Sparse stretching for solving sparse-dense linear least-squares problems

Scott, J. ORCID: https://orcid.org/0000-0003-2130-1091 and Tuma, M. (2019) Sparse stretching for solving sparse-dense linear least-squares problems. SIAM Journal on Scientific Computing, 41 (3). A1604-A1625. ISSN 1095-7197 doi: 10.1137/18M1181353

Abstract/Summary

Large-scale linear least-squares problems arise in a wide range of practical applications. In some cases, the system matrix contains a small number of dense rows. These make the problem significantly harder to solve because their presence limits the direct applicability of sparse matrix techniques. In particular, the normal matrix is (close to) dense, so that forming it is impractical. One way to help overcome the dense row problem is to employ matrix stretching. Stretching is a sparse matrix technique that improves sparsity by making the least-squares problem larger. We show that standard stretching can still result in the normal matrix for the stretched problem having an unacceptably large amount of fill. This motivates us to propose a new sparse stretching strategy that performs the stretching so as to limit the fill in the normal matrix and its Cholesky factor. Numerical examples from real problems are used to illustrate the potential gains.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/82828
Identification Number/DOI	10.1137/18M1181353
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Publisher	Society for Industrial and Applied Mathematics
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