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The state-of-the-art of preconditioners for sparse linear least-squares problems. (English) Zbl 1380.65064
Summary: 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, 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.
Reviewer: Reviewer (Berlin)

65F08 Preconditioners for iterative methods
65F05 Direct numerical methods for linear systems and matrix inversion
65F50 Computational methods for sparse matrices
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