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Factorized approximate inverses with adaptive dropping
Type of publication: Article
Citation:
Publication status: Published
Journal: SIAM Journal on Scientific Computing
Volume: 38
Number: 3
Year: 2016
Pages: A1807–A1820
DOI: 10.1137/15M1030315
Abstract: This paper presents a new approach to construct factorized approximate inverses for a symmetric and positive definite matrix A. The proposed strategy is based on adaptive dropping that reflects the quality of preserving the relation UZ = I between the direct factor U and the inverse factor Z satisfying A = UT U and A−1 = ZZT . An important part of the approach is column pivoting used to minimize the growth of the condition number of leading principal submatrices of U that occurs explicitly in the dropping criterion. Numerical experiments demonstrate that the resulting approximate inverse factorization is robust as a preconditioner for solving large and sparse systems of linear equations.
Preprint project: NCMM
Preprint year: 2016
Preprint number: 02
Preprint ID: NCMM/2016/02
Keywords: Approximate inverses, Gram–Schmidt orthogonalization, incomplete factorization, preconditioned iterative methods
Authors Kopal, Jiří
Rozložník, Miroslav
Tůma, Miroslav
Added by: [JP]
Total mark: 0
Attachments
  • 20160127074010.pdf
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