Bibtex de la publication

@Unpublished{ AmAsBoBuL'We2012.1,
author = {Amestoy, Patrick and Ashcraft, Cleve and Boiteau , Olivier and Buttari, Alfredo and L'Excellent, Jean-Yves and Weisbecker, Clément},
title = "{Improving Multifrontal methods by means of Low-Rank Approximations techniques (2012 SIAM Conference on Applied Linear Algebra, Valencia, Spain, 18/06/2012-22/06/2012)}",
year = {2012},
language = {anglais},
URL = {http://amestoy.perso.enseeiht.fr/doc/weisbecker_la12.pdf},
keywords = {multifrontal, low-rank, linear algebra, sparse matrix, approximations},
abstract = {Matrices coming from elliptic PDEs have been shown to have a low-rank property : their off-diagonal blocks can be approximated by low-rank blocks. This representation offers a substantial reduction of the memory requirement and gives efficient means to perform many of the basic algebra operations. In this talk, we present how these results can be used to significantly improve multifrontal solver. Low-rank blocks matrices and hierarchical matrices are analyzed and compared. The first one approximates the fronts blockwise. The second one defines a hierarchy of blocks to recursivly approximate the fronts. Both decrease memory consumption, complexity and parallel communication costs.}
}