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

Scott, J. 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

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To link to this item DOI: 10.1145/3014057


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
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
ID Code:70342


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