The state-of-the-art of preconditioners for sparse linear least-squares problems

Scott, J. ORCID: https://orcid.org/0000-0003-2130-1091 and Gould, N. (2017) The state-of-the-art of preconditioners for sparse linear least-squares problems. ACM Transactions on Mathematical Software (TOMS), 43 (4). 36. ISSN 0098-3500 doi: 10.1145/3014057

Abstract/Summary

In recent years, a variety of preconditioners have been proposed for use in solving large sparse linear least-squares problems. These include simple diagonal preconditioning, preconditioners based on incomplete factorizations and stationary inner iterations used with Krylov subspace methods. In this study, we briefly review preconditioners for which software has been made available and then present a numerical evaluation of them using performance profiles and a large set of problems arising from practical applications. Comparisons are made with state-of-the-art sparse direct methods.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/70342
Identification Number/DOI	10.1145/3014057
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Publisher	ACM
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