On using Cholesky-based factorizations for solving rank-deficient sparse linear least-squares problems

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Persistent URL	http://purl.org/net/epubs/work/24428837
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Record Id	24428837
Title	On using Cholesky-based factorizations for solving rank-deficient sparse linear least-squares problems
Contributors	JA Scott (STFC Rutherford Appleton Lab.)
Abstract	By examining the performance of modern parallel sparse direct solvers and exploiting our knowledge of the algorithms behind them, we perform numerical experiments to study how they can be used to efficiently solve rank-deficient sparse linear least-squares problems arising from practical applications. We consider both the regularized normal equations and the regularized augmented system. We employ the computed factors of the regularized systems as preconditioners with an iterative solver to obtain the solution of the original (unregularized) problem. Furthermore, we look at using limited-memory incomplete Cholesky-based factorizations and how these can offer the potential to solve very large problems.
Organisation	STFC , SCI-COMP , SCI-COMP-CM
Keywords	augmented system , Cholesky factorizations , iterative methods , least-squares problems , preconditioning , direct methods , normal equations , sparse matrices
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Language	English (EN)

Type	Details	URI(s)	Local file(s)	Year
Preprint	RAL Preprints RAL-P-2016-005, SIAM J Sci Comput 2016.		RAL-P-2016-005.pdf	2016