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LU Preconditioning for Overdetermined Sparse Least Squares Problems

Gary Howell 1 Marc Baboulin 2, 3
1 NC State - North Carolina State University [Raleigh]
2 LRI - Laboratoire de Recherche en Informatique
3 POSTALE - Performance Optimization by Software Transformation and Algorithms & Librairies Enhancement
LRI - Laboratoire de Recherche en Informatique, Inria Saclay - Ile de France
Abstract : We investigate how to use an LU factorization with the classical LSQR routine for solving overdetermined sparse least squares problems. Usually L is much better conditioned than A and iterating with L instead of A results in faster convergence. When a runtime test indicates that L is not sufficiently well-conditioned, a partial orthogonalization of L accelerates the convergence. Numerical experiments illustrate the good behavior of our algorithm in terms of storage and convergence.
Keywords : iterative methods preconditioning conjugate gradient algorithm LSQR algorithm Sparse linear least squares
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Conference papers
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https://hal.inria.fr/hal-01223069
Contributor : Marc Baboulin <>
Submitted on : Sunday, November 1, 2015 - 8:51:18 PM
Last modification on : Wednesday, September 16, 2020 - 5:24:43 PM

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CNRS | INRIA | UMR8623 | LRI-PARSYS | CENTRALESUPELEC

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Gary Howell, Marc Baboulin. LU Preconditioning for Overdetermined Sparse Least Squares Problems. International Conference on Parallel Processing and Applied Mathematics, Sep 2015, Krakow, Poland. pp.128-137, ⟨10.1007/978-3-319-32149-3_13⟩. ⟨hal-01223069⟩

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