Data Types List

Here are the data types with brief descriptions:

[detail level 12]

►Mblas	A module providing explicit interfaces to BLAS routines
Cddot
Cdgbmv
Cdgemm
Cdgemv
Cdscal
Cdtrsm
Cdtrsv
Czdscal
Czgbmv
Czgemm
Czgemv
Czscal
Cztrsm
Cztrsv
►Mlapack
Cdgeev
Cdgelqf
Cdgels
Cdgelss
Cdgelsy
Cdgeqp3
Cdgeqrf
Cdgesvd
Cdgetrf
Cdgetri
Cdgetrs
Cdggev
Cdlaic1
Cdlamch
Cdlaset
Cdlasrt
Cdorglq
Cdorgqr
Cdormlq
Cdormqr
Cdormrz
Cdpotrf
Cdpotrs
Cdsyev
Cdtzrzf
Czgeev
Czgelqf
Czgels
Czgelss
Czgelsy
Czgeqp3
Czgeqrf
Czgesvd
Czgetrf
Czgetri
Czgetrs
Czlaic1
Czpotrf
Czpotrs
Cztzrzf
Czunglq
Czungqr
Czunmlq
Czunmqr
Czunmrz
►Mlinalg	Provides a set of common linear algebra routines
Cband_diag_mtx_mult	Multiplies a banded matrix, A, with a diagonal matrix, B, such that A = alpha * A * B, or A = alpha * B * A
Cband_mtx_mult	Multiplies a banded matrix, A, by a vector x such that alpha * op(A) * x + beta * y = y
Cband_mtx_to_full_mtx	Converts a banded matrix stored in dense form to a full matrix
Cbanded_to_dense	Converts a banded matrix to a dense matrix
Ccholesky_factor	Computes the Cholesky factorization of a symmetric, positive definite matrix
Ccholesky_rank1_downdate	Computes the rank 1 downdate to a Cholesky factored matrix (upper triangular)
Ccholesky_rank1_update	Computes the rank 1 update to a Cholesky factored matrix (upper triangular)
Ccsr_matrix	A sparse matrix stored in compressed sparse row (CSR) format
Cdense_to_banded	Converts a dense matrix to a banded matrix
Cdet	Computes the determinant of a square matrix
Cdiag_mtx_mult	Multiplies a diagonal matrix with another matrix or array
Ceigen	Computes the eigenvalues, and optionally the eigenvectors, of a matrix
Cextract_diagonal	Extracts the diagonal of a matrix
Cform_lq	Forms the orthogonal matrix Q from the elementary reflectors returned by the LQ factorization algorithm
Cform_lu	Extracts the L and U matrices from the condensed [L\U] storage format used by the lu_factor
Cform_qr	Forms the full M-by-M orthogonal matrix Q from the elementary reflectors returned by the base QR factorization algorithm
Clq_factor	Computes the LQ factorization of an M-by-N matrix
Clu_factor	Computes the LU factorization of an M-by-N matrix
Cmatmul	Performs sparse matrix multiplication C = A * B
Cmsr_matrix	A sparse matrix stored in modified sparse row format. This format is convenient for situations where the diagonal is fully populated
Cmtx_inverse	Computes the inverse of a square matrix
Cmtx_mult	Performs the matrix operation: \( C = \alpha op(A) op(B) + \beta C \)
Cmtx_pinverse	Computes the Moore-Penrose pseudo-inverse of a M-by-N matrix using the singular value decomposition of the matrix
Cmtx_rank	Computes the rank of a matrix
Cmult_lq	Multiplies a general matrix by the orthogonal matrix Q from a LQ factorization
Cmult_qr	Multiplies a general matrix by the orthogonal matrix Q from a QR factorization
Cmult_rz	Multiplies a general matrix by the orthogonal matrix Z from an RZ factorization
Cnonzero_count	Determines the number of nonzero entries in a sparse matrix
Coperator(*)	Multiplies a sparse matrix and a scalar
Coperator(+)	Adds two sparse matrices
Coperator(-)	Subtracts two sparse matrices
Coperator(/)	Multiplies a sparse matrix by a scalar
Cpgmres_solver	A preconditioned GMRES solver
Cqr_factor	Computes the QR factorization of an M-by-N matrix
Cqr_rank1_update	Computes the rank 1 update to an M-by-N QR factored matrix A (M >= N) where \( A = Q R \), and \( A1 = A + U V^T \) such that \( A1 = Q1 R1 \). In the event \( V \) is complex-valued, \( V^H \) is computed instead of \( V^T \)
Crank1_update	Performs the rank-1 update to matrix A such that: \( A = \alpha X Y^T + A \), where \( A \) is an M-by-N matrix, \( \alpha \)is a scalar, \( X \) is an M-element array, and \( Y \) is an N-element array. In the event that \( Y \) is complex, \( Y^H \) is used instead of \( Y^T \)
Crecip_mult_array	Multiplies a vector by the reciprocal of a real scalar
Crz_factor	Factors an upper trapezoidal matrix by means of orthogonal transformations such that \( A = R Z = (R 0) Z \). Z is an orthogonal matrix of dimension N-by-N, and R is an M-by-M upper triangular matrix
Csize	Determines the size of the requested dimension of the supplied sparse matrix
Csolve_cholesky	Solves a system of Cholesky factored equations
Csolve_least_squares	Solves the overdetermined or underdetermined system \( A X = B \) of M equations of N unknowns. Notice, it is assumed that matrix A has full rank
Csolve_least_squares_full	Solves the overdetermined or underdetermined system \( A X = B \) of M equations of N unknowns, but uses a full orthogonal factorization of the system
Csolve_least_squares_svd	Solves the overdetermined or underdetermined system \( A X = B \) of M equations of N unknowns using a singular value decomposition of matrix A
Csolve_lq	Solves a system of M LQ-factored equations of N unknowns. N must be greater than or equal to M
Csolve_lu	Solves a system of LU-factored equations
Csolve_qr	Solves a system of M QR-factored equations of N unknowns
Csolve_triangular_system	Solves a triangular system of equations
Csort	Sorts an array
Csparse_direct_solve	Provides a direct solution to a square, sparse system
Csvd	Computes the singular value decomposition of a matrix A. The SVD is defined as: \( A = U S V^T \), where \( U \) is an M-by-M orthogonal matrix, \( S \) is an M-by-N diagonal matrix, and \( V \) is an N-by-N orthogonal matrix. In the event that \( V \) is complex valued, \( V^H \) is computed instead of \( V^T \)
Cswap	Swaps the contents of two arrays
Ctrace	Computes the trace of a matrix (the sum of the main diagonal elements)
Ctranspose	Provides the transpose of a sparse matrix
Ctri_mtx_mult	Computes the triangular matrix operation: \( B = \alpha A^T A + \beta B \), or \( B = \alpha A A^T + \beta B \), where A is a triangular matrix
►Mqrupdate	A module providing explicit interfaces for the QRUPDATE library
Cdch1dn
Cdch1up
Cdqr1up
Czch1dn
Czch1up
Czqr1up
►Msparskit	An interface to the SPARSKIT library available at https://www-users.cse.umn.edu/~saad/software/SPARSKIT/
Camub	Computes the matrix product: C = A * B
Caplb	Computes the matrix sum: C = A + B, where the matrices are given in CSR format
Caplsb	Computes the matrix sum: C = A + s * B, where the matrices are given in CSR format
Cbndcsr	Converts the LINPACK, BLAS, LAPACK banded matrix format into a CSR format
Cclncsr	@breif Cleans up a CSR matrix
Ccoocsr	Converte a matrix stored in coordinate format to CSR format
Ccsort	Sorces the elements of a CSR matrix in increasing order of their column indices within each row
Ccsrcsc2	Converts a CSR matrix into a CSC matrix (transposition)
Ccsrmsr	Converts a CSR matrix to an MSR matrix
Cgetdia	Extracts the diagonal from a matrix
Cgetelm	Gets element A(i,j) of matrix A for any pair (i,j)
Cilud	Computes the incomplete LU factorization of a sparse matrix in CSR format with standard dropping strategy
Ciludp	Computes the incomplete LU factorization of a sparse matrix in CSR format with standard dropping strategy
Cilut	Computes the incomplete LU factorization of a sparse matrix in CSR format using a dual truncation mechanism
Cilutp	Computes the incomplete LU factorization of a sparse matrix in CSR format using a dual truncation mechanism and pivoting
Clusol	Solves the LU-factored system (LU) x = y
Cmsrcsr	Converts and MSR matrix to a CSR matrix
Cpgmres	An ILUT preconditioned GMRES algorithm. This routine utilizes the L and U matrices generated by the ILUT routine to precondition the GMRES algorithm. The stopping criteria utilized is based simply on reducing the residual norm to the requested tolerance