Problem Formulation: Matrix multiplication of two sparse matrices is a fundamental operation in linear Bayesian inverse problems for computing covariance matrices of observations and a posteriori ...

Sparse matrices, which are common in scientific applications, are matrices in which most elements are zero. To save space and running time it is critical to only store the nonzero elements. A standard ...

Sparse matrices are a special type of data structure where most of the values are zero. These matrices find applications in various fields, such as graphics, simulations, and neural networks, due to ...

In sparse matrices, zero values can be discarded from storage or computations to accelerate execution. In order to represent only non-zero values in sparse matrices, the cuSPARSE library supports ...

In this paper, we propose a co-sparse non-negative matrix factorization framework to impose sparsity in both the coding matrix and the basis matrix. The co-sparsity is realized by limiting the total ...

Improving on this has been an open problem even for sparse linear systems with poly (n) condition number. In this paper, we present an algorithm that solves linear systems in sparse matrices ...