//
// Math.NET Numerics, part of the Math.NET Project
// http://mathnet.opensourcedotnet.info
// Copyright (c) 2009 Math.NET
// Permission is hereby granted, free of charge, to any person
// obtaining a copy of this software and associated documentation
// files (the "Software"), to deal in the Software without
// restriction, including without limitation the rights to use,
// copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the
// Software is furnished to do so, subject to the following
// conditions:
// The above copyright notice and this permission notice shall be
// included in all copies or substantial portions of the Software.
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// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
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// OTHER DEALINGS IN THE SOFTWARE.
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/* This file is automatically generated - do not modify it.
Change NativeLinearAlgebraProvider.include instead.
Last generated on: 14/11/2009 20:09:22
*/
namespace MathNet.Numerics.Algorithms.LinearAlgebra.Atlas
{
using System;
using Properties;
///
/// The managed linear algebra provider.
///
public class AtlasLinearAlgebraProvider : ILinearAlgebraProvider
{
#region ILinearAlgebraProvider Members
///
/// Adds a scaled vector to another: y += alpha*x.
///
/// The vector to update.
/// The value to scale by.
/// The vector to add to .
/// This equivalent to the AXPY BLAS routine.
public void AddVectorToScaledVector(double[] y, double alpha, double[] x)
{
if (y == null)
{
throw new ArgumentNullException("y");
}
if (x == null)
{
throw new ArgumentNullException("x");
}
if (y.Length != x.Length)
{
throw new ArgumentException(Resources.ArgumentVectorsSameLength);
}
if (alpha == 0.0)
{
return;
}
SafeNativeMethods.d_axpy(y.Length, alpha, x, y);
}
///
/// Scales an array. Can be used to scale a vector and a matrix.
///
/// The scalar.
/// The values to scale.
/// This is equivalent to the SCAL BLAS routine.
public void ScaleArray(double alpha, double[] x)
{
if (alpha == 1.0)
{
return;
}
SafeNativeMethods.d_scale(x.Length, alpha, x);
}
///
/// Queries the provider for the optimal, workspace block size
/// for the given routine.
///
/// Name of the method to query.
///
/// -1 if the provider cannot compute the workspace size; otherwise
/// the suggested block size.
///
public int QueryWorkspaceBlockSize(string methodName)
{
throw new NotImplementedException();
}
///
/// Computes the dot product of x and y.
///
/// The vector x.
/// The vector y.
/// The dot product of x and y.
/// This is equivalent to the DOT BLAS routine.
public double DotProduct(double[] x, double[] y)
{
throw new NotImplementedException();
}
///
/// Does a point wise add of two arrays z = x + y. This can be used
/// to add vectors or matrices.
///
/// The array x.
/// The array y.
/// The result of the addition.
/// There is no equivalent BLAS routine, but many libraries
/// provide optimized (parallel and/or vectorized) versions of this
/// routine.
public void AddArrays(double[] x, double[] y, double[] result)
{
throw new NotImplementedException();
}
///
/// Does a point wise subtraction of two arrays z = x - y. This can be used
/// to subtract vectors or matrices.
///
/// The array x.
/// The array y.
/// The result of the subtraction.
/// There is no equivalent BLAS routine, but many libraries
/// provide optimized (parallel and/or vectorized) versions of this
/// routine.
public void SubtractArrays(double[] x, double[] y, double[] result)
{
throw new NotImplementedException();
}
///
/// Does a point wise multiplication of two arrays z = x * y. This can be used
/// to multiple elements of vectors or matrices.
///
/// The array x.
/// The array y.
/// The result of the point wise multiplication.
/// There is no equivalent BLAS routine, but many libraries
/// provide optimized (parallel and/or vectorized) versions of this
/// routine.
public void PointWiseMultiplyArrays(double[] x, double[] y, double[] result)
{
throw new NotImplementedException();
}
///
/// Computes the requested of the matrix.
///
/// The type of norm to compute.
/// The matrix to compute the norm from.
///
/// The requested of the matrix.
///
public double MatrixNorm(Norm norm, double[] matrix)
{
throw new NotImplementedException();
}
///
/// Computes the requested of the matrix.
///
/// The type of norm to compute.
/// The matrix to compute the norm from.
/// The work array. Only used when
/// and needs to be have a length of at least M (number of rows of .
///
/// The requested of the matrix.
///
public double MatrixNorm(Norm norm, double[] matrix, double[] work)
{
throw new NotImplementedException();
}
///
/// Multiples two matrices. result = x * y
///
/// The x matrix.
/// The number of rows in the x matrix.
/// The number of columns in the x matrix.
/// The y matrix.
/// The number of rows in the y matrix.
/// The number of columns in the y matrix.
/// Where to store the result of the multiplication.
/// 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.
public void MatrixMultiply(double[] x, int xRows, int xColumns, double[] y, int yRows, int yColumns, double[] result)
{
throw new NotImplementedException();
}
///
/// Multiplies two matrices and updates another with the result. c = alpha*op(a)*op(b) + beta*c
///
/// How to transpose the matrix.
/// How to transpose the matrix.
/// The value to scale matrix.
/// The a matrix.
/// The number of rows in the matrix.
/// The number of columns in the matrix.
/// The b matrix
/// The number of rows in the matrix.
/// The number of columns in the matrix.
/// The value to scale the matrix.
/// The c matrix.
public void MatrixMultiplyWithUpdate(Transpose transposeA, Transpose transposeB, double alpha, double[] a,
int aRows, int aColumns, double[] b, int bRows, int bColumns, double beta, double[] c)
{
throw new NotImplementedException();
}
///
/// Computes the LU factorization of A.
///
/// An m by n matrix. The matrix is overwritten with the
/// the LU factorization On exit.
/// On exit, it contains the pivot indices. The size
/// of the array must be min(m,n).
/// This is equivalent to the GETRF LAPACK routine.
public void LUFactor(double[] a, int[] ipiv)
{
throw new NotImplementedException();
}
///
/// Computes the inverse of matrix using LU factorization.
///
/// The N by N matrix to invert. Contains the inverse On exit.
/// This is equivalent to the GETRF and GETRI LAPACK routines.
public void LUInverse(double[] a)
{
throw new NotImplementedException();
}
///
/// Computes the inverse of a previously factored matrix.
///
/// The LU factored N by N matrix. Contains the inverse On exit.
/// The pivot indices of .
/// This is equivalent to the GETRI LAPACK routine.
public void LUInverseFactored(double[] a, int[] ipiv)
{
throw new NotImplementedException();
}
///
/// Computes the inverse of matrix using LU factorization.
///
/// The N by N matrix to invert. Contains the inverse On exit.
/// The work array. The array must have a length of at least N,
/// but should be N*blocksize. The blocksize is machine dependent. Use
/// to determine the optimal size of the work array. On exit, work[0] contains the optimal
/// work size value.
/// This is equivalent to the GETRF and GETRI LAPACK routines.
public void LUInverse(double[] a, double[] work)
{
throw new NotImplementedException();
}
///
/// Computes the inverse of a previously factored matrix.
///
/// The LU factored N by N matrix. Contains the inverse On exit.
/// The pivot indices of .
/// The work array. The array must have a length of at least N,
/// but should be N*blocksize. The blocksize is machine dependent. Use
/// to determine the optimal size of the work array. On exit, work[0] contains the optimal
/// work size value.
/// This is equivalent to the GETRI LAPACK routine.
public void LUInverseFactored(double[] a, int[] ipiv, double[] work)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using LU factorization.
///
/// The number of columns of B.
/// The square matrix A.
/// The B matrix.
/// This is equivalent to the GETRF and GETRS LAPACK routines.
public void LUSolve(int columnsOfB, double[] a, double[] b)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using a previously factored A matrix.
///
/// The number of columns of B.
/// The factored A matrix.
/// The pivot indices of .
/// The B matrix.
/// This is equivalent to the GETRS LAPACK routine.
public void LUSolveFactored(int columnsOfB, double[] a, int ipiv, double[] b)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using LU factorization.
///
/// How to transpose the matrix.
/// The number of columns of B.
/// The square matrix A.
/// The B matrix.
/// This is equivalent to the GETRF and GETRS LAPACK routines.
public void LUSolve(Transpose transposeA, int columnsOfB, double[] a, double[] b)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using a previously factored A matrix.
///
/// How to transpose the matrix.
/// The number of columns of B.
/// The factored A matrix.
/// The pivot indices of .
/// The B matrix.
/// This is equivalent to the GETRS LAPACK routine.
public void LUSolveFactored(Transpose transposeA, int columnsOfB, double[] a, int ipiv, double[] b)
{
throw new NotImplementedException();
}
///
/// Computes the Cholesky factorization of A.
///
/// On entry, a square, positive definite matrix. On exit, the matrix is overwritten with the
/// the Cholesky factorization.
/// This is equivalent to the POTRF LAPACK routine.
public void CholeskyFactor(double[] a)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using Cholesky factorization.
///
/// The number of columns of B.
/// The square, positive definite matrix A.
/// The B matrix.
/// This is equivalent to the POTRF add POTRS LAPACK routines.
public void CholeskySolve(int columnsOfB, double[] a, double[] b)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using a previously factored A matrix.
///
/// The number of columns of B.
/// The factored A matrix.
/// The B matrix.
/// This is equivalent to the POTRS LAPACK routine.
public void CholeskySolveFactored(int columnsOfB, double[] a, double[] b)
{
throw new NotImplementedException();
}
///
/// Computes the QR factorization of 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.
/// On exit, A M by M matrix that holds the Q matrix of the
/// QR factorization.
/// This is similar to the GEQRF and ORGQR LAPACK routines.
public void QRFactor(double[] r, double[] q)
{
throw new NotImplementedException();
}
///
/// Computes the QR factorization of 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.
/// On exit, A M by M matrix that holds the Q matrix of the
/// QR factorization.
/// The work array. The array must have a length of at least N,
/// but should be N*blocksize. The blocksize is machine dependent. Use
/// to determine the optimal size of the work array. On exit, work[0] contains the optimal
/// work size value.
public void QRFactor(double[] r, double[] q, double[] work)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using QR factorization of A.
///
/// The number of columns of B.
/// 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.
/// On exit, A M by M matrix that holds the Q matrix of the
/// QR factorization.
/// The B matrix.
/// On exit, the solution matrix.
public void QRSolve(int columnsOfB, double[] r, double[] q, double[] b, double[] x)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using QR factorization of A.
///
/// The number of columns of B.
/// 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.
/// On exit, A M by M matrix that holds the Q matrix of the
/// QR factorization.
/// The B matrix.
/// On exit, the solution matrix.
/// The work array. The array must have a length of at least N,
/// but should be N*blocksize. The blocksize is machine dependent. Use
/// to determine the optimal size of the work array. On exit, work[0] contains the optimal
/// work size value.
public void QRSolve(int columnsOfB, double[] r, double[] q, double[] b, double[] x, double[] work)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using a previously QR factored matrix.
///
/// The number of columns of B.
/// The Q matrix obtained by calling .
/// The R matrix obtained by calling .
/// The B matrix.
/// On exit, the solution matrix.
public void QRSolveFactored(int columnsOfB, double[] q, double[] r, double[] b, double[] x)
{
throw new NotImplementedException();
}
///
/// Computes the singular value decomposition of A.
///
/// Compute the singular U and VT vectors or not.
/// On entry, the M by N matrix to decompose. On exit, A may be overwritten.
/// The singular values of A in ascending value.
/// If is true, on exit U contains the left
/// singular vectors.
/// If is true, on exit VT contains the transposed
/// right singular vectors.
/// This is equivalent to the GESVD LAPACK routine.
public void SinguarValueDecomposition(bool computeVectors, double[] a, double[] s, double[] u, double[] vt)
{
throw new NotImplementedException();
}
///
/// Computes the singular value decomposition of A.
///
/// Compute the singular U and VT vectors or not.
/// On entry, the M by N matrix to decompose. On exit, A may be overwritten.
/// The singular values of A in ascending value.
/// If is true, on exit U contains the left
/// singular vectors.
/// If is true, on exit VT contains the transposed
/// right singular vectors.
/// The work array. For real matrices, the work array should be at least
/// Max(3*Min(M, N) + Max(M, N), 5*Min(M,N)). For complex matrices, 2*Min(M, N) + Max(M, N).
/// On exit, work[0] contains the optimal work size value.
/// This is equivalent to the GESVD LAPACK routine.
public void SingularValueDecomposition(bool computeVectors, double[] a, double[] s, double[] u, double[] vt, double[] work)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using the singular value decomposition of A.
///
/// On entry, the M by N matrix to decompose. On exit, A may be overwritten.
/// The singular values of A in ascending value.
/// On exit U contains the left singular vectors.
/// On exit VT contains the transposed right singular vectors.
/// The B matrix.
/// On exit, the solution matrix.
public void SvdSolve(double[] a, double[] s, double[] u, double[] vt, double[] b, double[] x)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using the singular value decomposition of A.
///
/// On entry, the M by N matrix to decompose. On exit, A may be overwritten.
/// The singular values of A in ascending value.
/// On exit U contains the left singular vectors.
/// On exit VT contains the transposed right singular vectors.
/// The B matrix.
/// On exit, the solution matrix.
/// The work array. For real matrices, the work array should be at least
/// Max(3*Min(M, N) + Max(M, N), 5*Min(M,N)). For complex matrices, 2*Min(M, N) + Max(M, N).
/// On exit, work[0] contains the optimal work size value.
public void SvdSolve(double[] a, double[] s, double[] u, double[] vt, double[] b, double[] x, double[] work)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using a previously SVD decomposed matrix.
///
/// The number of columns of B.
/// The s values returned by .
/// The left singular vectors returned by .
/// The right singular vectors returned by .
/// The B matrix.
/// On exit, the solution matrix.
public void SvdSolveFactored(int columnsOfB, double[] s, double[] u, double[] vt, double[] b, double[] x)
{
throw new NotImplementedException();
}
#endregion
#region ILinearAlgebraProvider Members
///
/// Adds a scaled vector to another: y += alpha*x.
///
/// The vector to update.
/// The value to scale by.
/// The vector to add to .
/// This equivalent to the AXPY BLAS routine.
public void AddVectorToScaledVector(float[] y, float alpha, float[] x)
{
if (y == null)
{
throw new ArgumentNullException("y");
}
if (x == null)
{
throw new ArgumentNullException("x");
}
if (y.Length != x.Length)
{
throw new ArgumentException(Resources.ArgumentVectorsSameLength);
}
if (alpha == 0.0f)
{
return;
}
SafeNativeMethods.s_axpy(y.Length, alpha, x, y);
}
///
/// Scales an array. Can be used to scale a vector and a matrix.
///
/// The scalar.
/// The values to scale.
/// This is equivalent to the SCAL BLAS routine.
public void ScaleArray(float alpha, float[] x)
{
if (alpha == 1.0)
{
return;
}
SafeNativeMethods.s_scale(x.Length, alpha, x);
}
///
/// Computes the dot product of x and y.
///
/// The vector x.
/// The vector y.
/// The dot product of x and y.
/// This is equivalent to the DOT BLAS routine.
public float DotProduct(float[] x, float[] y)
{
throw new NotImplementedException();
}
///
/// Does a point wise add of two arrays z = x + y. This can be used
/// to add vectors or matrices.
///
/// The array x.
/// The array y.
/// The result of the addition.
/// There is no equivalent BLAS routine, but many libraries
/// provide optimized (parallel and/or vectorized) versions of this
/// routine.
public void AddArrays(float[] x, float[] y, float[] result)
{
throw new NotImplementedException();
}
///
/// Does a point wise subtraction of two arrays z = x - y. This can be used
/// to subtract vectors or matrices.
///
/// The array x.
/// The array y.
/// The result of the subtraction.
/// There is no equivalent BLAS routine, but many libraries
/// provide optimized (parallel and/or vectorized) versions of this
/// routine.
public void SubtractArrays(float[] x, float[] y, float[] result)
{
throw new NotImplementedException();
}
///
/// Does a point wise multiplication of two arrays z = x * y. This can be used
/// to multiple elements of vectors or matrices.
///
/// The array x.
/// The array y.
/// The result of the point wise multiplication.
/// There is no equivalent BLAS routine, but many libraries
/// provide optimized (parallel and/or vectorized) versions of this
/// routine.
public void PointWiseMultiplyArrays(float[] x, float[] y, float[] result)
{
throw new NotImplementedException();
}
///
/// Computes the requested of the matrix.
///
/// The type of norm to compute.
/// The matrix to compute the norm from.
///
/// The requested of the matrix.
///
public float MatrixNorm(Norm norm, float[] matrix)
{
throw new NotImplementedException();
}
///
/// Computes the requested of the matrix.
///
/// The type of norm to compute.
/// The matrix to compute the norm from.
/// The work array. Only used when
/// and needs to be have a length of at least M (number of rows of .
///
/// The requested of the matrix.
///
public float MatrixNorm(Norm norm, float[] matrix, float[] work)
{
throw new NotImplementedException();
}
///
/// Multiples two matrices. result = x * y
///
/// The x matrix.
/// The number of rows in the x matrix.
/// The number of columns in the x matrix.
/// The y matrix.
/// The number of rows in the y matrix.
/// The number of columns in the y matrix.
/// Where to store the result of the multiplication.
/// 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.
public void MatrixMultiply(float[] x, int xRows, int xColumns, float[] y, int yRows, int yColumns, float[] result)
{
throw new NotImplementedException();
}
///
/// Multiplies two matrices and updates another with the result. c = alpha*op(a)*op(b) + beta*c
///
/// How to transpose the matrix.
/// How to transpose the matrix.
/// The value to scale matrix.
/// The a matrix.
/// The number of rows in the matrix.
/// The number of columns in the matrix.
/// The b matrix
/// The number of rows in the matrix.
/// The number of columns in the matrix.
/// The value to scale the matrix.
/// The c matrix.
public void MatrixMultiplyWithUpdate(Transpose transposeA, Transpose transposeB, float alpha, float[] a,
int aRows, int aColumns, float[] b, int bRows, int bColumns, float beta, float[] c)
{
throw new NotImplementedException();
}
///
/// Computes the LU factorization of A.
///
/// An m by n matrix. The matrix is overwritten with the
/// the LU factorization On exit.
/// On exit, it contains the pivot indices. The size
/// of the array must be min(m,n).
/// This is equivalent to the GETRF LAPACK routine.
public void LUFactor(float[] a, int[] ipiv)
{
throw new NotImplementedException();
}
///
/// Computes the inverse of matrix using LU factorization.
///
/// The N by N matrix to invert. Contains the inverse On exit.
/// This is equivalent to the GETRF and GETRI LAPACK routines.
public void LUInverse(float[] a)
{
throw new NotImplementedException();
}
///
/// Computes the inverse of a previously factored matrix.
///
/// The LU factored N by N matrix. Contains the inverse On exit.
/// The pivot indices of .
/// This is equivalent to the GETRI LAPACK routine.
public void LUInverseFactored(float[] a, int[] ipiv)
{
throw new NotImplementedException();
}
///
/// Computes the inverse of matrix using LU factorization.
///
/// The N by N matrix to invert. Contains the inverse On exit.
/// The work array. The array must have a length of at least N,
/// but should be N*blocksize. The blocksize is machine dependent. Use
/// to determine the optimal size of the work array. On exit, work[0] contains the optimal
/// work size value.
/// This is equivalent to the GETRF and GETRI LAPACK routines.
public void LUInverse(float[] a, float[] work)
{
throw new NotImplementedException();
}
///
/// Computes the inverse of a previously factored matrix.
///
/// The LU factored N by N matrix. Contains the inverse On exit.
/// The pivot indices of .
/// The work array. The array must have a length of at least N,
/// but should be N*blocksize. The blocksize is machine dependent. Use
/// to determine the optimal size of the work array. On exit, work[0] contains the optimal
/// work size value.
/// This is equivalent to the GETRI LAPACK routine.
public void LUInverseFactored(float[] a, int[] ipiv, float[] work)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using LU factorization.
///
/// The number of columns of B.
/// The square matrix A.
/// The B matrix.
/// This is equivalent to the GETRF and GETRS LAPACK routines.
public void LUSolve(int columnsOfB, float[] a, float[] b)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using a previously factored A matrix.
///
/// The number of columns of B.
/// The factored A matrix.
/// The pivot indices of .
/// The B matrix.
/// This is equivalent to the GETRS LAPACK routine.
public void LUSolveFactored(int columnsOfB, float[] a, int ipiv, float[] b)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using LU factorization.
///
/// How to transpose the matrix.
/// The number of columns of B.
/// The square matrix A.
/// The B matrix.
/// This is equivalent to the GETRF and GETRS LAPACK routines.
public void LUSolve(Transpose transposeA, int columnsOfB, float[] a, float[] b)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using a previously factored A matrix.
///
/// How to transpose the matrix.
/// The number of columns of B.
/// The factored A matrix.
/// The pivot indices of .
/// The B matrix.
/// This is equivalent to the GETRS LAPACK routine.
public void LUSolveFactored(Transpose transposeA, int columnsOfB, float[] a, int ipiv, float[] b)
{
throw new NotImplementedException();
}
///
/// Computes the Cholesky factorization of A.
///
/// On entry, a square, positive definite matrix. On exit, the matrix is overwritten with the
/// the Cholesky factorization.
/// This is equivalent to the POTRF LAPACK routine.
public void CholeskyFactor(float[] a)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using Cholesky factorization.
///
/// The number of columns of B.
/// The square, positive definite matrix A.
/// The B matrix.
/// This is equivalent to the POTRF add POTRS LAPACK routines.
public void CholeskySolve(int columnsOfB, float[] a, float[] b)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using a previously factored A matrix.
///
/// The number of columns of B.
/// The factored A matrix.
/// The B matrix.
/// This is equivalent to the POTRS LAPACK routine.
public void CholeskySolveFactored(int columnsOfB, float[] a, float[] b)
{
throw new NotImplementedException();
}
///
/// Computes the QR factorization of 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.
/// On exit, A M by M matrix that holds the Q matrix of the
/// QR factorization.
/// This is similar to the GEQRF and ORGQR LAPACK routines.
public void QRFactor(float[] r, float[] q)
{
throw new NotImplementedException();
}
///
/// Computes the QR factorization of 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.
/// On exit, A M by M matrix that holds the Q matrix of the
/// QR factorization.
/// The work array. The array must have a length of at least N,
/// but should be N*blocksize. The blocksize is machine dependent. Use
/// to determine the optimal size of the work array. On exit, work[0] contains the optimal
/// work size value.
public void QRFactor(float[] r, float[] q, float[] work)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using QR factorization of A.
///
/// The number of columns of B.
/// 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.
/// On exit, A M by M matrix that holds the Q matrix of the
/// QR factorization.
/// The B matrix.
/// On exit, the solution matrix.
public void QRSolve(int columnsOfB, float[] r, float[] q, float[] b, float[] x)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using QR factorization of A.
///
/// The number of columns of B.
/// 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.
/// On exit, A M by M matrix that holds the Q matrix of the
/// QR factorization.
/// The B matrix.
/// On exit, the solution matrix.
/// The work array. The array must have a length of at least N,
/// but should be N*blocksize. The blocksize is machine dependent. Use
/// to determine the optimal size of the work array. On exit, work[0] contains the optimal
/// work size value.
public void QRSolve(int columnsOfB, float[] r, float[] q, float[] b, float[] x, float[] work)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using a previously QR factored matrix.
///
/// The number of columns of B.
/// The Q matrix obtained by calling .
/// The R matrix obtained by calling .
/// The B matrix.
/// On exit, the solution matrix.
public void QRSolveFactored(int columnsOfB, float[] q, float[] r, float[] b, float[] x)
{
throw new NotImplementedException();
}
///
/// Computes the singular value decomposition of A.
///
/// Compute the singular U and VT vectors or not.
/// On entry, the M by N matrix to decompose. On exit, A may be overwritten.
/// The singular values of A in ascending value.
/// If is true, on exit U contains the left
/// singular vectors.
/// If is true, on exit VT contains the transposed
/// right singular vectors.
/// This is equivalent to the GESVD LAPACK routine.
public void SinguarValueDecomposition(bool computeVectors, float[] a, float[] s, float[] u, float[] vt)
{
throw new NotImplementedException();
}
///
/// Computes the singular value decomposition of A.
///
/// Compute the singular U and VT vectors or not.
/// On entry, the M by N matrix to decompose. On exit, A may be overwritten.
/// The singular values of A in ascending value.
/// If is true, on exit U contains the left
/// singular vectors.
/// If is true, on exit VT contains the transposed
/// right singular vectors.
/// The work array. For real matrices, the work array should be at least
/// Max(3*Min(M, N) + Max(M, N), 5*Min(M,N)). For complex matrices, 2*Min(M, N) + Max(M, N).
/// On exit, work[0] contains the optimal work size value.
/// This is equivalent to the GESVD LAPACK routine.
public void SingularValueDecomposition(bool computeVectors, float[] a, float[] s, float[] u, float[] vt, float[] work)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using the singular value decomposition of A.
///
/// On entry, the M by N matrix to decompose. On exit, A may be overwritten.
/// The singular values of A in ascending value.
/// On exit U contains the left singular vectors.
/// On exit VT contains the transposed right singular vectors.
/// The B matrix.
/// On exit, the solution matrix.
public void SvdSolve(float[] a, float[] s, float[] u, float[] vt, float[] b, float[] x)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using the singular value decomposition of A.
///
/// On entry, the M by N matrix to decompose. On exit, A may be overwritten.
/// The singular values of A in ascending value.
/// On exit U contains the left singular vectors.
/// On exit VT contains the transposed right singular vectors.
/// The B matrix.
/// On exit, the solution matrix.
/// The work array. For real matrices, the work array should be at least
/// Max(3*Min(M, N) + Max(M, N), 5*Min(M,N)). For complex matrices, 2*Min(M, N) + Max(M, N).
/// On exit, work[0] contains the optimal work size value.
public void SvdSolve(float[] a, float[] s, float[] u, float[] vt, float[] b, float[] x, float[] work)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using a previously SVD decomposed matrix.
///
/// The number of columns of B.
/// The s values returned by .
/// The left singular vectors returned by .
/// The right singular vectors returned by .
/// The B matrix.
/// On exit, the solution matrix.
public void SvdSolveFactored(int columnsOfB, float[] s, float[] u, float[] vt, float[] b, float[] x)
{
throw new NotImplementedException();
}
#endregion
#region ILinearAlgebraProvider Members
///
/// Adds a scaled vector to another: y += alpha*x.
///
/// The vector to update.
/// The value to scale by.
/// The vector to add to .
/// This equivalent to the AXPY BLAS routine.
public void AddVectorToScaledVector(Complex[] y, Complex alpha, Complex[] x)
{
if (y == null)
{
throw new ArgumentNullException("y");
}
if (x == null)
{
throw new ArgumentNullException("x");
}
if (y.Length != x.Length)
{
throw new ArgumentException(Resources.ArgumentVectorsSameLength);
}
if (alpha.IsZero)
{
return;
}
SafeNativeMethods.z_axpy(y.Length, ref alpha, x, y);
}
///
/// Scales an array. Can be used to scale a vector and a matrix.
///
/// The scalar.
/// The values to scale.
/// This is equivalent to the SCAL BLAS routine.
public void ScaleArray(Complex alpha, Complex[] x)
{
if (alpha.IsOne)
{
return;
}
SafeNativeMethods.z_scale(x.Length, ref alpha, x);
}
///
/// Computes the dot product of x and y.
///
/// The vector x.
/// The vector y.
/// The dot product of x and y.
/// This is equivalent to the DOT BLAS routine.
public Complex DotProduct(Complex[] x, Complex[] y)
{
throw new NotImplementedException();
}
///
/// Does a point wise add of two arrays z = x + y. This can be used
/// to add vectors or matrices.
///
/// The array x.
/// The array y.
/// The result of the addition.
/// There is no equivalent BLAS routine, but many libraries
/// provide optimized (parallel and/or vectorized) versions of this
/// routine.
public void AddArrays(Complex[] x, Complex[] y, Complex[] result)
{
throw new NotImplementedException();
}
///
/// Does a point wise subtraction of two arrays z = x - y. This can be used
/// to subtract vectors or matrices.
///
/// The array x.
/// The array y.
/// The result of the subtraction.
/// There is no equivalent BLAS routine, but many libraries
/// provide optimized (parallel and/or vectorized) versions of this
/// routine.
public void SubtractArrays(Complex[] x, Complex[] y, Complex[] result)
{
throw new NotImplementedException();
}
///
/// Does a point wise multiplication of two arrays z = x * y. This can be used
/// to multiple elements of vectors or matrices.
///
/// The array x.
/// The array y.
/// The result of the point wise multiplication.
/// There is no equivalent BLAS routine, but many libraries
/// provide optimized (parallel and/or vectorized) versions of this
/// routine.
public void PointWiseMultiplyArrays(Complex[] x, Complex[] y, Complex[] result)
{
throw new NotImplementedException();
}
///
/// Computes the requested of the matrix.
///
/// The type of norm to compute.
/// The matrix to compute the norm from.
///
/// The requested of the matrix.
///
public Complex MatrixNorm(Norm norm, Complex[] matrix)
{
throw new NotImplementedException();
}
///
/// Computes the requested of the matrix.
///
/// The type of norm to compute.
/// The matrix to compute the norm from.
/// The work array. Only used when
/// and needs to be have a length of at least M (number of rows of .
///
/// The requested of the matrix.
///
public Complex MatrixNorm(Norm norm, Complex[] matrix, Complex[] work)
{
throw new NotImplementedException();
}
///
/// Multiples two matrices. result = x * y
///
/// The x matrix.
/// The number of rows in the x matrix.
/// The number of columns in the x matrix.
/// The y matrix.
/// The number of rows in the y matrix.
/// The number of columns in the y matrix.
/// Where to store the result of the multiplication.
/// 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.
public void MatrixMultiply(Complex[] x, int xRows, int xColumns, Complex[] y, int yRows, int yColumns, Complex[] result)
{
throw new NotImplementedException();
}
///
/// Multiplies two matrices and updates another with the result. c = alpha*op(a)*op(b) + beta*c
///
/// How to transpose the matrix.
/// How to transpose the matrix.
/// The value to scale matrix.
/// The a matrix.
/// The number of rows in the matrix.
/// The number of columns in the matrix.
/// The b matrix
/// The number of rows in the matrix.
/// The number of columns in the matrix.
/// The value to scale the matrix.
/// The c matrix.
public void MatrixMultiplyWithUpdate(Transpose transposeA, Transpose transposeB, Complex alpha, Complex[] a,
int aRows, int aColumns, Complex[] b, int bRows, int bColumns, Complex beta, Complex[] c)
{
throw new NotImplementedException();
}
///
/// Computes the LU factorization of A.
///
/// An m by n matrix. The matrix is overwritten with the
/// the LU factorization On exit.
/// On exit, it contains the pivot indices. The size
/// of the array must be min(m,n).
/// This is equivalent to the GETRF LAPACK routine.
public void LUFactor(Complex[] a, int[] ipiv)
{
throw new NotImplementedException();
}
///
/// Computes the inverse of matrix using LU factorization.
///
/// The N by N matrix to invert. Contains the inverse On exit.
/// This is equivalent to the GETRF and GETRI LAPACK routines.
public void LUInverse(Complex[] a)
{
throw new NotImplementedException();
}
///
/// Computes the inverse of a previously factored matrix.
///
/// The LU factored N by N matrix. Contains the inverse On exit.
/// The pivot indices of .
/// This is equivalent to the GETRI LAPACK routine.
public void LUInverseFactored(Complex[] a, int[] ipiv)
{
throw new NotImplementedException();
}
///
/// Computes the inverse of matrix using LU factorization.
///
/// The N by N matrix to invert. Contains the inverse On exit.
/// The work array. The array must have a length of at least N,
/// but should be N*blocksize. The blocksize is machine dependent. Use
/// to determine the optimal size of the work array. On exit, work[0] contains the optimal
/// work size value.
/// This is equivalent to the GETRF and GETRI LAPACK routines.
public void LUInverse(Complex[] a, Complex[] work)
{
throw new NotImplementedException();
}
///
/// Computes the inverse of a previously factored matrix.
///
/// The LU factored N by N matrix. Contains the inverse On exit.
/// The pivot indices of .
/// The work array. The array must have a length of at least N,
/// but should be N*blocksize. The blocksize is machine dependent. Use
/// to determine the optimal size of the work array. On exit, work[0] contains the optimal
/// work size value.
/// This is equivalent to the GETRI LAPACK routine.
public void LUInverseFactored(Complex[] a, int[] ipiv, Complex[] work)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using LU factorization.
///
/// The number of columns of B.
/// The square matrix A.
/// The B matrix.
/// This is equivalent to the GETRF and GETRS LAPACK routines.
public void LUSolve(int columnsOfB, Complex[] a, Complex[] b)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using a previously factored A matrix.
///
/// The number of columns of B.
/// The factored A matrix.
/// The pivot indices of .
/// The B matrix.
/// This is equivalent to the GETRS LAPACK routine.
public void LUSolveFactored(int columnsOfB, Complex[] a, int ipiv, Complex[] b)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using LU factorization.
///
/// How to transpose the matrix.
/// The number of columns of B.
/// The square matrix A.
/// The B matrix.
/// This is equivalent to the GETRF and GETRS LAPACK routines.
public void LUSolve(Transpose transposeA, int columnsOfB, Complex[] a, Complex[] b)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using a previously factored A matrix.
///
/// How to transpose the matrix.
/// The number of columns of B.
/// The factored A matrix.
/// The pivot indices of .
/// The B matrix.
/// This is equivalent to the GETRS LAPACK routine.
public void LUSolveFactored(Transpose transposeA, int columnsOfB, Complex[] a, int ipiv, Complex[] b)
{
throw new NotImplementedException();
}
///
/// Computes the Cholesky factorization of A.
///
/// On entry, a square, positive definite matrix. On exit, the matrix is overwritten with the
/// the Cholesky factorization.
/// This is equivalent to the POTRF LAPACK routine.
public void CholeskyFactor(Complex[] a)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using Cholesky factorization.
///
/// The number of columns of B.
/// The square, positive definite matrix A.
/// The B matrix.
/// This is equivalent to the POTRF add POTRS LAPACK routines.
public void CholeskySolve(int columnsOfB, Complex[] a, Complex[] b)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using a previously factored A matrix.
///
/// The number of columns of B.
/// The factored A matrix.
/// The B matrix.
/// This is equivalent to the POTRS LAPACK routine.
public void CholeskySolveFactored(int columnsOfB, Complex[] a, Complex[] b)
{
throw new NotImplementedException();
}
///
/// Computes the QR factorization of 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.
/// On exit, A M by M matrix that holds the Q matrix of the
/// QR factorization.
/// This is similar to the GEQRF and ORGQR LAPACK routines.
public void QRFactor(Complex[] r, Complex[] q)
{
throw new NotImplementedException();
}
///
/// Computes the QR factorization of 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.
/// On exit, A M by M matrix that holds the Q matrix of the
/// QR factorization.
/// The work array. The array must have a length of at least N,
/// but should be N*blocksize. The blocksize is machine dependent. Use
/// to determine the optimal size of the work array. On exit, work[0] contains the optimal
/// work size value.
public void QRFactor(Complex[] r, Complex[] q, Complex[] work)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using QR factorization of A.
///
/// The number of columns of B.
/// 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.
/// On exit, A M by M matrix that holds the Q matrix of the
/// QR factorization.
/// The B matrix.
/// On exit, the solution matrix.
public void QRSolve(int columnsOfB, Complex[] r, Complex[] q, Complex[] b, Complex[] x)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using QR factorization of A.
///
/// The number of columns of B.
/// 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.
/// On exit, A M by M matrix that holds the Q matrix of the
/// QR factorization.
/// The B matrix.
/// On exit, the solution matrix.
/// The work array. The array must have a length of at least N,
/// but should be N*blocksize. The blocksize is machine dependent. Use
/// to determine the optimal size of the work array. On exit, work[0] contains the optimal
/// work size value.
public void QRSolve(int columnsOfB, Complex[] r, Complex[] q, Complex[] b, Complex[] x, Complex[] work)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using a previously QR factored matrix.
///
/// The number of columns of B.
/// The Q matrix obtained by calling .
/// The R matrix obtained by calling .
/// The B matrix.
/// On exit, the solution matrix.
public void QRSolveFactored(int columnsOfB, Complex[] q, Complex[] r, Complex[] b, Complex[] x)
{
throw new NotImplementedException();
}
///
/// Computes the singular value decomposition of A.
///
/// Compute the singular U and VT vectors or not.
/// On entry, the M by N matrix to decompose. On exit, A may be overwritten.
/// The singular values of A in ascending value.
/// If is true, on exit U contains the left
/// singular vectors.
/// If is true, on exit VT contains the transposed
/// right singular vectors.
/// This is equivalent to the GESVD LAPACK routine.
public void SinguarValueDecomposition(bool computeVectors, Complex[] a, Complex[] s, Complex[] u, Complex[] vt)
{
throw new NotImplementedException();
}
///
/// Computes the singular value decomposition of A.
///
/// Compute the singular U and VT vectors or not.
/// On entry, the M by N matrix to decompose. On exit, A may be overwritten.
/// The singular values of A in ascending value.
/// If is true, on exit U contains the left
/// singular vectors.
/// If is true, on exit VT contains the transposed
/// right singular vectors.
/// The work array. For real matrices, the work array should be at least
/// Max(3*Min(M, N) + Max(M, N), 5*Min(M,N)). For complex matrices, 2*Min(M, N) + Max(M, N).
/// On exit, work[0] contains the optimal work size value.
/// This is equivalent to the GESVD LAPACK routine.
public void SingularValueDecomposition(bool computeVectors, Complex[] a, Complex[] s, Complex[] u, Complex[] vt, Complex[] work)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using the singular value decomposition of A.
///
/// On entry, the M by N matrix to decompose. On exit, A may be overwritten.
/// The singular values of A in ascending value.
/// On exit U contains the left singular vectors.
/// On exit VT contains the transposed right singular vectors.
/// The B matrix.
/// On exit, the solution matrix.
public void SvdSolve(Complex[] a, Complex[] s, Complex[] u, Complex[] vt, Complex[] b, Complex[] x)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using the singular value decomposition of A.
///
/// On entry, the M by N matrix to decompose. On exit, A may be overwritten.
/// The singular values of A in ascending value.
/// On exit U contains the left singular vectors.
/// On exit VT contains the transposed right singular vectors.
/// The B matrix.
/// On exit, the solution matrix.
/// The work array. For real matrices, the work array should be at least
/// Max(3*Min(M, N) + Max(M, N), 5*Min(M,N)). For complex matrices, 2*Min(M, N) + Max(M, N).
/// On exit, work[0] contains the optimal work size value.
public void SvdSolve(Complex[] a, Complex[] s, Complex[] u, Complex[] vt, Complex[] b, Complex[] x, Complex[] work)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using a previously SVD decomposed matrix.
///
/// The number of columns of B.
/// The s values returned by .
/// The left singular vectors returned by .
/// The right singular vectors returned by .
/// The B matrix.
/// On exit, the solution matrix.
public void SvdSolveFactored(int columnsOfB, Complex[] s, Complex[] u, Complex[] vt, Complex[] b, Complex[] x)
{
throw new NotImplementedException();
}
#endregion
#region ILinearAlgebraProvider Members
///
/// Adds a scaled vector to another: y += alpha*x.
///
/// The vector to update.
/// The value to scale by.
/// The vector to add to .
/// This equivalent to the AXPY BLAS routine.
public void AddVectorToScaledVector(Complex32[] y, Complex32 alpha, Complex32[] x)
{
if (y == null)
{
throw new ArgumentNullException("y");
}
if (x == null)
{
throw new ArgumentNullException("x");
}
if (y.Length != x.Length)
{
throw new ArgumentException(Resources.ArgumentVectorsSameLength);
}
if (alpha.IsZero)
{
return;
}
SafeNativeMethods.c_axpy(y.Length, ref alpha, x, y);
}
///
/// Scales an array. Can be used to scale a vector and a matrix.
///
/// The scalar.
/// The values to scale.
/// This is equivalent to the SCAL BLAS routine.
public void ScaleArray(Complex32 alpha, Complex32[] x)
{
if (alpha.IsOne)
{
return;
}
SafeNativeMethods.c_scale(x.Length, ref alpha, x);
}
///
/// Computes the dot product of x and y.
///
/// The vector x.
/// The vector y.
/// The dot product of x and y.
/// This is equivalent to the DOT BLAS routine.
public Complex32 DotProduct(Complex32[] x, Complex32[] y)
{
throw new NotImplementedException();
}
///
/// Does a point wise add of two arrays z = x + y. This can be used
/// to add vectors or matrices.
///
/// The array x.
/// The array y.
/// The result of the addition.
/// There is no equivalent BLAS routine, but many libraries
/// provide optimized (parallel and/or vectorized) versions of this
/// routine.
public void AddArrays(Complex32[] x, Complex32[] y, Complex32[] result)
{
throw new NotImplementedException();
}
///
/// Does a point wise subtraction of two arrays z = x - y. This can be used
/// to subtract vectors or matrices.
///
/// The array x.
/// The array y.
/// The result of the subtraction.
/// There is no equivalent BLAS routine, but many libraries
/// provide optimized (parallel and/or vectorized) versions of this
/// routine.
public void SubtractArrays(Complex32[] x, Complex32[] y, Complex32[] result)
{
throw new NotImplementedException();
}
///
/// Does a point wise multiplication of two arrays z = x * y. This can be used
/// to multiple elements of vectors or matrices.
///
/// The array x.
/// The array y.
/// The result of the point wise multiplication.
/// There is no equivalent BLAS routine, but many libraries
/// provide optimized (parallel and/or vectorized) versions of this
/// routine.
public void PointWiseMultiplyArrays(Complex32[] x, Complex32[] y, Complex32[] result)
{
throw new NotImplementedException();
}
///
/// Computes the requested of the matrix.
///
/// The type of norm to compute.
/// The matrix to compute the norm from.
///
/// The requested of the matrix.
///
public Complex32 MatrixNorm(Norm norm, Complex32[] matrix)
{
throw new NotImplementedException();
}
///
/// Computes the requested of the matrix.
///
/// The type of norm to compute.
/// The matrix to compute the norm from.
/// The work array. Only used when
/// and needs to be have a length of at least M (number of rows of .
///
/// The requested of the matrix.
///
public Complex32 MatrixNorm(Norm norm, Complex32[] matrix, Complex32[] work)
{
throw new NotImplementedException();
}
///
/// Multiples two matrices. result = x * y
///
/// The x matrix.
/// The number of rows in the x matrix.
/// The number of columns in the x matrix.
/// The y matrix.
/// The number of rows in the y matrix.
/// The number of columns in the y matrix.
/// Where to store the result of the multiplication.
/// 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.
public void MatrixMultiply(Complex32[] x, int xRows, int xColumns, Complex32[] y, int yRows, int yColumns, Complex32[] result)
{
throw new NotImplementedException();
}
///
/// Multiplies two matrices and updates another with the result. c = alpha*op(a)*op(b) + beta*c
///
/// How to transpose the matrix.
/// How to transpose the matrix.
/// The value to scale matrix.
/// The a matrix.
/// The number of rows in the matrix.
/// The number of columns in the matrix.
/// The b matrix
/// The number of rows in the matrix.
/// The number of columns in the matrix.
/// The value to scale the matrix.
/// The c matrix.
public void MatrixMultiplyWithUpdate(Transpose transposeA, Transpose transposeB, Complex32 alpha, Complex32[] a,
int aRows, int aColumns, Complex32[] b, int bRows, int bColumns, Complex32 beta, Complex32[] c)
{
throw new NotImplementedException();
}
///
/// Computes the LU factorization of A.
///
/// An m by n matrix. The matrix is overwritten with the
/// the LU factorization On exit.
/// On exit, it contains the pivot indices. The size
/// of the array must be min(m,n).
/// This is equivalent to the GETRF LAPACK routine.
public void LUFactor(Complex32[] a, int[] ipiv)
{
throw new NotImplementedException();
}
///
/// Computes the inverse of matrix using LU factorization.
///
/// The N by N matrix to invert. Contains the inverse On exit.
/// This is equivalent to the GETRF and GETRI LAPACK routines.
public void LUInverse(Complex32[] a)
{
throw new NotImplementedException();
}
///
/// Computes the inverse of a previously factored matrix.
///
/// The LU factored N by N matrix. Contains the inverse On exit.
/// The pivot indices of .
/// This is equivalent to the GETRI LAPACK routine.
public void LUInverseFactored(Complex32[] a, int[] ipiv)
{
throw new NotImplementedException();
}
///
/// Computes the inverse of matrix using LU factorization.
///
/// The N by N matrix to invert. Contains the inverse On exit.
/// The work array. The array must have a length of at least N,
/// but should be N*blocksize. The blocksize is machine dependent. Use
/// to determine the optimal size of the work array. On exit, work[0] contains the optimal
/// work size value.
/// This is equivalent to the GETRF and GETRI LAPACK routines.
public void LUInverse(Complex32[] a, Complex32[] work)
{
throw new NotImplementedException();
}
///
/// Computes the inverse of a previously factored matrix.
///
/// The LU factored N by N matrix. Contains the inverse On exit.
/// The pivot indices of .
/// The work array. The array must have a length of at least N,
/// but should be N*blocksize. The blocksize is machine dependent. Use
/// to determine the optimal size of the work array. On exit, work[0] contains the optimal
/// work size value.
/// This is equivalent to the GETRI LAPACK routine.
public void LUInverseFactored(Complex32[] a, int[] ipiv, Complex32[] work)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using LU factorization.
///
/// The number of columns of B.
/// The square matrix A.
/// The B matrix.
/// This is equivalent to the GETRF and GETRS LAPACK routines.
public void LUSolve(int columnsOfB, Complex32[] a, Complex32[] b)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using a previously factored A matrix.
///
/// The number of columns of B.
/// The factored A matrix.
/// The pivot indices of .
/// The B matrix.
/// This is equivalent to the GETRS LAPACK routine.
public void LUSolveFactored(int columnsOfB, Complex32[] a, int ipiv, Complex32[] b)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using LU factorization.
///
/// How to transpose the matrix.
/// The number of columns of B.
/// The square matrix A.
/// The B matrix.
/// This is equivalent to the GETRF and GETRS LAPACK routines.
public void LUSolve(Transpose transposeA, int columnsOfB, Complex32[] a, Complex32[] b)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using a previously factored A matrix.
///
/// How to transpose the matrix.
/// The number of columns of B.
/// The factored A matrix.
/// The pivot indices of .
/// The B matrix.
/// This is equivalent to the GETRS LAPACK routine.
public void LUSolveFactored(Transpose transposeA, int columnsOfB, Complex32[] a, int ipiv, Complex32[] b)
{
throw new NotImplementedException();
}
///
/// Computes the Cholesky factorization of A.
///
/// On entry, a square, positive definite matrix. On exit, the matrix is overwritten with the
/// the Cholesky factorization.
/// This is equivalent to the POTRF LAPACK routine.
public void CholeskyFactor(Complex32[] a)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using Cholesky factorization.
///
/// The number of columns of B.
/// The square, positive definite matrix A.
/// The B matrix.
/// This is equivalent to the POTRF add POTRS LAPACK routines.
public void CholeskySolve(int columnsOfB, Complex32[] a, Complex32[] b)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using a previously factored A matrix.
///
/// The number of columns of B.
/// The factored A matrix.
/// The B matrix.
/// This is equivalent to the POTRS LAPACK routine.
public void CholeskySolveFactored(int columnsOfB, Complex32[] a, Complex32[] b)
{
throw new NotImplementedException();
}
///
/// Computes the QR factorization of 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.
/// On exit, A M by M matrix that holds the Q matrix of the
/// QR factorization.
/// This is similar to the GEQRF and ORGQR LAPACK routines.
public void QRFactor(Complex32[] r, Complex32[] q)
{
throw new NotImplementedException();
}
///
/// Computes the QR factorization of 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.
/// On exit, A M by M matrix that holds the Q matrix of the
/// QR factorization.
/// The work array. The array must have a length of at least N,
/// but should be N*blocksize. The blocksize is machine dependent. Use
/// to determine the optimal size of the work array. On exit, work[0] contains the optimal
/// work size value.
public void QRFactor(Complex32[] r, Complex32[] q, Complex32[] work)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using QR factorization of A.
///
/// The number of columns of B.
/// 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.
/// On exit, A M by M matrix that holds the Q matrix of the
/// QR factorization.
/// The B matrix.
/// On exit, the solution matrix.
public void QRSolve(int columnsOfB, Complex32[] r, Complex32[] q, Complex32[] b, Complex32[] x)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using QR factorization of A.
///
/// The number of columns of B.
/// 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.
/// On exit, A M by M matrix that holds the Q matrix of the
/// QR factorization.
/// The B matrix.
/// On exit, the solution matrix.
/// The work array. The array must have a length of at least N,
/// but should be N*blocksize. The blocksize is machine dependent. Use
/// to determine the optimal size of the work array. On exit, work[0] contains the optimal
/// work size value.
public void QRSolve(int columnsOfB, Complex32[] r, Complex32[] q, Complex32[] b, Complex32[] x, Complex32[] work)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using a previously QR factored matrix.
///
/// The number of columns of B.
/// The Q matrix obtained by calling .
/// The R matrix obtained by calling .
/// The B matrix.
/// On exit, the solution matrix.
public void QRSolveFactored(int columnsOfB, Complex32[] q, Complex32[] r, Complex32[] b, Complex32[] x)
{
throw new NotImplementedException();
}
///
/// Computes the singular value decomposition of A.
///
/// Compute the singular U and VT vectors or not.
/// On entry, the M by N matrix to decompose. On exit, A may be overwritten.
/// The singular values of A in ascending value.
/// If is true, on exit U contains the left
/// singular vectors.
/// If is true, on exit VT contains the transposed
/// right singular vectors.
/// This is equivalent to the GESVD LAPACK routine.
public void SinguarValueDecomposition(bool computeVectors, Complex32[] a, Complex32[] s, Complex32[] u, Complex32[] vt)
{
throw new NotImplementedException();
}
///
/// Computes the singular value decomposition of A.
///
/// Compute the singular U and VT vectors or not.
/// On entry, the M by N matrix to decompose. On exit, A may be overwritten.
/// The singular values of A in ascending value.
/// If is true, on exit U contains the left
/// singular vectors.
/// If is true, on exit VT contains the transposed
/// right singular vectors.
/// The work array. For real matrices, the work array should be at least
/// Max(3*Min(M, N) + Max(M, N), 5*Min(M,N)). For complex matrices, 2*Min(M, N) + Max(M, N).
/// On exit, work[0] contains the optimal work size value.
/// This is equivalent to the GESVD LAPACK routine.
public void SingularValueDecomposition(bool computeVectors, Complex32[] a, Complex32[] s, Complex32[] u, Complex32[] vt, Complex32[] work)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using the singular value decomposition of A.
///
/// On entry, the M by N matrix to decompose. On exit, A may be overwritten.
/// The singular values of A in ascending value.
/// On exit U contains the left singular vectors.
/// On exit VT contains the transposed right singular vectors.
/// The B matrix.
/// On exit, the solution matrix.
public void SvdSolve(Complex32[] a, Complex32[] s, Complex32[] u, Complex32[] vt, Complex32[] b, Complex32[] x)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using the singular value decomposition of A.
///
/// On entry, the M by N matrix to decompose. On exit, A may be overwritten.
/// The singular values of A in ascending value.
/// On exit U contains the left singular vectors.
/// On exit VT contains the transposed right singular vectors.
/// The B matrix.
/// On exit, the solution matrix.
/// The work array. For real matrices, the work array should be at least
/// Max(3*Min(M, N) + Max(M, N), 5*Min(M,N)). For complex matrices, 2*Min(M, N) + Max(M, N).
/// On exit, work[0] contains the optimal work size value.
public void SvdSolve(Complex32[] a, Complex32[] s, Complex32[] u, Complex32[] vt, Complex32[] b, Complex32[] x, Complex32[] work)
{
throw new NotImplementedException();
}
///
/// Solves A*X=B for X using a previously SVD decomposed matrix.
///
/// The number of columns of B.
/// The s values returned by .
/// The left singular vectors returned by .
/// The right singular vectors returned by .
/// The B matrix.
/// On exit, the solution matrix.
public void SvdSolveFactored(int columnsOfB, Complex32[] s, Complex32[] u, Complex32[] vt, Complex32[] b, Complex32[] x)
{
throw new NotImplementedException();
}
#endregion
}
}