Vector-sparse solver for unsymmetrical matrices
In this work, efficient algorithms for sparse computations (reordering algorithms, storage schemes, symbolic factorization, master degree-of-freedom, L?D?U numerical factorization, forward and backward solutions) are developed and integrated into the proposed procedures. In order to exploit fast saxpy operations offered by many vector computers, take advantage of available cache in many workstations, and minimize data movements into fast memory, special storage schemes are designed to store the coefficient (unsymmetrical) matrix. Thus, the upper triangular portion of the coefficient matrix is stored in a compressed sparse row format, while the lower triangular portion of the same matrix is stored in a compressed sparse column format. A reordering algorithm is applied on one portion of the matrix to minimize fill-ins. Unsymmetrical matrixvector multiplication has also been sparsely computed for error-norm check purpose. The entire sparse procedures have been coded in standard Fortran language.
unsymmetrical matrices fortran language fortran modified minimum degree transa sparse solver