Class CompressedSparseRow stores general sparse matrix data in compressed row format.

Class DoubleBandFact represents the factorization of a banded matrix of double-precision floating point numbers.

Class DoubleBandMatrix represents a banded matrix of double-precision floating point values. A banded matrix is a matrix that has all its non-zero entries near the diagonal.

Class DoubleBisquareWeightingFunction implements the bisquare weighting function for Iteratively Reweighted Least Squares (IRLS).

Class DoubleCholeskyLeastSq solves least square problems by using the Cholesky factorization to solve the normal equations.

Class DoubleComplexBandFact represents the factorization of a banded matrix of complex double-precision floating point numbers.

Class DoubleComplexBandMatrix represents a banded matrix of double-precision complex numbers. A banded matrix is a matrix that has all its non-zero entries near the diagonal.

Class DoubleComplexCholeskyLeastSq solves least square problems by using the Cholesky factorization to solve the normal equations.

Class DoubleComplexCsrSparseMatrix stores a general sparse matrix using Compressed Row (CSR) Storage format.

Class DoubleComplexEigDecomp computes the eigenvalues and left and right eigenvectors of a general matrix, with preliminary balancing.

Class DoubleComplexEigDecompServer creates eigenvalue decompositions. A server instance can be configured to perform preliminary balancing, and to compute left eigenvectors, right eigenvectors, both, or neither.

Class DoubleComplexGSVDecomp computes the generalized singular value decomposition (GSVD) of a pair of general rectangular matrices.

Class for serving up generalized singular value decompositions (GSVD) in the form of DoubleComplexGSVDecomp instances.

Class DoubleComplexLowerTriMatrix represents a lower triangular matrix of double-precision complex numbers. A lower triangular matrix is a square matrix with all elements above the main diagonal equal to zero.

Class DoubleComplexQRDecomp represents the QR decomposition of a general matrix.

Class DoubleComplexQRDecompServer allows control over how the pivoting is done in the creation of DoubleComplexQRDecomp objects.

Class DoubleComplexQRLeastSq solves least squares problems by using a QR decomposition.

Class DoubleComplexSparseFact performs general sparse matrix factorizations.

Class DoubleComplexSparseVector encapsulates a general sparse vector.

Class DoubleComplexSVDecomp represents the singular value decomposition (SVD) of a matrix.

Class DoubleComplexSVDecompServer constructs instances of the DoubleComplexSVDecomp class.

Class DoubleComplexSVDLeastSq solves least squares problems by using a singular value decomposition.

Class DoubleComplexTriDiagFact represents the LU factorization of a tridiagonal matrix of double-precision complex floating point numbers.

Class DoubleComplexTriDiagMatrix represents a tridiagonal matrix of double-precision complex numbers. A tridiagonal matrix is a matrix which has all its non-zero entries on the main diagonal, the super diagonal, and the subdiagonal.

Class DoubleComplexComplexUpperTriMatrix represents an upper triangular matrix of double-precision complex numbers. An upper triangular matrix is a square matrix with all elements below the main diagonal equal to zero.

Class DoubleCOWeightedLeastSq solves weighted least squares problems by using a Complete Orthogonal (CO) decomposition technique.

Class DoubleCsrSparseMatrix stores a general sparse matrix using Compressed Row (CSR) storage format.

Class DoubleEigDecomp computes the eigenvalues and left and right eigenvectors of a general matrix, with preliminary balancing.

Class DoubleEigDecompServer creates eigenvalue decompositions. A server instance can be configured to perform preliminary balancing, and to compute left eigenvectors, right eigenvectors, both, or neither.

Class DoubleFairWeightingFunction implements the fair weighting function for Iteratively Reweighted Least Squares (IRLS).

Class DoubleGSVDecomp computes the generalized singular value decomposition (GSVD) of a pair of general rectangular matrices.

Class for serving up generalized singular value decompositions (GSVD) in the form of DoubleGSVDecomp instances.

Class DoubleHermCsrSparseMatrix stores a general sparse Hermitian matrix using the Compressed Row (CSR) storage format.

Class DoubleHermitianBandMatrix represents an Hermitian banded matrix of double-precision floating point values. An Hermitian banded matrix is an Hermitian matrix that has all its non-zero entries near the diagonal.

Class DoubleHermitianEigDecomp computes the eigenvalues and eigenvectors of a symmetrix matrix.

Class DoubleHermitianEigDecompServer creates eigenvalue decompositions. A server instance can be configured to compute eigenvalues only, or both eigenvalues and eigenvectors. In addition, the server can be configured to compute only the eigenvalues in a given range. A tolerance for the convergence of the algorithm may also be specified.

Class DoubleHermitianFact represents the factorization of a Hermitian, matrix of complex double-precision floating point numbers.

Class DoubleHermitianMatrix represents a matrix of double-precision floating point complex values.

Class DoubleHermitianPDBandFact represents the factorization of a Hermitian, positive definite, banded matrix of complex double-precision floating point numbers.

Class DoubleHermitianPDFact represents the Cholesky factorization of a Hermitian, positive definite, matrix of double-precision complex floating point numbers. In a Cholesky factorization a Hermitian, positive definite matrix A is factored as A = UU' where U is upper triangular and U' is the conjugate transpose of U.

Class DoubleHermPDTriDiagFact represents the LDL' factorization of a Hermitian, positive definite, tridiagonal matrix of complex double-precision floating point numbers.

Class DoubleIterativelyReweightedLeastSq solves a least squares problems by iteratively applying a weighted least squares fit.

Abstract base class for least squares weighting functions used in the Iteratively Reweighted Least Squares algorithm.

Class DoubleLowerTriMatrix represents a lower triangular matrix of double-precision floating point values. A lower triangular matrix is a square matrix with all elements above the main diagonal equal to zero.

Class DoubleQRDecomp represents the QR decomposition of a general matrix.

Class DoubleQRDecompServer allows control over how the pivoting is done in the creation of DoubleQRDecomp objects.

Class DoubleQRLeastSq solves least squares problems by using a QR decomposition.

Class DoubleSparseFact performs general sparse matrix factorizations.

Class DoubleSparseHermFact performs Hermitian sparse matrix factorizations.

Class DoubleSparseHermPDFact performs sparse Hermitian Positive Definite matrix factorizations.

Class DoubleSparseSymFact performs sparse symmetric matrix factorizations.

Class DoubleSparseSymPDFact performs sparse positive definite symmetric matrix factorizations.

Class DoubleSparseVector encapsulates a general sparse vector.

Class DoubleSVDecomp represents the singular value decomposition (SVD) of a matrix.

Class DoubleSVDecompServer constructs instances of the DoubleSVDecomp class.

Class DoubleSVDLeastSq solves least squares problems by using a singular value decomposition.

Class DoubleSymBandMatrix represents a symmetric banded matrix of double-precision floating point values. A symmetric banded matrix is a symmetric matrix that has all its non-zero entries near the diagonal.

Class DoubleSymCsrSparseMatrix stores a sparse symmetric matrix using the CompreSsed Row (CSR) storage format.

Class DoubleSymEigDecomp computes the eigenvalues and eigenvectors of a symmetrix matrix.

Class DoubleSymEigDecompServer creates eigenvalue decompositions. A server instance can be configured to compute eigenvalues only, or both eigenvalues and eigenvectors. In addition, the server can be configured to compute only the eigenvalues in a given range. A tolerance for the convergence of the algorithm may also be specified.

Class DoubleSymFact represents the factorization of a symmetric, matrix of double-precision floating point numbers.

Class DoubleSymmetricMatrix represents a symmetric matrix of double-precision floating point values.

Class DoubleSymPDBandFact represents the factorization of a symmetric, positive definite, banded matrix of double-precision floating point numbers.

Class DoubleSymPDFact represents the Cholesky factorization of a symmetric, positive definite, matrix of double-precision floating point numbers. In a Cholesky factorization a symmetric, positive definite matrix A is factored as A = UU' where U is upper triangular and U' is the transpose of U.

Class DoubleSymPDTriDiagFact represents the LDL' factorization of a symmetric, positive definite, tridiagonal matrix of double-precision floating point numbers.

Class DoubleTriDiagFact represents the LU factorization of a tridiagonal matrix of double-precision floating point numbers.

Class DoubleTriDiagMatrix represents a tridiagonal matrix of double-precision floating point values. A tridiagonal matrix is a matrix which has all its non-zero entries on the main diagonal, the super diagonal, and the subdiagonal.

Class DoubleUpperTriMatrix represents an upper triangular matrix of double-precision floating point values. An upper triangular matrix is a square matrix with all elements below the main diagonal equal to zero.

Class FloatBandFact represents the factorization of a banded matrix of single-precision floating point numbers.

Class FloatBandMatrix represents a banded matrix of single-precision floating point values. A banded matrix is a matrix that has all its non-zero entries near the diagonal.

Class FloatCholeskyLeastSq solves least square problems by using the Cholesky factorization to solve the normal equations.

Class FloatComplexBandFact represents the factorization of a banded matrix of complex single-precision floating point numbers.

Class FloatComplexBandMatrix represents a banded matrix of single-precision complex numbers. A banded matrix is a matrix that has all its non-zero entries near the diagonal.

Class FloatComplexCholeskyLeastSq solves least square problems by using the Cholesky factorization to solve the normal equations.

Class FloatComplexEigDecomp computes the eigenvalues and left and right eigenvectors of a general matrix, with preliminary balancing.

Class FloatComplexEigDecompServer creates eigenvalue decompositions. A server instance can be configured to perform preliminary balancing, and to compute left eigenvectors, right eigenvectors, both, or neither.

Class FloatComplexGSVDecomp computes the generalized singular value decomposition (GSVD) of a pair of general rectangular matrices.

Class for serving up generalized singular value decompositions (GSVD) in the form of FloatComplexGSVDecomp instances.

Class FloatComplexLowerTriMatrix represents a lower triangular matrix of single-precision complex numbers. A lower triangular matrix is a square matrix with all elements above the main diagonal equal to zero.

Class FloatComplexQRDecomp represents the QR decomposition of a general matrix.

Class FloatComplexQRDecompServer allows control over how the pivoting is done in the creation of FloatComplexQRDecomp objects.

Class FloatComplexQRLeastSq solves least squares problems by using a QR decomposition.

Class FloatComplexSVDecomp represents the singular value decomposition (SVD) of a matrix.

Class FloatComplexSVDecompServer constructs instances of the FloatComplexSVDecomp class.

Class FloatComplexSVDLeastSq solves least squares problems by using a singular value decomposition.

Class FloatComplexTriDiagFact represents the LU factorization of a tridiagonal matrix of single-precision complex floating point numbers.

Class FloatComplexTriDiagMatrix represents a tridiagonal matrix of single-precision complex numbers. A tridiagonal matrix is a matrix which has all its non-zero entries on the main diagonal, the super diagonal, and the subdiagonal.

Class FloatComplexUpperTriMatrix represents an upper triangular matrix of single-precision complex numbers. An upper triangular matrix is a square matrix with all elements below the main diagonal equal to zero.

Class FloatEigDecomp computes the eigenvalues and left and right eigenvectors of a general matrix, with preliminary balancing.

Class FloatEigDecompServer creates eigenvalue decompositions. A server instance can be configured to perform preliminary balancing, and to compute left eigenvectors, right eigenvectors, both, or neither.

Class FloatGSVDecomp computes the generalized singular value decomposition (GSVD) of a pair of general rectangular matrices.

Class for serving up generalized singular value decompositions (GSVD) in the form of FloatGSVDecomp instances.

Class FloatHermitianBandMatrix represents an Hermitian banded matrix of double-precision floating point values. An Hermitian banded matrix is an Hermitian matrix that has all its non-zero entries near the diagonal.

Class FloatHermitianEigDecomp computes the eigenvalues and eigenvectors of a symmetrix matrix.

Class FloatHermitianEigDecompServer creates eigenvalue decompositions. A server instance can be configured to compute eigenvalues only, or both eigenvalues and eigenvectors. In addition, the server can be configured to compute only the eigenvalues in a given range. A tolerance for the convergence of the algorithm may also be specified.

Class FloatHermitianFact represents the factorization of a Hermitian, matrix of complex single-precision floating point numbers.

Class FloatHermitianMatrix represents a matrix of single-precision floating point complex values.

Class FloatHermitianPDBandFact represents the factorization of a Hermitian, positive definite, banded matrix of complex single-precision floating point numbers.

Class FloatHermitianPDFact represents the Cholesky factorization of a Hermitian, positive definite, matrix of single-precision complex floating point numbers. In a Cholesky factorization a Hermitian, positive definite matrix A is factored as A = UU' where U is upper triangular and U' is the conjugate transpose of U.

Class FloatHermPDTriDiagFact represents the LDL' factorization of a Hermitian, positive definite, tridiagonal matrix of complex single-precision floating point numbers.

Class FloatLowerTriMatrix represents a lower triangular matrix of single-precision floating point values. A lower triangular matrix is a square matrix with all elements above the main diagonal equal to zero.

Class FloatQRDecomp represents the QR decomposition of a general matrix.

Class FloatQRDecompServer allows control over how the pivoting is done in the creation of FloatQRDecomp objects.

Class FloatQRLeastSq solves least squares problems by using a QR decomposition.

Class FloatSVDecomp represents the singular value decomposition (SVD) of a matrix.

Class FloatSVDecompServer constructs instances of the FloatSVDecomp class.

Class FloatSVDLeastSq solves least squares problems by using a singular value decomposition.

Class FloatSymBandMatrix represents a symmetric banded matrix of single-precision floating point values. A symmetric banded matrix is a symmetric matrix that has all its non-zero entries near the diagonal.

Class FloatSymEigDecomp computes the eigenvalues and eigenvectors of a symmetrix matrix.

Class FloatSymEigDecompServer creates eigenvalue decompositions. A server instance can be configured to compute eigenvalues only, or both eigenvalues and eigenvectors. In addition the server can be configured to compute only the eigenvalues in a given range. A tolerance for the convergence of the algorithm may also be specified.

Class FloatSymFact represents the factorization of a symmetric matrix of single-precision floating point numbers.

Class FloatSymmetricMatrix represents a symmetric matrix of float-precision floating point values.

Class FloatSymPDBandFact represents the factorization of a symmetric, positive definite, banded matrix of single-precision floating point numbers.

Class FloatSymPDFact represents the Cholesky factorization of a symmetric, positive definite, matrix of single-precision floating point numbers. In a Cholesky factorization a symmetric, positive definite matrix A is factored as A = UU' where U is upper triangular and U' is the transpose of U.

Class FloatSymPDTriDiagFact represents the LDL' factorization of a symmetric, positive definite, tridiagonal matrix of single-precision floating point numbers.

Class FloatTriDiagFact represents the LU factorization of a tridiagonal matrix of single-precision floating point numbers.

Class FloatTriDiagMatrix represents a tridiagonal matrix of single-precision floating point values. A tridiagonal matrix is a matrix which has all its non-zero entries on the main diagonal, the super diagonal, and the subdiagonal.

Class FloatUpperTriMatrix represents an upper triangular matrix of single-precision floating point values. An upper triangular matrix is a square matrix with all elements below the main diagonal equal to zero.

Class IndexArray presents 0-based indexices to the user, but uses 1-based indices internally.

Class MatrixFunctions provides standard mathematical functions for NMath Matrix structured sparse matrix types.

Exception thrown when an attempt is made to change the value of an element in a structured matrix that cannot be changed.

Class SparseMatrixData stores general sparse matrix data.

Abstract base class for sparse matrix factorizations using the Parallel Direct Sparse Solver Interface (PARDISO).

Class SparseVectorData stores sparse vector data.

Class IntPair represents a pair of integers.

Interface for least squares weighting functions.

Interface for general sparse matrix storage formats.

Tolerance met function delegate.

Enumeration for specifying balancing options in eigenvalue decompositions.

Enumeration for specifying possible return values for errors.

Enumeration for specifying the types of matrices that can be factored.