The relationship between the features of sparse matrix and the matrix solving status

Dianwei Han, Shuting Xu, Jun Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Solving very large sparse linear systems are often encountered in many scientific and engineering applications. Generally there are two classes of methods available to solve the sparse linear systems. The first class is the direct solution methods, represented by the Gauss elimination method. The second class is the iterative solution methods, of which the preconditioned Krylov subspace methods are considered to be the most effective ones currently available in this field. The sparsity structure and the numerical value distribution which are considered as features of the sparse matrices may have important effect on the iterative solution of linear systems. We first extract the matrix features, and then preconditioned iterative methods are used to the linear system. Our experiments show that a few features that may affect, positively or negatively, the solving status of a sparse matrix with the level-based preconditioners.

Original languageEnglish
Title of host publicationProceedings of the 46th Annual Southeast Regional Conference on XX, ACM-SE 46
Pages501-506
Number of pages6
DOIs
StatePublished - 2008
Event46th Annual Southeast Regional Conference on XX, ACM-SE 46 - Auburn, AL, United States
Duration: Mar 28 2009Mar 29 2009

Publication series

NameProceedings of the 46th Annual Southeast Regional Conference on XX, ACM-SE 46

Conference

Conference46th Annual Southeast Regional Conference on XX, ACM-SE 46
Country/TerritoryUnited States
CityAuburn, AL
Period3/28/093/29/09

Keywords

  • ACM proceedings
  • Features of matrices
  • Iterative methods
  • Preconditioner

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Hardware and Architecture
  • Software

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