palf.#ctor(System.Int32,System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Palf"/> class.
            </summary>
            <param name="seed">The seed value.</param>
            <param name="threadSafe">if set to <c>true</c> , the class is thread safe.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.Palf.#ctor(System.Int32,System.Boolean,System.Int32,System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Palf"/> class.
            </summary>
            <param name="seed">The seed value.</param>
            <param name="threadSafe">if set to <c>true</c>, the class is thread safe.</param>
            <param name="shortLag">The ShortLag value</param>
            <param name="longLag">TheLongLag value</param>
        </member>
        <member name="P:MathNet.Numerics.Random.Palf.ShortLag">
            <summary>
            Gets the short lag of the Lagged Fibonacci pseudo-random number generator.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Random.Palf.LongLag">
            <summary>
            Gets the long lag of the Lagged Fibonacci pseudo-random number generator.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.Palf._x">
            <summary>
            Stores an array of <see cref="P:MathNet.Numerics.Random.Palf.LongLag"/> random numbers
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.Palf._k">
            <summary>
            Stores an index for the random number array element that will be accessed next.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Palf.Fill">
            <summary>
            Fills the array <see cref="F:MathNet.Numerics.Random.Palf._x"/> with <see cref="P:MathNet.Numerics.Random.Palf.LongLag"/> new unsigned random numbers.
            </summary>
            <remarks>
            Generated random numbers are 32-bit unsigned integers greater than or equal to 0
            and less than or equal to <see cref="F:System.Int32.MaxValue"/>.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Palf.DoSample">
            <summary>
            Returns a random double-precision floating point number greater than or equal to 0.0, and less than 1.0.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Palf.DoSampleInteger">
            <summary>
            Returns a random 32-bit signed integer greater than or equal to zero and less than <see cref="F:System.Int32.MaxValue"/>
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Palf.Doubles(System.Double[],System.Int32)">
            <summary>
            Fills an array with random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Palf.Doubles(System.Int32,System.Int32)">
            <summary>
            Returns an array of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Palf.DoubleSequence(System.Int32)">
            <summary>
            Returns an infinite sequence of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads, but the result must be enumerated from a single thread each.</remarks>
        </member>
        <member name="T:MathNet.Numerics.Random.RandomExtensions">
            <summary>
            This class implements extension methods for the System.Random class. The extension methods generate
            pseudo-random distributed numbers for types other than double and int32.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomExtensions.NextDoubles(System.Random,System.Double[])">
            <summary>
            Fills an array with uniform random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <param name="rnd">The random number generator.</param>
            <param name="values">The array to fill with random values.</param>
            <remarks>
            This extension is thread-safe if and only if called on an random number
            generator provided by Math.NET Numerics or derived from the RandomSource class.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomExtensions.NextDoubles(System.Random,System.Int32)">
            <summary>
            Returns an array of uniform random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <param name="rnd">The random number generator.</param>
            <param name="count">The size of the array to fill.</param>
            <remarks>
            This extension is thread-safe if and only if called on an random number
            generator provided by Math.NET Numerics or derived from the RandomSource class.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomExtensions.NextDoubleSequence(System.Random)">
            <summary>
            Returns an infinite sequence of uniform random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>
            This extension is thread-safe if and only if called on an random number
            generator provided by Math.NET Numerics or derived from the RandomSource class.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomExtensions.NextBytes(System.Random,System.Int32)">
            <summary>
            Returns an array of uniform random bytes.
            </summary>
            <param name="rnd">The random number generator.</param>
            <param name="count">The size of the array to fill.</param>
            <remarks>
            This extension is thread-safe if and only if called on an random number
            generator provided by Math.NET Numerics or derived from the RandomSource class.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomExtensions.NextInt32s(System.Random,System.Int32[])">
            <summary>
            Fills an array with uniform random 32-bit signed integers greater than or equal to zero and less than <see cref="F:System.Int32.MaxValue"/>.
            </summary>
            <param name="rnd">The random number generator.</param>
            <param name="values">The array to fill with random values.</param>
            <remarks>
            This extension is thread-safe if and only if called on an random number
            generator provided by Math.NET Numerics or derived from the RandomSource class.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomExtensions.NextInt32s(System.Random,System.Int32[],System.Int32,System.Int32)">
            <summary>
            Fills an array with uniform random 32-bit signed integers within the specified range.
            </summary>
            <param name="rnd">The random number generator.</param>
            <param name="values">The array to fill with random values.</param>
            <param name="minInclusive">Lower bound, inclusive.</param>
            <param name="maxExclusive">Upper bound, exclusive.</param>
            <remarks>
            This extension is thread-safe if and only if called on an random number
            generator provided by Math.NET Numerics or derived from the RandomSource class.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomExtensions.NextInt32Sequence(System.Random,System.Int32,System.Int32)">
            <summary>
            Returns an infinite sequence of uniform random 32-bit signed integers within the specified range.
            </summary>
            <remarks>
            This extension is thread-safe if and only if called on an random number
            generator provided by Math.NET Numerics or derived from the RandomSource class.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomExtensions.NextBigIntegerSequence(System.Random,System.Numerics.BigInteger,System.Numerics.BigInteger)">
            <summary>
            Returns an infinite sequence of uniform random <see cref="T:System.Numerics.BigInteger"/> within the specified range.
            </summary>
            <remarks>
            This extension is thread-safe if and only if called on an random number
            generator provided by Math.NET Numerics or derived from the RandomSource class.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomExtensions.NextInt64(System.Random)">
            <summary>
            Returns a nonnegative random number less than <see cref="F:System.Int64.MaxValue"/>.
            </summary>
            <param name="rnd">The random number generator.</param>
            <returns>
            A 64-bit signed integer greater than or equal to 0, and less than <see cref="F:System.Int64.MaxValue"/>; that is,
            the range of return values includes 0 but not <see cref="F:System.Int64.MaxValue"/>.
            </returns>
            <seealso cref="M:MathNet.Numerics.Random.RandomExtensions.NextFullRangeInt64(System.Random)"/>
            <remarks>
            This extension is thread-safe if and only if called on an random number
            generator provided by Math.NET Numerics or derived from the RandomSource class.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomExtensions.NextFullRangeInt32(System.Random)">
            <summary>
            Returns a random number of the full Int32 range.
            </summary>
            <param name="rnd">The random number generator.</param>
            <returns>
            A 32-bit signed integer of the full range, including 0, negative numbers,
            <see cref="F:System.Int32.MaxValue"/> and <see cref="F:System.Int32.MinValue"/>.
            </returns>
            <seealso cref="M:System.Random.Next"/>
            <remarks>
            This extension is thread-safe if and only if called on an random number
            generator provided by Math.NET Numerics or derived from the RandomSource class.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomExtensions.NextFullRangeInt64(System.Random)">
            <summary>
            Returns a random number of the full Int64 range.
            </summary>
            <param name="rnd">The random number generator.</param>
            <returns>
            A 64-bit signed integer of the full range, including 0, negative numbers,
            <see cref="F:System.Int64.MaxValue"/> and <see cref="F:System.Int64.MinValue"/>.
            </returns>
            <seealso cref="M:MathNet.Numerics.Random.RandomExtensions.NextInt64(System.Random)"/>
            <remarks>
            This extension is thread-safe if and only if called on an random number
            generator provided by Math.NET Numerics or derived from the RandomSource class.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomExtensions.NextDecimal(System.Random)">
            <summary>
            Returns a nonnegative decimal floating point random number less than 1.0.
            </summary>
            <param name="rnd">The random number generator.</param>
            <returns>
            A decimal floating point number greater than or equal to 0.0, and less than 1.0; that is,
            the range of return values includes 0.0 but not 1.0.
            </returns>
            <remarks>
            This extension is thread-safe if and only if called on an random number
            generator provided by Math.NET Numerics or derived from the RandomSource class.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomExtensions.NextBoolean(System.Random)">
            <summary>
            Returns a random boolean.
            </summary>
            <param name="rnd">The random number generator.</param>
            <remarks>
            This extension is thread-safe if and only if called on an random number
            generator provided by Math.NET Numerics or derived from the RandomSource class.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSeed.Time">
            <summary>
            Provides a time-dependent seed value, matching the default behavior of System.Random.
            WARNING: There is no randomness in this seed and quick repeated calls can cause
            the same seed value. Do not use for cryptography!
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSeed.Guid">
            <summary>
            Provides a seed based on time and unique GUIDs.
            WARNING: There is only low randomness in this seed, but at least quick repeated
            calls will result in different seed values. Do not use for cryptography!
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSeed.Robust">
            <summary>
            Provides a seed based on an internal random number generator (crypto if available), time and unique GUIDs.
            WARNING: There is only medium randomness in this seed, but quick repeated
            calls will result in different seed values. Do not use for cryptography!
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Random.RandomSource">
            <summary>
            Base class for random number generators. This class introduces a layer between <see cref="T:System.Random"/>
            and the Math.NET Numerics random number generators to provide thread safety.
            When used directly it use the System.Random as random number source.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.RandomSource"/> class using
            the value of <see cref="P:MathNet.Numerics.Control.ThreadSafeRandomNumberGenerators"/> to set whether
            the instance is thread safe or not.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.#ctor(System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.RandomSource"/> class.
            </summary>
            <param name="threadSafe">if set to <c>true</c> , the class is thread safe.</param>
            <remarks>Thread safe instances are two and half times slower than non-thread
            safe classes.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.NextDoubles(System.Double[])">
            <summary>
            Fills an array with uniform random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <param name="values">The array to fill with random values.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.NextDoubles(System.Int32)">
            <summary>
            Returns an array of uniform random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <param name="count">The size of the array to fill.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.NextDoubleSequence">
            <summary>
            Returns an infinite sequence of uniform random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.Next">
            <summary>
            Returns a random 32-bit signed integer greater than or equal to zero and less than <see cref="F:System.Int32.MaxValue"/>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.Next(System.Int32)">
            <summary>
            Returns a random number less then a specified maximum.
            </summary>
            <param name="maxExclusive">The exclusive upper bound of the random number returned. Range: maxExclusive ≥ 1.</param>
            <returns>A 32-bit signed integer less than <paramref name="maxExclusive"/>.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException"><paramref name="maxExclusive"/> is zero or negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.Next(System.Int32,System.Int32)">
            <summary>
            Returns a random number within a specified range.
            </summary>
            <param name="minInclusive">The inclusive lower bound of the random number returned.</param>
            <param name="maxExclusive">The exclusive upper bound of the random number returned. Range: maxExclusive > minExclusive.</param>
            <returns>
            A 32-bit signed integer greater than or equal to <paramref name="minInclusive"/> and less than <paramref name="maxExclusive"/>; that is, the range of return values includes <paramref name="minInclusive"/> but not <paramref name="maxExclusive"/>. If <paramref name="minInclusive"/> equals <paramref name="maxExclusive"/>, <paramref name="minInclusive"/> is returned.
            </returns>
            <exception cref="T:System.ArgumentOutOfRangeException"><paramref name="minInclusive"/> is greater than <paramref name="maxExclusive"/>. </exception>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.NextInt32s(System.Int32[])">
            <summary>
            Fills an array with random 32-bit signed integers greater than or equal to zero and less than <see cref="F:System.Int32.MaxValue"/>.
            </summary>
            <param name="values">The array to fill with random values.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.NextInt32s(System.Int32)">
            <summary>
            Returns an array with random 32-bit signed integers greater than or equal to zero and less than <see cref="F:System.Int32.MaxValue"/>.
            </summary>
            <param name="count">The size of the array to fill.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.NextInt32s(System.Int32[],System.Int32)">
            <summary>
            Fills an array with random numbers within a specified range.
            </summary>
            <param name="values">The array to fill with random values.</param>
            <param name="maxExclusive">The exclusive upper bound of the random number returned. Range: maxExclusive ≥ 1.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.NextInt32s(System.Int32,System.Int32)">
            <summary>
            Returns an array with random 32-bit signed integers within the specified range.
            </summary>
            <param name="count">The size of the array to fill.</param>
            <param name="maxExclusive">The exclusive upper bound of the random number returned. Range: maxExclusive ≥ 1.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.NextInt32s(System.Int32[],System.Int32,System.Int32)">
            <summary>
            Fills an array with random numbers within a specified range.
            </summary>
            <param name="values">The array to fill with random values.</param>
            <param name="minInclusive">The inclusive lower bound of the random number returned.</param>
            <param name="maxExclusive">The exclusive upper bound of the random number returned. Range: maxExclusive > minExclusive.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.NextInt32s(System.Int32,System.Int32,System.Int32)">
            <summary>
            Returns an array with random 32-bit signed integers within the specified range.
            </summary>
            <param name="count">The size of the array to fill.</param>
            <param name="minInclusive">The inclusive lower bound of the random number returned.</param>
            <param name="maxExclusive">The exclusive upper bound of the random number returned. Range: maxExclusive > minExclusive.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.NextInt32Sequence">
            <summary>
            Returns an infinite sequence of random 32-bit signed integers greater than or equal to zero and less than <see cref="F:System.Int32.MaxValue"/>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.NextInt32Sequence(System.Int32,System.Int32)">
            <summary>
            Returns an infinite sequence of random numbers within a specified range.
            </summary>
            <param name="minInclusive">The inclusive lower bound of the random number returned.</param>
            <param name="maxExclusive">The exclusive upper bound of the random number returned. Range: maxExclusive > minExclusive.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.NextBytes(System.Byte[])">
            <summary>
            Fills the elements of a specified array of bytes with random numbers.
            </summary>
            <param name="buffer">An array of bytes to contain random numbers.</param>
            <exception cref="T:System.ArgumentNullException"><paramref name="buffer"/> is null. </exception>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.Sample">
            <summary>
            Returns a random number between 0.0 and 1.0.
            </summary>
            <returns>A double-precision floating point number greater than or equal to 0.0, and less than 1.0.</returns>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.DoSample">
            <summary>
            Returns a random double-precision floating point number greater than or equal to 0.0, and less than 1.0.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.DoSampleInteger">
            <summary>
            Returns a random 32-bit signed integer greater than or equal to zero and less than 2147483647 (<see cref="F:System.Int32.MaxValue"/>).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.DoSampleBytes(System.Byte[])">
            <summary>
            Fills the elements of a specified array of bytes with random numbers in full range, including zero and 255 (<see cref="F:System.Byte.MaxValue"/>).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.DoSampleInt32WithNBits(System.Int32)">
            <summary>
            Returns a random N-bit signed integer greater than or equal to zero and less than 2^N.
            N (bit count) is expected to be greater than zero and less than 32 (not verified).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.DoSampleInt64WithNBits(System.Int32)">
            <summary>
            Returns a random N-bit signed long integer greater than or equal to zero and less than 2^N.
            N (bit count) is expected to be greater than zero and less than 64 (not verified).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.DoSampleInteger(System.Int32)">
            <summary>
            Returns a random 32-bit signed integer within the specified range.
            </summary>
            <param name="maxExclusive">The exclusive upper bound of the random number returned. Range: maxExclusive ≥ 2 (not verified, must be ensured by caller).</param>
        </member>
        <member name="M:MathNet.Numerics.Random.RandomSource.DoSampleInteger(System.Int32,System.Int32)">
            <summary>
            Returns a random 32-bit signed integer within the specified range.
            </summary>
            <param name="minInclusive">The inclusive lower bound of the random number returned.</param>
            <param name="maxExclusive">The exclusive upper bound of the random number returned. Range: maxExclusive ≥ minExclusive + 2 (not verified, must be ensured by caller).</param>
        </member>
        <member name="T:MathNet.Numerics.Random.SystemRandomSource">
            <summary>
            A random number generator based on the <see cref="T:System.Random"/> class in the .NET library.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.SystemRandomSource.#ctor">
            <summary>
            Construct a new random number generator with a random seed.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.SystemRandomSource.#ctor(System.Boolean)">
            <summary>
            Construct a new random number generator with random seed.
            </summary>
            <param name="threadSafe">if set to <c>true</c> , the class is thread safe.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.SystemRandomSource.#ctor(System.Int32)">
            <summary>
            Construct a new random number generator with random seed.
            </summary>
            <param name="seed">The seed value.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.SystemRandomSource.#ctor(System.Int32,System.Boolean)">
            <summary>
            Construct a new random number generator with random seed.
            </summary>
            <param name="seed">The seed value.</param>
            <param name="threadSafe">if set to <c>true</c> , the class is thread safe.</param>
        </member>
        <member name="P:MathNet.Numerics.Random.SystemRandomSource.Default">
            <summary>
            Default instance, thread-safe.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.SystemRandomSource.DoSample">
            <summary>
            Returns a random double-precision floating point number greater than or equal to 0.0, and less than 1.0.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.SystemRandomSource.DoSampleInteger">
            <summary>
            Returns a random 32-bit signed integer greater than or equal to zero and less than <see cref="F:System.Int32.MaxValue"/>
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.SystemRandomSource.DoSampleInteger(System.Int32)">
            <summary>
            Returns a random 32-bit signed integer within the specified range.
            </summary>
            <param name="maxExclusive">The exclusive upper bound of the random number returned. Range: maxExclusive ≥ 2 (not verified, must be ensured by caller).</param>
        </member>
        <member name="M:MathNet.Numerics.Random.SystemRandomSource.DoSampleInteger(System.Int32,System.Int32)">
            <summary>
            Returns a random 32-bit signed integer within the specified range.
            </summary>
            <param name="minInclusive">The inclusive lower bound of the random number returned.</param>
            <param name="maxExclusive">The exclusive upper bound of the random number returned. Range: maxExclusive ≥ minExclusive + 2 (not verified, must be ensured by caller).</param>
        </member>
        <member name="M:MathNet.Numerics.Random.SystemRandomSource.DoSampleBytes(System.Byte[])">
            <summary>
            Fills the elements of a specified array of bytes with random numbers in full range, including zero and 255 (<see cref="F:System.Byte.MaxValue"/>).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.SystemRandomSource.FastDoubles(System.Double[])">
            <summary>
            Fill an array with uniform random numbers greater than or equal to 0.0 and less than 1.0.
            WARNING: potentially very short random sequence length, can generate repeated partial sequences.
            </summary>
            <remarks>Parallelized on large length, but also supports being called in parallel from multiple threads</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.SystemRandomSource.FastDoubles(System.Int32)">
            <summary>
            Returns an array of uniform random numbers greater than or equal to 0.0 and less than 1.0.
            WARNING: potentially very short random sequence length, can generate repeated partial sequences.
            </summary>
            <remarks>Parallelized on large length, but also supports being called in parallel from multiple threads</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.SystemRandomSource.DoubleSequence">
            <summary>
            Returns an infinite sequence of uniform random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads, but the result must be enumerated from a single thread each.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.SystemRandomSource.Doubles(System.Double[],System.Int32)">
            <summary>
            Fills an array with random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.SystemRandomSource.Doubles(System.Int32,System.Int32)">
            <summary>
            Returns an array of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.SystemRandomSource.DoubleSequence(System.Int32)">
            <summary>
            Returns an infinite sequence of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads, but the result must be enumerated from a single thread each.</remarks>
        </member>
        <member name="T:MathNet.Numerics.Random.WH1982">
            <summary>
            Wichmann-Hill’s 1982 combined multiplicative congruential generator.
            </summary>
            <remarks>See: Wichmann, B. A. &amp; Hill, I. D. (1982), "Algorithm AS 183:
            An efficient and portable pseudo-random number generator". Applied Statistics 31 (1982) 188-190
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.WH1982.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.WH1982"/> class using
            a seed based on time and unique GUIDs.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.WH1982.#ctor(System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.WH1982"/> class using
            a seed based on time and unique GUIDs.
            </summary>
            <param name="threadSafe">if set to <c>true</c> , the class is thread safe.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.WH1982.#ctor(System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.WH1982"/> class.
            </summary>
            <param name="seed">The seed value.</param>
            <remarks>If the seed value is zero, it is set to one. Uses the
            value of <see cref="P:MathNet.Numerics.Control.ThreadSafeRandomNumberGenerators"/> to
            set whether the instance is thread safe.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.WH1982.#ctor(System.Int32,System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.WH1982"/> class.
            </summary>
            <param name="seed">The seed value.</param>
            <remarks>The seed is set to 1, if the zero is used as the seed.</remarks>
            <param name="threadSafe">if set to <c>true</c> , the class is thread safe.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.WH1982.DoSample">
            <summary>
            Returns a random double-precision floating point number greater than or equal to 0.0, and less than 1.0.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.WH1982.Doubles(System.Double[],System.Int32)">
            <summary>
            Fills an array with random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.WH1982.Doubles(System.Int32,System.Int32)">
            <summary>
            Returns an array of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.WH1982.DoubleSequence(System.Int32)">
            <summary>
            Returns an infinite sequence of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads, but the result must be enumerated from a single thread each.</remarks>
        </member>
        <member name="T:MathNet.Numerics.Random.WH2006">
            <summary>
            Wichmann-Hill’s 2006 combined multiplicative congruential generator.
            </summary>
            <remarks>See: Wichmann, B. A. &amp; Hill, I. D. (2006), "Generating good pseudo-random numbers".
            Computational Statistics &amp; Data Analysis 51:3 (2006) 1614-1622
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.WH2006.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.WH2006"/> class using
            a seed based on time and unique GUIDs.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.WH2006.#ctor(System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.WH2006"/> class using
            a seed based on time and unique GUIDs.
            </summary>
            <param name="threadSafe">if set to <c>true</c> , the class is thread safe.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.WH2006.#ctor(System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.WH2006"/> class.
            </summary>
            <param name="seed">The seed value.</param>
            <remarks>If the seed value is zero, it is set to one. Uses the
            value of <see cref="P:MathNet.Numerics.Control.ThreadSafeRandomNumberGenerators"/> to
            set whether the instance is thread safe.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.WH2006.#ctor(System.Int32,System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.WH2006"/> class.
            </summary>
            <param name="seed">The seed value.</param>
            <remarks>The seed is set to 1, if the zero is used as the seed.</remarks>
            <param name="threadSafe">if set to <c>true</c> , the class is thread safe.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.WH2006.DoSample">
            <summary>
            Returns a random double-precision floating point number greater than or equal to 0.0, and less than 1.0.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.WH2006.Doubles(System.Double[],System.Int32)">
            <summary>
            Fills an array with random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.WH2006.Doubles(System.Int32,System.Int32)">
            <summary>
            Returns an array of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.WH2006.DoubleSequence(System.Int32)">
            <summary>
            Returns an infinite sequence of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads, but the result must be enumerated from a single thread each.</remarks>
        </member>
        <member name="T:MathNet.Numerics.Random.Xorshift">
            <summary>
            Implements a multiply-with-carry Xorshift pseudo random number generator (RNG) specified in Marsaglia, George. (2003). Xorshift RNGs.
            <code>Xn = a * Xn−3 + c mod 2^32</code>
            http://www.jstatsoft.org/v08/i14/paper
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.Xorshift.YSeed">
            <summary>
            The default value for X1.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.Xorshift.ZSeed">
            <summary>
            The default value for X2.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.Xorshift.ASeed">
            <summary>
            The default value for the multiplier.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.Xorshift.CSeed">
            <summary>
            The default value for the carry over.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.Xorshift.UlongToDoubleMultiplier">
            <summary>
            The multiplier to compute a double-precision floating point number [0, 1)
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.Xorshift._x">
            <summary>
            Seed or last but three unsigned random number.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.Xorshift._y">
            <summary>
            Last but two unsigned random number.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.Xorshift._z">
            <summary>
            Last but one unsigned random number.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.Xorshift._c">
            <summary>
            The value of the carry over.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Random.Xorshift._a">
            <summary>
            The multiplier.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Xorshift.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Xorshift"/> class using
            a seed based on time and unique GUIDs.
            </summary>
            <remarks>If the seed value is zero, it is set to one. Uses the
            value of <see cref="P:MathNet.Numerics.Control.ThreadSafeRandomNumberGenerators"/> to
            set whether the instance is thread safe.
            Uses the default values of:
            <list>
            <item>a = 916905990</item>
            <item>c = 13579</item>
            <item>X1 = 77465321</item>
            <item>X2 = 362436069</item>
            </list></remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Xorshift.#ctor(System.Int64,System.Int64,System.Int64,System.Int64)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Xorshift"/> class using
            a seed based on time and unique GUIDs.
            </summary>
            <param name="a">The multiply value</param>
            <param name="c">The initial carry value.</param>
            <param name="x1">The initial value if X1.</param>
            <param name="x2">The initial value if X2.</param>
            <remarks>If the seed value is zero, it is set to one. Uses the
            value of <see cref="P:MathNet.Numerics.Control.ThreadSafeRandomNumberGenerators"/> to
            set whether the instance is thread safe.
            Note: <paramref name="c"/> must be less than <paramref name="a"/>.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Xorshift.#ctor(System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Xorshift"/> class using
            a seed based on time and unique GUIDs.
            </summary>
            <param name="threadSafe">if set to <c>true</c> , the class is thread safe.</param>
            <remarks>
            Uses the default values of:
            <list>
            <item>a = 916905990</item>
            <item>c = 13579</item>
            <item>X1 = 77465321</item>
            <item>X2 = 362436069</item>
            </list></remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Xorshift.#ctor(System.Boolean,System.Int64,System.Int64,System.Int64,System.Int64)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Xorshift"/> class using
            a seed based on time and unique GUIDs.
            </summary>
            <param name="threadSafe">if set to <c>true</c> , the class is thread safe.</param>
            <param name="a">The multiply value</param>
            <param name="c">The initial carry value.</param>
            <param name="x1">The initial value if X1.</param>
            <param name="x2">The initial value if X2.</param>
            <remarks><paramref name="c"/> must be less than <paramref name="a"/>.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Xorshift.#ctor(System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Xorshift"/> class.
            </summary>
            <param name="seed">The seed value.</param>
            <remarks>If the seed value is zero, it is set to one. Uses the
            value of <see cref="P:MathNet.Numerics.Control.ThreadSafeRandomNumberGenerators"/> to
            set whether the instance is thread safe.
            Uses the default values of:
            <list>
            <item>a = 916905990</item>
            <item>c = 13579</item>
            <item>X1 = 77465321</item>
            <item>X2 = 362436069</item>
            </list></remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Xorshift.#ctor(System.Int32,System.Int64,System.Int64,System.Int64,System.Int64)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Xorshift"/> class.
            </summary>
            <param name="seed">The seed value.</param>
            <remarks>If the seed value is zero, it is set to one. Uses the
            value of <see cref="P:MathNet.Numerics.Control.ThreadSafeRandomNumberGenerators"/> to
            set whether the instance is thread safe.</remarks>
            <param name="a">The multiply value</param>
            <param name="c">The initial carry value.</param>
            <param name="x1">The initial value if X1.</param>
            <param name="x2">The initial value if X2.</param>
            <remarks><paramref name="c"/> must be less than <paramref name="a"/>.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Xorshift.#ctor(System.Int32,System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Xorshift"/> class.
            </summary>
            <param name="seed">The seed value.</param>
            <param name="threadSafe">if set to <c>true</c>, the class is thread safe.</param>
            <remarks>
            Uses the default values of:
            <list>
            <item>a = 916905990</item>
            <item>c = 13579</item>
            <item>X1 = 77465321</item>
            <item>X2 = 362436069</item>
            </list></remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Xorshift.#ctor(System.Int32,System.Boolean,System.Int64,System.Int64,System.Int64,System.Int64)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Random.Xorshift"/> class.
            </summary>
            <param name="seed">The seed value.</param>
            <param name="threadSafe">if set to <c>true</c>, the class is thread safe.</param>
            <param name="a">The multiply value</param>
            <param name="c">The initial carry value.</param>
            <param name="x1">The initial value if X1.</param>
            <param name="x2">The initial value if X2.</param>
            <remarks><paramref name="c"/> must be less than <paramref name="a"/>.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Xorshift.DoSample">
            <summary>
            Returns a random double-precision floating point number greater than or equal to 0.0, and less than 1.0.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Xorshift.DoSampleInteger">
            <summary>
            Returns a random 32-bit signed integer greater than or equal to zero and less than <see cref="F:System.Int32.MaxValue"/>
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Xorshift.DoSampleBytes(System.Byte[])">
            <summary>
            Fills the elements of a specified array of bytes with random numbers in full range, including zero and 255 (<see cref="F:System.Byte.MaxValue"/>).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Xorshift.Doubles(System.Double[],System.Int32,System.UInt64,System.UInt64,System.UInt64,System.UInt64)">
            <summary>
            Fills an array with random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Xorshift.Doubles(System.Int32,System.Int32,System.UInt64,System.UInt64,System.UInt64,System.UInt64)">
            <summary>
            Returns an array of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Xorshift.DoubleSequence(System.Int32,System.UInt64,System.UInt64,System.UInt64,System.UInt64)">
            <summary>
            Returns an infinite sequence of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads, but the result must be enumerated from a single thread each.</remarks>
        </member>
        <member name="T:MathNet.Numerics.Random.Xoshiro256StarStar">
             <summary>
             Xoshiro256** pseudo random number generator.
             A random number generator based on the <see cref="T:System.Random"/> class in the .NET library.
             </summary>
             <remarks>
             This is xoshiro256** 1.0, our all-purpose, rock-solid generator. It has
             excellent(sub-ns) speed, a state space(256 bits) that is large enough
             for any parallel application, and it passes all tests we are aware of.
            
             For generating just floating-point numbers, xoshiro256+ is even faster.
            
             The state must be seeded so that it is not everywhere zero.If you have
             a 64-bit seed, we suggest to seed a splitmix64 generator and use its
             output to fill s.
            
             For further details see:
             David Blackman &amp; Sebastiano Vigna (2018), "Scrambled Linear Pseudorandom Number Generators".
             https://arxiv.org/abs/1805.01407
             </remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Xoshiro256StarStar.#ctor">
            <summary>
            Construct a new random number generator with a random seed.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Xoshiro256StarStar.#ctor(System.Boolean)">
            <summary>
            Construct a new random number generator with random seed.
            </summary>
            <param name="threadSafe">if set to <c>true</c> , the class is thread safe.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.Xoshiro256StarStar.#ctor(System.Int32)">
            <summary>
            Construct a new random number generator with random seed.
            </summary>
            <param name="seed">The seed value.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.Xoshiro256StarStar.#ctor(System.Int32,System.Boolean)">
            <summary>
            Construct a new random number generator with random seed.
            </summary>
            <param name="seed">The seed value.</param>
            <param name="threadSafe">if set to <c>true</c> , the class is thread safe.</param>
        </member>
        <member name="M:MathNet.Numerics.Random.Xoshiro256StarStar.DoSample">
            <summary>
            Returns a random double-precision floating point number greater than or equal to 0.0, and less than 1.0.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Xoshiro256StarStar.DoSampleInteger">
            <summary>
            Returns a random 32-bit signed integer greater than or equal to zero and less than <see cref="F:System.Int32.MaxValue"/>
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Xoshiro256StarStar.DoSampleBytes(System.Byte[])">
            <summary>
            Fills the elements of a specified array of bytes with random numbers in full range, including zero and 255 (<see cref="F:System.Byte.MaxValue"/>).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Xoshiro256StarStar.DoSampleInt32WithNBits(System.Int32)">
            <summary>
            Returns a random N-bit signed integer greater than or equal to zero and less than 2^N.
            N (bit count) is expected to be greater than zero and less than 32 (not verified).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Xoshiro256StarStar.DoSampleInt64WithNBits(System.Int32)">
            <summary>
            Returns a random N-bit signed long integer greater than or equal to zero and less than 2^N.
            N (bit count) is expected to be greater than zero and less than 64 (not verified).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Random.Xoshiro256StarStar.Doubles(System.Double[],System.Int32)">
            <summary>
            Fills an array with random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Xoshiro256StarStar.Doubles(System.Int32,System.Int32)">
            <summary>
            Returns an array of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Xoshiro256StarStar.DoubleSequence(System.Int32)">
            <summary>
            Returns an infinite sequence of random numbers greater than or equal to 0.0 and less than 1.0.
            </summary>
            <remarks>Supports being called in parallel from multiple threads, but the result must be enumerated from a single thread each.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Random.Xoshiro256StarStar.Splitmix64(System.UInt64@)">
            <summary>
            Splitmix64 RNG.
            </summary>
            <param name="x">RNG state. This can take any value, including zero.</param>
            <returns>A new random UInt64.</returns>
            <remarks>
            Splitmix64 produces equidistributed outputs, thus if a zero is generated then the
            next zero will be after a further 2^64 outputs.
            </remarks>
        </member>
        <member name="T:MathNet.Numerics.RootFinding.Bisection">
            <summary>
            Bisection root-finding algorithm.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.Bisection.FindRootExpand(System.Func{System.Double,System.Double},System.Double,System.Double,System.Double,System.Int32,System.Double,System.Int32)">
            <summary>Find a solution of the equation f(x)=0.</summary>
            <param name="f">The function to find roots from.</param>
            <param name="guessLowerBound">Guess for the low value of the range where the root is supposed to be. Will be expanded if needed.</param>
            <param name="guessUpperBound">Guess for the  high value of the range where the root is supposed to be. Will be expanded if needed.</param>
            <param name="accuracy">Desired accuracy. The root will be refined until the accuracy or the maximum number of iterations is reached. Default 1e-8. Must be greater than 0.</param>
            <param name="maxIterations">Maximum number of iterations. Default 100.</param>
            <param name="expandFactor">Factor at which to expand the bounds, if needed. Default 1.6.</param>
            <param name="maxExpandIteratons">Maximum number of expand iterations. Default 100.</param>
            <returns>Returns the root with the specified accuracy.</returns>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.Bisection.FindRoot(System.Func{System.Double,System.Double},System.Double,System.Double,System.Double,System.Int32)">
            <summary>Find a solution of the equation f(x)=0.</summary>
            <param name="f">The function to find roots from.</param>
            <param name="lowerBound">The low value of the range where the root is supposed to be.</param>
            <param name="upperBound">The high value of the range where the root is supposed to be.</param>
            <param name="accuracy">Desired accuracy. The root will be refined until the accuracy or the maximum number of iterations is reached. Default 1e-8. Must be greater than 0.</param>
            <param name="maxIterations">Maximum number of iterations. Default 100.</param>
            <returns>Returns the root with the specified accuracy.</returns>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.Bisection.TryFindRoot(System.Func{System.Double,System.Double},System.Double,System.Double,System.Double,System.Int32,System.Double@)">
            <summary>Find a solution of the equation f(x)=0.</summary>
            <param name="f">The function to find roots from.</param>
            <param name="lowerBound">The low value of the range where the root is supposed to be.</param>
            <param name="upperBound">The high value of the range where the root is supposed to be.</param>
            <param name="accuracy">Desired accuracy for both the root and the function value at the root. The root will be refined until the accuracy or the maximum number of iterations is reached. Must be greater than 0.</param>
            <param name="maxIterations">Maximum number of iterations. Usually 100.</param>
            <param name="root">The root that was found, if any. Undefined if the function returns false.</param>
            <returns>True if a root with the specified accuracy was found, else false.</returns>
        </member>
        <member name="T:MathNet.Numerics.RootFinding.Brent">
            <summary>
            Algorithm by Brent, Van Wijngaarden, Dekker et al.
            Implementation inspired by Press, Teukolsky, Vetterling, and Flannery, "Numerical Recipes in C", 2nd edition, Cambridge University Press
            </summary>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.Brent.FindRootExpand(System.Func{System.Double,System.Double},System.Double,System.Double,System.Double,System.Int32,System.Double,System.Int32)">
            <summary>Find a solution of the equation f(x)=0.</summary>
            <param name="f">The function to find roots from.</param>
            <param name="guessLowerBound">Guess for the low value of the range where the root is supposed to be. Will be expanded if needed.</param>
            <param name="guessUpperBound">Guess for the  high value of the range where the root is supposed to be. Will be expanded if needed.</param>
            <param name="accuracy">Desired accuracy. The root will be refined until the accuracy or the maximum number of iterations is reached. Default 1e-8. Must be greater than 0.</param>
            <param name="maxIterations">Maximum number of iterations. Default 100.</param>
            <param name="expandFactor">Factor at which to expand the bounds, if needed. Default 1.6.</param>
            <param name="maxExpandIteratons">Maximum number of expand iterations. Default 100.</param>
            <returns>Returns the root with the specified accuracy.</returns>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.Brent.FindRoot(System.Func{System.Double,System.Double},System.Double,System.Double,System.Double,System.Int32)">
            <summary>Find a solution of the equation f(x)=0.</summary>
            <param name="f">The function to find roots from.</param>
            <param name="lowerBound">The low value of the range where the root is supposed to be.</param>
            <param name="upperBound">The high value of the range where the root is supposed to be.</param>
            <param name="accuracy">Desired accuracy. The root will be refined until the accuracy or the maximum number of iterations is reached. Default 1e-8. Must be greater than 0.</param>
            <param name="maxIterations">Maximum number of iterations. Default 100.</param>
            <returns>Returns the root with the specified accuracy.</returns>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.Brent.TryFindRoot(System.Func{System.Double,System.Double},System.Double,System.Double,System.Double,System.Int32,System.Double@)">
            <summary>Find a solution of the equation f(x)=0.</summary>
            <param name="f">The function to find roots from.</param>
            <param name="lowerBound">The low value of the range where the root is supposed to be.</param>
            <param name="upperBound">The high value of the range where the root is supposed to be.</param>
            <param name="accuracy">Desired accuracy. The root will be refined until the accuracy or the maximum number of iterations is reached. Must be greater than 0.</param>
            <param name="maxIterations">Maximum number of iterations. Usually 100.</param>
            <param name="root">The root that was found, if any. Undefined if the function returns false.</param>
            <returns>True if a root with the specified accuracy was found, else false.</returns>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.Brent.Sign(System.Double,System.Double)">
            <summary>Helper method useful for preventing rounding errors.</summary>
            <returns>a*sign(b)</returns>
        </member>
        <member name="T:MathNet.Numerics.RootFinding.Broyden">
            <summary>
            Algorithm by Broyden.
            Implementation inspired by Press, Teukolsky, Vetterling, and Flannery, "Numerical Recipes in C", 2nd edition, Cambridge University Press
            </summary>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.Broyden.FindRoot(System.Func{System.Double[],System.Double[]},System.Double[],System.Double,System.Int32,System.Double)">
            <summary>Find a solution of the equation f(x)=0.</summary>
            <param name="f">The function to find roots from.</param>
            <param name="initialGuess">Initial guess of the root.</param>
            <param name="accuracy">Desired accuracy. The root will be refined until the accuracy or the maximum number of iterations is reached. Default 1e-8. Must be greater than 0.</param>
            <param name="maxIterations">Maximum number of iterations. Default 100.</param>
            <param name="jacobianStepSize">Relative step size for calculating the Jacobian matrix at first step. Default 1.0e-4</param>
            <returns>Returns the root with the specified accuracy.</returns>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.Broyden.TryFindRootWithJacobianStep(System.Func{System.Double[],System.Double[]},System.Double[],System.Double,System.Int32,System.Double,System.Double[]@)">
            <summary>Find a solution of the equation f(x)=0.</summary>
            <param name="f">The function to find roots from.</param>
            <param name="initialGuess">Initial guess of the root.</param>
            <param name="accuracy">Desired accuracy. The root will be refined until the accuracy or the maximum number of iterations is reached. Must be greater than 0.</param>
            <param name="maxIterations">Maximum number of iterations. Usually 100.</param>
            <param name="jacobianStepSize">Relative step size for calculating the Jacobian matrix at first step.</param>
            <param name="root">The root that was found, if any. Undefined if the function returns false.</param>
            <returns>True if a root with the specified accuracy was found, else false.</returns>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.Broyden.TryFindRoot(System.Func{System.Double[],System.Double[]},System.Double[],System.Double,System.Int32,System.Double[]@)">
            <summary>Find a solution of the equation f(x)=0.</summary>
            <param name="f">The function to find roots from.</param>
            <param name="initialGuess">Initial guess of the root.</param>
            <param name="accuracy">Desired accuracy. The root will be refined until the accuracy or the maximum number of iterations is reached. Must be greater than 0.</param>
            <param name="maxIterations">Maximum number of iterations. Usually 100.</param>
            <param name="root">The root that was found, if any. Undefined if the function returns false.</param>
            <returns>True if a root with the specified accuracy was found, else false.</returns>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.Broyden.CalculateApproximateJacobian(System.Func{System.Double[],System.Double[]},System.Double[],System.Double[],System.Double)">
            <summary>
            Helper method to calculate an approximation of the Jacobian.
            </summary>
            <param name="f">The function.</param>
            <param name="x0">The argument (initial guess).</param>
            <param name="y0">The result (of initial guess).</param>
            <param name="jacobianStepSize">Relative step size for calculating the Jacobian.</param>
        </member>
        <member name="T:MathNet.Numerics.RootFinding.Cubic">
            <summary>
            Finds roots to the cubic equation x^3 + a2*x^2 + a1*x + a0 = 0
            Implements the cubic formula in http://mathworld.wolfram.com/CubicFormula.html
            </summary>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.Cubic.QR(System.Double,System.Double,System.Double,System.Double@,System.Double@)">
            <summary>
            Q and R are transformed variables.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.Cubic.PowThird(System.Double)">
            <summary>
            n^(1/3) - work around a negative double raised to (1/3)
            </summary>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.Cubic.RealRoots(System.Double,System.Double,System.Double)">
            <summary>
            Find all real-valued roots of the cubic equation a0 + a1*x + a2*x^2 + x^3 = 0.
            Note the special coefficient order ascending by exponent (consistent with polynomials).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.Cubic.Roots(System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Find all three complex roots of the cubic equation d + c*x + b*x^2 + a*x^3 = 0.
            Note the special coefficient order ascending by exponent (consistent with polynomials).
            </summary>
        </member>
        <member name="T:MathNet.Numerics.RootFinding.NewtonRaphson">
            <summary>
            Pure Newton-Raphson root-finding algorithm without any recovery measures in cases it behaves badly.
            The algorithm aborts immediately if the root leaves the bound interval.
            </summary>
            <seealso cref="T:MathNet.Numerics.RootFinding.RobustNewtonRaphson"/>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.NewtonRaphson.FindRoot(System.Func{System.Double,System.Double},System.Func{System.Double,System.Double},System.Double,System.Double,System.Double,System.Int32)">
            <summary>Find a solution of the equation f(x)=0.</summary>
            <param name="f">The function to find roots from.</param>
            <param name="df">The first derivative of the function to find roots from.</param>
            <param name="lowerBound">The low value of the range where the root is supposed to be. Aborts if it leaves the interval.</param>
            <param name="upperBound">The high value of the range where the root is supposed to be. Aborts if it leaves the interval.</param>
            <param name="accuracy">Desired accuracy. The root will be refined until the accuracy or the maximum number of iterations is reached. Default 1e-8. Must be greater than 0.</param>
            <param name="maxIterations">Maximum number of iterations. Default 100.</param>
            <returns>Returns the root with the specified accuracy.</returns>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.NewtonRaphson.FindRootNearGuess(System.Func{System.Double,System.Double},System.Func{System.Double,System.Double},System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>Find a solution of the equation f(x)=0.</summary>
            <param name="f">The function to find roots from.</param>
            <param name="df">The first derivative of the function to find roots from.</param>
            <param name="initialGuess">Initial guess of the root.</param>
            <param name="lowerBound">The low value of the range where the root is supposed to be. Aborts if it leaves the interval. Default MinValue.</param>
            <param name="upperBound">The high value of the range where the root is supposed to be. Aborts if it leaves the interval. Default MaxValue.</param>
            <param name="accuracy">Desired accuracy. The root will be refined until the accuracy or the maximum number of iterations is reached. Default 1e-8. Must be greater than 0.</param>
            <param name="maxIterations">Maximum number of iterations. Default 100.</param>
            <returns>Returns the root with the specified accuracy.</returns>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.NewtonRaphson.TryFindRoot(System.Func{System.Double,System.Double},System.Func{System.Double,System.Double},System.Double,System.Double,System.Double,System.Double,System.Int32,System.Double@)">
            <summary>Find a solution of the equation f(x)=0.</summary>
            <param name="f">The function to find roots from.</param>
            <param name="df">The first derivative of the function to find roots from.</param>
            <param name="initialGuess">Initial guess of the root.</param>
            <param name="lowerBound">The low value of the range where the root is supposed to be. Aborts if it leaves the interval.</param>
            <param name="upperBound">The high value of the range where the root is supposed to be. Aborts if it leaves the interval.</param>
            <param name="accuracy">Desired accuracy. The root will be refined until the accuracy or the maximum number of iterations is reached. Example: 1e-14. Must be greater than 0.</param>
            <param name="maxIterations">Maximum number of iterations. Example: 100.</param>
            <param name="root">The root that was found, if any. Undefined if the function returns false.</param>
            <returns>True if a root with the specified accuracy was found, else false.</returns>
        </member>
        <member name="T:MathNet.Numerics.RootFinding.RobustNewtonRaphson">
            <summary>
            Robust Newton-Raphson root-finding algorithm that falls back to bisection when overshooting or converging too slow, or to subdivision on lacking bracketing.
            </summary>
            <seealso cref="T:MathNet.Numerics.RootFinding.NewtonRaphson"/>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.RobustNewtonRaphson.FindRoot(System.Func{System.Double,System.Double},System.Func{System.Double,System.Double},System.Double,System.Double,System.Double,System.Int32,System.Int32)">
            <summary>Find a solution of the equation f(x)=0.</summary>
            <param name="f">The function to find roots from.</param>
            <param name="df">The first derivative of the function to find roots from.</param>
            <param name="lowerBound">The low value of the range where the root is supposed to be.</param>
            <param name="upperBound">The high value of the range where the root is supposed to be.</param>
            <param name="accuracy">Desired accuracy. The root will be refined until the accuracy or the maximum number of iterations is reached. Default 1e-8. Must be greater than 0.</param>
            <param name="maxIterations">Maximum number of iterations. Default 100.</param>
            <param name="subdivision">How many parts an interval should be split into for zero crossing scanning in case of lacking bracketing. Default 20.</param>
            <returns>Returns the root with the specified accuracy.</returns>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.RobustNewtonRaphson.TryFindRoot(System.Func{System.Double,System.Double},System.Func{System.Double,System.Double},System.Double,System.Double,System.Double,System.Int32,System.Int32,System.Double@)">
            <summary>Find a solution of the equation f(x)=0.</summary>
            <param name="f">The function to find roots from.</param>
            <param name="df">The first derivative of the function to find roots from.</param>
            <param name="lowerBound">The low value of the range where the root is supposed to be.</param>
            <param name="upperBound">The high value of the range where the root is supposed to be.</param>
            <param name="accuracy">Desired accuracy. The root will be refined until the accuracy or the maximum number of iterations is reached. Example: 1e-14. Must be greater than 0.</param>
            <param name="maxIterations">Maximum number of iterations. Example: 100.</param>
            <param name="subdivision">How many parts an interval should be split into for zero crossing scanning in case of lacking bracketing. Example: 20.</param>
            <param name="root">The root that was found, if any. Undefined if the function returns false.</param>
            <returns>True if a root with the specified accuracy was found, else false.</returns>
        </member>
        <member name="T:MathNet.Numerics.RootFinding.Secant">
            <summary>
            Pure Secant root-finding algorithm without any recovery measures in cases it behaves badly.
            The algorithm aborts immediately if the root leaves the bound interval.
            </summary>
            <seealso cref="T:MathNet.Numerics.RootFinding.Brent"/>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.Secant.FindRoot(System.Func{System.Double,System.Double},System.Double,System.Double,System.Double,System.Double,System.Double,System.Int32)">
            <summary>Find a solution of the equation f(x)=0.</summary>
            <param name="f">The function to find roots from.</param>
            <param name="guess">The first guess of the root within the bounds specified.</param>
            <param name="secondGuess">The second guess of the root within the bounds specified.</param>
            <param name="lowerBound">The low value of the range where the root is supposed to be. Aborts if it leaves the interval. Default MinValue.</param>
            <param name="upperBound">The high value of the range where the root is supposed to be. Aborts if it leaves the interval. Default MaxValue.</param>
            <param name="accuracy">Desired accuracy. The root will be refined until the accuracy or the maximum number of iterations is reached. Default 1e-8. Must be greater than 0.</param>
            <param name="maxIterations">Maximum number of iterations. Default 100.</param>
            <returns>Returns the root with the specified accuracy.</returns>
            <exception cref="T:MathNet.Numerics.NonConvergenceException"></exception>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.Secant.TryFindRoot(System.Func{System.Double,System.Double},System.Double,System.Double,System.Double,System.Double,System.Double,System.Int32,System.Double@)">
            <summary>Find a solution of the equation f(x)=0.</summary>
            <param name="f">The function to find roots from.</param>
            <param name="guess">The first guess of the root within the bounds specified.</param>
            <param name="secondGuess">The second guess of the root within the bounds specified.</param>
            <param name="lowerBound">The low value of the range where the root is supposed to be. Aborts if it leaves the interval.</param>
            <param name="upperBound">The low value of the range where the root is supposed to be. Aborts if it leaves the interval.</param>
            <param name="accuracy">Desired accuracy. The root will be refined until the accuracy or the maximum number of iterations is reached. Example: 1e-14. Must be greater than 0.</param>
            <param name="maxIterations">Maximum number of iterations. Example: 100.</param>
            <param name="root">The root that was found, if any. Undefined if the function returns false.</param>
            <returns>True if a root with the specified accuracy was found, else false</returns>
        </member>
        <member name="M:MathNet.Numerics.RootFinding.ZeroCrossingBracketing.Expand(System.Func{System.Double,System.Double},System.Double@,System.Double@,System.Double,System.Int32)">
            <summary>Detect a range containing at least one root.</summary>
            <param name="f">The function to detect roots from.</param>
            <param name="lowerBound">Lower value of the range.</param>
            <param name="upperBound">Upper value of the range</param>
            <param name="factor">The growing factor of research. Usually 1.6.</param>
            <param name="maxIterations">Maximum number of iterations. Usually 50.</param>
            <returns>True if the bracketing operation succeeded, false otherwise.</returns>
            <remarks>This iterative methods stops when two values with opposite signs are found.</remarks>
        </member>
        <member name="M:MathNet.Numerics.Series.Evaluate(System.Func{System.Double})">
            <summary>
            Numerically stable series summation (stops automatically).
            </summary>
            <param name="nextSummand">provides the summands sequentially</param>
            <returns>Sum</returns>
        </member>
        <member name="M:MathNet.Numerics.Series.Evaluate(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Numerically stable series summation (stops automatically).
            </summary>
            <param name="infiniteSummands">provides the summands sequentially</param>
            <returns>Sum</returns>
        </member>
        <member name="T:MathNet.Numerics.Sorting">
            <summary>
            Sorting algorithms for single, tuple and triple lists.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Sorting.Sort``1(System.Collections.Generic.IList{``0},System.Collections.Generic.IComparer{``0})">
            <summary>
            Sort a list of keys, in place using the quick sort algorithm using the quick sort algorithm.
            </summary>
            <typeparam name="T">The type of elements in the key list.</typeparam>
            <param name="keys">List to sort.</param>
            <param name="comparer">Comparison, defining the sort order.</param>
        </member>
        <member name="M:MathNet.Numerics.Sorting.Sort``2(System.Collections.Generic.IList{``0},System.Collections.Generic.IList{``1},System.Collections.Generic.IComparer{``0})">
            <summary>
            Sort a list of keys and items with respect to the keys, in place using the quick sort algorithm.
            </summary>
            <typeparam name="TKey">The type of elements in the key list.</typeparam>
            <typeparam name="TItem">The type of elements in the item list.</typeparam>
            <param name="keys">List to sort.</param>
            <param name="items">List to permute the same way as the key list.</param>
            <param name="comparer">Comparison, defining the sort order.</param>
        </member>
        <member name="M:MathNet.Numerics.Sorting.Sort``3(System.Collections.Generic.IList{``0},System.Collections.Generic.IList{``1},System.Collections.Generic.IList{``2},System.Collections.Generic.IComparer{``0})">
            <summary>
            Sort a list of keys, items1 and items2 with respect to the keys, in place using the quick sort algorithm.
            </summary>
            <typeparam name="TKey">The type of elements in the key list.</typeparam>
            <typeparam name="TItem1">The type of elements in the first item list.</typeparam>
            <typeparam name="TItem2">The type of elements in the second item list.</typeparam>
            <param name="keys">List to sort.</param>
            <param name="items1">First list to permute the same way as the key list.</param>
            <param name="items2">Second list to permute the same way as the key list.</param>
            <param name="comparer">Comparison, defining the sort order.</param>
        </member>
        <member name="M:MathNet.Numerics.Sorting.Sort``1(System.Collections.Generic.IList{``0},System.Int32,System.Int32,System.Collections.Generic.IComparer{``0})">
            <summary>
            Sort a range of a list of keys, in place using the quick sort algorithm.
            </summary>
            <typeparam name="T">The type of element in the list.</typeparam>
            <param name="keys">List to sort.</param>
            <param name="index">The zero-based starting index of the range to sort.</param>
            <param name="count">The length of the range to sort.</param>
            <param name="comparer">Comparison, defining the sort order.</param>
        </member>
        <member name="M:MathNet.Numerics.Sorting.Sort``2(System.Collections.Generic.IList{``0},System.Collections.Generic.IList{``1},System.Int32,System.Int32,System.Collections.Generic.IComparer{``0})">
            <summary>
            Sort a list of keys and items with respect to the keys, in place using the quick sort algorithm.
            </summary>
            <typeparam name="TKey">The type of elements in the key list.</typeparam>
            <typeparam name="TItem">The type of elements in the item list.</typeparam>
            <param name="keys">List to sort.</param>
            <param name="items">List to permute the same way as the key list.</param>
            <param name="index">The zero-based starting index of the range to sort.</param>
            <param name="count">The length of the range to sort.</param>
            <param name="comparer">Comparison, defining the sort order.</param>
        </member>
        <member name="M:MathNet.Numerics.Sorting.Sort``3(System.Collections.Generic.IList{``0},System.Collections.Generic.IList{``1},System.Collections.Generic.IList{``2},System.Int32,System.Int32,System.Collections.Generic.IComparer{``0})">
            <summary>
            Sort a list of keys, items1 and items2 with respect to the keys, in place using the quick sort algorithm.
            </summary>
            <typeparam name="TKey">The type of elements in the key list.</typeparam>
            <typeparam name="TItem1">The type of elements in the first item list.</typeparam>
            <typeparam name="TItem2">The type of elements in the second item list.</typeparam>
            <param name="keys">List to sort.</param>
            <param name="items1">First list to permute the same way as the key list.</param>
            <param name="items2">Second list to permute the same way as the key list.</param>
            <param name="index">The zero-based starting index of the range to sort.</param>
            <param name="count">The length of the range to sort.</param>
            <param name="comparer">Comparison, defining the sort order.</param>
        </member>
        <member name="M:MathNet.Numerics.Sorting.SortAll``2(System.Collections.Generic.IList{``0},System.Collections.Generic.IList{``1},System.Collections.Generic.IComparer{``0},System.Collections.Generic.IComparer{``1})">
            <summary>
            Sort a list of keys and items with respect to the keys, in place using the quick sort algorithm.
            </summary>
            <typeparam name="T1">The type of elements in the primary list.</typeparam>
            <typeparam name="T2">The type of elements in the secondary list.</typeparam>
            <param name="primary">List to sort.</param>
            <param name="secondary">List to sort on duplicate primary items, and permute the same way as the key list.</param>
            <param name="primaryComparer">Comparison, defining the primary sort order.</param>
            <param name="secondaryComparer">Comparison, defining the secondary sort order.</param>
        </member>
        <member name="M:MathNet.Numerics.Sorting.QuickSort``1(System.Collections.Generic.IList{``0},System.Collections.Generic.IComparer{``0},System.Int32,System.Int32)">
            <summary>
            Recursive implementation for an in place quick sort on a list.
            </summary>
            <typeparam name="T">The type of the list on which the quick sort is performed.</typeparam>
            <param name="keys">The list which is sorted using quick sort.</param>
            <param name="comparer">The method with which to compare two elements of the quick sort.</param>
            <param name="left">The left boundary of the quick sort.</param>
            <param name="right">The right boundary of the quick sort.</param>
        </member>
        <member name="M:MathNet.Numerics.Sorting.QuickSort``2(System.Collections.Generic.IList{``0},System.Collections.Generic.IList{``1},System.Collections.Generic.IComparer{``0},System.Int32,System.Int32)">
            <summary>
            Recursive implementation for an in place quick sort on a list while reordering one other list accordingly.
            </summary>
            <typeparam name="T">The type of the list on which the quick sort is performed.</typeparam>
            <typeparam name="TItems">The type of the list which is automatically reordered accordingly.</typeparam>
            <param name="keys">The list which is sorted using quick sort.</param>
            <param name="items">The list which is automatically reordered accordingly.</param>
            <param name="comparer">The method with which to compare two elements of the quick sort.</param>
            <param name="left">The left boundary of the quick sort.</param>
            <param name="right">The right boundary of the quick sort.</param>
        </member>
        <member name="M:MathNet.Numerics.Sorting.QuickSort``3(System.Collections.Generic.IList{``0},System.Collections.Generic.IList{``1},System.Collections.Generic.IList{``2},System.Collections.Generic.IComparer{``0},System.Int32,System.Int32)">
            <summary>
            Recursive implementation for an in place quick sort on one list while reordering two other lists accordingly.
            </summary>
            <typeparam name="T">The type of the list on which the quick sort is performed.</typeparam>
            <typeparam name="TItems1">The type of the first list which is automatically reordered accordingly.</typeparam>
            <typeparam name="TItems2">The type of the second list which is automatically reordered accordingly.</typeparam>
            <param name="keys">The list which is sorted using quick sort.</param>
            <param name="items1">The first list which is automatically reordered accordingly.</param>
            <param name="items2">The second list which is automatically reordered accordingly.</param>
            <param name="comparer">The method with which to compare two elements of the quick sort.</param>
            <param name="left">The left boundary of the quick sort.</param>
            <param name="right">The right boundary of the quick sort.</param>
        </member>
        <member name="M:MathNet.Numerics.Sorting.QuickSortAll``2(System.Collections.Generic.IList{``0},System.Collections.Generic.IList{``1},System.Collections.Generic.IComparer{``0},System.Collections.Generic.IComparer{``1},System.Int32,System.Int32)">
            <summary>
            Recursive implementation for an in place quick sort on the primary and then by the secondary list while reordering one secondary list accordingly.
            </summary>
            <typeparam name="T1">The type of the primary list.</typeparam>
            <typeparam name="T2">The type of the secondary list.</typeparam>
            <param name="primary">The list which is sorted using quick sort.</param>
            <param name="secondary">The list which is sorted secondarily (on primary duplicates) and automatically reordered accordingly.</param>
            <param name="primaryComparer">The method with which to compare two elements of the primary list.</param>
            <param name="secondaryComparer">The method with which to compare two elements of the secondary list.</param>
            <param name="left">The left boundary of the quick sort.</param>
            <param name="right">The right boundary of the quick sort.</param>
        </member>
        <member name="M:MathNet.Numerics.Sorting.Swap``1(System.Collections.Generic.IList{``0},System.Int32,System.Int32)">
            <summary>
            Performs an in place swap of two elements in a list.
            </summary>
            <typeparam name="T">The type of elements stored in the list.</typeparam>
            <param name="keys">The list in which the elements are stored.</param>
            <param name="a">The index of the first element of the swap.</param>
            <param name="b">The index of the second element of the swap.</param>
        </member>
        <member name="T:MathNet.Numerics.SpecialFunctions">
            <summary>
            This partial implementation of the SpecialFunctions class contains all methods related to the Airy functions.
            </summary>
            <summary>
            This partial implementation of the SpecialFunctions class contains all methods related to the Bessel functions.
            </summary>
            <summary>
            This partial implementation of the SpecialFunctions class contains all methods related to the error function.
            </summary>
            <summary>
            This partial implementation of the SpecialFunctions class contains all methods related to the Hankel function.
            </summary>
            <summary>
            This partial implementation of the SpecialFunctions class contains all methods related to the harmonic function.
            </summary>
            <summary>
            This partial implementation of the SpecialFunctions class contains all methods related to the modified Bessel function.
            </summary>
            <summary>
            This partial implementation of the SpecialFunctions class contains all methods related to the log1p function.
            </summary>
            <summary>
            This partial implementation of the SpecialFunctions class contains all methods related to the logistic function.
            </summary>
            <summary>
            This partial implementation of the SpecialFunctions class contains all methods related to the Marcum-Q functions.
            </summary>
            <summary>
            This partial implementation of the SpecialFunctions class contains all methods related to the modified Bessel function.
            </summary>
            <summary>
            This partial implementation of the SpecialFunctions class contains all methods related to the modified Bessel function.
            </summary>
            <summary>
            This partial implementation of the SpecialFunctions class contains all methods related to the spherical Bessel functions.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.AiryAi(System.Numerics.Complex)">
            <summary>
            Returns the Airy function Ai.
            <para>AiryAi(z) is a solution to the Airy equation, y'' - y * z = 0.</para>
            </summary>
            <param name="z">The value to compute the Airy function of.</param>
            <returns>The Airy function Ai.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.AiryAiScaled(System.Numerics.Complex)">
            <summary>
            Returns the exponentially scaled Airy function Ai.
            <para>ScaledAiryAi(z) is given by Exp(zta) * AiryAi(z), where zta = (2/3) * z * Sqrt(z).</para>
            </summary>
            <param name="z">The value to compute the Airy function of.</param>
            <returns>The exponentially scaled Airy function Ai.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.AiryAi(System.Double)">
            <summary>
            Returns the Airy function Ai.
            <para>AiryAi(z) is a solution to the Airy equation, y'' - y * z = 0.</para>
            </summary>
            <param name="z">The value to compute the Airy function of.</param>
            <returns>The Airy function Ai.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.AiryAiScaled(System.Double)">
            <summary>
            Returns the exponentially scaled Airy function Ai.
            <para>ScaledAiryAi(z) is given by Exp(zta) * AiryAi(z), where zta = (2/3) * z * Sqrt(z).</para>
            </summary>
            <param name="z">The value to compute the Airy function of.</param>
            <returns>The exponentially scaled Airy function Ai.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.AiryAiPrime(System.Numerics.Complex)">
            <summary>
            Returns the derivative of the Airy function Ai.
            <para>AiryAiPrime(z) is defined as d/dz AiryAi(z).</para>
            </summary>
            <param name="z">The value to compute the derivative of the Airy function of.</param>
            <returns>The derivative of the Airy function Ai.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.AiryAiPrimeScaled(System.Numerics.Complex)">
            <summary>
            Returns the exponentially scaled derivative of Airy function Ai
            <para>ScaledAiryAiPrime(z) is given by Exp(zta) * AiryAiPrime(z), where zta = (2/3) * z * Sqrt(z).</para>
            </summary>
            <param name="z">The value to compute the derivative of the Airy function of.</param>
            <returns>The exponentially scaled derivative of Airy function Ai.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.AiryAiPrime(System.Double)">
            <summary>
            Returns the derivative of the Airy function Ai.
            <para>AiryAiPrime(z) is defined as d/dz AiryAi(z).</para>
            </summary>
            <param name="z">The value to compute the derivative of the Airy function of.</param>
            <returns>The derivative of the Airy function Ai.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.AiryAiPrimeScaled(System.Double)">
            <summary>
            Returns the exponentially scaled derivative of the Airy function Ai.
            <para>ScaledAiryAiPrime(z) is given by Exp(zta) * AiryAiPrime(z), where zta = (2/3) * z * Sqrt(z).</para>
            </summary>
            <param name="z">The value to compute the derivative of the Airy function of.</param>
            <returns>The exponentially scaled derivative of the Airy function Ai.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.AiryBi(System.Numerics.Complex)">
            <summary>
            Returns the Airy function Bi.
            <para>AiryBi(z) is a solution to the Airy equation, y'' - y * z = 0.</para>
            </summary>
            <param name="z">The value to compute the Airy function of.</param>
            <returns>The Airy function Bi.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.AiryBiScaled(System.Numerics.Complex)">
            <summary>
            Returns the exponentially scaled Airy function Bi.
            <para>ScaledAiryBi(z) is given by Exp(-Abs(zta.Real)) * AiryBi(z) where zta = (2 / 3) * z * Sqrt(z).</para>
            </summary>
            <param name="z">The value to compute the Airy function of.</param>
            <returns>The exponentially scaled Airy function Bi(z).</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.AiryBi(System.Double)">
            <summary>
            Returns the Airy function Bi.
            <para>AiryBi(z) is a solution to the Airy equation, y'' - y * z = 0.</para>
            </summary>
            <param name="z">The value to compute the Airy function of.</param>
            <returns>The Airy function Bi.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.AiryBiScaled(System.Double)">
            <summary>
            Returns the exponentially scaled Airy function Bi.
            <para>ScaledAiryBi(z) is given by Exp(-Abs(zta.Real)) * AiryBi(z) where zta = (2 / 3) * z * Sqrt(z).</para>
            </summary>
            <param name="z">The value to compute the Airy function of.</param>
            <returns>The exponentially scaled Airy function Bi.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.AiryBiPrime(System.Numerics.Complex)">
            <summary>
            Returns the derivative of the Airy function Bi.
            <para>AiryBiPrime(z) is defined as d/dz AiryBi(z).</para>
            </summary>
            <param name="z">The value to compute the derivative of the Airy function of.</param>
            <returns>The derivative of the Airy function Bi.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.AiryBiPrimeScaled(System.Numerics.Complex)">
            <summary>
            Returns the exponentially scaled derivative of the Airy function Bi.
            <para>ScaledAiryBiPrime(z) is given by Exp(-Abs(zta.Real)) * AiryBiPrime(z) where zta = (2 / 3) * z * Sqrt(z).</para>
            </summary>
            <param name="z">The value to compute the derivative of the Airy function of.</param>
            <returns>The exponentially scaled derivative of the Airy function Bi.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.AiryBiPrime(System.Double)">
            <summary>
            Returns the derivative of the Airy function Bi.
            <para>AiryBiPrime(z) is defined as d/dz AiryBi(z).</para>
            </summary>
            <param name="z">The value to compute the derivative of the Airy function of.</param>
            <returns>The derivative of the Airy function Bi.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.AiryBiPrimeScaled(System.Double)">
            <summary>
            Returns the exponentially scaled derivative of the Airy function Bi.
            <para>ScaledAiryBiPrime(z) is given by Exp(-Abs(zta.Real)) * AiryBiPrime(z) where zta = (2 / 3) * z * Sqrt(z).</para>
            </summary>
            <param name="z">The value to compute the derivative of the Airy function of.</param>
            <returns>The exponentially scaled derivative of the Airy function Bi.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselJ(System.Double,System.Numerics.Complex)">
            <summary>
            Returns the Bessel function of the first kind.
            <para>BesselJ(n, z) is a solution to the Bessel differential equation.</para>
            </summary>
            <param name="n">The order of the Bessel function.</param>
            <param name="z">The value to compute the Bessel function of.</param>
            <returns>The Bessel function of the first kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselJScaled(System.Double,System.Numerics.Complex)">
            <summary>
            Returns the exponentially scaled Bessel function of the first kind.
            <para>ScaledBesselJ(n, z) is given by Exp(-Abs(z.Imaginary)) * BesselJ(n, z).</para>
            </summary>
            <param name="n">The order of the Bessel function.</param>
            <param name="z">The value to compute the Bessel function of.</param>
            <returns>The exponentially scaled Bessel function of the first kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselJ(System.Double,System.Double)">
            <summary>
            Returns the Bessel function of the first kind.
            <para>BesselJ(n, z) is a solution to the Bessel differential equation.</para>
            </summary>
            <param name="n">The order of the Bessel function.</param>
            <param name="z">The value to compute the Bessel function of.</param>
            <returns>The Bessel function of the first kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselJScaled(System.Double,System.Double)">
            <summary>
            Returns the exponentially scaled Bessel function of the first kind.
            <para>ScaledBesselJ(n, z) is given by Exp(-Abs(z.Imaginary)) * BesselJ(n, z).</para>
            </summary>
            <param name="n">The order of the Bessel function.</param>
            <param name="z">The value to compute the Bessel function of.</param>
            <returns>The exponentially scaled Bessel function of the first kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselY(System.Double,System.Numerics.Complex)">
            <summary>
            Returns the Bessel function of the second kind.
            <para>BesselY(n, z) is a solution to the Bessel differential equation.</para>
            </summary>
            <param name="n">The order of the Bessel function.</param>
            <param name="z">The value to compute the Bessel function of.</param>
            <returns>The Bessel function of the second kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselYScaled(System.Double,System.Numerics.Complex)">
            <summary>
            Returns the exponentially scaled Bessel function of the second kind.
            <para>ScaledBesselY(n, z) is given by Exp(-Abs(z.Imaginary)) * Y(n, z).</para>
            </summary>
            <param name="n">The order of the Bessel function.</param>
            <param name="z">The value to compute the Bessel function of.</param>
            <returns>The exponentially scaled Bessel function of the second kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselY(System.Double,System.Double)">
            <summary>
            Returns the Bessel function of the second kind.
            <para>BesselY(n, z) is a solution to the Bessel differential equation.</para>
            </summary>
            <param name="n">The order of the Bessel function.</param>
            <param name="z">The value to compute the Bessel function of.</param>
            <returns>The Bessel function of the second kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselYScaled(System.Double,System.Double)">
            <summary>
            Returns the exponentially scaled Bessel function of the second kind.
            <para>ScaledBesselY(n, z) is given by Exp(-Abs(z.Imaginary)) * BesselY(n, z).</para>
            </summary>
            <param name="n">The order of the Bessel function.</param>
            <param name="z">The value to compute the Bessel function of.</param>
            <returns>The exponentially scaled Bessel function of the second kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselI(System.Double,System.Numerics.Complex)">
            <summary>
            Returns the modified Bessel function of the first kind.
            <para>BesselI(n, z) is a solution to the modified Bessel differential equation.</para>
            </summary>
            <param name="n">The order of the modified Bessel function.</param>
            <param name="z">The value to compute the modified Bessel function of.</param>
            <returns>The modified Bessel function of the first kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselIScaled(System.Double,System.Numerics.Complex)">
            <summary>
            Returns the exponentially scaled modified Bessel function of the first kind.
            <para>ScaledBesselI(n, z) is given by Exp(-Abs(z.Real)) * BesselI(n, z).</para>
            </summary>
            <param name="n">The order of the modified Bessel function.</param>
            <param name="z">The value to compute the modified Bessel function of.</param>
            <returns>The exponentially scaled modified Bessel function of the first kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselI(System.Double,System.Double)">
            <summary>
            Returns the modified Bessel function of the first kind.
            <para>BesselI(n, z) is a solution to the modified Bessel differential equation.</para>
            </summary>
            <param name="n">The order of the modified Bessel function.</param>
            <param name="z">The value to compute the modified Bessel function of.</param>
            <returns>The modified Bessel function of the first kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselIScaled(System.Double,System.Double)">
            <summary>
            Returns the exponentially scaled modified Bessel function of the first kind.
            <para>ScaledBesselI(n, z) is given by Exp(-Abs(z.Real)) * BesselI(n, z).</para>
            </summary>
            <param name="n">The order of the modified Bessel function.</param>
            <param name="z">The value to compute the modified Bessel function of.</param>
            <returns>The exponentially scaled modified Bessel function of the first kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselK(System.Double,System.Numerics.Complex)">
            <summary>
            Returns the modified Bessel function of the second kind.
            <para>BesselK(n, z) is a solution to the modified Bessel differential equation.</para>
            </summary>
            <param name="n">The order of the modified Bessel function.</param>
            <param name="z">The value to compute the modified Bessel function of.</param>
            <returns>The modified Bessel function of the second kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselKScaled(System.Double,System.Numerics.Complex)">
            <summary>
            Returns the exponentially scaled modified Bessel function of the second kind.
            <para>ScaledBesselK(n, z) is given by Exp(z) * BesselK(n, z).</para>
            </summary>
            <param name="n">The order of the modified Bessel function.</param>
            <param name="z">The value to compute the modified Bessel function of.</param>
            <returns>The exponentially scaled modified Bessel function of the second kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselK(System.Double,System.Double)">
            <summary>
            Returns the modified Bessel function of the second kind.
            <para>BesselK(n, z) is a solution to the modified Bessel differential equation.</para>
            </summary>
            <param name="n">The order of the modified Bessel function.</param>
            <param name="z">The value to compute the modified Bessel function of.</param>
            <returns>The modified Bessel function of the second kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselKScaled(System.Double,System.Double)">
            <summary>
            Returns the exponentially scaled modified Bessel function of the second kind.
            <para>ScaledBesselK(n, z) is given by Exp(z) * BesselK(n, z).</para>
            </summary>
            <param name="n">The order of the modified Bessel function.</param>
            <param name="z">The value to compute the modified Bessel function of.</param>
            <returns>The exponentially scaled modified Bessel function of the second kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BetaLn(System.Double,System.Double)">
            <summary>
            Computes the logarithm of the Euler Beta function.
            </summary>
            <param name="z">The first Beta parameter, a positive real number.</param>
            <param name="w">The second Beta parameter, a positive real number.</param>
            <returns>The logarithm of the Euler Beta function evaluated at z,w.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="z"/> or <paramref name="w"/> are not positive.</exception>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.Beta(System.Double,System.Double)">
            <summary>
            Computes the Euler Beta function.
            </summary>
            <param name="z">The first Beta parameter, a positive real number.</param>
            <param name="w">The second Beta parameter, a positive real number.</param>
            <returns>The Euler Beta function evaluated at z,w.</returns>
            <exception cref="T:System.ArgumentException">If <paramref name="z"/> or <paramref name="w"/> are not positive.</exception>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BetaIncomplete(System.Double,System.Double,System.Double)">
            <summary>
            Returns the lower incomplete (unregularized) beta function
            B(a,b,x) = int(t^(a-1)*(1-t)^(b-1),t=0..x) for real a &gt; 0, b &gt; 0, 1 &gt;= x &gt;= 0.
            </summary>
            <param name="a">The first Beta parameter, a positive real number.</param>
            <param name="b">The second Beta parameter, a positive real number.</param>
            <param name="x">The upper limit of the integral.</param>
            <returns>The lower incomplete (unregularized) beta function.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BetaRegularized(System.Double,System.Double,System.Double)">
            <summary>
            Returns the regularized lower incomplete beta function
            I_x(a,b) = 1/Beta(a,b) * int(t^(a-1)*(1-t)^(b-1),t=0..x) for real a &gt; 0, b &gt; 0, 1 &gt;= x &gt;= 0.
            </summary>
            <param name="a">The first Beta parameter, a positive real number.</param>
            <param name="b">The second Beta parameter, a positive real number.</param>
            <param name="x">The upper limit of the integral.</param>
            <returns>The regularized lower incomplete beta function.</returns>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpAn">
            <summary>
            **************************************
            COEFFICIENTS FOR METHOD ErfImp       *
            **************************************
            </summary>
            <summary> Polynomial coefficients for a numerator of ErfImp
            calculation for Erf(x) in the interval [1e-10, 0.5].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpAd">
            <summary> Polynomial coefficients for  a denominator of ErfImp
            calculation for Erf(x) in the interval [1e-10, 0.5].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpBn">
            <summary> Polynomial coefficients for a numerator in ErfImp
            calculation for Erfc(x) in the interval [0.5, 0.75].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpBd">
            <summary> Polynomial coefficients for a denominator in ErfImp
            calculation for Erfc(x) in the interval [0.5, 0.75].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpCn">
            <summary> Polynomial coefficients for a numerator in ErfImp
            calculation for Erfc(x) in the interval [0.75, 1.25].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpCd">
            <summary> Polynomial coefficients for a denominator in ErfImp
            calculation for Erfc(x) in the interval [0.75, 1.25].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpDn">
            <summary> Polynomial coefficients for a numerator in ErfImp
            calculation for Erfc(x) in the interval [1.25, 2.25].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpDd">
            <summary> Polynomial coefficients for a denominator in ErfImp
            calculation for Erfc(x) in the interval [1.25, 2.25].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpEn">
            <summary> Polynomial coefficients for a numerator in ErfImp
            calculation for Erfc(x) in the interval [2.25, 3.5].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpEd">
            <summary> Polynomial coefficients for a denominator in ErfImp
            calculation for Erfc(x) in the interval [2.25, 3.5].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpFn">
            <summary> Polynomial coefficients for a numerator in ErfImp
            calculation for Erfc(x) in the interval [3.5, 5.25].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpFd">
            <summary> Polynomial coefficients for a denominator in ErfImp
            calculation for Erfc(x) in the interval [3.5, 5.25].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpGn">
            <summary> Polynomial coefficients for a numerator in ErfImp
            calculation for Erfc(x) in the interval [5.25, 8].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpGd">
            <summary> Polynomial coefficients for a denominator in ErfImp
            calculation for Erfc(x) in the interval [5.25, 8].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpHn">
            <summary> Polynomial coefficients for a numerator in ErfImp
            calculation for Erfc(x) in the interval [8, 11.5].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpHd">
            <summary> Polynomial coefficients for a denominator in ErfImp
            calculation for Erfc(x) in the interval [8, 11.5].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpIn">
            <summary> Polynomial coefficients for a numerator in ErfImp
            calculation for Erfc(x) in the interval [11.5, 17].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpId">
            <summary> Polynomial coefficients for a denominator in ErfImp
            calculation for Erfc(x) in the interval [11.5, 17].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpJn">
            <summary> Polynomial coefficients for a numerator in ErfImp
            calculation for Erfc(x) in the interval [17, 24].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpJd">
            <summary> Polynomial coefficients for a denominator in ErfImp
            calculation for Erfc(x) in the interval [17, 24].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpKn">
            <summary> Polynomial coefficients for a numerator in ErfImp
            calculation for Erfc(x) in the interval [24, 38].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpKd">
            <summary> Polynomial coefficients for a denominator in ErfImp
            calculation for Erfc(x) in the interval [24, 38].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpLn">
            <summary> Polynomial coefficients for a numerator in ErfImp
            calculation for Erfc(x) in the interval [38, 60].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpLd">
            <summary> Polynomial coefficients for a denominator in ErfImp
            calculation for Erfc(x) in the interval [38, 60].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpMn">
            <summary> Polynomial coefficients for a numerator in ErfImp
            calculation for Erfc(x) in the interval [60, 85].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpMd">
            <summary> Polynomial coefficients for a denominator in ErfImp
            calculation for Erfc(x) in the interval [60, 85].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpNn">
            <summary> Polynomial coefficients for a numerator in ErfImp
            calculation for Erfc(x) in the interval [85, 110].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErfImpNd">
            <summary> Polynomial coefficients for a denominator in ErfImp
            calculation for Erfc(x) in the interval [85, 110].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErvInvImpAn">
            <summary>
            **************************************
            COEFFICIENTS FOR METHOD ErfInvImp    *
            **************************************
            </summary>
            <summary> Polynomial coefficients for a numerator of ErfInvImp
            calculation for Erf^-1(z) in the interval [0, 0.5].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErvInvImpAd">
            <summary> Polynomial coefficients for a denominator of ErfInvImp
            calculation for Erf^-1(z) in the interval [0, 0.5].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErvInvImpBn">
            <summary> Polynomial coefficients for a numerator of ErfInvImp
            calculation for Erf^-1(z) in the interval [0.5, 0.75].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErvInvImpBd">
            <summary> Polynomial coefficients for a denominator of ErfInvImp
            calculation for Erf^-1(z) in the interval [0.5, 0.75].
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErvInvImpCn">
            <summary> Polynomial coefficients for a numerator of ErfInvImp
            calculation for Erf^-1(z) in the interval [0.75, 1] with x less than 3.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErvInvImpCd">
            <summary> Polynomial coefficients for a denominator of ErfInvImp
            calculation for Erf^-1(z) in the interval [0.75, 1] with x less than 3.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErvInvImpDn">
            <summary> Polynomial coefficients for a numerator of ErfInvImp
            calculation for Erf^-1(z) in the interval [0.75, 1] with x between 3 and 6.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErvInvImpDd">
            <summary> Polynomial coefficients for a denominator of ErfInvImp
            calculation for Erf^-1(z) in the interval [0.75, 1] with x between 3 and 6.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErvInvImpEn">
            <summary> Polynomial coefficients for a numerator of ErfInvImp
            calculation for Erf^-1(z) in the interval [0.75, 1] with x between 6 and 18.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErvInvImpEd">
            <summary> Polynomial coefficients for a denominator of ErfInvImp
            calculation for Erf^-1(z) in the interval [0.75, 1] with x between 6 and 18.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErvInvImpFn">
            <summary> Polynomial coefficients for a numerator of ErfInvImp
            calculation for Erf^-1(z) in the interval [0.75, 1] with x between 18 and 44.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErvInvImpFd">
            <summary> Polynomial coefficients for a denominator of ErfInvImp
            calculation for Erf^-1(z) in the interval [0.75, 1] with x between 18 and 44.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErvInvImpGn">
            <summary> Polynomial coefficients for a numerator of ErfInvImp
            calculation for Erf^-1(z) in the interval [0.75, 1] with x greater than 44.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.ErvInvImpGd">
            <summary> Polynomial coefficients for a denominator of ErfInvImp
            calculation for Erf^-1(z) in the interval [0.75, 1] with x greater than 44.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.Erf(System.Double)">
            <summary>Calculates the error function.</summary>
            <param name="x">The value to evaluate.</param>
            <returns>the error function evaluated at given value.</returns>
            <remarks>
                <list type="bullet">
                    <item>returns 1 if <c>x == double.PositiveInfinity</c>.</item>
                    <item>returns -1 if <c>x == double.NegativeInfinity</c>.</item>
                </list>
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.Erfc(System.Double)">
            <summary>Calculates the complementary error function.</summary>
            <param name="x">The value to evaluate.</param>
            <returns>the complementary error function evaluated at given value.</returns>
            <remarks>
                <list type="bullet">
                    <item>returns 0 if <c>x == double.PositiveInfinity</c>.</item>
                    <item>returns 2 if <c>x == double.NegativeInfinity</c>.</item>
                </list>
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.ErfInv(System.Double)">
            <summary>Calculates the inverse error function evaluated at z.</summary>
            <returns>The inverse error function evaluated at given value.</returns>
            <remarks>
              <list type="bullet">
                <item>returns double.PositiveInfinity if <c>z &gt;= 1.0</c>.</item>
                <item>returns double.NegativeInfinity if <c>z &lt;= -1.0</c>.</item>
              </list>
            </remarks>
            <summary>Calculates the inverse error function evaluated at z.</summary>
            <param name="z">value to evaluate.</param>
            <returns>the inverse error function evaluated at Z.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.ErfImp(System.Double,System.Boolean)">
            <summary>
            Implementation of the error function.
            </summary>
            <param name="z">Where to evaluate the error function.</param>
            <param name="invert">Whether to compute 1 - the error function.</param>
            <returns>the error function.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.ErfcInv(System.Double)">
            <summary>Calculates the complementary inverse error function evaluated at z.</summary>
            <returns>The complementary inverse error function evaluated at given value.</returns>
            <remarks> We have tested this implementation against the arbitrary precision mpmath library
            and found cases where we can only guarantee 9 significant figures correct.
                <list type="bullet">
                    <item>returns double.PositiveInfinity if <c>z &lt;= 0.0</c>.</item>
                    <item>returns double.NegativeInfinity if <c>z &gt;= 2.0</c>.</item>
                </list>
            </remarks>
            <summary>calculates the complementary inverse error function evaluated at z.</summary>
            <param name="z">value to evaluate.</param>
            <returns>the complementary inverse error function evaluated at Z.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.ErfInvImpl(System.Double,System.Double,System.Double)">
            <summary>
            The implementation of the inverse error function.
            </summary>
            <param name="p">First intermediate parameter.</param>
            <param name="q">Second intermediate parameter.</param>
            <param name="s">Third intermediate parameter.</param>
            <returns>the inverse error function.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.Expm1(System.Double)">
            <summary>
            Numerically stable exponential minus one, i.e. <code>x -> exp(x)-1</code>
            </summary>
            <param name="power">A number specifying a power.</param>
            <returns>Returns <code>exp(power)-1</code>.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.ExponentialMinusOne(System.Double)">
            <summary>
            Numerically stable exponential minus one, i.e. <code>x -> exp(x)-1</code>
            </summary>
            <param name="power">A number specifying a power.</param>
            <returns>Returns <code>exp(power)-1</code>.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.ExponentialIntegral(System.Double,System.Int32)">
            <summary>
            Computes the generalized Exponential Integral function (En).
            </summary>
            <param name="x">The argument of the Exponential Integral function.</param>
            <param name="n">Integer power of the denominator term. Generalization index.</param>
            <returns>The value of the Exponential Integral function.</returns>
            <remarks>
            <para>This implementation of the computation of the Exponential Integral function follows the derivation in
                "Handbook of Mathematical Functions, Applied Mathematics Series, Volume 55", Abramowitz, M., and Stegun, I.A. 1964,  reprinted 1968 by
                Dover Publications, New York), Chapters 6, 7, and 26.
                AND
                "Advanced mathematical methods for scientists and engineers", Bender, Carl M.; Steven A. Orszag (1978). page 253
            </para>
            <para>
                for x &gt; 1  uses continued fraction approach that is often used to compute incomplete gamma.
                for 0 &lt; x &lt;= 1 uses Taylor series expansion
            </para>
            <para>Our unit tests suggest that the accuracy of the Exponential Integral function is correct up to 13 floating point digits.</para>
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.Factorial(System.Int32)">
            <summary>
            Computes the factorial function x -> x! of an integer number > 0. The function can represent all number up
            to 22! exactly, all numbers up to 170! using a double representation. All larger values will overflow.
            </summary>
            <returns>A value value! for value > 0</returns>
            <remarks>
            If you need to multiply or divide various such factorials, consider using the logarithmic version
            <see cref="M:MathNet.Numerics.SpecialFunctions.FactorialLn(System.Int32)"/> instead so you can add instead of multiply and subtract instead of divide, and
            then exponentiate the result using <see cref="M:System.Math.Exp(System.Double)"/>. This will also circumvent the problem that
            factorials become very large even for small parameters.
            </remarks>
            <exception cref="T:System.ArgumentOutOfRangeException" />
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.Factorial(System.Numerics.BigInteger)">
            <summary>
            Computes the factorial of an integer.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.FactorialLn(System.Int32)">
            <summary>
            Computes the logarithmic factorial function x -> ln(x!) of an integer number > 0.
            </summary>
            <returns>A value value! for value > 0</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.Binomial(System.Int32,System.Int32)">
            <summary>
            Computes the binomial coefficient: n choose k.
            </summary>
            <param name="n">A nonnegative value n.</param>
            <param name="k">A nonnegative value h.</param>
            <returns>The binomial coefficient: n choose k.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BinomialLn(System.Int32,System.Int32)">
            <summary>
            Computes the natural logarithm of the binomial coefficient: ln(n choose k).
            </summary>
            <param name="n">A nonnegative value n.</param>
            <param name="k">A nonnegative value h.</param>
            <returns>The logarithmic binomial coefficient: ln(n choose k).</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.Multinomial(System.Int32,System.Int32[])">
            <summary>
            Computes the multinomial coefficient: n choose n1, n2, n3, ...
            </summary>
            <param name="n">A nonnegative value n.</param>
            <param name="ni">An array of nonnegative values that sum to <paramref name="n"/>.</param>
            <returns>The multinomial coefficient.</returns>
            <exception cref="T:System.ArgumentNullException">if <paramref name="ni"/> is <see langword="null" />.</exception>
            <exception cref="T:System.ArgumentException">If <paramref name="n"/> or any of the <paramref name="ni"/> are negative.</exception>
            <exception cref="T:System.ArgumentException">If the sum of all <paramref name="ni"/> is not equal to <paramref name="n"/>.</exception>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.GammaN">
            <summary>
            The order of the <see cref="M:MathNet.Numerics.SpecialFunctions.GammaLn(System.Double)"/> approximation.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.GammaR">
            <summary>
            Auxiliary variable when evaluating the <see cref="M:MathNet.Numerics.SpecialFunctions.GammaLn(System.Double)"/> function.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.GammaDk">
            <summary>
            Polynomial coefficients for the <see cref="M:MathNet.Numerics.SpecialFunctions.GammaLn(System.Double)"/> approximation.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.GammaLn(System.Double)">
            <summary>
            Computes the logarithm of the Gamma function.
            </summary>
            <param name="z">The argument of the gamma function.</param>
            <returns>The logarithm of the gamma function.</returns>
            <remarks>
            <para>This implementation of the computation of the gamma and logarithm of the gamma function follows the derivation in
                "An Analysis Of The Lanczos Gamma Approximation", Glendon Ralph Pugh, 2004.
            We use the implementation listed on p. 116 which achieves an accuracy of 16 floating point digits. Although 16 digit accuracy
            should be sufficient for double values, improving accuracy is possible (see p. 126 in Pugh).</para>
            <para>Our unit tests suggest that the accuracy of the Gamma function is correct up to 14 floating point digits.</para>
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.Gamma(System.Double)">
            <summary>
            Computes the Gamma function.
            </summary>
            <param name="z">The argument of the gamma function.</param>
            <returns>The logarithm of the gamma function.</returns>
            <remarks>
            <para>
            This implementation of the computation of the gamma and logarithm of the gamma function follows the derivation in
                "An Analysis Of The Lanczos Gamma Approximation", Glendon Ralph Pugh, 2004.
            We use the implementation listed on p. 116 which should achieve an accuracy of 16 floating point digits. Although 16 digit accuracy
            should be sufficient for double values, improving accuracy is possible (see p. 126 in Pugh).
            </para>
            <para>Our unit tests suggest that the accuracy of the Gamma function is correct up to 13 floating point digits.</para>
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.GammaUpperRegularized(System.Double,System.Double)">
            <summary>
            Returns the upper incomplete regularized gamma function
            Q(a,x) = 1/Gamma(a) * int(exp(-t)t^(a-1),t=0..x) for real a &gt; 0, x &gt; 0.
            </summary>
            <param name="a">The argument for the gamma function.</param>
            <param name="x">The lower integral limit.</param>
            <returns>The upper incomplete regularized gamma function.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.GammaUpperIncomplete(System.Double,System.Double)">
            <summary>
            Returns the upper incomplete gamma function
            Gamma(a,x) = int(exp(-t)t^(a-1),t=0..x) for real a &gt; 0, x &gt; 0.
            </summary>
            <param name="a">The argument for the gamma function.</param>
            <param name="x">The lower integral limit.</param>
            <returns>The upper incomplete gamma function.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.GammaLowerIncomplete(System.Double,System.Double)">
            <summary>
            Returns the lower incomplete gamma function
            gamma(a,x) = int(exp(-t)t^(a-1),t=0..x) for real a &gt; 0, x &gt; 0.
            </summary>
            <param name="a">The argument for the gamma function.</param>
            <param name="x">The upper integral limit.</param>
            <returns>The lower incomplete gamma function.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.GammaLowerRegularized(System.Double,System.Double)">
            <summary>
            Returns the lower incomplete regularized gamma function
            P(a,x) = 1/Gamma(a) * int(exp(-t)t^(a-1),t=0..x) for real a &gt; 0, x &gt; 0.
            </summary>
            <param name="a">The argument for the gamma function.</param>
            <param name="x">The upper integral limit.</param>
            <returns>The lower incomplete gamma function.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.GammaLowerRegularizedInv(System.Double,System.Double)">
            <summary>
            Returns the inverse P^(-1) of the regularized lower incomplete gamma function
            P(a,x) = 1/Gamma(a) * int(exp(-t)t^(a-1),t=0..x) for real a &gt; 0, x &gt; 0,
            such that P^(-1)(a,P(a,x)) == x.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.DiGamma(System.Double)">
            <summary>
            Computes the Digamma function which is mathematically defined as the derivative of the logarithm of the gamma function.
            This implementation is based on
                Jose Bernardo
                Algorithm AS 103:
                Psi ( Digamma ) Function,
                Applied Statistics,
                Volume 25, Number 3, 1976, pages 315-317.
            Using the modifications as in Tom Minka's lightspeed toolbox.
            </summary>
            <param name="x">The argument of the digamma function.</param>
            <returns>The value of the DiGamma function at <paramref name="x"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.DiGammaInv(System.Double)">
            <summary>
            <para>Computes the inverse Digamma function: this is the inverse of the logarithm of the gamma function. This function will
            only return solutions that are positive.</para>
            <para>This implementation is based on the bisection method.</para>
            </summary>
            <param name="p">The argument of the inverse digamma function.</param>
            <returns>The positive solution to the inverse DiGamma function at <paramref name="p"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.RisingFactorial(System.Double,System.Int32)">
            <summary>
            Computes the Rising Factorial (Pochhammer function)  x -> (x)n, n>= 0. see: https://en.wikipedia.org/wiki/Falling_and_rising_factorials
            </summary>
            <returns>The real value of the Rising Factorial for x and n</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.FallingFactorial(System.Double,System.Int32)">
            <summary>
            Computes the Falling Factorial (Pochhammer function)  x -> x(n), n>= 0. see: https://en.wikipedia.org/wiki/Falling_and_rising_factorials
            </summary>
            <returns>The real value of the Falling Factorial for x and n</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.GeneralizedHypergeometric(System.Double[],System.Double[],System.Int32)">
            <summary>
            A generalized hypergeometric series is a power series in which the ratio of successive coefficients indexed by n is a rational function of n.
            This is the most common pFq(a1, ..., ap; b1,...,bq; z) representation
            see: https://en.wikipedia.org/wiki/Generalized_hypergeometric_function
            </summary>
            <param name="a">The list of coefficients in the numerator</param>
            <param name="b">The list of coefficients in the denominator</param>
            <param name="z">The variable in the power series</param>
            <returns>The value of the Generalized HyperGeometric Function.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.HankelH1(System.Double,System.Numerics.Complex)">
            <summary>
            Returns the Hankel function of the first kind.
            <para>HankelH1(n, z) is defined as BesselJ(n, z) + j * BesselY(n, z).</para>
            </summary>
            <param name="n">The order of the Hankel function.</param>
            <param name="z">The value to compute the Hankel function of.</param>
            <returns>The Hankel function of the first kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.HankelH1Scaled(System.Double,System.Numerics.Complex)">
            <summary>
            Returns the exponentially scaled Hankel function of the first kind.
            <para>ScaledHankelH1(n, z) is given by Exp(-z * j) * HankelH1(n, z) where j = Sqrt(-1).</para>
            </summary>
            <param name="n">The order of the Hankel function.</param>
            <param name="z">The value to compute the Hankel function of.</param>
            <returns>The exponentially scaled Hankel function of the first kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.HankelH2(System.Double,System.Numerics.Complex)">
            <summary>
            Returns the Hankel function of the second kind.
            <para>HankelH2(n, z) is defined as BesselJ(n, z) - j * BesselY(n, z).</para>
            </summary>
            <param name="n">The order of the Hankel function.</param>
            <param name="z">The value to compute the Hankel function of.</param>
            <returns>The Hankel function of the second kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.HankelH2Scaled(System.Double,System.Numerics.Complex)">
            <summary>
            Returns the exponentially scaled Hankel function of the second kind.
            <para>ScaledHankelH2(n, z) is given by Exp(z * j) * HankelH2(n, z) where j = Sqrt(-1).</para>
            </summary>
            <param name="n">The order of the Hankel function.</param>
            <param name="z">The value to compute the Hankel function of.</param>
            <returns>The exponentially scaled Hankel function of the second kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.Harmonic(System.Int32)">
            <summary>
            Computes the <paramref name="t"/>'th Harmonic number.
            </summary>
            <param name="t">The Harmonic number which needs to be computed.</param>
            <returns>The t'th Harmonic number.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.GeneralHarmonic(System.Int32,System.Double)">
            <summary>
            Compute the generalized harmonic number of order n of m. (1 + 1/2^m + 1/3^m + ... + 1/n^m)
            </summary>
            <param name="n">The order parameter.</param>
            <param name="m">The power parameter.</param>
            <returns>General Harmonic number.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.KelvinBe(System.Double,System.Double)">
            <summary>
            Returns the Kelvin function of the first kind.
            <para>KelvinBe(nu, x) is given by BesselJ(0, j * sqrt(j) * x) where j = sqrt(-1).</para>
            <para>KelvinBer(nu, x) and KelvinBei(nu, x) are the real and imaginary parts of the KelvinBe(nu, x)</para>
            </summary>
            <param name="nu">the order of the the Kelvin function.</param>
            <param name="x">The value to compute the Kelvin function of.</param>
            <returns>The Kelvin function of the first kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.KelvinBer(System.Double,System.Double)">
            <summary>
            Returns the Kelvin function ber.
            <para>KelvinBer(nu, x) is given by the real part of BesselJ(nu, j * sqrt(j) * x) where j = sqrt(-1).</para>
            </summary>
            <param name="nu">the order of the the Kelvin function.</param>
            <param name="x">The value to compute the Kelvin function of.</param>
            <returns>The Kelvin function ber.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.KelvinBer(System.Double)">
            <summary>
            Returns the Kelvin function ber.
            <para>KelvinBer(x) is given by the real part of BesselJ(0, j * sqrt(j) * x) where j = sqrt(-1).</para>
            <para>KelvinBer(x) is equivalent to KelvinBer(0, x).</para>
            </summary>
            <param name="x">The value to compute the Kelvin function of.</param>
            <returns>The Kelvin function ber.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.KelvinBei(System.Double,System.Double)">
            <summary>
            Returns the Kelvin function bei.
            <para>KelvinBei(nu, x) is given by the imaginary part of BesselJ(nu, j * sqrt(j) * x) where j = sqrt(-1).</para>
            </summary>
            <param name="nu">the order of the the Kelvin function.</param>
            <param name="x">The value to compute the Kelvin function of.</param>
            <returns>The Kelvin function bei.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.KelvinBei(System.Double)">
            <summary>
            Returns the Kelvin function bei.
            <para>KelvinBei(x) is given by the imaginary part of BesselJ(0, j * sqrt(j) * x) where j = sqrt(-1).</para>
            <para>KelvinBei(x) is equivalent to KelvinBei(0, x).</para>
            </summary>
            <param name="x">The value to compute the Kelvin function of.</param>
            <returns>The Kelvin function bei.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.KelvinBerPrime(System.Double,System.Double)">
            <summary>
            Returns the derivative of the Kelvin function ber.
            </summary>
            <param name="nu">The order of the Kelvin function.</param>
            <param name="x">The value to compute the derivative of the Kelvin function of.</param>
            <returns>the derivative of the Kelvin function ber</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.KelvinBerPrime(System.Double)">
            <summary>
            Returns the derivative of the Kelvin function ber.
            </summary>
            <param name="x">The value to compute the derivative of the Kelvin function of.</param>
            <returns>The derivative of the Kelvin function ber.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.KelvinBeiPrime(System.Double,System.Double)">
            <summary>
            Returns the derivative of the Kelvin function bei.
            </summary>
            <param name="nu">The order of the Kelvin function.</param>
            <param name="x">The value to compute the derivative of the Kelvin function of.</param>
            <returns>the derivative of the Kelvin function bei.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.KelvinBeiPrime(System.Double)">
            <summary>
            Returns the derivative of the Kelvin function bei.
            </summary>
            <param name="x">The value to compute the derivative of the Kelvin function of.</param>
            <returns>The derivative of the Kelvin function bei.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.KelvinKe(System.Double,System.Double)">
            <summary>
            Returns the Kelvin function of the second kind
            <para>KelvinKe(nu, x) is given by Exp(-nu * pi * j / 2) * BesselK(nu, x * sqrt(j)) where j = sqrt(-1).</para>
            <para>KelvinKer(nu, x) and KelvinKei(nu, x) are the real and imaginary parts of the KelvinBe(nu, x)</para>
            </summary>
            <param name="nu">The order of the Kelvin function.</param>
            <param name="x">The value to calculate the kelvin function of,</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.KelvinKer(System.Double,System.Double)">
            <summary>
            Returns the Kelvin function ker.
            <para>KelvinKer(nu, x) is given by the real part of Exp(-nu * pi * j / 2) * BesselK(nu, sqrt(j) * x) where j = sqrt(-1).</para>
            </summary>
            <param name="nu">the order of the the Kelvin function.</param>
            <param name="x">The non-negative real value to compute the Kelvin function of.</param>
            <returns>The Kelvin function ker.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.KelvinKer(System.Double)">
            <summary>
            Returns the Kelvin function ker.
            <para>KelvinKer(x) is given by the real part of Exp(-nu * pi * j / 2) * BesselK(0, sqrt(j) * x) where j = sqrt(-1).</para>
            <para>KelvinKer(x) is equivalent to KelvinKer(0, x).</para>
            </summary>
            <param name="x">The non-negative real value to compute the Kelvin function of.</param>
            <returns>The Kelvin function ker.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.KelvinKei(System.Double,System.Double)">
            <summary>
            Returns the Kelvin function kei.
            <para>KelvinKei(nu, x) is given by the imaginary part of Exp(-nu * pi * j / 2) * BesselK(nu, sqrt(j) * x) where j = sqrt(-1).</para>
            </summary>
            <param name="nu">the order of the the Kelvin function.</param>
            <param name="x">The non-negative real value to compute the Kelvin function of.</param>
            <returns>The Kelvin function kei.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.KelvinKei(System.Double)">
            <summary>
            Returns the Kelvin function kei.
            <para>KelvinKei(x) is given by the imaginary part of Exp(-nu * pi * j / 2) * BesselK(0, sqrt(j) * x) where j = sqrt(-1).</para>
            <para>KelvinKei(x) is equivalent to KelvinKei(0, x).</para>
            </summary>
            <param name="x">The non-negative real value to compute the Kelvin function of.</param>
            <returns>The Kelvin function kei.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.KelvinKerPrime(System.Double,System.Double)">
            <summary>
            Returns the derivative of the Kelvin function ker.
            </summary>
            <param name="nu">The order of the Kelvin function.</param>
            <param name="x">The non-negative real value to compute the derivative of the Kelvin function of.</param>
            <returns>The derivative of the Kelvin function ker.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.KelvinKerPrime(System.Double)">
            <summary>
            Returns the derivative of the Kelvin function ker.
            </summary>
            <param name="x">The value to compute the derivative of the Kelvin function of.</param>
            <returns>The derivative of the Kelvin function ker.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.KelvinKeiPrime(System.Double,System.Double)">
            <summary>
            Returns the derivative of the Kelvin function kei.
            </summary>
            <param name="nu">The order of the Kelvin function.</param>
            <param name="x">The value to compute the derivative of the Kelvin function of.</param>
            <returns>The derivative of the Kelvin function kei.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.KelvinKeiPrime(System.Double)">
            <summary>
            Returns the derivative of the Kelvin function kei.
            </summary>
            <param name="x">The value to compute the derivative of the Kelvin function of.</param>
            <returns>The derivative of the Kelvin function kei.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.Log1p(System.Double)">
            <summary>
            Computes ln(1+x) with good relative precision when |x| is small
            </summary>
            <param name="x">The parameter for which to compute the log1p function. Range: x > 0.</param>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.Logistic(System.Double)">
            <summary>
            Computes the logistic function. see: http://en.wikipedia.org/wiki/Logistic
            </summary>
            <param name="p">The parameter for which to compute the logistic function.</param>
            <returns>The logistic function of <paramref name="p"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.Logit(System.Double)">
            <summary>
            Computes the logit function, the inverse of the sigmoid logistic function. see: http://en.wikipedia.org/wiki/Logit
            </summary>
            <param name="p">The parameter for which to compute the logit function. This number should be
            between 0 and 1.</param>
            <returns>The logarithm of <paramref name="p"/> divided by 1.0 - <paramref name="p"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.MarcumQ(System.Double,System.Double,System.Double)">
            <summary>
            Returns the Marcum Q-function Q[ν](a,b). <a href="https://en.wikipedia.org/wiki/Marcum_Q-function">Marcum Q-function (Wikipedia)</a>
            <para>References: A. Gil, J. Segura and N.M. Temme. Efficient and accurate algorithms for the
            computation and inversion of the incomplete gamma function ratios. SIAM J Sci Comput. (2012) 34(6), A2965-A2981</para>
            </summary>
            <param name="nu">The order of generalized Marcum Q-function. Range: 1≦ν≦10000</param>
            <param name="a">The value to compute the Marcum Q-function of. Range: 0≦a≦10000</param>
            <param name="b">The value to compute the Marcum Q-function of. Range: 0≦b≦10000</param>
            <returns>The Marcum Q-function Q[ν](a,b)</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.MarcumQ(System.Double,System.Double,System.Double,System.Int32@)">
            <summary>
            Returns the Marcum Q-function Q[ν](a,b). <a href="https://en.wikipedia.org/wiki/Marcum_Q-function">Marcum Q-function (Wikipedia)</a>
            <para>References: A. Gil, J. Segura and N.M. Temme. Efficient and accurate algorithms for the
            computation and inversion of the incomplete gamma function ratios. SIAM J Sci Comput. (2012) 34(6), A2965-A2981</para>
            </summary>
            <param name="nu">The order of generalized Marcum Q-function. Range: 1≦ν≦10000</param>
            <param name="a">The value to compute the Marcum Q-function of. Range: 0≦a≦10000</param>
            <param name="b">The value to compute the Marcum Q-function of. Range: 0≦b≦10000</param>
            <param name="err">Error flag
            <para>0: Computation succesful.</para>
            <para>1: Underflow problems. The function value is set to one.</para>
            <para>2: Any of the arguments of the function is out of range.The function value is set to zero.</para>
            </param>
            <returns>The Marcum Q-function Q[ν](a,b)</returns>
        </member>
        <member name="T:MathNet.Numerics.SpecialFunctions.MarcumQFunction">
            <summary>
            <para>Marcum Q-functions</para>
            <para>References</para>
            <para>[1] A. Gil, J. Segura and N.M. Temme, Accompanying paper in ACM Trans Math Soft</para>
            <para>[2] A. Gil, J. Segura and N.M. Temme. Efficient and accurate algorithms for the
            computation and inversion of the incomplete gamma function ratios. SIAM J Sci Comput.
            (2012) 34(6), A2965-A2981</para>
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.MarcumQFunction.Tiny">
            <summary>
            Fortran 95 TINY(0.0d0)
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.MarcumQFunction.Huge">
            <summary>
            Fortran 95 HUGE(0.0d0)
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.MarcumQFunction.TwoExp1Over4">
            <summary>
            The number 2**(1/4)
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.MarcumQFunction.MachTol">
            <summary>
            machine-epsilon
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.MarcumQFunction.LnSqrt2Pi">
            <summary>
            The number log[e](sqrt(2*pi))
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.MarcumQFunction.Dwarf">
            <summary>
            Safe underflow limit
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.MarcumQFunction.Giant">
            <summary>
            Safe overflow limit
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.MarcumQFunction.ExpLow">
            <summary>
            Lower limit of exponential
            </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.MarcumQFunction.Epss">
            <summary>
            Demanded accuracy
            </summary>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.MarcumQFunction.Marcum(System.Double,System.Double,System.Double,System.Double@,System.Double@,System.Int32@)">
            <summary>
            Calculation of the Marcum Q-functions P_mu(x,y) and Q_mu(x,y). <a href="https://en.wikipedia.org/wiki/Marcum_Q-function">Marcum Q-function (Wikipedia)</a>
            </summary>
            <remarks>
            <para>
            In order to avoid, overflow/underflow problems in IEEE double precision arithmetic, the admissible parameter ranges for computation are:
            </para>
            <para>
            0≦x≦10000, 0≦y≦10000, 1≦mu≦10000
            </para>
            <para>
            The aimed relative accuracy is close to 1.0e-11 in the previous parameter domain.
            </para>
            </remarks>
            <param name="mu">argument of the functions</param>
            <param name="x">argument of the functions</param>
            <param name="y">argument of the functions</param>
            <param name="p">function P_mu(a,x)</param>
            <param name="q">function Q_mu(a,x)</param>
            <param name="ierr">error flag
            <para>0: Computation succesful</para>
            <para>1: Underflow problems. The function values are set to zero and one.</para>
            <para>2: Any of the arguments of the function is out of range.The function values (P_mu(a, x) and Q_mu(a, x)) are set to zero.</para>
            </param>
        </member>
        <member name="T:MathNet.Numerics.SpecialFunctions.MarcumQFunction.IncompleteGamma">
            <summary>
            computation of the incomplete gamma function ratios
            </summary>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.MarcumQFunction.IncompleteGamma.Incgam(System.Double,System.Double,System.Double@,System.Double@,System.Int32@)">
            <summary>
            Calculation of the incomplete gamma functions ratios P(a,x) and Q(a,x).
            </summary>
            <param name="a">argument of the functions</param>
            <param name="x">argument of the functions</param>
            <param name="p">function P(a,x)</param>
            <param name="q">function Q(a,x)</param>
            <param name="ierr">error flag</param>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.MarcumQFunction.IncompleteGamma.Invincgam(System.Double,System.Double,System.Double,System.Double@,System.Int32@)">
            <summary>
            Computes xr in the equations P(a,xr)=p and Q(a,xr)=q with a as a given positive parameter. In most cases, we invert the equation with min(p,q)
            </summary>
            <param name="a">argument of the functions</param>
            <param name="p">function P(a,x)</param>
            <param name="q">function Q(a,x)</param>
            <param name="xr">soluction of the equations P(a,xr)=p and Q(a,xr)=q with a as a given positive parameter.</param>
            <param name="ierr">error flag</param>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.MarcumQFunction.IncompleteGamma.Dompart(System.Double,System.Double,System.Boolean)">
            <summary>
            dompart is approx. of  x^a * exp(-x) / gamma(a+1)
            </summary>
            <param name="a">argument of the functions</param>
            <param name="x">argument of the functions</param>
            <param name="qt">argument of the functions</param>
            <returns></returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.MarcumQFunction.IncompleteGamma.Loggam(System.Double)">
            <summary>
            Computation of ln(gamma(x)), x>0
            </summary>
            <param name="x">argument of the functions</param>
            <returns></returns>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.BesselI0A">
             <summary>
             **************************************
             COEFFICIENTS FOR METHODS bessi0      *
             **************************************
             </summary>
             <summary> Chebyshev coefficients for exp(-x) I0(x)
             in the interval [0, 8].
            
             lim(x->0){ exp(-x) I0(x) } = 1.
             </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.BesselI0B">
             <summary> Chebyshev coefficients for exp(-x) sqrt(x) I0(x)
             in the inverted interval [8, infinity].
            
             lim(x->inf){ exp(-x) sqrt(x) I0(x) } = 1/sqrt(2pi).
             </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.BesselI1A">
             <summary>
             **************************************
             COEFFICIENTS FOR METHODS bessi1      *
             **************************************
             </summary>
             <summary> Chebyshev coefficients for exp(-x) I1(x) / x
             in the interval [0, 8].
            
             lim(x->0){ exp(-x) I1(x) / x } = 1/2.
             </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.BesselI1B">
             <summary> Chebyshev coefficients for exp(-x) sqrt(x) I1(x)
             in the inverted interval [8, infinity].
            
             lim(x->inf){ exp(-x) sqrt(x) I1(x) } = 1/sqrt(2pi).
             </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.BesselK0A">
             <summary>
             **************************************
             COEFFICIENTS FOR METHODS bessk0, bessk0e *
             **************************************
             </summary>
             <summary> Chebyshev coefficients for K0(x) + log(x/2) I0(x)
             in the interval [0, 2].  The odd order coefficients are all
             zero; only the even order coefficients are listed.
            
             lim(x->0){ K0(x) + log(x/2) I0(x) } = -EUL.
             </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.BesselK0B">
             <summary> Chebyshev coefficients for exp(x) sqrt(x) K0(x)
             in the inverted interval [2, infinity].
            
             lim(x->inf){ exp(x) sqrt(x) K0(x) } = sqrt(pi/2).
             </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.BesselK1A">
             <summary>
             **************************************
             COEFFICIENTS FOR METHODS bessk1, bessk1e *
             **************************************
             </summary>
             <summary> Chebyshev coefficients for x(K1(x) - log(x/2) I1(x))
             in the interval [0, 2].
            
             lim(x->0){ x(K1(x) - log(x/2) I1(x)) } = 1.
             </summary>
        </member>
        <member name="F:MathNet.Numerics.SpecialFunctions.BesselK1B">
             <summary> Chebyshev coefficients for exp(x) sqrt(x) K1(x)
             in the interval [2, infinity].
            
             lim(x->inf){ exp(x) sqrt(x) K1(x) } = sqrt(pi/2).
             </summary>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselI0(System.Double)">
            <summary>Returns the modified Bessel function of first kind, order 0 of the argument.
            <p/>
            The function is defined as <tt>i0(x) = j0( ix )</tt>.
            <p/>
            The range is partitioned into the two intervals [0, 8] and
            (8, infinity). Chebyshev polynomial expansions are employed
            in each interval.
            </summary>
            <param name="x">The value to compute the Bessel function of.
            </param>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselI1(System.Double)">
            <summary>Returns the modified Bessel function of first kind,
            order 1 of the argument.
            <p/>
            The function is defined as <tt>i1(x) = -i j1( ix )</tt>.
            <p/>
            The range is partitioned into the two intervals [0, 8] and
            (8, infinity). Chebyshev polynomial expansions are employed
            in each interval.
            </summary>
            <param name="x">The value to compute the Bessel function of.
            </param>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselK0(System.Double)">
            <summary> Returns the modified Bessel function of the second kind
            of order 0 of the argument.
            <p/>
            The range is partitioned into the two intervals [0, 8] and
            (8, infinity). Chebyshev polynomial expansions are employed
            in each interval.
            </summary>
            <param name="x">The value to compute the Bessel function of.
            </param>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselK0e(System.Double)">
            <summary>Returns the exponentially scaled modified Bessel function
            of the second kind of order 0 of the argument.
            </summary>
            <param name="x">The value to compute the Bessel function of.
            </param>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselK1(System.Double)">
            <summary> Returns the modified Bessel function of the second kind
            of order 1 of the argument.
            <p/>
            The range is partitioned into the two intervals [0, 2] and
            (2, infinity). Chebyshev polynomial expansions are employed
            in each interval.
            </summary>
            <param name="x">The value to compute the Bessel function of.
            </param>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselK1e(System.Double)">
            <summary> Returns the exponentially scaled modified Bessel function
            of the second kind of order 1 of the argument.
            <p/>
            <tt>k1e(x) = exp(x) * k1(x)</tt>.
            </summary>
            <param name="x">The value to compute the Bessel function of.
            </param>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.StruveL0(System.Double)">
            <summary>
            Returns the modified Struve function of order 0.
            </summary>
            <param name="x">The value to compute the function of.</param>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.StruveL1(System.Double)">
            <summary>
            Returns the modified Struve function of order 1.
            </summary>
            <param name="x">The value to compute the function of.</param>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselI0MStruveL0(System.Double)">
            <summary>
            Returns the difference between the Bessel I0 and Struve L0 functions.
            </summary>
            <param name="x">The value to compute the function of.</param>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.BesselI1MStruveL1(System.Double)">
            <summary>
            Returns the difference between the Bessel I1 and Struve L1 functions.
            </summary>
            <param name="x">The value to compute the function of.</param>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.SphericalBesselJ(System.Double,System.Numerics.Complex)">
            <summary>
            Returns the spherical Bessel function of the first kind.
            <para>SphericalBesselJ(n, z) is given by Sqrt(pi/2) / Sqrt(z) * BesselJ(n + 1/2, z).</para>
            </summary>
            <param name="n">The order of the spherical Bessel function.</param>
            <param name="z">The value to compute the spherical Bessel function of.</param>
            <returns>The spherical Bessel function of the first kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.SphericalBesselJ(System.Double,System.Double)">
            <summary>
            Returns the spherical Bessel function of the first kind.
            <para>SphericalBesselJ(n, z) is given by Sqrt(pi/2) / Sqrt(z) * BesselJ(n + 1/2, z).</para>
            </summary>
            <param name="n">The order of the spherical Bessel function.</param>
            <param name="z">The value to compute the spherical Bessel function of.</param>
            <returns>The spherical Bessel function of the first kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.SphericalBesselY(System.Double,System.Numerics.Complex)">
            <summary>
            Returns the spherical Bessel function of the second kind.
            <para>SphericalBesselY(n, z) is given by Sqrt(pi/2) / Sqrt(z) * BesselY(n + 1/2, z).</para>
            </summary>
            <param name="n">The order of the spherical Bessel function.</param>
            <param name="z">The value to compute the spherical Bessel function of.</param>
            <returns>The spherical Bessel function of the second kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.SphericalBesselY(System.Double,System.Double)">
            <summary>
            Returns the spherical Bessel function of the second kind.
            <para>SphericalBesselY(n, z) is given by Sqrt(pi/2) / Sqrt(z) * BesselY(n + 1/2, z).</para>
            </summary>
            <param name="n">The order of the spherical Bessel function.</param>
            <param name="z">The value to compute the spherical Bessel function of.</param>
            <returns>The spherical Bessel function of the second kind.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.Hypotenuse(System.Numerics.Complex,System.Numerics.Complex)">
            <summary>
            Numerically stable hypotenuse of a right angle triangle, i.e. <code>(a,b) -> sqrt(a^2 + b^2)</code>
            </summary>
            <param name="a">The length of side a of the triangle.</param>
            <param name="b">The length of side b of the triangle.</param>
            <returns>Returns <code>sqrt(a<sup>2</sup> + b<sup>2</sup>)</code> without underflow/overflow.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.Hypotenuse(MathNet.Numerics.Complex32,MathNet.Numerics.Complex32)">
            <summary>
            Numerically stable hypotenuse of a right angle triangle, i.e. <code>(a,b) -> sqrt(a^2 + b^2)</code>
            </summary>
            <param name="a">The length of side a of the triangle.</param>
            <param name="b">The length of side b of the triangle.</param>
            <returns>Returns <code>sqrt(a<sup>2</sup> + b<sup>2</sup>)</code> without underflow/overflow.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.Hypotenuse(System.Double,System.Double)">
            <summary>
            Numerically stable hypotenuse of a right angle triangle, i.e. <code>(a,b) -> sqrt(a^2 + b^2)</code>
            </summary>
            <param name="a">The length of side a of the triangle.</param>
            <param name="b">The length of side b of the triangle.</param>
            <returns>Returns <code>sqrt(a<sup>2</sup> + b<sup>2</sup>)</code> without underflow/overflow.</returns>
        </member>
        <member name="M:MathNet.Numerics.SpecialFunctions.Hypotenuse(System.Single,System.Single)">
            <summary>
            Numerically stable hypotenuse of a right angle triangle, i.e. <code>(a,b) -> sqrt(a^2 + b^2)</code>
            </summary>
            <param name="a">The length of side a of the triangle.</param>
            <param name="b">The length of side b of the triangle.</param>
            <returns>Returns <code>sqrt(a<sup>2</sup> + b<sup>2</sup>)</code> without underflow/overflow.</returns>
        </member>
        <member name="T:MathNet.Numerics.Evaluate">
            <summary>
            Evaluation functions, useful for function approximation.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Evaluate.ChebyshevA(System.Double[],System.Double)">
            <summary> Evaluates the series of Chebyshev polynomials Ti at argument x/2.
            The series is given by
            <pre>
                  N-1
                   - '
            y  =   >   coef[i] T (x/2)
                   -            i
                  i=0
            </pre>
            Coefficients are stored in reverse order, i.e. the zero
            order term is last in the array.  Note N is the number of
            coefficients, not the order.
            <p/>
            If coefficients are for the interval a to b, x must
            have been transformed to x -> 2(2x - b - a)/(b-a) before
            entering the routine.  This maps x from (a, b) to (-1, 1),
            over which the Chebyshev polynomials are defined.
            <p/>
            If the coefficients are for the inverted interval, in
            which (a, b) is mapped to (1/b, 1/a), the transformation
            required is x -> 2(2ab/x - b - a)/(b-a).  If b is infinity,
            this becomes x -> 4a/x - 1.
            <p/>
            SPEED:
            <p/>
            Taking advantage of the recurrence properties of the
            Chebyshev polynomials, the routine requires one more
            addition per loop than evaluating a nested polynomial of
            the same degree.
            </summary>
            <param name="coefficients">The coefficients of the polynomial.</param>
            <param name="x">Argument to the polynomial.</param>
            <remarks>
            Reference: https://bpm2.svn.codeplex.com/svn/Common.Numeric/Arithmetic.cs
            <p/>
            Marked as Deprecated in
            http://people.apache.org/~isabel/mahout_site/mahout-matrix/apidocs/org/apache/mahout/jet/math/Arithmetic.html
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Evaluate.ChebyshevSum(System.Int32,System.Double[],System.Double)">
            <summary>
            Summation of Chebyshev polynomials, using the Clenshaw method with Reinsch modification.
            </summary>
            <param name="n">The no. of terms in the sequence.</param>
            <param name="coefficients">The coefficients of the Chebyshev series, length n+1.</param>
            <param name="x">The value at which the series is to be evaluated.</param>
            <remarks>
            ORIGINAL AUTHOR:
               Dr. Allan J. MacLeod; Dept. of Mathematics and Statistics, University of Paisley; High St., PAISLEY, SCOTLAND
            REFERENCES:
               "An error analysis of the modified Clenshaw method for evaluating Chebyshev and Fourier series"
               J. Oliver, J.I.M.A., vol. 20, 1977, pp379-391
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.TestFunctions.Rosenbrock(System.Double,System.Double)">
            <summary>
            Valley-shaped Rosenbrock function for 2 dimensions: (x,y) -> (1-x)^2 + 100*(y-x^2)^2.
            This function has a global minimum at (1,1) with f(1,1) = 0.
            Common range: [-5,10] or [-2.048,2.048].
            </summary>
            <remarks>
            https://en.wikipedia.org/wiki/Rosenbrock_function
            http://www.sfu.ca/~ssurjano/rosen.html
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.TestFunctions.Rosenbrock(System.Double[])">
            <summary>
            Valley-shaped Rosenbrock function for 2 or more dimensions.
            This function have a global minimum of all ones and, for 8 > N > 3, a local minimum at (-1,1,...,1).
            </summary>
            <remarks>
            https://en.wikipedia.org/wiki/Rosenbrock_function
            http://www.sfu.ca/~ssurjano/rosen.html
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.TestFunctions.Himmelblau(System.Double,System.Double)">
            <summary>
            Himmelblau, a multi-modal function: (x,y) -> (x^2+y-11)^2 + (x+y^2-7)^2
            This function has 4 global minima with f(x,y) = 0.
            Common range: [-6,6].
            Named after David Mautner Himmelblau
            </summary>
            <remarks>
            https://en.wikipedia.org/wiki/Himmelblau%27s_function
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.TestFunctions.Rastrigin(System.Double[])">
            <summary>
            Rastrigin, a highly multi-modal function with many local minima.
            Global minimum of all zeros with f(0) = 0.
            Common range: [-5.12,5.12].
            </summary>
            <remarks>
            https://en.wikipedia.org/wiki/Rastrigin_function
            http://www.sfu.ca/~ssurjano/rastr.html
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.TestFunctions.DropWave(System.Double,System.Double)">
            <summary>
            Drop-Wave, a multi-modal and highly complex function with many local minima.
            Global minimum of all zeros with f(0) = -1.
            Common range: [-5.12,5.12].
            </summary>
            <remarks>
            http://www.sfu.ca/~ssurjano/drop.html
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.TestFunctions.Ackley(System.Double[])">
            <summary>
            Ackley, a function with many local minima. It is nearly flat in outer regions but has a large hole at the center.
            Global minimum of all zeros with f(0) = 0.
            Common range: [-32.768, 32.768].
            </summary>
            <remarks>
            http://www.sfu.ca/~ssurjano/ackley.html
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.TestFunctions.Bohachevsky1(System.Double,System.Double)">
            <summary>
            Bowl-shaped first Bohachevsky function.
            Global minimum of all zeros with f(0,0) = 0.
            Common range: [-100, 100]
            </summary>
            <remarks>
            http://www.sfu.ca/~ssurjano/boha.html
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.TestFunctions.Matyas(System.Double,System.Double)">
            <summary>
            Plate-shaped Matyas function.
            Global minimum of all zeros with f(0,0) = 0.
            Common range: [-10, 10].
            </summary>
            <remarks>
            http://www.sfu.ca/~ssurjano/matya.html
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.TestFunctions.SixHumpCamel(System.Double,System.Double)">
            <summary>
            Valley-shaped six-hump camel back function.
            Two global minima and four local minima. Global minima with f(x) ) -1.0316 at (0.0898,-0.7126) and (-0.0898,0.7126).
            Common range: x in [-3,3], y in [-2,2].
            </summary>
            <remarks>
            http://www.sfu.ca/~ssurjano/camel6.html
            </remarks>
        </member>
        <member name="T:MathNet.Numerics.Statistics.ArrayStatistics">
            <summary>
            Statistics operating on arrays assumed to be unsorted.
            WARNING: Methods with the Inplace-suffix may modify the data array by reordering its entries.
            </summary>
            <seealso cref="T:MathNet.Numerics.Statistics.SortedArrayStatistics"/>
            <seealso cref="T:MathNet.Numerics.Statistics.StreamingStatistics"/>
            <seealso cref="T:MathNet.Numerics.Statistics.Statistics"/>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.MinimumMagnitudePhase(System.Numerics.Complex[])">
            <summary>
            Returns the smallest absolute value from the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.MinimumMagnitudePhase(MathNet.Numerics.Complex32[])">
            <summary>
            Returns the smallest absolute value from the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.MaximumMagnitudePhase(System.Numerics.Complex[])">
            <summary>
            Returns the largest absolute value from the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.MaximumMagnitudePhase(MathNet.Numerics.Complex32[])">
            <summary>
            Returns the largest absolute value from the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.Minimum(System.Double[])">
            <summary>
            Returns the smallest value from the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.Maximum(System.Double[])">
            <summary>
            Returns the largest value from the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.MinimumAbsolute(System.Double[])">
            <summary>
            Returns the smallest absolute value from the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.MaximumAbsolute(System.Double[])">
            <summary>
            Returns the largest absolute value from the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.Mean(System.Double[])">
            <summary>
            Estimates the arithmetic sample mean from the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.GeometricMean(System.Double[])">
            <summary>
            Evaluates the geometric mean of the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.HarmonicMean(System.Double[])">
            <summary>
            Evaluates the harmonic mean of the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.Variance(System.Double[])">
            <summary>
            Estimates the unbiased population variance from the provided samples as unsorted array.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.PopulationVariance(System.Double[])">
            <summary>
            Evaluates the population variance from the full population provided as unsorted array.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.StandardDeviation(System.Double[])">
            <summary>
            Estimates the unbiased population standard deviation from the provided samples as unsorted array.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(System.Double[])">
            <summary>
            Evaluates the population standard deviation from the full population provided as unsorted array.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.MeanVariance(System.Double[])">
            <summary>
            Estimates the arithmetic sample mean and the unbiased population variance from the provided samples as unsorted array.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN for mean if data is empty or any entry is NaN and NaN for variance if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.MeanStandardDeviation(System.Double[])">
            <summary>
            Estimates the arithmetic sample mean and the unbiased population standard deviation from the provided samples as unsorted array.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN for mean if data is empty or any entry is NaN and NaN for standard deviation if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.Covariance(System.Double[],System.Double[])">
            <summary>
            Estimates the unbiased population covariance from the provided two sample arrays.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples1">First sample array.</param>
            <param name="samples2">Second sample array.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.PopulationCovariance(System.Double[],System.Double[])">
            <summary>
            Evaluates the population covariance from the full population provided as two arrays.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population1">First population array.</param>
            <param name="population2">Second population array.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.RootMeanSquare(System.Double[])">
            <summary>
            Estimates the root mean square (RMS) also known as quadratic mean from the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.OrderStatisticInplace(System.Double[],System.Int32)">
            <summary>
            Returns the order statistic (order 1..N) from the unsorted data array.
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
            <param name="data">Sample array, no sorting is assumed. Will be reordered.</param>
            <param name="order">One-based order of the statistic, must be between 1 and N (inclusive).</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.MedianInplace(System.Double[])">
            <summary>
            Estimates the median value from the unsorted data array.
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
            <param name="data">Sample array, no sorting is assumed. Will be reordered.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.PercentileInplace(System.Double[],System.Int32)">
            <summary>
            Estimates the p-Percentile value from the unsorted data array.
            If a non-integer Percentile is needed, use Quantile instead.
            Approximately median-unbiased regardless of the sample distribution (R8).
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
            <param name="data">Sample array, no sorting is assumed. Will be reordered.</param>
            <param name="p">Percentile selector, between 0 and 100 (inclusive).</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.LowerQuartileInplace(System.Double[])">
            <summary>
            Estimates the first quartile value from the unsorted data array.
            Approximately median-unbiased regardless of the sample distribution (R8).
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
            <param name="data">Sample array, no sorting is assumed. Will be reordered.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.UpperQuartileInplace(System.Double[])">
            <summary>
            Estimates the third quartile value from the unsorted data array.
            Approximately median-unbiased regardless of the sample distribution (R8).
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
            <param name="data">Sample array, no sorting is assumed. Will be reordered.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.InterquartileRangeInplace(System.Double[])">
            <summary>
            Estimates the inter-quartile range from the unsorted data array.
            Approximately median-unbiased regardless of the sample distribution (R8).
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
            <param name="data">Sample array, no sorting is assumed. Will be reordered.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.FiveNumberSummaryInplace(System.Double[])">
            <summary>
            Estimates {min, lower-quantile, median, upper-quantile, max} from the unsorted data array.
            Approximately median-unbiased regardless of the sample distribution (R8).
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
            <param name="data">Sample array, no sorting is assumed. Will be reordered.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.QuantileInplace(System.Double[],System.Double)">
            <summary>
            Estimates the tau-th quantile from the unsorted data array.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau.
            Approximately median-unbiased regardless of the sample distribution (R8).
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
            <param name="data">Sample array, no sorting is assumed. Will be reordered.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive).</param>
            <remarks>
            R-8, SciPy-(1/3,1/3):
            Linear interpolation of the approximate medians for order statistics.
            When tau &lt; (2/3) / (N + 1/3), use x1. When tau &gt;= (N - 1/3) / (N + 1/3), use xN.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.QuantileCustomInplace(System.Double[],System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Estimates the tau-th quantile from the unsorted data array.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified
            by 4 parameters a, b, c and d, consistent with Mathematica.
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
            <param name="data">Sample array, no sorting is assumed. Will be reordered.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive)</param>
            <param name="a">a-parameter</param>
            <param name="b">b-parameter</param>
            <param name="c">c-parameter</param>
            <param name="d">d-parameter</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.QuantileCustomInplace(System.Double[],System.Double,MathNet.Numerics.Statistics.QuantileDefinition)">
            <summary>
            Estimates the tau-th quantile from the unsorted data array.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified to be compatible
            with an existing system.
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
            <param name="data">Sample array, no sorting is assumed. Will be reordered.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive)</param>
            <param name="definition">Quantile definition, to choose what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.RanksInplace(System.Double[],MathNet.Numerics.Statistics.RankDefinition)">
            <summary>
            Evaluates the rank of each entry of the unsorted data array.
            The rank definition can be specified to be compatible
            with an existing system.
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.Mean(System.Int32[])">
            <summary>
            Estimates the arithmetic sample mean from the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.GeometricMean(System.Int32[])">
            <summary>
            Evaluates the geometric mean of the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.HarmonicMean(System.Int32[])">
            <summary>
            Evaluates the harmonic mean of the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.Variance(System.Int32[])">
            <summary>
            Estimates the unbiased population variance from the provided samples as unsorted array.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.PopulationVariance(System.Int32[])">
            <summary>
            Evaluates the population variance from the full population provided as unsorted array.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.StandardDeviation(System.Int32[])">
            <summary>
            Estimates the unbiased population standard deviation from the provided samples as unsorted array.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(System.Int32[])">
            <summary>
            Evaluates the population standard deviation from the full population provided as unsorted array.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.MeanVariance(System.Int32[])">
            <summary>
            Estimates the arithmetic sample mean and the unbiased population variance from the provided samples as unsorted array.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN for mean if data is empty or any entry is NaN and NaN for variance if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.MeanStandardDeviation(System.Int32[])">
            <summary>
            Estimates the arithmetic sample mean and the unbiased population standard deviation from the provided samples as unsorted array.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN for mean if data is empty or any entry is NaN and NaN for standard deviation if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.Covariance(System.Int32[],System.Int32[])">
            <summary>
            Estimates the unbiased population covariance from the provided two sample arrays.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples1">First sample array.</param>
            <param name="samples2">Second sample array.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.PopulationCovariance(System.Int32[],System.Int32[])">
            <summary>
            Evaluates the population covariance from the full population provided as two arrays.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population1">First population array.</param>
            <param name="population2">Second population array.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.RootMeanSquare(System.Int32[])">
            <summary>
            Estimates the root mean square (RMS) also known as quadratic mean from the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.Minimum(System.Single[])">
            <summary>
            Returns the smallest value from the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.Maximum(System.Single[])">
            <summary>
            Returns the smallest value from the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.MinimumAbsolute(System.Single[])">
            <summary>
            Returns the smallest absolute value from the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.MaximumAbsolute(System.Single[])">
            <summary>
            Returns the largest absolute value from the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.Mean(System.Single[])">
            <summary>
            Estimates the arithmetic sample mean from the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.GeometricMean(System.Single[])">
            <summary>
            Evaluates the geometric mean of the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.HarmonicMean(System.Single[])">
            <summary>
            Evaluates the harmonic mean of the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.Variance(System.Single[])">
            <summary>
            Estimates the unbiased population variance from the provided samples as unsorted array.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.PopulationVariance(System.Single[])">
            <summary>
            Evaluates the population variance from the full population provided as unsorted array.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.StandardDeviation(System.Single[])">
            <summary>
            Estimates the unbiased population standard deviation from the provided samples as unsorted array.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(System.Single[])">
            <summary>
            Evaluates the population standard deviation from the full population provided as unsorted array.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.MeanVariance(System.Single[])">
            <summary>
            Estimates the arithmetic sample mean and the unbiased population variance from the provided samples as unsorted array.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN for mean if data is empty or any entry is NaN and NaN for variance if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.MeanStandardDeviation(System.Single[])">
            <summary>
            Estimates the arithmetic sample mean and the unbiased population standard deviation from the provided samples as unsorted array.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN for mean if data is empty or any entry is NaN and NaN for standard deviation if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.Covariance(System.Single[],System.Single[])">
            <summary>
            Estimates the unbiased population covariance from the provided two sample arrays.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples1">First sample array.</param>
            <param name="samples2">Second sample array.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.PopulationCovariance(System.Single[],System.Single[])">
            <summary>
            Evaluates the population covariance from the full population provided as two arrays.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population1">First population array.</param>
            <param name="population2">Second population array.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.RootMeanSquare(System.Single[])">
            <summary>
            Estimates the root mean square (RMS) also known as quadratic mean from the unsorted data array.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="data">Sample array, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.OrderStatisticInplace(System.Single[],System.Int32)">
            <summary>
            Returns the order statistic (order 1..N) from the unsorted data array.
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
            <param name="data">Sample array, no sorting is assumed. Will be reordered.</param>
            <param name="order">One-based order of the statistic, must be between 1 and N (inclusive).</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.MedianInplace(System.Single[])">
            <summary>
            Estimates the median value from the unsorted data array.
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
            <param name="data">Sample array, no sorting is assumed. Will be reordered.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.PercentileInplace(System.Single[],System.Int32)">
            <summary>
            Estimates the p-Percentile value from the unsorted data array.
            If a non-integer Percentile is needed, use Quantile instead.
            Approximately median-unbiased regardless of the sample distribution (R8).
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
            <param name="data">Sample array, no sorting is assumed. Will be reordered.</param>
            <param name="p">Percentile selector, between 0 and 100 (inclusive).</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.LowerQuartileInplace(System.Single[])">
            <summary>
            Estimates the first quartile value from the unsorted data array.
            Approximately median-unbiased regardless of the sample distribution (R8).
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
            <param name="data">Sample array, no sorting is assumed. Will be reordered.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.UpperQuartileInplace(System.Single[])">
            <summary>
            Estimates the third quartile value from the unsorted data array.
            Approximately median-unbiased regardless of the sample distribution (R8).
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
            <param name="data">Sample array, no sorting is assumed. Will be reordered.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.InterquartileRangeInplace(System.Single[])">
            <summary>
            Estimates the inter-quartile range from the unsorted data array.
            Approximately median-unbiased regardless of the sample distribution (R8).
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
            <param name="data">Sample array, no sorting is assumed. Will be reordered.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.FiveNumberSummaryInplace(System.Single[])">
            <summary>
            Estimates {min, lower-quantile, median, upper-quantile, max} from the unsorted data array.
            Approximately median-unbiased regardless of the sample distribution (R8).
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
            <param name="data">Sample array, no sorting is assumed. Will be reordered.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.QuantileInplace(System.Single[],System.Double)">
            <summary>
            Estimates the tau-th quantile from the unsorted data array.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau.
            Approximately median-unbiased regardless of the sample distribution (R8).
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
            <param name="data">Sample array, no sorting is assumed. Will be reordered.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive).</param>
            <remarks>
            R-8, SciPy-(1/3,1/3):
            Linear interpolation of the approximate medians for order statistics.
            When tau &lt; (2/3) / (N + 1/3), use x1. When tau &gt;= (N - 1/3) / (N + 1/3), use xN.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.QuantileCustomInplace(System.Single[],System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Estimates the tau-th quantile from the unsorted data array.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified
            by 4 parameters a, b, c and d, consistent with Mathematica.
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
            <param name="data">Sample array, no sorting is assumed. Will be reordered.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive)</param>
            <param name="a">a-parameter</param>
            <param name="b">b-parameter</param>
            <param name="c">c-parameter</param>
            <param name="d">d-parameter</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.QuantileCustomInplace(System.Single[],System.Double,MathNet.Numerics.Statistics.QuantileDefinition)">
            <summary>
            Estimates the tau-th quantile from the unsorted data array.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified to be compatible
            with an existing system.
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
            <param name="data">Sample array, no sorting is assumed. Will be reordered.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive)</param>
            <param name="definition">Quantile definition, to choose what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.ArrayStatistics.RanksInplace(System.Single[],MathNet.Numerics.Statistics.RankDefinition)">
            <summary>
            Evaluates the rank of each entry of the unsorted data array.
            The rank definition can be specified to be compatible
            with an existing system.
            WARNING: Works inplace and can thus causes the data array to be reordered.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Statistics.Correlation">
            <summary>
            A class with correlation measures between two datasets.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Correlation.Auto(System.Double[])">
            <summary>
            Auto-correlation function (ACF) based on FFT for all possible lags k.
            </summary>
            <param name="x">Data array to calculate auto correlation for.</param>
            <returns>An array with the ACF as a function of the lags k.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Correlation.Auto(System.Double[],System.Int32,System.Int32)">
            <summary>
            Auto-correlation function (ACF) based on FFT for lags between kMin and kMax.
            </summary>
            <param name="x">The data array to calculate auto correlation for.</param>
            <param name="kMax">Max lag to calculate ACF for must be positive and smaller than x.Length.</param>
            <param name="kMin">Min lag to calculate ACF for (0 = no shift with acf=1) must be zero or positive and smaller than x.Length.</param>
            <returns>An array with the ACF as a function of the lags k.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Correlation.Auto(System.Double[],System.Int32[])">
            <summary>
            Auto-correlation function based on FFT for lags k.
            </summary>
            <param name="x">The data array to calculate auto correlation for.</param>
            <param name="k">Array with lags to calculate ACF for.</param>
            <returns>An array with the ACF as a function of the lags k.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Correlation.AutoCorrelationFft(System.Double[],System.Int32,System.Int32)">
            <summary>
            The internal method for calculating the auto-correlation.
            </summary>
            <param name="x">The data array to calculate auto-correlation for</param>
            <param name="kLow">Min lag to calculate ACF for (0 = no shift with acf=1) must be zero or positive and smaller than x.Length</param>
            <param name="kHigh">Max lag (EXCLUSIVE) to calculate ACF for must be positive and smaller than x.Length</param>
            <returns>An array with the ACF as a function of the lags k.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Correlation.Pearson(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Computes the Pearson Product-Moment Correlation coefficient.
            </summary>
            <param name="dataA">Sample data A.</param>
            <param name="dataB">Sample data B.</param>
            <returns>The Pearson product-moment correlation coefficient.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Correlation.WeightedPearson(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Computes the Weighted Pearson Product-Moment Correlation coefficient.
            </summary>
            <param name="dataA">Sample data A.</param>
            <param name="dataB">Sample data B.</param>
            <param name="weights">Corresponding weights of data.</param>
            <returns>The Weighted Pearson product-moment correlation coefficient.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Correlation.PearsonMatrix(System.Double[][])">
            <summary>
            Computes the Pearson Product-Moment Correlation matrix.
            </summary>
            <param name="vectors">Array of sample data vectors.</param>
            <returns>The Pearson product-moment correlation matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Correlation.PearsonMatrix(System.Collections.Generic.IEnumerable{System.Double[]})">
            <summary>
            Computes the Pearson Product-Moment Correlation matrix.
            </summary>
            <param name="vectors">Enumerable of sample data vectors.</param>
            <returns>The Pearson product-moment correlation matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Correlation.Spearman(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Computes the Spearman Ranked Correlation coefficient.
            </summary>
            <param name="dataA">Sample data series A.</param>
            <param name="dataB">Sample data series B.</param>
            <returns>The Spearman ranked correlation coefficient.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Correlation.SpearmanMatrix(System.Double[][])">
            <summary>
            Computes the Spearman Ranked Correlation matrix.
            </summary>
            <param name="vectors">Array of sample data vectors.</param>
            <returns>The Spearman ranked correlation matrix.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Correlation.SpearmanMatrix(System.Collections.Generic.IEnumerable{System.Double[]})">
            <summary>
            Computes the Spearman Ranked Correlation matrix.
            </summary>
            <param name="vectors">Enumerable of sample data vectors.</param>
            <returns>The Spearman ranked correlation matrix.</returns>
        </member>
        <member name="T:MathNet.Numerics.Statistics.DescriptiveStatistics">
            <summary>
            Computes the basic statistics of data set. The class meets the
            NIST standard of accuracy for mean, variance, and standard deviation
            (the only statistics they provide exact values for) and exceeds them
            in increased accuracy mode.
            Recommendation: consider to use RunningStatistics instead.
            </summary>
            <remarks>
            This type declares a DataContract for out of the box ephemeral serialization
            with engines like DataContractSerializer, Protocol Buffers and FsPickler,
            but does not guarantee any compatibility between versions.
            It is not recommended to rely on this mechanism for durable persistence.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Statistics.DescriptiveStatistics.#ctor(System.Collections.Generic.IEnumerable{System.Double},System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Statistics.DescriptiveStatistics"/> class.
            </summary>
            <param name="data">The sample data.</param>
            <param name="increasedAccuracy">
            If set to <c>true</c>, increased accuracy mode used.
            Increased accuracy mode uses <see cref="T:System.Decimal"/> types for internal calculations.
            </param>
            <remarks>
            Don't use increased accuracy for data sets containing large values (in absolute value).
            This may cause the calculations to overflow.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Statistics.DescriptiveStatistics.#ctor(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}},System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Statistics.DescriptiveStatistics"/> class.
            </summary>
            <param name="data">The sample data.</param>
            <param name="increasedAccuracy">
            If set to <c>true</c>, increased accuracy mode used.
            Increased accuracy mode uses <see cref="T:System.Decimal"/> types for internal calculations.
            </param>
            <remarks>
            Don't use increased accuracy for data sets containing large values (in absolute value).
            This may cause the calculations to overflow.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Statistics.DescriptiveStatistics.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Statistics.DescriptiveStatistics"/> class.
            </summary>
            <remarks>
            Used for Json serialization
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.Statistics.DescriptiveStatistics.Count">
            <summary>
            Gets the size of the sample.
            </summary>
            <value>The size of the sample.</value>
        </member>
        <member name="P:MathNet.Numerics.Statistics.DescriptiveStatistics.Mean">
            <summary>
            Gets the sample mean.
            </summary>
            <value>The sample mean.</value>
        </member>
        <member name="P:MathNet.Numerics.Statistics.DescriptiveStatistics.Variance">
            <summary>
            Gets the unbiased population variance estimator (on a dataset of size N will use an N-1 normalizer).
            </summary>
            <value>The sample variance.</value>
        </member>
        <member name="P:MathNet.Numerics.Statistics.DescriptiveStatistics.StandardDeviation">
            <summary>
            Gets the unbiased population standard deviation (on a dataset of size N will use an N-1 normalizer).
            </summary>
            <value>The sample standard deviation.</value>
        </member>
        <member name="P:MathNet.Numerics.Statistics.DescriptiveStatistics.Skewness">
            <summary>
            Gets the sample skewness.
            </summary>
            <value>The sample skewness.</value>
            <remarks>Returns zero if <see cref="P:MathNet.Numerics.Statistics.DescriptiveStatistics.Count"/> is less than three. </remarks>
        </member>
        <member name="P:MathNet.Numerics.Statistics.DescriptiveStatistics.Kurtosis">
            <summary>
            Gets the sample kurtosis.
            </summary>
            <value>The sample kurtosis.</value>
            <remarks>Returns zero if <see cref="P:MathNet.Numerics.Statistics.DescriptiveStatistics.Count"/> is less than four. </remarks>
        </member>
        <member name="P:MathNet.Numerics.Statistics.DescriptiveStatistics.Maximum">
            <summary>
            Gets the maximum sample value.
            </summary>
            <value>The maximum sample value.</value>
        </member>
        <member name="P:MathNet.Numerics.Statistics.DescriptiveStatistics.Minimum">
            <summary>
            Gets the minimum sample value.
            </summary>
            <value>The minimum sample value.</value>
        </member>
        <member name="M:MathNet.Numerics.Statistics.DescriptiveStatistics.Compute(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Computes descriptive statistics from a stream of data values.
            </summary>
            <param name="data">A sequence of datapoints.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.DescriptiveStatistics.Compute(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Computes descriptive statistics from a stream of nullable data values.
            </summary>
            <param name="data">A sequence of datapoints.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.DescriptiveStatistics.ComputeDecimal(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Computes descriptive statistics from a stream of data values.
            </summary>
            <param name="data">A sequence of datapoints.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.DescriptiveStatistics.ComputeDecimal(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Computes descriptive statistics from a stream of nullable data values.
            </summary>
            <param name="data">A sequence of datapoints.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.DescriptiveStatistics.SetStatistics(System.Double,System.Double,System.Double,System.Double,System.Double,System.Double,System.Int64)">
            <summary>
            Internal use. Method use for setting the statistics.
            </summary>
            <param name="mean">For setting Mean.</param>
            <param name="variance">For setting Variance.</param>
            <param name="skewness">For setting Skewness.</param>
            <param name="kurtosis">For setting Kurtosis.</param>
            <param name="minimum">For setting Minimum.</param>
            <param name="maximum">For setting Maximum.</param>
            <param name="n">For setting Count.</param>
        </member>
        <member name="T:MathNet.Numerics.Statistics.Bucket">
            <summary>
            A <see cref="T:MathNet.Numerics.Statistics.Histogram"/> consists of a series of <see cref="T:MathNet.Numerics.Statistics.Bucket"/>s,
            each representing a region limited by a lower bound (exclusive) and an upper bound (inclusive).
            </summary>
            <remarks>
            This type declares a DataContract for out of the box ephemeral serialization
            with engines like DataContractSerializer, Protocol Buffers and FsPickler,
            but does not guarantee any compatibility between versions.
            It is not recommended to rely on this mechanism for durable persistence.
            </remarks>
        </member>
        <member name="T:MathNet.Numerics.Statistics.Bucket.PointComparer">
            <summary>
            This <c>IComparer</c> performs comparisons between a point and a bucket.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Bucket.PointComparer.Compare(MathNet.Numerics.Statistics.Bucket,MathNet.Numerics.Statistics.Bucket)">
            <summary>
            Compares a point and a bucket. The point will be encapsulated in a bucket with width 0.
            </summary>
            <param name="bkt1">The first bucket to compare.</param>
            <param name="bkt2">The second bucket to compare.</param>
            <returns>-1 when the point is less than this bucket, 0 when it is in this bucket and 1 otherwise.</returns>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Bucket.LowerBound">
            <summary>
            Lower Bound of the Bucket.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Bucket.UpperBound">
            <summary>
            Upper Bound of the Bucket.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Bucket.Count">
            <summary>
            The number of datapoints in the bucket.
            </summary>
            <remarks>
            Value may be NaN if this was constructed as a <see cref="T:System.Collections.Generic.IComparer`1"/> argument.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Bucket.#ctor(System.Double,System.Double,System.Double)">
            <summary>
            Initializes a new instance of the Bucket class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Bucket.#ctor(System.Double)">
            <summary>
            Constructs a Bucket that can be used as an argument for a <see cref="T:System.Collections.Generic.IComparer`1"/>
            like <see cref="P:MathNet.Numerics.Statistics.Bucket.DefaultPointComparer"/> when performing a Binary search.
            </summary>
            <param name="targetValue">Value to look for</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Bucket.Clone">
            <summary>
            Creates a copy of the Bucket with the lowerbound, upperbound and counts exactly equal.
            </summary>
            <returns>A cloned Bucket object.</returns>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Bucket.Width">
            <summary>
            Width of the Bucket.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Bucket.IsSinglePoint">
            <summary>
            True if this is a single point argument for <see cref="T:System.Collections.Generic.IComparer`1"/>
            when performing a Binary search.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Bucket.DefaultPointComparer">
            <summary>
            Default comparer.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Bucket.Contains(System.Double)">
            <summary>
            This method check whether a point is contained within this bucket.
            </summary>
            <param name="x">The point to check.</param>
            <returns>
             0 if the point falls within the bucket boundaries;
            -1 if the point is smaller than the bucket,
            +1 if the point is larger than the bucket.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Bucket.CompareTo(MathNet.Numerics.Statistics.Bucket)">
            <summary>
            Comparison of two disjoint buckets. The buckets cannot be overlapping.
            </summary>
            <returns>
             0 if <c>UpperBound</c> and <c>LowerBound</c> are bit-for-bit equal
             1 if This bucket is lower that the compared bucket
            -1 otherwise
            </returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Bucket.Equals(System.Object)">
            <summary>
            Checks whether two Buckets are equal.
            </summary>
            <remarks>
            <c>UpperBound</c> and <c>LowerBound</c> are compared bit-for-bit, but This method tolerates a
            difference in <c>Count</c> given by  <seealso cref="M:MathNet.Numerics.Precision.AlmostEqual(System.Double,System.Double)"/>.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Bucket.GetHashCode">
            <summary>
            Provides a hash code for this bucket.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Bucket.ToString">
            <summary>
            Formats a human-readable string for this bucket.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Statistics.Histogram">
            <summary>
            A class which computes histograms of data.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Histogram._buckets">
            <summary>
            Contains all the <c>Bucket</c>s of the <c>Histogram</c>.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Histogram._areBucketsSorted">
            <summary>
            Indicates whether the elements of <c>buckets</c> are currently sorted.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Histogram.#ctor">
            <summary>
            Initializes a new instance of the Histogram class.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Histogram.#ctor(System.Collections.Generic.IEnumerable{System.Double},System.Int32)">
            <summary>
            Constructs a Histogram with a specific number of equally sized buckets. The upper and lower bound of the histogram
            will be set to the smallest and largest datapoint.
            </summary>
            <param name="data">The data sequence to build a histogram on.</param>
            <param name="nbuckets">The number of buckets to use.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Histogram.#ctor(System.Collections.Generic.IEnumerable{System.Double},System.Int32,System.Double,System.Double)">
            <summary>
            Constructs a Histogram with a specific number of equally sized buckets.
            </summary>
            <param name="data">The data sequence to build a histogram on.</param>
            <param name="nbuckets">The number of buckets to use.</param>
            <param name="lower">The histogram lower bound.</param>
            <param name="upper">The histogram upper bound.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Histogram.AddData(System.Double)">
            <summary>
            Add one data point to the histogram. If the datapoint falls outside the range of the histogram,
            the lowerbound or upperbound will automatically adapt.
            </summary>
            <param name="d">The datapoint which we want to add.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Histogram.AddData(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Add a sequence of data point to the histogram. If the datapoint falls outside the range of the histogram,
            the lowerbound or upperbound will automatically adapt.
            </summary>
            <param name="data">The sequence of datapoints which we want to add.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Histogram.AddBucket(MathNet.Numerics.Statistics.Bucket)">
            <summary>
            Adds a <c>Bucket</c> to the <c>Histogram</c>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Histogram.LazySort">
            <summary>
            Sort the buckets if needed.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Histogram.GetBucketOf(System.Double)">
            <summary>
            Returns the <c>Bucket</c> that contains the value <c>v</c>.
            </summary>
            <param name="v">The point to search the bucket for.</param>
            <returns>A copy of the bucket containing point <paramref name="v"/>.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Histogram.GetBucketIndexOf(System.Double)">
            <summary>
            Returns the index in the <c>Histogram</c> of the <c>Bucket</c>
            that contains the value <c>v</c>.
            </summary>
            <param name="v">The point to search the bucket index for.</param>
            <returns>The index of the bucket containing the point.</returns>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Histogram.LowerBound">
            <summary>
            Returns the lower bound of the histogram.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Histogram.UpperBound">
            <summary>
            Returns the upper bound of the histogram.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Histogram.Item(System.Int32)">
            <summary>
            Gets the <c>n</c>'th bucket.
            </summary>
            <param name="n">The index of the bucket to be returned.</param>
            <returns>A copy of the <c>n</c>'th bucket.</returns>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Histogram.BucketCount">
            <summary>
            Gets the number of buckets.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Histogram.DataCount">
            <summary>
            Gets the total number of datapoints in the histogram.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Histogram.ToString">
            <summary>
            Prints the buckets contained in the <see cref="T:MathNet.Numerics.Statistics.Histogram"/>.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Statistics.KernelDensity">
            <summary>
            Kernel density estimation (KDE).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.KernelDensity.Estimate(System.Double,System.Double,System.Collections.Generic.IList{System.Double},System.Func{System.Double,System.Double})">
            <summary>
            Estimate the probability density function of a random variable.
            </summary>
            <remarks>
            The routine assumes that the provided kernel is well defined, i.e. a real non-negative function that integrates to 1.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Statistics.KernelDensity.EstimateGaussian(System.Double,System.Double,System.Collections.Generic.IList{System.Double})">
            <summary>
            Estimate the probability density function of a random variable with a Gaussian kernel.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.KernelDensity.EstimateEpanechnikov(System.Double,System.Double,System.Collections.Generic.IList{System.Double})">
            <summary>
            Estimate the probability density function of a random variable with an Epanechnikov kernel.
            The Epanechnikov kernel is optimal in a mean square error sense.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.KernelDensity.EstimateUniform(System.Double,System.Double,System.Collections.Generic.IList{System.Double})">
            <summary>
            Estimate the probability density function of a random variable with a uniform kernel.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.KernelDensity.EstimateTriangular(System.Double,System.Double,System.Collections.Generic.IList{System.Double})">
            <summary>
            Estimate the probability density function of a random variable with a triangular kernel.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.KernelDensity.GaussianKernel(System.Double)">
            <summary>
            A Gaussian kernel (PDF of Normal distribution with mean 0 and variance 1).
            This kernel is the default.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.KernelDensity.EpanechnikovKernel(System.Double)">
            <summary>
            Epanechnikov Kernel:
            x =&gt; Math.Abs(x) &lt;= 1.0 ? 3.0/4.0(1.0-x^2) : 0.0
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.KernelDensity.UniformKernel(System.Double)">
            <summary>
            Uniform Kernel:
            x =&gt; Math.Abs(x) &lt;= 1.0 ? 1.0/2.0 : 0.0
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.KernelDensity.TriangularKernel(System.Double)">
            <summary>
            Triangular Kernel:
            x =&gt; Math.Abs(x) &lt;= 1.0 ? (1.0-Math.Abs(x)) : 0.0
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Statistics.Mcmc.HybridMC">
            <summary>
            A hybrid Monte Carlo sampler for multivariate distributions.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.HybridMC._length">
            <summary>
            Number of parameters in the density function.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.HybridMC._pDistribution">
            <summary>
            Distribution to sample momentum from.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.HybridMC._mpSdv">
            <summary>
            Standard deviations used in the sampling of different components of the
            momentum.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Mcmc.HybridMC.MomentumStdDev">
            <summary>
            Gets or sets the standard deviations used in the sampling of different components of the
            momentum.
            </summary>
            <exception cref="T:System.ArgumentOutOfRangeException">When the length of pSdv is not the same as Length.</exception>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMC.#ctor(System.Double[],MathNet.Numerics.Statistics.Mcmc.DensityLn{System.Double[]},System.Int32,System.Double,System.Int32)">
            <summary>
            Constructs a new Hybrid Monte Carlo sampler for a multivariate probability distribution.
            The components of the momentum will be sampled from a normal distribution with standard deviation
            1 using the default <see cref="T:System.Random"/> random
            number generator. A three point estimation will be used for differentiation.
            This constructor will set the burn interval.
            </summary>
            <param name="x0">The initial sample.</param>
            <param name="pdfLnP">The log density of the distribution we want to sample from.</param>
            <param name="frogLeapSteps">Number frog leap simulation steps.</param>
            <param name="stepSize">Size of the frog leap simulation steps.</param>
            <param name="burnInterval">The number of iterations in between returning samples.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">When the number of burnInterval iteration is negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMC.#ctor(System.Double[],MathNet.Numerics.Statistics.Mcmc.DensityLn{System.Double[]},System.Int32,System.Double,System.Int32,System.Double[])">
            <summary>
            Constructs a new Hybrid Monte Carlo sampler for a multivariate probability distribution.
            The components of the momentum will be sampled from a normal distribution with standard deviation
            specified by pSdv using the default <see cref="T:System.Random"/> random
            number generator. A three point estimation will be used for differentiation.
            This constructor will set the burn interval.
            </summary>
            <param name="x0">The initial sample.</param>
            <param name="pdfLnP">The log density of the distribution we want to sample from.</param>
            <param name="frogLeapSteps">Number frog leap simulation steps.</param>
            <param name="stepSize">Size of the frog leap simulation steps.</param>
            <param name="burnInterval">The number of iterations in between returning samples.</param>
            <param name="pSdv">The standard deviations of the normal distributions that are used to sample
            the components of the momentum.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">When the number of burnInterval iteration is negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMC.#ctor(System.Double[],MathNet.Numerics.Statistics.Mcmc.DensityLn{System.Double[]},System.Int32,System.Double,System.Int32,System.Double[],System.Random)">
            <summary>
            Constructs a new Hybrid Monte Carlo sampler for a multivariate probability distribution.
            The components of the momentum will be sampled from a normal distribution with standard deviation
            specified by pSdv using the a random number generator provided by the user.
            A three point estimation will be used for differentiation.
            This constructor will set the burn interval.
            </summary>
            <param name="x0">The initial sample.</param>
            <param name="pdfLnP">The log density of the distribution we want to sample from.</param>
            <param name="frogLeapSteps">Number frog leap simulation steps.</param>
            <param name="stepSize">Size of the frog leap simulation steps.</param>
            <param name="burnInterval">The number of iterations in between returning samples.</param>
            <param name="pSdv">The standard deviations of the normal distributions that are used to sample
            the components of the momentum.</param>
            <param name="randomSource">Random number generator used for sampling the momentum.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">When the number of burnInterval iteration is negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMC.#ctor(System.Double[],MathNet.Numerics.Statistics.Mcmc.DensityLn{System.Double[]},System.Int32,System.Double,System.Int32,System.Double[],System.Random,MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric{System.Double[]}.DiffMethod)">
            <summary>
            Constructs a new Hybrid Monte Carlo sampler for a multivariate probability distribution.
            The components of the momentum will be sampled from a normal distribution with standard deviations
            given by pSdv. This constructor will set the burn interval, the method used for
            numerical differentiation and the random number generator.
            </summary>
            <param name="x0">The initial sample.</param>
            <param name="pdfLnP">The log density of the distribution we want to sample from.</param>
            <param name="frogLeapSteps">Number frog leap simulation steps.</param>
            <param name="stepSize">Size of the frog leap simulation steps.</param>
            <param name="burnInterval">The number of iterations in between returning samples.</param>
            <param name="pSdv">The standard deviations of the normal distributions that are used to sample
            the components of the momentum.</param>
            <param name="randomSource">Random number generator used for sampling the momentum.</param>
            <param name="diff">The method used for numerical differentiation.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">When the number of burnInterval iteration is negative.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">When the length of pSdv is not the same as x0.</exception>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMC.Initialize(System.Double[])">
            <summary>
            Initialize parameters.
            </summary>
            <param name="x0">The current location of the sampler.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMC.CheckVariance(System.Double[])">
            <summary>
            Checking that the location and the momentum are of the same dimension and that each component is positive.
            </summary>
            <param name="pSdv">The standard deviations used for sampling the momentum.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">When the length of pSdv is not the same as Length or if any
            component is negative.</exception>
            <exception cref="T:System.ArgumentNullException">When pSdv is null.</exception>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMC.Copy(System.Double[])">
            <summary>
            Use for copying objects in the Burn method.
            </summary>
            <param name="source">The source of copying.</param>
            <returns>A copy of the source object.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMC.Create">
            <summary>
            Use for creating temporary objects in the Burn method.
            </summary>
            <returns>An object of type T.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMC.DoAdd(System.Double[]@,System.Double,System.Double[])">
            <inheritdoc/>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMC.DoSubtract(System.Double[]@,System.Double,System.Double[])">
            <inheritdoc/>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMC.DoProduct(System.Double[],System.Double[])">
            <inheritdoc/>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMC.RandomizeMomentum(System.Double[]@)">
            <summary>
            Samples the momentum from a normal distribution.
            </summary>
            <param name="p">The momentum to be randomized.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMC.Grad(MathNet.Numerics.Statistics.Mcmc.DensityLn{System.Double[]},System.Double[])">
            <summary>
            The default method used for computing the gradient. Uses a simple three point estimation.
            </summary>
            <param name="function">Function which the gradient is to be evaluated.</param>
            <param name="x">The location where the gradient is to be evaluated.</param>
            <returns>The gradient of the function at the point x.</returns>
        </member>
        <member name="T:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1">
            <summary>
            The Hybrid (also called Hamiltonian) Monte Carlo produces samples from distribution P using a set
            of Hamiltonian equations to guide the sampling process. It uses the negative of the log density as
            a potential energy, and a randomly generated momentum to set up a Hamiltonian system, which is then used
            to sample the distribution. This can result in a faster convergence than the random walk Metropolis sampler
            (<seealso cref="T:MathNet.Numerics.Statistics.Mcmc.MetropolisSampler`1"/>).
            </summary>
            <typeparam name="T">The type of samples this sampler produces.</typeparam>
        </member>
        <member name="T:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1.DiffMethod">
            <summary>
            The delegate type that defines a derivative evaluated at a certain point.
            </summary>
            <param name="f">Function to be differentiated.</param>
            <param name="x">Value where the derivative is computed.</param>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1._energy">
            <summary>
            Evaluates the energy function of the target distribution.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1.Current">
            <summary>
            The current location of the sampler.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1._burnInterval">
            <summary>
            The number of burn iterations between two samples.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1._stepSize">
            <summary>
            The size of each step in the Hamiltonian equation.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1._frogLeapSteps">
            <summary>
            The number of iterations in the Hamiltonian equation.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1._diff">
            <summary>
            The algorithm used for differentiation.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1.BurnInterval">
            <summary>
            Gets or sets the number of iterations in between returning samples.
            </summary>
            <exception cref="T:System.ArgumentOutOfRangeException">When burn interval is negative.</exception>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1.FrogLeapSteps">
            <summary>
            Gets or sets the number of iterations in the Hamiltonian equation.
            </summary>
            <exception cref="T:System.ArgumentOutOfRangeException">When frog leap steps is negative or zero.</exception>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1.StepSize">
            <summary>
            Gets or sets the size of each step in the Hamiltonian equation.
            </summary>
            <exception cref="T:System.ArgumentOutOfRangeException">When step size is negative or zero.</exception>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1.#ctor(`0,MathNet.Numerics.Statistics.Mcmc.DensityLn{`0},System.Int32,System.Double,System.Int32,System.Random,MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric{`0}.DiffMethod)">
            <summary>
            Constructs a new Hybrid Monte Carlo sampler.
            </summary>
            <param name="x0">The initial sample.</param>
            <param name="pdfLnP">The log density of the distribution we want to sample from.</param>
            <param name="frogLeapSteps">Number frog leap simulation steps.</param>
            <param name="stepSize">Size of the frog leap simulation steps.</param>
            <param name="burnInterval">The number of iterations in between returning samples.</param>
            <param name="randomSource">Random number generator used for sampling the momentum.</param>
            <param name="diff">The method used for differentiation.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">When the number of burnInterval iteration is negative.</exception>
            <exception cref="T:System.ArgumentNullException">When either x0, pdfLnP or diff is null.</exception>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1.Sample">
            <summary>
            Returns a sample from the distribution P.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1.Burn(System.Int32)">
            <summary>
            This method runs the sampler for a number of iterations without returning a sample
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1.Update(System.Double@,`0@,`0,`0,System.Double,System.Double)">
            <summary>
            Method used to update the sample location. Used in the end of the loop.
            </summary>
            <param name="e">The old energy.</param>
            <param name="gradient">The old gradient/derivative of the energy.</param>
            <param name="mNew">The new sample.</param>
            <param name="gNew">The new gradient/derivative of the energy.</param>
            <param name="enew">The new energy.</param>
            <param name="dh">The difference between the old Hamiltonian and new Hamiltonian. Use to determine
            if an update should take place. </param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1.Create">
            <summary>
            Use for creating temporary objects in the Burn method.
            </summary>
            <returns>An object of type T.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1.Copy(`0)">
            <summary>
            Use for copying objects in the Burn method.
            </summary>
            <param name="source">The source of copying.</param>
            <returns>A copy of the source object.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1.DoProduct(`0,`0)">
            <summary>
            Method for doing dot product.
            </summary>
            <param name="first">First vector/scalar in the product.</param>
            <param name="second">Second vector/scalar in the product.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1.DoAdd(`0@,System.Double,`0)">
            <summary>
            Method for adding, multiply the second vector/scalar by factor and then
            add it to the first vector/scalar.
            </summary>
            <param name="first">First vector/scalar.</param>
            <param name="factor">Scalar factor multiplying by the second vector/scalar.</param>
            <param name="second">Second vector/scalar.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1.DoSubtract(`0@,System.Double,`0)">
            <summary>
            Multiplying the second vector/scalar by factor and then subtract it from
            the first vector/scalar.
            </summary>
            <param name="first">First vector/scalar.</param>
            <param name="factor">Scalar factor to be multiplied to the second vector/scalar.</param>
            <param name="second">Second vector/scalar.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1.RandomizeMomentum(`0@)">
            <summary>
            Method for sampling a random momentum.
            </summary>
            <param name="p">Momentum to be randomized.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1.HamiltonianEquations(`0@,`0@,`0@)">
            <summary>
            The Hamiltonian equations that is used to produce the new sample.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1.Hamiltonian(`0,System.Double)">
            <summary>
            Method to compute the Hamiltonian used in the method.
            </summary>
            <param name="momentum">The momentum.</param>
            <param name="e">The energy.</param>
            <returns>Hamiltonian=E+p.p/2</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1.SetNonNegative(System.Int32)">
            <summary>
            Method to check and set a quantity to a non-negative value.
            </summary>
            <param name="value">Proposed value to be checked.</param>
            <returns>Returns value if it is greater than or equal to zero.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">Throws when value is negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1.SetPositive(System.Int32)">
            <summary>
            Method to check and set a quantity to a non-negative value.
            </summary>
            <param name="value">Proposed value to be checked.</param>
            <returns>Returns value if it is greater than to zero.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">Throws when value is negative or zero.</exception>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric`1.SetPositive(System.Double)">
            <summary>
            Method to check and set a quantity to a non-negative value.
            </summary>
            <param name="value">Proposed value to be checked.</param>
            <returns>Returns value if it is greater than zero.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">Throws when value is negative or zero.</exception>
        </member>
        <member name="T:MathNet.Numerics.Statistics.Mcmc.MCMCDiagnostics">
            <summary>
            Provides utilities to analysis the convergence of a set of samples from
            a <seealso cref="T:MathNet.Numerics.Statistics.Mcmc.McmcSampler`1"/>.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.MCMCDiagnostics.ACF``1(System.Collections.Generic.IEnumerable{``0},System.Int32,System.Func{``0,System.Double})">
            <summary>
            Computes the auto correlations of a series evaluated by a function f.
            </summary>
            <param name="series">The series for computing the auto correlation.</param>
            <param name="lag">The lag in the series</param>
            <param name="f">The function used to evaluate the series.</param>
            <returns>The auto correlation.</returns>
            <exception cref="T:System.ArgumentOutOfRangeException">Throws if lag is zero or if lag is
            greater than or equal to the length of Series.</exception>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.MCMCDiagnostics.EffectiveSize``1(System.Collections.Generic.IEnumerable{``0},System.Func{``0,System.Double})">
            <summary>
            Computes the effective size of the sample when evaluated by a function f.
            </summary>
            <param name="series">The samples.</param>
            <param name="f">The function use for evaluating the series.</param>
            <returns>The effective size when auto correlation is taken into account.</returns>
        </member>
        <member name="T:MathNet.Numerics.Statistics.Mcmc.GlobalProposalSampler`1">
            <summary>
            A method which samples datapoints from a proposal distribution. The implementation of this sampler
            is stateless: no variables are saved between two calls to Sample. This proposal is different from
            <seealso cref="T:MathNet.Numerics.Statistics.Mcmc.LocalProposalSampler`1"/> in that it doesn't take any parameters; it samples random
            variables from the whole domain.
            </summary>
            <typeparam name="T">The type of the datapoints.</typeparam>
            <returns>A sample from the proposal distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Statistics.Mcmc.LocalProposalSampler`1">
            <summary>
            A method which samples datapoints from a proposal distribution given an initial sample. The implementation
            of this sampler is stateless: no variables are saved between two calls to Sample. This proposal is different from
            <seealso cref="T:MathNet.Numerics.Statistics.Mcmc.GlobalProposalSampler`1"/> in that it samples locally around an initial point. In other words, it
            makes a small local move rather than producing a global sample from the proposal.
            </summary>
            <typeparam name="T">The type of the datapoints.</typeparam>
            <param name="init">The initial sample.</param>
            <returns>A sample from the proposal distribution.</returns>
        </member>
        <member name="T:MathNet.Numerics.Statistics.Mcmc.Density`1">
            <summary>
            A function which evaluates a density.
            </summary>
            <typeparam name="T">The type of data the distribution is over.</typeparam>
            <param name="sample">The sample we want to evaluate the density for.</param>
        </member>
        <member name="T:MathNet.Numerics.Statistics.Mcmc.DensityLn`1">
            <summary>
            A function which evaluates a log density.
            </summary>
            <typeparam name="T">The type of data the distribution is over.</typeparam>
            <param name="sample">The sample we want to evaluate the log density for.</param>
        </member>
        <member name="T:MathNet.Numerics.Statistics.Mcmc.TransitionKernelLn`1">
            <summary>
            A function which evaluates the log of a transition kernel probability.
            </summary>
            <typeparam name="T">The type for the space over which this transition kernel is defined.</typeparam>
            <param name="to">The new state in the transition.</param>
            <param name="from">The previous state in the transition.</param>
            <returns>The log probability of the transition.</returns>
        </member>
        <member name="T:MathNet.Numerics.Statistics.Mcmc.McmcSampler`1">
            <summary>
            The interface which every sampler must implement.
            </summary>
            <typeparam name="T">The type of samples this sampler produces.</typeparam>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.McmcSampler`1._randomNumberGenerator">
            <summary>
            The random number generator for this class.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.McmcSampler`1.Accepts">
            <summary>
            Keeps track of the number of accepted samples.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.McmcSampler`1.Samples">
            <summary>
            Keeps track of the number of calls to the proposal sampler.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.McmcSampler`1.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Statistics.Mcmc.McmcSampler`1"/> class.
            </summary>
            <remarks>Thread safe instances are two and half times slower than non-thread
            safe classes.</remarks>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Mcmc.McmcSampler`1.RandomSource">
            <summary>
            Gets or sets the random number generator.
            </summary>
            <exception cref="T:System.ArgumentNullException">When the random number generator is null.</exception>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.McmcSampler`1.Sample">
            <summary>
            Returns one sample.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.McmcSampler`1.Sample(System.Int32)">
            <summary>
            Returns a number of samples.
            </summary>
            <param name="n">The number of samples we want.</param>
            <returns>An array of samples.</returns>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Mcmc.McmcSampler`1.AcceptanceRate">
            <summary>
            Gets the acceptance rate of the sampler.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Statistics.Mcmc.MetropolisHastingsSampler`1">
             <summary>
             Metropolis-Hastings sampling produces samples from distribution P by sampling from a proposal distribution Q
             and accepting/rejecting based on the density of P. Metropolis-Hastings sampling doesn't require that the
             proposal distribution Q is symmetric in comparison to <seealso cref="T:MathNet.Numerics.Statistics.Mcmc.MetropolisSampler`1"/>. It does need to
             be able to evaluate the proposal sampler's log density though. All densities are required to be in log space.
            
             The Metropolis-Hastings sampler is a stateful sampler. It keeps track of where it currently is in the domain
             of the distribution P.
             </summary>
             <typeparam name="T">The type of samples this sampler produces.</typeparam>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.MetropolisHastingsSampler`1._pdfLnP">
            <summary>
            Evaluates the log density function of the target distribution.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.MetropolisHastingsSampler`1._krnlQ">
            <summary>
            Evaluates the log transition probability for the proposal distribution.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.MetropolisHastingsSampler`1._proposal">
            <summary>
            A function which samples from a proposal distribution.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.MetropolisHastingsSampler`1._current">
            <summary>
            The current location of the sampler.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.MetropolisHastingsSampler`1._currentDensityLn">
            <summary>
            The log density at the current location.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.MetropolisHastingsSampler`1._burnInterval">
            <summary>
            The number of burn iterations between two samples.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.MetropolisHastingsSampler`1.#ctor(`0,MathNet.Numerics.Statistics.Mcmc.DensityLn{`0},MathNet.Numerics.Statistics.Mcmc.TransitionKernelLn{`0},MathNet.Numerics.Statistics.Mcmc.LocalProposalSampler{`0},System.Int32)">
            <summary>
            Constructs a new Metropolis-Hastings sampler using the default <see cref="T:System.Random"/> random number generator. This
            constructor will set the burn interval.
            </summary>
            <param name="x0">The initial sample.</param>
            <param name="pdfLnP">The log density of the distribution we want to sample from.</param>
            <param name="krnlQ">The log transition probability for the proposal distribution.</param>
            <param name="proposal">A method that samples from the proposal distribution.</param>
            <param name="burnInterval">The number of iterations in between returning samples.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">When the number of burnInterval iteration is negative.</exception>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Mcmc.MetropolisHastingsSampler`1.BurnInterval">
            <summary>
            Gets or sets the number of iterations in between returning samples.
            </summary>
            <exception cref="T:System.ArgumentOutOfRangeException">When burn interval is negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.MetropolisHastingsSampler`1.Burn(System.Int32)">
            <summary>
            This method runs the sampler for a number of iterations without returning a sample
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.MetropolisHastingsSampler`1.Sample">
            <summary>
            Returns a sample from the distribution P.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Statistics.Mcmc.MetropolisSampler`1">
             <summary>
             Metropolis sampling produces samples from distribution P by sampling from a proposal distribution Q
             and accepting/rejecting based on the density of P. Metropolis sampling requires that the proposal
             distribution Q is symmetric. All densities are required to be in log space.
            
             The Metropolis sampler is a stateful sampler. It keeps track of where it currently is in the domain
             of the distribution P.
             </summary>
             <typeparam name="T">The type of samples this sampler produces.</typeparam>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.MetropolisSampler`1._pdfLnP">
            <summary>
            Evaluates the log density function of the sampling distribution.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.MetropolisSampler`1._proposal">
            <summary>
            A function which samples from a proposal distribution.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.MetropolisSampler`1._current">
            <summary>
            The current location of the sampler.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.MetropolisSampler`1._currentDensityLn">
            <summary>
            The log density at the current location.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.MetropolisSampler`1._burnInterval">
            <summary>
            The number of burn iterations between two samples.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.MetropolisSampler`1.#ctor(`0,MathNet.Numerics.Statistics.Mcmc.DensityLn{`0},MathNet.Numerics.Statistics.Mcmc.LocalProposalSampler{`0},System.Int32)">
            <summary>
            Constructs a new Metropolis sampler using the default <see cref="T:System.Random"/> random number generator.
            </summary>
            <param name="x0">The initial sample.</param>
            <param name="pdfLnP">The log density of the distribution we want to sample from.</param>
            <param name="proposal">A method that samples from the symmetric proposal distribution.</param>
            <param name="burnInterval">The number of iterations in between returning samples.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">When the number of burnInterval iteration is negative.</exception>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Mcmc.MetropolisSampler`1.BurnInterval">
            <summary>
            Gets or sets the number of iterations in between returning samples.
            </summary>
            <exception cref="T:System.ArgumentOutOfRangeException">When burn interval is negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.MetropolisSampler`1.Burn(System.Int32)">
            <summary>
            This method runs the sampler for a number of iterations without returning a sample
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.MetropolisSampler`1.Sample">
            <summary>
            Returns a sample from the distribution P.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Statistics.Mcmc.RejectionSampler`1">
            <summary>
            Rejection sampling produces samples from distribution P by sampling from a proposal distribution Q
            and accepting/rejecting based on the density of P and Q. The density of P and Q don't need to
            to be normalized, but we do need that for each x, P(x) &lt; Q(x).
            </summary>
            <typeparam name="T">The type of samples this sampler produces.</typeparam>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.RejectionSampler`1._pdfP">
            <summary>
            Evaluates the density function of the sampling distribution.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.RejectionSampler`1._pdfQ">
            <summary>
            Evaluates the density function of the proposal distribution.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.RejectionSampler`1._proposal">
            <summary>
            A function which samples from a proposal distribution.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.RejectionSampler`1.#ctor(MathNet.Numerics.Statistics.Mcmc.Density{`0},MathNet.Numerics.Statistics.Mcmc.Density{`0},MathNet.Numerics.Statistics.Mcmc.GlobalProposalSampler{`0})">
            <summary>
            Constructs a new rejection sampler using the default <see cref="T:System.Random"/> random number generator.
            </summary>
            <param name="pdfP">The density of the distribution we want to sample from.</param>
            <param name="pdfQ">The density of the proposal distribution.</param>
            <param name="proposal">A method that samples from the proposal distribution.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.RejectionSampler`1.Sample">
            <summary>
            Returns a sample from the distribution P.
            </summary>
            <exception cref="T:System.ArgumentOutOfRangeException">When the algorithms detects that the proposal
            distribution doesn't upper bound the target distribution.</exception>
        </member>
        <member name="T:MathNet.Numerics.Statistics.Mcmc.UnivariateHybridMC">
            <summary>
            A hybrid Monte Carlo sampler for univariate distributions.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.UnivariateHybridMC._distribution">
            <summary>
            Distribution to sample momentum from.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.UnivariateHybridMC._sdv">
            <summary>
            Standard deviations used in the sampling of the
            momentum.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Mcmc.UnivariateHybridMC.MomentumStdDev">
            <summary>
            Gets or sets the standard deviation used in the sampling of the
            momentum.
            </summary>
            <exception cref="T:System.ArgumentOutOfRangeException">When standard deviation is negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.UnivariateHybridMC.#ctor(System.Double,MathNet.Numerics.Statistics.Mcmc.DensityLn{System.Double},System.Int32,System.Double,System.Int32,System.Double)">
            <summary>
            Constructs a new Hybrid Monte Carlo sampler for a univariate probability distribution.
            The momentum will be sampled from a normal distribution with standard deviation
            specified by pSdv using the default <see cref="T:System.Random"/> random
            number generator. A three point estimation will be used for differentiation.
            This constructor will set the burn interval.
            </summary>
            <param name="x0">The initial sample.</param>
            <param name="pdfLnP">The log density of the distribution we want to sample from.</param>
            <param name="frogLeapSteps">Number frog leap simulation steps.</param>
            <param name="stepSize">Size of the frog leap simulation steps.</param>
            <param name="burnInterval">The number of iterations in between returning samples.</param>
            <param name="pSdv">The standard deviation of the normal distribution that is used to sample
            the momentum.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">When the number of burnInterval iteration is negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.UnivariateHybridMC.#ctor(System.Double,MathNet.Numerics.Statistics.Mcmc.DensityLn{System.Double},System.Int32,System.Double,System.Int32,System.Double,System.Random)">
            <summary>
            Constructs a new Hybrid Monte Carlo sampler for a univariate probability distribution.
            The momentum will be sampled from a normal distribution with standard deviation
            specified by pSdv using a random
            number generator provided by the user. A three point estimation will be used for differentiation.
            This constructor will set the burn interval.
            </summary>
            <param name="x0">The initial sample.</param>
            <param name="pdfLnP">The log density of the distribution we want to sample from.</param>
            <param name="frogLeapSteps">Number frog leap simulation steps.</param>
            <param name="stepSize">Size of the frog leap simulation steps.</param>
            <param name="burnInterval">The number of iterations in between returning samples.</param>
            <param name="pSdv">The standard deviation of the normal distribution that is used to sample
            the momentum.</param>
            <param name="randomSource">Random number generator used to sample the momentum.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">When the number of burnInterval iteration is negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.UnivariateHybridMC.#ctor(System.Double,MathNet.Numerics.Statistics.Mcmc.DensityLn{System.Double},System.Int32,System.Double,System.Int32,System.Double,System.Random,MathNet.Numerics.Statistics.Mcmc.HybridMCGeneric{System.Double}.DiffMethod)">
            <summary>
            Constructs a new Hybrid Monte Carlo sampler for a multivariate probability distribution.
            The momentum will be sampled from a normal distribution with standard deviation
            given by pSdv using a random
            number generator provided by the user.  This constructor will set both the burn interval and the method used for
            numerical differentiation.
            </summary>
            <param name="x0">The initial sample.</param>
            <param name="pdfLnP">The log density of the distribution we want to sample from.</param>
            <param name="frogLeapSteps">Number frog leap simulation steps.</param>
            <param name="stepSize">Size of the frog leap simulation steps.</param>
            <param name="burnInterval">The number of iterations in between returning samples.</param>
            <param name="pSdv">The standard deviation of the normal distribution that is used to sample
            the momentum.</param>
            <param name="diff">The method used for numerical differentiation.</param>
            <param name="randomSource">Random number generator used for sampling the momentum.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">When the number of burnInterval iteration is negative.</exception>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.UnivariateHybridMC.Copy(System.Double)">
            <summary>
            Use for copying objects in the Burn method.
            </summary>
            <param name="source">The source of copying.</param>
            <returns>A copy of the source object.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.UnivariateHybridMC.Create">
            <summary>
            Use for creating temporary objects in the Burn method.
            </summary>
            <returns>An object of type T.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.UnivariateHybridMC.DoAdd(System.Double@,System.Double,System.Double)">
            <inheritdoc/>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.UnivariateHybridMC.DoProduct(System.Double,System.Double)">
            <inheritdoc/>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.UnivariateHybridMC.DoSubtract(System.Double@,System.Double,System.Double)">
            <inheritdoc/>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.UnivariateHybridMC.RandomizeMomentum(System.Double@)">
            <summary>
            Samples the momentum from a normal distribution.
            </summary>
            <param name="p">The momentum to be randomized.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.UnivariateHybridMC.Grad(MathNet.Numerics.Statistics.Mcmc.DensityLn{System.Double},System.Double)">
            <summary>
            The default method used for computing the derivative. Uses a simple three point estimation.
            </summary>
            <param name="function">Function for which the derivative is to be evaluated.</param>
            <param name="x">The location where the derivative is to be evaluated.</param>
            <returns>The derivative of the function at the point x.</returns>
        </member>
        <member name="T:MathNet.Numerics.Statistics.Mcmc.UnivariateSliceSampler">
             <summary>
             Slice sampling produces samples from distribution P by uniformly sampling from under the pdf of P using
             a technique described in "Slice Sampling", R. Neal, 2003. All densities are required to be in log space.
            
             The slice sampler is a stateful sampler. It keeps track of where it currently is in the domain
             of the distribution P.
             </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.UnivariateSliceSampler._pdfLnP">
            <summary>
            Evaluates the log density function of the target distribution.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.UnivariateSliceSampler._current">
            <summary>
            The current location of the sampler.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.UnivariateSliceSampler._currentDensityLn">
            <summary>
            The log density at the current location.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.UnivariateSliceSampler._burnInterval">
            <summary>
            The number of burn iterations between two samples.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.Mcmc.UnivariateSliceSampler._scale">
            <summary>
            The scale of the slice sampler.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.UnivariateSliceSampler.#ctor(System.Double,MathNet.Numerics.Statistics.Mcmc.DensityLn{System.Double},System.Double)">
            <summary>
            Constructs a new Slice sampler using the default <see cref="T:System.Random"/> random
            number generator. The burn interval will be set to 0.
            </summary>
            <param name="x0">The initial sample.</param>
            <param name="pdfLnP">The density of the distribution we want to sample from.</param>
            <param name="scale">The scale factor of the slice sampler.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">When the scale of the slice sampler is not positive.</exception>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.UnivariateSliceSampler.#ctor(System.Double,MathNet.Numerics.Statistics.Mcmc.DensityLn{System.Double},System.Int32,System.Double)">
            <summary>
            Constructs a new slice sampler using the default <see cref="T:System.Random"/> random number generator. It
            will set the number of burnInterval iterations and run a burnInterval phase.
            </summary>
            <param name="x0">The initial sample.</param>
            <param name="pdfLnP">The density of the distribution we want to sample from.</param>
            <param name="burnInterval">The number of iterations in between returning samples.</param>
            <param name="scale">The scale factor of the slice sampler.</param>
            <exception cref="T:System.ArgumentOutOfRangeException">When the number of burnInterval iteration is negative.</exception>
            <exception cref="T:System.ArgumentOutOfRangeException">When the scale of the slice sampler is not positive.</exception>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Mcmc.UnivariateSliceSampler.BurnInterval">
            <summary>
            Gets or sets the number of iterations in between returning samples.
            </summary>
            <exception cref="T:System.ArgumentOutOfRangeException">When burn interval is negative.</exception>
        </member>
        <member name="P:MathNet.Numerics.Statistics.Mcmc.UnivariateSliceSampler.Scale">
            <summary>
            Gets or sets the scale of the slice sampler.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.UnivariateSliceSampler.Burn(System.Int32)">
            <summary>
            This method runs the sampler for a number of iterations without returning a sample
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Mcmc.UnivariateSliceSampler.Sample">
            <summary>
            Returns a sample from the distribution P.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Statistics.MovingStatistics">
            <summary>
            Running statistics over a window of data, allows updating by adding values.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.MovingStatistics.Count">
            <summary>
            Gets the total number of samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.MovingStatistics.Minimum">
            <summary>
            Returns the minimum value in the sample data.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.MovingStatistics.Maximum">
            <summary>
            Returns the maximum value in the sample data.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.MovingStatistics.Mean">
            <summary>
            Evaluates the sample mean, an estimate of the population mean.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.MovingStatistics.Variance">
            <summary>
            Estimates the unbiased population variance from the provided samples.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.MovingStatistics.PopulationVariance">
            <summary>
            Evaluates the variance from the provided full population.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.MovingStatistics.StandardDeviation">
            <summary>
            Estimates the unbiased population standard deviation from the provided samples.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.MovingStatistics.PopulationStandardDeviation">
            <summary>
            Evaluates the standard deviation from the provided full population.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.MovingStatistics.Push(System.Double)">
            <summary>
            Update the running statistics by adding another observed sample (in-place).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.MovingStatistics.PushRange(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Update the running statistics by adding a sequence of observed sample (in-place).
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.RankDefinition.Average">
            <summary>Replace ties with their mean (non-integer ranks). Default.</summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.RankDefinition.Min">
            <summary>Replace ties with their minimum (typical sports ranking).</summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.RankDefinition.Max">
            <summary>Replace ties with their maximum.</summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.RankDefinition.First">
            <summary>Permutation with increasing values at each index of ties.</summary>
        </member>
        <member name="T:MathNet.Numerics.Statistics.RunningStatistics">
            <summary>
            Running statistics accumulator, allows updating by adding values
            or by combining two accumulators.
            </summary>
            <remarks>
            This type declares a DataContract for out of the box ephemeral serialization
            with engines like DataContractSerializer, Protocol Buffers and FsPickler,
            but does not guarantee any compatibility between versions.
            It is not recommended to rely on this mechanism for durable persistence.
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningStatistics.Count">
            <summary>
            Gets the total number of samples.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningStatistics.Minimum">
            <summary>
            Returns the minimum value in the sample data.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningStatistics.Maximum">
            <summary>
            Returns the maximum value in the sample data.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningStatistics.Mean">
            <summary>
            Evaluates the sample mean, an estimate of the population mean.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningStatistics.Variance">
            <summary>
            Estimates the unbiased population variance from the provided samples.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningStatistics.PopulationVariance">
            <summary>
            Evaluates the variance from the provided full population.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningStatistics.StandardDeviation">
            <summary>
            Estimates the unbiased population standard deviation from the provided samples.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningStatistics.PopulationStandardDeviation">
            <summary>
            Evaluates the standard deviation from the provided full population.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningStatistics.Skewness">
            <summary>
            Estimates the unbiased population skewness from the provided samples.
            Uses a normalizer (Bessel's correction; type 2).
            Returns NaN if data has less than three entries or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningStatistics.PopulationSkewness">
            <summary>
            Evaluates the population skewness from the full population.
            Does not use a normalizer and would thus be biased if applied to a subset (type 1).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningStatistics.Kurtosis">
            <summary>
            Estimates the unbiased population excess kurtosis from the provided samples.
            Uses a normalizer (Bessel's correction; type 2).
            Returns NaN if data has less than four entries or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningStatistics.PopulationKurtosis">
            <summary>
            Evaluates the population excess kurtosis from the full population.
            Does not use a normalizer and would thus be biased if applied to a subset (type 1).
            Returns NaN if data has less than three entries or if any entry is NaN.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.RunningStatistics.Push(System.Double)">
            <summary>
            Update the running statistics by adding another observed sample (in-place).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.RunningStatistics.PushRange(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Update the running statistics by adding a sequence of observed sample (in-place).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.RunningStatistics.Combine(MathNet.Numerics.Statistics.RunningStatistics,MathNet.Numerics.Statistics.RunningStatistics)">
            <summary>
            Create a new running statistics over the combined samples of two existing running statistics.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Statistics.RunningWeightedStatistics">
            <summary>
            Running weighted statistics accumulator, allows updating by adding values
            or by combining two accumulators. Weights are reliability weights, not frequency weights.
            </summary>
            <remarks>
            This type declares a DataContract for out of the box ephemeral serialization
            with engines like DataContractSerializer, Protocol Buffers and FsPickler,
            but does not guarantee any compatibility between versions.
            It is not recommended to rely on this mechanism for durable persistence.
            </remarks>
        </member>
        <member name="F:MathNet.Numerics.Statistics.RunningWeightedStatistics._m2">
            <summary>
            Second moment
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.RunningWeightedStatistics._m3">
            <summary>
            Third moment
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.RunningWeightedStatistics._m4">
            <summary>
            Fourth moment
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.RunningWeightedStatistics._w1">
            <summary>
            Total weight
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.RunningWeightedStatistics._w2">
            <summary>
            Total of weights to the second power
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.RunningWeightedStatistics._w3">
            <summary>
            Total of weights to the third power
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.RunningWeightedStatistics._w4">
            <summary>
            Total of weights to the fourth power
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Statistics.RunningWeightedStatistics._den">
            <summary>
            The denominator in the variance calculation to perform a Bessel correction.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningWeightedStatistics.Count">
            <summary>
            Gets the total number of samples with non-zero weight.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningWeightedStatistics.Minimum">
            <summary>
            Returns the minimum value in the sample data.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningWeightedStatistics.Maximum">
            <summary>
            Returns the maximum value in the sample data.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningWeightedStatistics.Mean">
            <summary>
            Evaluates the sample mean, an estimate of the population mean.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningWeightedStatistics.Variance">
            <summary>
            Estimates the unbiased population variance from the provided samples.
            Will use the Bessel correction for reliability weighting.
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningWeightedStatistics.PopulationVariance">
            <summary>
            Evaluates the variance from the provided full population.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningWeightedStatistics.StandardDeviation">
            <summary>
            Estimates the unbiased population standard deviation from the provided samples.
            Will use the Bessel correction for reliability weighting.
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningWeightedStatistics.PopulationStandardDeviation">
            <summary>
            Evaluates the standard deviation from the provided full population.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningWeightedStatistics.Skewness">
            <summary>
            Estimates the unbiased population skewness from the provided samples.
            Will use the Bessel correction for reliability weighting.
            Returns NaN if data has less than three entries or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningWeightedStatistics.PopulationSkewness">
            <summary>
            Evaluates the population skewness from the full population.
            Does not use a normalizer and would thus be biased if applied to a subset (type 1).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningWeightedStatistics.Kurtosis">
            <summary>
            Estimates the unbiased population excess kurtosis from the provided samples.
            Will use the Bessel correction for reliability weighting.
            Returns NaN if data has less than four entries or if any entry is NaN.
            Equivalent formula for this for weighted distributions are unknown.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningWeightedStatistics.PopulationKurtosis">
            <summary>
            Evaluates the population excess kurtosis from the full population.
            Does not use a normalizer and would thus be biased if applied to a subset (type 1).
            Returns NaN if data has less than three entries or if any entry is NaN.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningWeightedStatistics.TotalWeight">
            <summary>
            Evaluates the total weight of the population.
            </summary>
        </member>
        <member name="P:MathNet.Numerics.Statistics.RunningWeightedStatistics.EffectiveSampleSize">
            <summary>
            The Kish's Effective Sample Size
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.RunningWeightedStatistics.Push(System.Double,System.Double)">
            <summary>
            Update the running statistics by adding another observed sample (in-place).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.RunningWeightedStatistics.PushRange(System.Collections.Generic.IEnumerable{System.Tuple{System.Double,System.Double}})">
            <summary>
            Update the running statistics by adding a sequence of weighted observatopms (in-place).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.RunningWeightedStatistics.PushRange(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Update the running statistics by adding a sequence of weighted observatopms (in-place).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.RunningWeightedStatistics.Combine(MathNet.Numerics.Statistics.RunningWeightedStatistics,MathNet.Numerics.Statistics.RunningWeightedStatistics)">
            <summary>
            Create a new running statistics over the combined samples of two existing running statistics.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Statistics.SortedArrayStatistics">
            <summary>
            Statistics operating on an array already sorted ascendingly.
            </summary>
            <seealso cref="T:MathNet.Numerics.Statistics.ArrayStatistics"/>
            <seealso cref="T:MathNet.Numerics.Statistics.StreamingStatistics"/>
            <seealso cref="T:MathNet.Numerics.Statistics.Statistics"/>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.Minimum(System.Double[])">
            <summary>
            Returns the smallest value from the sorted data array (ascending).
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.Maximum(System.Double[])">
            <summary>
            Returns the largest value from the sorted data array (ascending).
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.OrderStatistic(System.Double[],System.Int32)">
            <summary>
            Returns the order statistic (order 1..N) from the sorted data array (ascending).
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
            <param name="order">One-based order of the statistic, must be between 1 and N (inclusive).</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.Median(System.Double[])">
            <summary>
            Estimates the median value from the sorted data array (ascending).
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.Percentile(System.Double[],System.Int32)">
            <summary>
            Estimates the p-Percentile value from the sorted data array (ascending).
            If a non-integer Percentile is needed, use Quantile instead.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
            <param name="p">Percentile selector, between 0 and 100 (inclusive).</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.LowerQuartile(System.Double[])">
            <summary>
            Estimates the first quartile value from the sorted data array (ascending).
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.UpperQuartile(System.Double[])">
            <summary>
            Estimates the third quartile value from the sorted data array (ascending).
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.InterquartileRange(System.Double[])">
            <summary>
            Estimates the inter-quartile range from the sorted data array (ascending).
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.FiveNumberSummary(System.Double[])">
            <summary>
            Estimates {min, lower-quantile, median, upper-quantile, max} from the sorted data array (ascending).
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.Quantile(System.Double[],System.Double)">
            <summary>
            Estimates the tau-th quantile from the sorted data array (ascending).
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive).</param>
            <remarks>
            R-8, SciPy-(1/3,1/3):
            Linear interpolation of the approximate medians for order statistics.
            When tau &lt; (2/3) / (N + 1/3), use x1. When tau &gt;= (N - 1/3) / (N + 1/3), use xN.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.QuantileCustom(System.Double[],System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Estimates the tau-th quantile from the sorted data array (ascending).
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified
            by 4 parameters a, b, c and d, consistent with Mathematica.
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive).</param>
            <param name="a">a-parameter</param>
            <param name="b">b-parameter</param>
            <param name="c">c-parameter</param>
            <param name="d">d-parameter</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.QuantileCustom(System.Double[],System.Double,MathNet.Numerics.Statistics.QuantileDefinition)">
            <summary>
            Estimates the tau-th quantile from the sorted data array (ascending).
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified to be compatible
            with an existing system.
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive).</param>
            <param name="definition">Quantile definition, to choose what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.EmpiricalCDF(System.Double[],System.Double)">
            <summary>
            Estimates the empirical cumulative distribution function (CDF) at x from the sorted data array (ascending).
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="x">The value where to estimate the CDF at.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.QuantileRank(System.Double[],System.Double,MathNet.Numerics.Statistics.RankDefinition)">
            <summary>
            Estimates the quantile tau from the sorted data array (ascending).
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified to be compatible
            with an existing system.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="x">Quantile value.</param>
            <param name="definition">Rank definition, to choose how ties should be handled and what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.Ranks(System.Double[],MathNet.Numerics.Statistics.RankDefinition)">
            <summary>
            Evaluates the rank of each entry of the sorted data array (ascending).
            The rank definition can be specified to be compatible
            with an existing system.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.Minimum(System.Single[])">
            <summary>
            Returns the smallest value from the sorted data array (ascending).
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.Maximum(System.Single[])">
            <summary>
            Returns the largest value from the sorted data array (ascending).
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.OrderStatistic(System.Single[],System.Int32)">
            <summary>
            Returns the order statistic (order 1..N) from the sorted data array (ascending).
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
            <param name="order">One-based order of the statistic, must be between 1 and N (inclusive).</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.Median(System.Single[])">
            <summary>
            Estimates the median value from the sorted data array (ascending).
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.Percentile(System.Single[],System.Int32)">
            <summary>
            Estimates the p-Percentile value from the sorted data array (ascending).
            If a non-integer Percentile is needed, use Quantile instead.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
            <param name="p">Percentile selector, between 0 and 100 (inclusive).</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.LowerQuartile(System.Single[])">
            <summary>
            Estimates the first quartile value from the sorted data array (ascending).
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.UpperQuartile(System.Single[])">
            <summary>
            Estimates the third quartile value from the sorted data array (ascending).
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.InterquartileRange(System.Single[])">
            <summary>
            Estimates the inter-quartile range from the sorted data array (ascending).
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.FiveNumberSummary(System.Single[])">
            <summary>
            Estimates {min, lower-quantile, median, upper-quantile, max} from the sorted data array (ascending).
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.Quantile(System.Single[],System.Double)">
            <summary>
            Estimates the tau-th quantile from the sorted data array (ascending).
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive).</param>
            <remarks>
            R-8, SciPy-(1/3,1/3):
            Linear interpolation of the approximate medians for order statistics.
            When tau &lt; (2/3) / (N + 1/3), use x1. When tau &gt;= (N - 1/3) / (N + 1/3), use xN.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.QuantileCustom(System.Single[],System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Estimates the tau-th quantile from the sorted data array (ascending).
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified
            by 4 parameters a, b, c and d, consistent with Mathematica.
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive).</param>
            <param name="a">a-parameter</param>
            <param name="b">b-parameter</param>
            <param name="c">c-parameter</param>
            <param name="d">d-parameter</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.QuantileCustom(System.Single[],System.Double,MathNet.Numerics.Statistics.QuantileDefinition)">
            <summary>
            Estimates the tau-th quantile from the sorted data array (ascending).
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified to be compatible
            with an existing system.
            </summary>
            <param name="data">Sample array, must be sorted ascendingly.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive).</param>
            <param name="definition">Quantile definition, to choose what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.EmpiricalCDF(System.Single[],System.Single)">
            <summary>
            Estimates the empirical cumulative distribution function (CDF) at x from the sorted data array (ascending).
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="x">The value where to estimate the CDF at.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.QuantileRank(System.Single[],System.Single,MathNet.Numerics.Statistics.RankDefinition)">
            <summary>
            Estimates the quantile tau from the sorted data array (ascending).
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified to be compatible
            with an existing system.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="x">Quantile value.</param>
            <param name="definition">Rank definition, to choose how ties should be handled and what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.SortedArrayStatistics.Ranks(System.Single[],MathNet.Numerics.Statistics.RankDefinition)">
            <summary>
            Evaluates the rank of each entry of the sorted data array (ascending).
            The rank definition can be specified to be compatible
            with an existing system.
            </summary>
        </member>
        <member name="T:MathNet.Numerics.Statistics.Statistics">
            <summary>
            Extension methods to return basic statistics on set of data.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Minimum(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Returns the minimum value in the sample data.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="data">The sample data.</param>
            <returns>The minimum value in the sample data.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Minimum(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Returns the minimum value in the sample data.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="data">The sample data.</param>
            <returns>The minimum value in the sample data.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Minimum(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Returns the minimum value in the sample data.
            Returns NaN if data is empty or if any entry is NaN.
            Null-entries are ignored.
            </summary>
            <param name="data">The sample data.</param>
            <returns>The minimum value in the sample data.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Maximum(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Returns the maximum value in the sample data.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="data">The sample data.</param>
            <returns>The maximum value in the sample data.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Maximum(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Returns the maximum value in the sample data.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="data">The sample data.</param>
            <returns>The maximum value in the sample data.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Maximum(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Returns the maximum value in the sample data.
            Returns NaN if data is empty or if any entry is NaN.
            Null-entries are ignored.
            </summary>
            <param name="data">The sample data.</param>
            <returns>The maximum value in the sample data.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.MinimumAbsolute(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Returns the minimum absolute value in the sample data.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="data">The sample data.</param>
            <returns>The minimum value in the sample data.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.MinimumAbsolute(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Returns the minimum absolute value in the sample data.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="data">The sample data.</param>
            <returns>The minimum value in the sample data.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.MaximumAbsolute(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Returns the maximum absolute value in the sample data.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="data">The sample data.</param>
            <returns>The maximum value in the sample data.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.MaximumAbsolute(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Returns the maximum absolute value in the sample data.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="data">The sample data.</param>
            <returns>The maximum value in the sample data.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.MinimumMagnitudePhase(System.Collections.Generic.IEnumerable{System.Numerics.Complex})">
            <summary>
            Returns the minimum magnitude and phase value in the sample data.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="data">The sample data.</param>
            <returns>The minimum value in the sample data.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.MinimumMagnitudePhase(System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32})">
            <summary>
            Returns the minimum magnitude and phase value in the sample data.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="data">The sample data.</param>
            <returns>The minimum value in the sample data.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.MaximumMagnitudePhase(System.Collections.Generic.IEnumerable{System.Numerics.Complex})">
            <summary>
            Returns the maximum magnitude and phase value in the sample data.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="data">The sample data.</param>
            <returns>The minimum value in the sample data.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.MaximumMagnitudePhase(System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32})">
            <summary>
            Returns the maximum magnitude and phase value in the sample data.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="data">The sample data.</param>
            <returns>The minimum value in the sample data.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Mean(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Evaluates the sample mean, an estimate of the population mean.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="data">The data to calculate the mean of.</param>
            <returns>The mean of the sample.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Mean(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Evaluates the sample mean, an estimate of the population mean.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="data">The data to calculate the mean of.</param>
            <returns>The mean of the sample.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Mean(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Evaluates the sample mean, an estimate of the population mean.
            Returns NaN if data is empty or if any entry is NaN.
            Null-entries are ignored.
            </summary>
            <param name="data">The data to calculate the mean of.</param>
            <returns>The mean of the sample.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.GeometricMean(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Evaluates the geometric mean.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="data">The data to calculate the geometric mean of.</param>
            <returns>The geometric mean of the sample.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.GeometricMean(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Evaluates the geometric mean.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="data">The data to calculate the geometric mean of.</param>
            <returns>The geometric mean of the sample.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.HarmonicMean(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Evaluates the harmonic mean.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="data">The data to calculate the harmonic mean of.</param>
            <returns>The harmonic mean of the sample.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.HarmonicMean(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Evaluates the harmonic mean.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="data">The data to calculate the harmonic mean of.</param>
            <returns>The harmonic mean of the sample.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Variance(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the unbiased population variance from the provided samples.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">A subset of samples, sampled from the full population.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Variance(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates the unbiased population variance from the provided samples.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">A subset of samples, sampled from the full population.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Variance(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Estimates the unbiased population variance from the provided samples.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            Null-entries are ignored.
            </summary>
            <param name="samples">A subset of samples, sampled from the full population.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.PopulationVariance(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Evaluates the variance from the provided full population.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population">The full population data.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.PopulationVariance(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Evaluates the variance from the provided full population.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population">The full population data.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.PopulationVariance(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Evaluates the variance from the provided full population.
            On a dataset of size N will use an N normalize and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            Null-entries are ignored.
            </summary>
            <param name="population">The full population data.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.StandardDeviation(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the unbiased population standard deviation from the provided samples.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">A subset of samples, sampled from the full population.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.StandardDeviation(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates the unbiased population standard deviation from the provided samples.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">A subset of samples, sampled from the full population.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.StandardDeviation(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Estimates the unbiased population standard deviation from the provided samples.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            Null-entries are ignored.
            </summary>
            <param name="samples">A subset of samples, sampled from the full population.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.PopulationStandardDeviation(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Evaluates the standard deviation from the provided full population.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population">The full population data.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.PopulationStandardDeviation(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Evaluates the standard deviation from the provided full population.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population">The full population data.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.PopulationStandardDeviation(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Evaluates the standard deviation from the provided full population.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            Null-entries are ignored.
            </summary>
            <param name="population">The full population data.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Skewness(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the unbiased population skewness from the provided samples.
            Uses a normalizer (Bessel's correction; type 2).
            Returns NaN if data has less than three entries or if any entry is NaN.
            </summary>
            <param name="samples">A subset of samples, sampled from the full population.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Skewness(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Estimates the unbiased population skewness from the provided samples.
            Uses a normalizer (Bessel's correction; type 2).
            Returns NaN if data has less than three entries or if any entry is NaN.
            Null-entries are ignored.
            </summary>
            <param name="samples">A subset of samples, sampled from the full population.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.PopulationSkewness(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Evaluates the skewness from the full population.
            Does not use a normalizer and would thus be biased if applied to a subset (type 1).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="population">The full population data.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.PopulationSkewness(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Evaluates the skewness from the full population.
            Does not use a normalizer and would thus be biased if applied to a subset (type 1).
            Returns NaN if data has less than two entries or if any entry is NaN.
            Null-entries are ignored.
            </summary>
            <param name="population">The full population data.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Kurtosis(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the unbiased population kurtosis from the provided samples.
            Uses a normalizer (Bessel's correction; type 2).
            Returns NaN if data has less than four entries or if any entry is NaN.
            </summary>
            <param name="samples">A subset of samples, sampled from the full population.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Kurtosis(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Estimates the unbiased population kurtosis from the provided samples.
            Uses a normalizer (Bessel's correction; type 2).
            Returns NaN if data has less than four entries or if any entry is NaN.
            Null-entries are ignored.
            </summary>
            <param name="samples">A subset of samples, sampled from the full population.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.PopulationKurtosis(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Evaluates the kurtosis from the full population.
            Does not use a normalizer and would thus be biased if applied to a subset (type 1).
            Returns NaN if data has less than three entries or if any entry is NaN.
            </summary>
            <param name="population">The full population data.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.PopulationKurtosis(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Evaluates the kurtosis from the full population.
            Does not use a normalizer and would thus be biased if applied to a subset (type 1).
            Returns NaN if data has less than three entries or if any entry is NaN.
            Null-entries are ignored.
            </summary>
            <param name="population">The full population data.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.MeanVariance(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the sample mean and the unbiased population variance from the provided samples.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN for mean if data is empty or if any entry is NaN and NaN for variance if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">The data to calculate the mean of.</param>
            <returns>The mean of the sample.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.MeanVariance(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates the sample mean and the unbiased population variance from the provided samples.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN for mean if data is empty or if any entry is NaN and NaN for variance if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">The data to calculate the mean of.</param>
            <returns>The mean of the sample.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.MeanStandardDeviation(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the sample mean and the unbiased population standard deviation from the provided samples.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN for mean if data is empty or if any entry is NaN and NaN for standard deviation if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">The data to calculate the mean of.</param>
            <returns>The mean of the sample.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.MeanStandardDeviation(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates the sample mean and the unbiased population standard deviation from the provided samples.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN for mean if data is empty or if any entry is NaN and NaN for standard deviation if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">The data to calculate the mean of.</param>
            <returns>The mean of the sample.</returns>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.SkewnessKurtosis(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the unbiased population skewness and kurtosis from the provided samples in a single pass.
            Uses a normalizer (Bessel's correction; type 2).
            </summary>
            <param name="samples">A subset of samples, sampled from the full population.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.PopulationSkewnessKurtosis(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Evaluates the skewness and kurtosis from the full population.
            Does not use a normalizer and would thus be biased if applied to a subset (type 1).
            </summary>
            <param name="population">The full population data.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Covariance(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the unbiased population covariance from the provided samples.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples1">A subset of samples, sampled from the full population.</param>
            <param name="samples2">A subset of samples, sampled from the full population.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Covariance(System.Collections.Generic.IEnumerable{System.Single},System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates the unbiased population covariance from the provided samples.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples1">A subset of samples, sampled from the full population.</param>
            <param name="samples2">A subset of samples, sampled from the full population.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Covariance(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}},System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Estimates the unbiased population covariance from the provided samples.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            Null-entries are ignored.
            </summary>
            <param name="samples1">A subset of samples, sampled from the full population.</param>
            <param name="samples2">A subset of samples, sampled from the full population.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.PopulationCovariance(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Evaluates the population covariance from the provided full populations.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population1">The full population data.</param>
            <param name="population2">The full population data.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.PopulationCovariance(System.Collections.Generic.IEnumerable{System.Single},System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Evaluates the population covariance from the provided full populations.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population1">The full population data.</param>
            <param name="population2">The full population data.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.PopulationCovariance(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}},System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Evaluates the population covariance from the provided full populations.
            On a dataset of size N will use an N normalize and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            Null-entries are ignored.
            </summary>
            <param name="population1">The full population data.</param>
            <param name="population2">The full population data.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.RootMeanSquare(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Evaluates the root mean square (RMS) also known as quadratic mean.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="data">The data to calculate the RMS of.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.RootMeanSquare(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Evaluates the root mean square (RMS) also known as quadratic mean.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="data">The data to calculate the RMS of.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.RootMeanSquare(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Evaluates the root mean square (RMS) also known as quadratic mean.
            Returns NaN if data is empty or if any entry is NaN.
            Null-entries are ignored.
            </summary>
            <param name="data">The data to calculate the mean of.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Median(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the sample median from the provided samples (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Median(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates the sample median from the provided samples (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Median(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Estimates the sample median from the provided samples (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Quantile(System.Collections.Generic.IEnumerable{System.Double},System.Double)">
            <summary>
            Estimates the tau-th quantile from the provided samples.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive).</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Quantile(System.Collections.Generic.IEnumerable{System.Single},System.Double)">
            <summary>
            Estimates the tau-th quantile from the provided samples.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive).</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Quantile(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}},System.Double)">
            <summary>
            Estimates the tau-th quantile from the provided samples.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive).</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.QuantileFunc(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the tau-th quantile from the provided samples.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.QuantileFunc(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates the tau-th quantile from the provided samples.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.QuantileFunc(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Estimates the tau-th quantile from the provided samples.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.QuantileCustom(System.Collections.Generic.IEnumerable{System.Double},System.Double,MathNet.Numerics.Statistics.QuantileDefinition)">
            <summary>
            Estimates the tau-th quantile from the provided samples.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified to be compatible
            with an existing system.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive).</param>
            <param name="definition">Quantile definition, to choose what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.QuantileCustom(System.Collections.Generic.IEnumerable{System.Single},System.Double,MathNet.Numerics.Statistics.QuantileDefinition)">
            <summary>
            Estimates the tau-th quantile from the provided samples.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified to be compatible
            with an existing system.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive).</param>
            <param name="definition">Quantile definition, to choose what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.QuantileCustom(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}},System.Double,MathNet.Numerics.Statistics.QuantileDefinition)">
            <summary>
            Estimates the tau-th quantile from the provided samples.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified to be compatible
            with an existing system.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive).</param>
            <param name="definition">Quantile definition, to choose what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.QuantileCustomFunc(System.Collections.Generic.IEnumerable{System.Double},MathNet.Numerics.Statistics.QuantileDefinition)">
            <summary>
            Estimates the tau-th quantile from the provided samples.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified to be compatible
            with an existing system.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="definition">Quantile definition, to choose what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.QuantileCustomFunc(System.Collections.Generic.IEnumerable{System.Single},MathNet.Numerics.Statistics.QuantileDefinition)">
            <summary>
            Estimates the tau-th quantile from the provided samples.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified to be compatible
            with an existing system.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="definition">Quantile definition, to choose what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.QuantileCustomFunc(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}},MathNet.Numerics.Statistics.QuantileDefinition)">
            <summary>
            Estimates the tau-th quantile from the provided samples.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified to be compatible
            with an existing system.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="definition">Quantile definition, to choose what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Percentile(System.Collections.Generic.IEnumerable{System.Double},System.Int32)">
            <summary>
            Estimates the p-Percentile value from the provided samples.
            If a non-integer Percentile is needed, use Quantile instead.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="p">Percentile selector, between 0 and 100 (inclusive).</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Percentile(System.Collections.Generic.IEnumerable{System.Single},System.Int32)">
            <summary>
            Estimates the p-Percentile value from the provided samples.
            If a non-integer Percentile is needed, use Quantile instead.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="p">Percentile selector, between 0 and 100 (inclusive).</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Percentile(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}},System.Int32)">
            <summary>
            Estimates the p-Percentile value from the provided samples.
            If a non-integer Percentile is needed, use Quantile instead.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="p">Percentile selector, between 0 and 100 (inclusive).</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.PercentileFunc(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the p-Percentile value from the provided samples.
            If a non-integer Percentile is needed, use Quantile instead.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.PercentileFunc(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates the p-Percentile value from the provided samples.
            If a non-integer Percentile is needed, use Quantile instead.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.PercentileFunc(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Estimates the p-Percentile value from the provided samples.
            If a non-integer Percentile is needed, use Quantile instead.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.LowerQuartile(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the first quartile value from the provided samples.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.LowerQuartile(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates the first quartile value from the provided samples.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.LowerQuartile(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Estimates the first quartile value from the provided samples.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.UpperQuartile(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the third quartile value from the provided samples.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.UpperQuartile(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates the third quartile value from the provided samples.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.UpperQuartile(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Estimates the third quartile value from the provided samples.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.InterquartileRange(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the inter-quartile range from the provided samples.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.InterquartileRange(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates the inter-quartile range from the provided samples.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.InterquartileRange(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Estimates the inter-quartile range from the provided samples.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.FiveNumberSummary(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates {min, lower-quantile, median, upper-quantile, max} from the provided samples.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.FiveNumberSummary(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates {min, lower-quantile, median, upper-quantile, max} from the provided samples.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.FiveNumberSummary(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Estimates {min, lower-quantile, median, upper-quantile, max} from the provided samples.
            Approximately median-unbiased regardless of the sample distribution (R8).
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.OrderStatistic(System.Collections.Generic.IEnumerable{System.Double},System.Int32)">
            <summary>
            Returns the order statistic (order 1..N) from the provided samples.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="order">One-based order of the statistic, must be between 1 and N (inclusive).</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.OrderStatistic(System.Collections.Generic.IEnumerable{System.Single},System.Int32)">
            <summary>
            Returns the order statistic (order 1..N) from the provided samples.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="order">One-based order of the statistic, must be between 1 and N (inclusive).</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.OrderStatisticFunc(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Returns the order statistic (order 1..N) from the provided samples.
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.OrderStatisticFunc(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Returns the order statistic (order 1..N) from the provided samples.
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Ranks(System.Collections.Generic.IEnumerable{System.Double},MathNet.Numerics.Statistics.RankDefinition)">
            <summary>
            Evaluates the rank of each entry of the provided samples.
            The rank definition can be specified to be compatible
            with an existing system.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="definition">Rank definition, to choose how ties should be handled and what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Ranks(System.Collections.Generic.IEnumerable{System.Single},MathNet.Numerics.Statistics.RankDefinition)">
            <summary>
            Evaluates the rank of each entry of the provided samples.
            The rank definition can be specified to be compatible
            with an existing system.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="definition">Rank definition, to choose how ties should be handled and what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Ranks(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}},MathNet.Numerics.Statistics.RankDefinition)">
            <summary>
            Evaluates the rank of each entry of the provided samples.
            The rank definition can be specified to be compatible
            with an existing system.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="definition">Rank definition, to choose how ties should be handled and what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.QuantileRank(System.Collections.Generic.IEnumerable{System.Double},System.Double,MathNet.Numerics.Statistics.RankDefinition)">
            <summary>
            Estimates the quantile tau from the provided samples.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified to be compatible
            with an existing system.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="x">Quantile value.</param>
            <param name="definition">Rank definition, to choose how ties should be handled and what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.QuantileRank(System.Collections.Generic.IEnumerable{System.Single},System.Single,MathNet.Numerics.Statistics.RankDefinition)">
            <summary>
            Estimates the quantile tau from the provided samples.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified to be compatible
            with an existing system.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="x">Quantile value.</param>
            <param name="definition">Rank definition, to choose how ties should be handled and what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.QuantileRank(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}},System.Double,MathNet.Numerics.Statistics.RankDefinition)">
            <summary>
            Estimates the quantile tau from the provided samples.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified to be compatible
            with an existing system.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="x">Quantile value.</param>
            <param name="definition">Rank definition, to choose how ties should be handled and what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.QuantileRankFunc(System.Collections.Generic.IEnumerable{System.Double},MathNet.Numerics.Statistics.RankDefinition)">
            <summary>
            Estimates the quantile tau from the provided samples.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified to be compatible
            with an existing system.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="definition">Rank definition, to choose how ties should be handled and what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.QuantileRankFunc(System.Collections.Generic.IEnumerable{System.Single},MathNet.Numerics.Statistics.RankDefinition)">
            <summary>
            Estimates the quantile tau from the provided samples.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified to be compatible
            with an existing system.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="definition">Rank definition, to choose how ties should be handled and what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.QuantileRankFunc(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}},MathNet.Numerics.Statistics.RankDefinition)">
            <summary>
            Estimates the quantile tau from the provided samples.
            The tau-th quantile is the data value where the cumulative distribution
            function crosses tau. The quantile definition can be specified to be compatible
            with an existing system.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="definition">Rank definition, to choose how ties should be handled and what product/definition it should be consistent with</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(System.Collections.Generic.IEnumerable{System.Double},System.Double)">
            <summary>
            Estimates the empirical cumulative distribution function (CDF) at x from the provided samples.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="x">The value where to estimate the CDF at.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(System.Collections.Generic.IEnumerable{System.Single},System.Single)">
            <summary>
            Estimates the empirical cumulative distribution function (CDF) at x from the provided samples.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="x">The value where to estimate the CDF at.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}},System.Double)">
            <summary>
            Estimates the empirical cumulative distribution function (CDF) at x from the provided samples.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="x">The value where to estimate the CDF at.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.EmpiricalCDFFunc(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the empirical cumulative distribution function (CDF) at x from the provided samples.
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.EmpiricalCDFFunc(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates the empirical cumulative distribution function (CDF) at x from the provided samples.
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.EmpiricalCDFFunc(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Estimates the empirical cumulative distribution function (CDF) at x from the provided samples.
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.EmpiricalInvCDF(System.Collections.Generic.IEnumerable{System.Double},System.Double)">
            <summary>
            Estimates the empirical inverse CDF at tau from the provided samples.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive).</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.EmpiricalInvCDF(System.Collections.Generic.IEnumerable{System.Single},System.Double)">
            <summary>
            Estimates the empirical inverse CDF at tau from the provided samples.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive).</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.EmpiricalInvCDF(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}},System.Double)">
            <summary>
            Estimates the empirical inverse CDF at tau from the provided samples.
            </summary>
            <param name="data">The data sample sequence.</param>
            <param name="tau">Quantile selector, between 0.0 and 1.0 (inclusive).</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.EmpiricalInvCDFFunc(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the empirical inverse CDF at tau from the provided samples.
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.EmpiricalInvCDFFunc(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates the empirical inverse CDF at tau from the provided samples.
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.EmpiricalInvCDFFunc(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Estimates the empirical inverse CDF at tau from the provided samples.
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Entropy(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Calculates the entropy of a stream of double values in bits.
            Returns NaN if any of the values in the stream are NaN.
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.Entropy(System.Collections.Generic.IEnumerable{System.Nullable{System.Double}})">
            <summary>
            Calculates the entropy of a stream of double values in bits.
            Returns NaN if any of the values in the stream are NaN.
            Null-entries are ignored.
            </summary>
            <param name="data">The data sample sequence.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.Statistics.MovingAverage(System.Collections.Generic.IEnumerable{System.Double},System.Int32)">
            <summary>
            Evaluates the sample mean over a moving window, for each samples.
            Returns NaN if no data is empty or if any entry is NaN.
            </summary>
            <param name="samples">The sample stream to calculate the mean of.</param>
            <param name="windowSize">The number of last samples to consider.</param>
        </member>
        <member name="T:MathNet.Numerics.Statistics.StreamingStatistics">
            <summary>
            Statistics operating on an IEnumerable in a single pass, without keeping the full data in memory.
            Can be used in a streaming way, e.g. on large datasets not fitting into memory.
            </summary>
            <seealso cref="T:MathNet.Numerics.Statistics.SortedArrayStatistics"/>
            <seealso cref="T:MathNet.Numerics.Statistics.StreamingStatistics"/>
            <seealso cref="T:MathNet.Numerics.Statistics.Statistics"/>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.Minimum(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Returns the smallest value from the enumerable, in a single pass without memoization.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="stream">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.Minimum(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Returns the smallest value from the enumerable, in a single pass without memoization.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="stream">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.Maximum(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Returns the largest value from the enumerable, in a single pass without memoization.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="stream">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.Maximum(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Returns the largest value from the enumerable, in a single pass without memoization.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="stream">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.MinimumAbsolute(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Returns the smallest absolute value from the enumerable, in a single pass without memoization.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="stream">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.MinimumAbsolute(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Returns the smallest absolute value from the enumerable, in a single pass without memoization.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="stream">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.MaximumAbsolute(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Returns the largest absolute value from the enumerable, in a single pass without memoization.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="stream">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.MaximumAbsolute(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Returns the largest absolute value from the enumerable, in a single pass without memoization.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="stream">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.MinimumMagnitudePhase(System.Collections.Generic.IEnumerable{System.Numerics.Complex})">
            <summary>
            Returns the smallest absolute value from the enumerable, in a single pass without memoization.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="stream">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.MinimumMagnitudePhase(System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32})">
            <summary>
            Returns the smallest absolute value from the enumerable, in a single pass without memoization.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="stream">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.MaximumMagnitudePhase(System.Collections.Generic.IEnumerable{System.Numerics.Complex})">
            <summary>
            Returns the largest absolute value from the enumerable, in a single pass without memoization.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="stream">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.MaximumMagnitudePhase(System.Collections.Generic.IEnumerable{MathNet.Numerics.Complex32})">
            <summary>
            Returns the largest absolute value from the enumerable, in a single pass without memoization.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="stream">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.Mean(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the arithmetic sample mean from the enumerable, in a single pass without memoization.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="stream">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.Mean(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates the arithmetic sample mean from the enumerable, in a single pass without memoization.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="stream">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.GeometricMean(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Evaluates the geometric mean of the enumerable, in a single pass without memoization.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="stream">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.GeometricMean(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Evaluates the geometric mean of the enumerable, in a single pass without memoization.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="stream">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.HarmonicMean(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Evaluates the harmonic mean of the enumerable, in a single pass without memoization.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="stream">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.HarmonicMean(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Evaluates the harmonic mean of the enumerable, in a single pass without memoization.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="stream">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.Variance(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the unbiased population variance from the provided samples as enumerable sequence, in a single pass without memoization.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.Variance(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates the unbiased population variance from the provided samples as enumerable sequence, in a single pass without memoization.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.PopulationVariance(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Evaluates the population variance from the full population provided as enumerable sequence, in a single pass without memoization.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.PopulationVariance(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Evaluates the population variance from the full population provided as enumerable sequence, in a single pass without memoization.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.StandardDeviation(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the unbiased population standard deviation from the provided samples as enumerable sequence, in a single pass without memoization.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.StandardDeviation(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates the unbiased population standard deviation from the provided samples as enumerable sequence, in a single pass without memoization.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.PopulationStandardDeviation(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Evaluates the population standard deviation from the full population provided as enumerable sequence, in a single pass without memoization.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.PopulationStandardDeviation(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Evaluates the population standard deviation from the full population provided as enumerable sequence, in a single pass without memoization.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.MeanVariance(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the arithmetic sample mean and the unbiased population variance from the provided samples as enumerable sequence, in a single pass without memoization.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN for mean if data is empty or any entry is NaN, and NaN for variance if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.MeanVariance(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates the arithmetic sample mean and the unbiased population variance from the provided samples as enumerable sequence, in a single pass without memoization.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN for mean if data is empty or any entry is NaN, and NaN for variance if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.MeanStandardDeviation(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the arithmetic sample mean and the unbiased population standard deviation from the provided samples as enumerable sequence, in a single pass without memoization.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN for mean if data is empty or any entry is NaN, and NaN for standard deviation if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.MeanStandardDeviation(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates the arithmetic sample mean and the unbiased population standard deviation from the provided samples as enumerable sequence, in a single pass without memoization.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN for mean if data is empty or any entry is NaN, and NaN for standard deviation if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.Covariance(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the unbiased population covariance from the provided two sample enumerable sequences, in a single pass without memoization.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples1">First sample stream.</param>
            <param name="samples2">Second sample stream.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.Covariance(System.Collections.Generic.IEnumerable{System.Single},System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates the unbiased population covariance from the provided two sample enumerable sequences, in a single pass without memoization.
            On a dataset of size N will use an N-1 normalizer (Bessel's correction).
            Returns NaN if data has less than two entries or if any entry is NaN.
            </summary>
            <param name="samples1">First sample stream.</param>
            <param name="samples2">Second sample stream.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.PopulationCovariance(System.Collections.Generic.IEnumerable{System.Double},System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Evaluates the population covariance from the full population provided as two enumerable sequences, in a single pass without memoization.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population1">First population stream.</param>
            <param name="population2">Second population stream.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.PopulationCovariance(System.Collections.Generic.IEnumerable{System.Single},System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Evaluates the population covariance from the full population provided as two enumerable sequences, in a single pass without memoization.
            On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
            Returns NaN if data is empty or if any entry is NaN.
            </summary>
            <param name="population1">First population stream.</param>
            <param name="population2">Second population stream.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.RootMeanSquare(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Estimates the root mean square (RMS) also known as quadratic mean from the enumerable, in a single pass without memoization.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="stream">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.RootMeanSquare(System.Collections.Generic.IEnumerable{System.Single})">
            <summary>
            Estimates the root mean square (RMS) also known as quadratic mean from the enumerable, in a single pass without memoization.
            Returns NaN if data is empty or any entry is NaN.
            </summary>
            <param name="stream">Sample stream, no sorting is assumed.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.StreamingStatistics.Entropy(System.Collections.Generic.IEnumerable{System.Double})">
            <summary>
            Calculates the entropy of a stream of double values.
            Returns NaN if any of the values in the stream are NaN.
            </summary>
            <param name="stream">The input stream to evaluate.</param>
            <returns></returns>
        </member>
        <member name="T:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics">
            <summary>
            Computes the basic statistics of data set. The class meets the
            NIST standard of accuracy for mean, variance, and standard deviation
            (the only statistics they provide exact values for) and exceeds them
            in increased accuracy mode.
            Recommendation: consider to use RunningWeightedStatistics instead.
            </summary>
            <remarks>
            This type declares a DataContract for out of the box ephemeral serialization
            with engines like DataContractSerializer, Protocol Buffers and FsPickler,
            but does not guarantee any compatibility between versions.
            It is not recommended to rely on this mechanism for durable persistence.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics.#ctor(System.Collections.Generic.IEnumerable{System.Tuple{System.Double,System.Double}},System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics"/> class.
            </summary>
            <param name="data">The sample data.</param>
            <param name="increasedAccuracy">
            If set to <c>true</c>, increased accuracy mode used.
            Increased accuracy mode uses <see cref="T:System.Decimal"/> types for internal calculations.
            </param>
            <remarks>
            Don't use increased accuracy for data sets containing large values (in absolute value).
            This may cause the calculations to overflow.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics.#ctor(System.Collections.Generic.IEnumerable{System.ValueTuple{System.Double,System.Double}},System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics"/> class.
            </summary>
            <param name="data">The sample data.</param>
            <param name="increasedAccuracy">
            If set to <c>true</c>, increased accuracy mode used.
            Increased accuracy mode uses <see cref="T:System.Decimal"/> types for internal calculations.
            </param>
            <remarks>
            Don't use increased accuracy for data sets containing large values (in absolute value).
            This may cause the calculations to overflow.
            </remarks>
        </member>
        <member name="M:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:MathNet.Numerics.Statistics.DescriptiveStatistics"/> class.
            </summary>
            <remarks>
            Used for Json serialization
            </remarks>
        </member>
        <member name="P:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics.Count">
            <summary>
            Gets the size of the sample.
            </summary>
            <value>The size of the sample.</value>
        </member>
        <member name="P:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics.Mean">
            <summary>
            Gets the sample mean.
            </summary>
            <value>The sample mean.</value>
        </member>
        <member name="P:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics.Variance">
            <summary>
            Gets the unbiased population variance estimator (on a dataset of size N will use an N-1 normalizer).
            </summary>
            <value>The sample variance.</value>
        </member>
        <member name="P:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics.StandardDeviation">
            <summary>
            Gets the unbiased population standard deviation (on a dataset of size N will use an N-1 normalizer).
            </summary>
            <value>The sample standard deviation.</value>
        </member>
        <member name="P:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics.Skewness">
            <summary>
            Gets the unbiased estimator of the population skewness.
            </summary>
            <value>The sample skewness.</value>
            <remarks>Returns zero if <see cref="P:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics.Count"/> is less than three. </remarks>
        </member>
        <member name="P:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics.Kurtosis">
            <summary>
            Gets the unbiased estimator of the population excess kurtosis using the G_2 estimator.
            </summary>
            <value>The sample kurtosis.</value>
            <remarks>Returns zero if <see cref="P:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics.Count"/> is less than four. </remarks>
        </member>
        <member name="P:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics.Maximum">
            <summary>
            Gets the maximum sample value.
            </summary>
            <value>The maximum sample value.</value>
        </member>
        <member name="P:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics.Minimum">
            <summary>
            Gets the minimum sample value.
            </summary>
            <value>The minimum sample value.</value>
        </member>
        <member name="P:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics.TotalWeight">
            <summary>
            Gets the total weight. When used with unweighted data, returns the number of samples.
            </summary>
            <value>The total weight.</value>
        </member>
        <member name="P:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics.EffectiveSampleSize">
            <summary>
            The Kish's effective sample size https://en.wikipedia.org/wiki/Effective_sample_size
            </summary>
            <value>The Kish's effective sample size.</value>
        </member>
        <member name="M:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics.Compute(System.Collections.Generic.IEnumerable{System.ValueTuple{System.Double,System.Double}})">
            <summary>
            Computes descriptive statistics from a stream of data values and reliability weights.
            </summary>
            <param name="data">A sequence of datapoints.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics.ComputeDecimal(System.Collections.Generic.IEnumerable{System.ValueTuple{System.Double,System.Double}})">
            <summary>
            Computes descriptive statistics from a stream of data values and reliability weights.
            </summary>
            <param name="data">A sequence of datapoints.</param>
        </member>
        <member name="M:MathNet.Numerics.Statistics.WeightedDescriptiveStatistics.SetStatisticsWeighted(System.Double,System.Double,System.Double,System.Double,System.Double,System.Double,System.Int64,System.Double,System.Double,System.Double,System.Double,System.Double)">
            <summary>
            Internal use. Method use for setting the statistics.
            </summary>
            <param name="mean">For setting Mean.</param>
            <param name="variance">For setting Variance.</param>
            <param name="skewness">For setting Skewness.</param>
            <param name="kurtosis">For setting Kurtosis.</param>
            <param name="minimum">For setting Minimum.</param>
            <param name="maximum">For setting Maximum.</param>
            <param name="n">For setting Count.</param>
        </member>
        <member name="T:MathNet.Numerics.Threading.CommonParallel">
            <summary>
            Used to simplify parallel code, particularly between the .NET 4.0 and Silverlight Code.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Threading.CommonParallel.For(System.Int32,System.Int32,System.Action{System.Int32,System.Int32})">
            <summary>
            Executes a for loop in which iterations may run in parallel.
            </summary>
            <param name="fromInclusive">The start index, inclusive.</param>
            <param name="toExclusive">The end index, exclusive.</param>
            <param name="body">The body to be invoked for each iteration range.</param>
        </member>
        <member name="M:MathNet.Numerics.Threading.CommonParallel.For(System.Int32,System.Int32,System.Int32,System.Action{System.Int32,System.Int32})">
            <summary>
            Executes a for loop in which iterations may run in parallel.
            </summary>
            <param name="fromInclusive">The start index, inclusive.</param>
            <param name="toExclusive">The end index, exclusive.</param>
            <param name="rangeSize">The partition size for splitting work into smaller pieces.</param>
            <param name="body">The body to be invoked for each iteration range.</param>
        </member>
        <member name="M:MathNet.Numerics.Threading.CommonParallel.Invoke(System.Action[])">
            <summary>
            Executes each of the provided actions inside a discrete, asynchronous task.
            </summary>
            <param name="actions">An array of actions to execute.</param>
            <exception cref="T:System.ArgumentException">The actions array contains a <c>null</c> element.</exception>
            <exception cref="T:System.AggregateException">At least one invocation of the actions threw an exception.</exception>
        </member>
        <member name="M:MathNet.Numerics.Threading.CommonParallel.Aggregate``1(System.Int32,System.Int32,System.Func{System.Int32,``0},System.Func{``0[],``0})">
            <summary>
            Selects an item (such as Max or Min).
            </summary>
            <param name="fromInclusive">Starting index of the loop.</param>
            <param name="toExclusive">Ending index of the loop</param>
            <param name="select">The function to select items over a subset.</param>
            <param name="reduce">The function to select the item of selection from the subsets.</param>
            <returns>The selected value.</returns>
        </member>
        <member name="M:MathNet.Numerics.Threading.CommonParallel.Aggregate``2(``0[],System.Func{System.Int32,``0,``1},System.Func{``1[],``1})">
            <summary>
            Selects an item (such as Max or Min).
            </summary>
            <param name="array">The array to iterate over.</param>
            <param name="select">The function to select items over a subset.</param>
            <param name="reduce">The function to select the item of selection from the subsets.</param>
            <returns>The selected value.</returns>
        </member>
        <member name="M:MathNet.Numerics.Threading.CommonParallel.Aggregate``1(System.Int32,System.Int32,System.Func{System.Int32,``0},System.Func{``0,``0,``0},``0)">
            <summary>
            Selects an item (such as Max or Min).
            </summary>
            <param name="fromInclusive">Starting index of the loop.</param>
            <param name="toExclusive">Ending index of the loop</param>
            <param name="select">The function to select items over a subset.</param>
            <param name="reducePair">The function to select the item of selection from the subsets.</param>
            <param name="reduceDefault">Default result of the reduce function on an empty set.</param>
            <returns>The selected value.</returns>
        </member>
        <member name="M:MathNet.Numerics.Threading.CommonParallel.Aggregate``2(``0[],System.Func{System.Int32,``0,``1},System.Func{``1,``1,``1},``1)">
            <summary>
            Selects an item (such as Max or Min).
            </summary>
            <param name="array">The array to iterate over.</param>
            <param name="select">The function to select items over a subset.</param>
            <param name="reducePair">The function to select the item of selection from the subsets.</param>
            <param name="reduceDefault">Default result of the reduce function on an empty set.</param>
            <returns>The selected value.</returns>
        </member>
        <member name="T:MathNet.Numerics.Trig">
            <summary>
            Double-precision trigonometry toolkit.
            </summary>
        </member>
        <member name="F:MathNet.Numerics.Trig.DegreeToGradConstant">
            <summary>
            Constant to convert a degree to grad.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Trig.DegreeToGrad(System.Double)">
            <summary>
            Converts a degree (360-periodic) angle to a grad (400-periodic) angle.
            </summary>
            <param name="degree">The degree to convert.</param>
            <returns>The converted grad angle.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.DegreeToRadian(System.Double)">
            <summary>
            Converts a degree (360-periodic) angle to a radian (2*Pi-periodic) angle.
            </summary>
            <param name="degree">The degree to convert.</param>
            <returns>The converted radian angle.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.GradToDegree(System.Double)">
            <summary>
            Converts a grad (400-periodic) angle to a degree (360-periodic) angle.
            </summary>
            <param name="grad">The grad to convert.</param>
            <returns>The converted degree.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.GradToRadian(System.Double)">
            <summary>
            Converts a grad (400-periodic) angle to a radian (2*Pi-periodic) angle.
            </summary>
            <param name="grad">The grad to convert.</param>
            <returns>The converted radian.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.RadianToDegree(System.Double)">
            <summary>
            Converts a radian (2*Pi-periodic) angle to a degree (360-periodic) angle.
            </summary>
            <param name="radian">The radian to convert.</param>
            <returns>The converted degree.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.RadianToGrad(System.Double)">
            <summary>
            Converts a radian (2*Pi-periodic) angle to a grad (400-periodic) angle.
            </summary>
            <param name="radian">The radian to convert.</param>
            <returns>The converted grad.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Sinc(System.Double)">
            <summary>
            Normalized Sinc function. sinc(x) = sin(pi*x)/(pi*x).
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Trig.Sin(System.Double)">
            <summary>
            Trigonometric Sine of an angle in radian, or opposite / hypotenuse.
            </summary>
            <param name="radian">The angle in radian.</param>
            <returns>The sine of the radian angle.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Sin(System.Numerics.Complex)">
            <summary>
            Trigonometric Sine of a <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The sine of the complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Cos(System.Double)">
            <summary>
            Trigonometric Cosine of an angle in radian, or adjacent / hypotenuse.
            </summary>
            <param name="radian">The angle in radian.</param>
            <returns>The cosine of an angle in radian.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Cos(System.Numerics.Complex)">
            <summary>
            Trigonometric Cosine of a <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The cosine of a complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Tan(System.Double)">
            <summary>
            Trigonometric Tangent of an angle in radian, or opposite / adjacent.
            </summary>
            <param name="radian">The angle in radian.</param>
            <returns>The tangent of the radian angle.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Tan(System.Numerics.Complex)">
            <summary>
            Trigonometric Tangent of a <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The tangent of the complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Cot(System.Double)">
            <summary>
            Trigonometric Cotangent of an angle in radian, or adjacent / opposite. Reciprocal of the tangent.
            </summary>
            <param name="radian">The angle in radian.</param>
            <returns>The cotangent of an angle in radian.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Cot(System.Numerics.Complex)">
            <summary>
            Trigonometric Cotangent of a <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The cotangent of the complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Sec(System.Double)">
            <summary>
            Trigonometric Secant of an angle in radian, or hypotenuse / adjacent. Reciprocal of the cosine.
            </summary>
            <param name="radian">The angle in radian.</param>
            <returns>The secant of the radian angle.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Sec(System.Numerics.Complex)">
            <summary>
            Trigonometric Secant of a <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The secant of the complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Csc(System.Double)">
            <summary>
            Trigonometric Cosecant of an angle in radian, or hypotenuse / opposite. Reciprocal of the sine.
            </summary>
            <param name="radian">The angle in radian.</param>
            <returns>Cosecant of an angle in radian.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Csc(System.Numerics.Complex)">
            <summary>
            Trigonometric Cosecant of a <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The cosecant of a complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Asin(System.Double)">
            <summary>
            Trigonometric principal Arc Sine in radian
            </summary>
            <param name="opposite">The opposite for a unit hypotenuse (i.e. opposite / hypotenuse).</param>
            <returns>The angle in radian.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Asin(System.Numerics.Complex)">
            <summary>
            Trigonometric principal Arc Sine of this <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The arc sine of a complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Acos(System.Double)">
            <summary>
            Trigonometric principal Arc Cosine in radian
            </summary>
            <param name="adjacent">The adjacent for a unit hypotenuse (i.e. adjacent / hypotenuse).</param>
            <returns>The angle in radian.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Acos(System.Numerics.Complex)">
            <summary>
            Trigonometric principal Arc Cosine of this <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The arc cosine of a complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Atan(System.Double)">
            <summary>
            Trigonometric principal Arc Tangent  in radian
            </summary>
            <param name="opposite">The opposite for a unit adjacent (i.e. opposite / adjacent).</param>
            <returns>The angle in radian.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Atan(System.Numerics.Complex)">
            <summary>
            Trigonometric principal Arc Tangent of this <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The arc tangent of a complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Acot(System.Double)">
            <summary>
            Trigonometric principal Arc Cotangent in radian
            </summary>
            <param name="adjacent">The adjacent for a unit opposite (i.e. adjacent / opposite).</param>
            <returns>The angle in radian.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Acot(System.Numerics.Complex)">
            <summary>
            Trigonometric principal Arc Cotangent of this <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The arc cotangent of a complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Asec(System.Double)">
            <summary>
            Trigonometric principal Arc Secant in radian
            </summary>
            <param name="hypotenuse">The hypotenuse for a unit adjacent (i.e. hypotenuse / adjacent).</param>
            <returns>The angle in radian.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Asec(System.Numerics.Complex)">
            <summary>
            Trigonometric principal Arc Secant of this <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The arc secant of a complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Acsc(System.Double)">
            <summary>
            Trigonometric principal Arc Cosecant in radian
            </summary>
            <param name="hypotenuse">The hypotenuse for a unit opposite (i.e. hypotenuse / opposite).</param>
            <returns>The angle in radian.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Acsc(System.Numerics.Complex)">
            <summary>
            Trigonometric principal Arc Cosecant of this <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The arc cosecant of a complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Sinh(System.Double)">
            <summary>
            Hyperbolic Sine
            </summary>
            <param name="angle">The hyperbolic angle, i.e. the area of the hyperbolic sector.</param>
            <returns>The hyperbolic sine of the angle.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Sinh(System.Numerics.Complex)">
            <summary>
            Hyperbolic Sine of a <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The hyperbolic sine of a complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Cosh(System.Double)">
            <summary>
            Hyperbolic Cosine
            </summary>
            <param name="angle">The hyperbolic angle, i.e. the area of the hyperbolic sector.</param>
            <returns>The hyperbolic Cosine of the angle.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Cosh(System.Numerics.Complex)">
            <summary>
            Hyperbolic Cosine of a <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The hyperbolic cosine of a complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Tanh(System.Double)">
            <summary>
            Hyperbolic Tangent in radian
            </summary>
            <param name="angle">The hyperbolic angle, i.e. the area of the hyperbolic sector.</param>
            <returns>The hyperbolic tangent of the angle.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Tanh(System.Numerics.Complex)">
            <summary>
            Hyperbolic Tangent of a <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The hyperbolic tangent of a complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Coth(System.Double)">
            <summary>
            Hyperbolic Cotangent
            </summary>
            <param name="angle">The hyperbolic angle, i.e. the area of the hyperbolic sector.</param>
            <returns>The hyperbolic cotangent of the angle.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Coth(System.Numerics.Complex)">
            <summary>
            Hyperbolic Cotangent of a <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The hyperbolic cotangent of a complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Sech(System.Double)">
            <summary>
            Hyperbolic Secant
            </summary>
            <param name="angle">The hyperbolic angle, i.e. the area of the hyperbolic sector.</param>
            <returns>The hyperbolic secant of the angle.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Sech(System.Numerics.Complex)">
            <summary>
            Hyperbolic Secant of a <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The hyperbolic secant of a complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Csch(System.Double)">
            <summary>
            Hyperbolic Cosecant
            </summary>
            <param name="angle">The hyperbolic angle, i.e. the area of the hyperbolic sector.</param>
            <returns>The hyperbolic cosecant of the angle.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Csch(System.Numerics.Complex)">
            <summary>
            Hyperbolic Cosecant of a <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The hyperbolic cosecant of a complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Asinh(System.Double)">
            <summary>
            Hyperbolic Area Sine
            </summary>
            <param name="value">The real value.</param>
            <returns>The hyperbolic angle, i.e. the area of its hyperbolic sector.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Asinh(System.Numerics.Complex)">
            <summary>
            Hyperbolic Area Sine of this <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The hyperbolic arc sine of a complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Acosh(System.Double)">
            <summary>
            Hyperbolic Area Cosine
            </summary>
            <param name="value">The real value.</param>
            <returns>The hyperbolic angle, i.e. the area of its hyperbolic sector.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Acosh(System.Numerics.Complex)">
            <summary>
            Hyperbolic Area Cosine of this <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The hyperbolic arc cosine of a complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Atanh(System.Double)">
            <summary>
            Hyperbolic Area Tangent
            </summary>
            <param name="value">The real value.</param>
            <returns>The hyperbolic angle, i.e. the area of its hyperbolic sector.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Atanh(System.Numerics.Complex)">
            <summary>
            Hyperbolic Area Tangent of this <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The hyperbolic arc tangent of a complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Acoth(System.Double)">
            <summary>
            Hyperbolic Area Cotangent
            </summary>
            <param name="value">The real value.</param>
            <returns>The hyperbolic angle, i.e. the area of its hyperbolic sector.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Acoth(System.Numerics.Complex)">
            <summary>
            Hyperbolic Area Cotangent of this <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The hyperbolic arc cotangent of a complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Asech(System.Double)">
            <summary>
            Hyperbolic Area Secant
            </summary>
            <param name="value">The real value.</param>
            <returns>The hyperbolic angle, i.e. the area of its hyperbolic sector.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Asech(System.Numerics.Complex)">
            <summary>
            Hyperbolic Area Secant of this <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The hyperbolic arc secant of a complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Acsch(System.Double)">
            <summary>
            Hyperbolic Area Cosecant
            </summary>
            <param name="value">The real value.</param>
            <returns>The hyperbolic angle, i.e. the area of its hyperbolic sector.</returns>
        </member>
        <member name="M:MathNet.Numerics.Trig.Acsch(System.Numerics.Complex)">
            <summary>
            Hyperbolic Area Cosecant of this <c>Complex</c> number.
            </summary>
            <param name="value">The complex value.</param>
            <returns>The hyperbolic arc cosecant of a complex number.</returns>
        </member>
        <member name="M:MathNet.Numerics.Window.Hamming(System.Int32)">
            <summary>
            Hamming window. Named after Richard Hamming.
            Symmetric version, useful e.g. for filter design purposes.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Window.HammingPeriodic(System.Int32)">
            <summary>
            Hamming window. Named after Richard Hamming.
            Periodic version, useful e.g. for FFT purposes.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Window.Hann(System.Int32)">
            <summary>
            Hann window. Named after Julius von Hann.
            Symmetric version, useful e.g. for filter design purposes.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Window.HannPeriodic(System.Int32)">
            <summary>
            Hann window. Named after Julius von Hann.
            Periodic version, useful e.g. for FFT purposes.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Window.Cosine(System.Int32)">
            <summary>
            Cosine window.
            Symmetric version, useful e.g. for filter design purposes.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Window.CosinePeriodic(System.Int32)">
            <summary>
            Cosine window.
            Periodic version, useful e.g. for FFT purposes.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Window.Lanczos(System.Int32)">
            <summary>
            Lanczos window.
            Symmetric version, useful e.g. for filter design purposes.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Window.LanczosPeriodic(System.Int32)">
            <summary>
            Lanczos window.
            Periodic version, useful e.g. for FFT purposes.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Window.Gauss(System.Int32,System.Double)">
            <summary>
            Gauss window.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Window.Blackman(System.Int32)">
            <summary>
            Blackman window.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Window.BlackmanHarris(System.Int32)">
            <summary>
            Blackman-Harris window.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Window.BlackmanNuttall(System.Int32)">
            <summary>
            Blackman-Nuttall window.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Window.Bartlett(System.Int32)">
            <summary>
            Bartlett window.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Window.BartlettHann(System.Int32)">
            <summary>
            Bartlett-Hann window.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Window.Nuttall(System.Int32)">
            <summary>
            Nuttall window.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Window.FlatTop(System.Int32)">
            <summary>
            Flat top window.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Window.Dirichlet(System.Int32)">
            <summary>
            Uniform rectangular (Dirichlet) window.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Window.Triangular(System.Int32)">
            <summary>
            Triangular window.
            </summary>
        </member>
        <member name="M:MathNet.Numerics.Window.Tukey(System.Int32,System.Double)">
            <summary>
            Tukey tapering window. A rectangular window bounded
            by half a cosine window on each side.
            </summary>
            <param name="width">Width of the window</param>
            <param name="r">Fraction of the window occupied by the cosine parts</param>
        </member>
    </members>
</doc>
