A Robust Preconditioner with Low Memory Requirements for Large Sparse Least Squares Problems

0404727 - UIVT-O 20030115 RIV US eng J - Článek v odborném periodiku
Benzi, M. - Tůma, Miroslav
A Robust Preconditioner with Low Memory Requirements for Large Sparse Least Squares Problems.
SIAM Journal on Scientific Computing. Roč. 25, č. 2 (2003), s. 499-512. ISSN 1064-8275. E-ISSN 1095-7197
Grant CEP: GA AV ČR IAA1030103; GA AV ČR IAA2030801
Výzkumný záměr: AV0Z1030915
Klíčová slova: large sparse least squares problems * preconditioned CGLS * robust incomplete factorization * incomplete C-orthogonalization * incomplete QR
Kód oboru RIV: BA - Obecná matematika
Impakt faktor: 1.379, rok: 2003

This paper describes a technique for constructing robust preconditioners for the CGLS method applied to the solution of large and sparse least squares problems. The algorithm computes an incomplete LDLT factorization of the normal equations matrix without the need to form the normal matrix itself. The preconditioner is reliable 9 pivot breakdowns cannot occur0 and has low intermediate storage requirements. Numerical experiments illustrating the performance of the preconditioner are presented. A comparison with incomplete QR preconditioners is also included.
Trvalý link: http://hdl.handle.net/11104/0124965