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

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Persistent URL	http://purl.org/net/epubs/work/23392972
Record Status	Checked
Record Id	23392972
Title	The state-of-the-art of preconditioners for sparse linear least-squares problems
Contributors	NIM Gould (STFC Rutherford Appleton Lab.), JA Scott (STFC Rutherford Appleton Lab.)
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 inner iterations used with Krylov subspace methods. In this study, we briefl y 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 , iterative solvers , least-squares problems , preconditioning , normal equations , AMS(MOS) subject classifications: 65F05, 65F50 , sparse matrices
Funding Information
Related Research Object(s):	23411221 , 23342934 , 30453497 , 30453617 , 32874110
Licence Information:
Language	English (EN)

Type	Details	URI(s)	Local file(s)	Year
Preprint	RAL Preprints RAL-P-2015-010, ACM Trans Math Software 2015.		RAL-P-2015-010.pdf	2015