NDLI: An improvement of SAINV and RIF preconditionings of CG method by double dropping strategy

Content Provider	IEEE Xplore Digital Library
Author	Fujino, S. Ikeda, Y.
Copyright Year	2004
Description	Author affiliation: Comput. & Commun. Center, Kyushu Univ., Japan (Fujino, S.)
Abstract	Preconditioning based on incomplete factorization of the matrix A is among the best known and most popular methods for solving a linear system of equations with symmetric positive definite coefficient matrix. However, the existence of an incomplete factorization is a delicate issue which must be overcame if one has a desire to design reliable preconditioning. Stabilized AINV (approximate in-verse) and RIF (robust incomplete factorization) preconditionings with single dropping have been proposed. Dropping procedure is a key to improvement of efficiency of computation. In this paper, new dropping strategy for improvement of both SAINV and RIF preconditionings are proposed. Moreover comparisons with other incomplete factorization and original SAINV and RIF preconditionings using challenging linear systems from realistic structural analysis are presented. We discuss double dropping strategy in the context of computation time of CG method with preconditioning for successful convergence and memory requirement for factorization.
Sponsorship	High Performance Comput. SIG Information Processing Soc. of Japan IEEE Comput. Soc. Japan Chapter
Starting Page	142
Ending Page	149
File Size	493505
Page Count	8
File Format	PDF
ISBN	076952138X
DOI	10.1109/HPCASIA.2004.1324029
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-07-22
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Character generation Linear systems Symmetric matrices Equations Robustness Sparse matrices Vectors Computer networks Information science Electronic mail
Content Type	Text
Resource Type	Article

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