Another benefit of using sparse matrices is that they can speed up some numerical linear algebra operations, such as solving linear systems, eigenvalue problems, or singular value decomposition.

Sparse Matrix is an implementation of basic matrix operations with a dense and sparse representation. The dense matrix allows for reading from mtx or csr files, with in-line parameters for specifying ...

The dot product calculation for each row of the sparse matrix is independent of the dot product for other rows; i.e., all iterations of the outer loop (Line 1) are logically independent of each other.

This representation can be used for matrices with arbitrary patterns of nonzero elements since each subsequence can be of a different size. A common operation on sparse matrices is to multiply them by ...

A large portion of the computation in sparse neural networks comprises of multiplying a sparse matrix with a dense matrix, denoted SDMM in this paper. The SDMM operation with an unstructured sparsity ...

Abstract: In recent years, the matrix enhancement and matrix pencil method (MEMP) as well as the forward-backward MEMP (FBMEMP) have been successfully applied to reduce the number of antenna elements ...

Yuan, M. (2010) High Dimensional Inverse Covariance Matrix Estimation via Linear Programming. Journal of Machine Learning Research, 11, 2261-2286. Cai, T., Liu, W. and Luo, X. (2011) A Constrained ...