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Persistent URL http://purl.org/net/epubs/work/30453497
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Record Id 30453497
Title The state-of-the-art of preconditioners for sparse linear least-squares problems
Contributors
Abstract 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 a number of different approaches to incomplete factorization and stationary inneriterations used with Krylov subspace methods. In this study,we briefly review available 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.
Organisation STFC , SCI-COMP , SCI-COMP-CM
Keywords augmented system , least-squares problems , iterative solvers , preconditioning , normal equations , sparse matrices
Funding Information EPSRC (EP/I013067/1); EPSRC (EP/M025179/1)
Related Research Object(s): 23392972 , 30453617 , 23411221 , 23342934 , 32874110
Language English (EN)
Type Details URI(s) Local file(s) Year
Report RAL Preprints RAL-P-2015-010 (corrected). 2016. RAL-P-2015-010 - corr.pdf 2016
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