bicg1 cg1 coo_csr csr_coo csr_dense csr_dusmm csr_dusmv csr_dussm csr_dussv csr_ssr csr_trans dense_csr ssr_csr ssr_dusmv

csr_dusmm() def csr_dusmm ( trans  , order  , m  , n  , l  , alpha  , val  , rowptr  , colind  , base  , b  , beta  , c    ) C <- αAB + βC or C <- αATB + βC (CSR) Purpose This function performs one of the following matrix-vector operations for a sparse matrix in CSR format. C <- αAB + βC or C <- αA^TB + βC where α and β are scalars, A or A^T is an m x n sparse matrix, B is an n x l dense matrix, and C is an m x l dense matrix. Returns info (int) = 0: Successful exit. = i < 0: The (-i)-th argument is invalid. Parameters [in] trans Specifies the operation to be performed. = 'N': C <- αAB + βC. = 'T' or 'C': C <- αA^TB + βC. [in] order Storage order of b and c. = 'C': Column major. = 'R': Row major. [in] m Number of rows of matrix A. (m >= 0) (If m = 0, returns without computation) [in] n Number of columns of matrix A. (n >= 0) (If n = 0, returns without computation) [in] l Number of columns of matrices B and C. (l >= 0) (if l = 0, returns without computation) [in] alpha Scalar α [in] val Numpy ndarray (1-dimensional, float, nnz) Values of nonzero elements of matrix A (where nnz is the number of nonzero elements). [in] rowptr Numpy ndarray (1-dimensional, int32, m + 1) Row pointers of matrix A. [in] colind Numpy ndarray (1-dimensional, int32, nnz) Column indices of matrix A (where nnz is the number of nonzero elements). [in] base Indexing of rowptr and colind. = 0: Zero-based (C style) indexing: Starting index is 0. = 1: One-based (Fortran style) indexing: Starting index is 1. [in] b Numpy ndarray (2-dimensional, float, n x l) Matrix B. [in] beta Scalar β. [in,out] c Numpy ndarray (2-dimensional, float, m x l) [in] Input matrix C (If beta is supplied as zero, c needs not be set on input). [out] Output matrix (= αAB + βC).