Stochastic preconditioning for diagonally dominant matrices

Haifeng Qian, Sachin S. Sapatnekar

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

This paper presents a new stochastic preconditioning approach for large sparse matrices. For the class of matrices that are rowwise and columnwise irreducibly diagonally dominant, we prove that an incomplete LDLT factorization in a symmetric case or an incomplete LDU factorization in an asymmetric case can be obtained from random walks and used as a preconditioner. It is argued that our factor matrices have better quality, i.e., better accuracy-size trade-offs, than preconditioners produced by existing incomplete factorization methods. Therefore a resulting preconditioned Krylov-subspace iterative solver requires less computation than traditional methods to solve a set of linear equations with the same error tolerance. The advantage increases for larger and denser matrices. These claims are verified by numerical tests, and we provide techniques that can potentially extend the theory to non-diagonally-dominant matrices.

Original languageEnglish (US)
Pages (from-to)1178-1204
Number of pages27
JournalSIAM Journal on Scientific Computing
Volume30
Issue number3
DOIs
StatePublished - Dec 1 2007

Keywords

  • Incomplete factorization
  • Iterative solver
  • Preconditioning
  • Random walk

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