One of us has shown in his thesis [21] and subsequent publications [7, 8] that structural linear dependence among the columns of a

sparse matrix A can be identified using matchings in bipartite graphs, and that matching based methods are faster than numerical methods based on matrix factorizations.

For a

sparse matrix K with at most b non-zero entries per row, the matrix-vector multiplications cost O(nbp) (1).

T], which can be easily performed by, respectively, forward and backward substitutions, and the product of the

sparse matrix M by an n-sized vector.

Editors Bouwmans, Serhat Aybat, and Zahzah present readers with a collection of professional, academic, and research perspectives on the contemporary use of robust low-rank and

sparse matrix decomposition in image and video processing.

Due to A being as a

sparse matrix, the bandwidth of non-zero matrix storage technique is adopted to store the non-zero element of the matrix A.

However, it is not accurate enough to get the initial phase of propagation paths by directly using the element's argument of the

sparse matrix.

In this aspect this paper proposes to construct sub set of

sparse matrix for different attributes as a major contribution.

We also consider the orsreg1 matrix of order 2205 obtained from the University of Florida

Sparse Matrix Collection [11].

BSR

sparse matrix format in cuSPARSE routines Support for Block Sparse Row matrix format added to more

sparse matrix operations.

Sparse matrix solvers on the gpu: conjugate gradients and multigrid.

The other one is to decompose the FE-BI matrix into the FE

sparse matrix and the BI dense matrix, and handle the FE matrix by ILU factorization method independently [10,11].

Algorithm in the worst case complexity is Q(n (m + n)), for the

sparse matrix can be simplified as O([n.