LU Preconditioning for Overdetermined Sparse Least Squares Problems

Gary Howell 1 Marc Baboulin 2, 3
1 NCSU - 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
Type de document :
Communication dans un congrès
International Conference on Parallel Processing and Applied Mathematics, Sep 2015, Krakow, Poland. Springer, 9573, pp.128-137, 2015, Lecture Notes in Computer Science. 〈http://ppam.pl〉. 〈10.1007/978-3-319-32149-3_13〉
Liste complète des métadonnées
https://hal.inria.fr/hal-01223069
Contributeur : Marc Baboulin <>
Soumis le : dimanche 1 novembre 2015 - 20:51:18
Dernière modification le : samedi 14 avril 2018 - 18:04:09

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INRIA | UMR8623 | LRI-PARSYS | CENTRALESUPELEC | UNIV-PARIS-SACLAY

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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. Springer, 9573, pp.128-137, 2015, Lecture Notes in Computer Science. 〈http://ppam.pl〉. 〈10.1007/978-3-319-32149-3_13〉. 〈hal-01223069〉

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