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 ...

A collection of C programs for creating, displaying and converting sparse matrices into efficient formats - jackr276/Sparse-Matrix-Utilities ...

Learn what are the benefits and limitations of using sparse matrices in numerical linear algebra, and how to handle them efficiently and accurately.

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 ...

Summary <p>This chapter examines the seven variants of sparse matrices implemented in the Scipy library. These seven variants include: coordinate sparse matrix, compressed sparse column matrix, ...

We present compiler technology for synthesizing sparse matrix code from (i) dense matrix code, and (ii) a description of the index structure of a sparse matrix. Our approach is to embed statement ...

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 ...

To this end, we introduce a co-sparse non-negative matrix factorization method to high-dimensional data by simultaneously imposing sparsity in both two decomposed matrices.

Using a larger set of connection matrices, we have shown that the usage of different matrix formats can help to improve the performance on CPUs as well as GPUs by distinguishing between sparse and ...