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Partial factorization of a dense symmetric indefinite matrix

Published: 05 January 2012 Publication History

Abstract

At the heart of a frontal or multifrontal solver for the solution of sparse symmetric sets of linear equations, there is the need to partially factorize dense matrices (the frontal matrices) and to be able to use their factorizations in subsequent forward and backward substitutions. For a large problem, packing (holding only the lower or upper triangular part) is important to save memory. It has long been recognized that blocking is the key to efficiency and this has become particularly relevant on modern hardware. For stability in the indefinite case, the use of interchanges and 2 × 2 pivots as well as 1 × 1 pivots is equally well established. In this article, the challenge of using these three ideas (packing, blocking, and pivoting) together is addressed to achieve stable factorizations of large real-world symmetric indefinite problems with good execution speed.
The ideas are not restricted to frontal and multifrontal solvers and are applicable whenever partial or complete factorizations of dense symmetric indefinite matrices are needed.

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cover image ACM Transactions on Mathematical Software
ACM Transactions on Mathematical Software  Volume 38, Issue 2
December 2011
136 pages
ISSN:0098-3500
EISSN:1557-7295
DOI:10.1145/2049673
Issue’s Table of Contents
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Publication History

Published: 05 January 2012
Accepted: 01 May 2011
Revised: 01 April 2011
Received: 01 November 2010
Published in TOMS Volume 38, Issue 2

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Author Tags

  1. LDLT factorization
  2. Symmetric linear systems
  3. frontal
  4. indefinite matrices
  5. multifrontal

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