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/30453497
Record Status	Checked
Record Id	30453497
Title	The state-of-the-art of preconditioners for sparse linear least-squares problems
Contributors	NIM Gould (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 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
Licence Information:
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