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 | |
| Added by: | [JP] |
| Total mark: | 0 |
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