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Block filtering decomposition
Author(s) -
Fezzani Riadh,
Grigori Laura,
Nataf Frédéric,
Wang Ke
Publication year - 2014
Publication title -
numerical linear algebra with applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.02
H-Index - 53
eISSN - 1099-1506
pISSN - 1070-5325
DOI - 10.1002/nla.1921
Subject(s) - preconditioner , discretization , convergence (economics) , sparse matrix , matrix (chemical analysis) , algorithm , iterative method , mathematics , computer science , computation , block (permutation group theory) , mathematical optimization , mathematical analysis , combinatorics , physics , materials science , quantum mechanics , economics , composite material , gaussian , economic growth
SUMMARY This paper introduces a new preconditioning technique that is suitable for matrices arising from the discretization of a system of PDEs on unstructured grids. The preconditioner satisfies a so‐called filtering property, which ensures that the input matrix is identical with the preconditioner on a given filtering vector. This vector is chosen to alleviate the effect of low‐frequency modes on convergence and so decrease or eliminate the plateau that is often observed in the convergence of iterative methods. In particular, the paper presents a general approach that allows to ensure that the filtering condition is satisfied in a matrix decomposition. The input matrix can have an arbitrary sparse structure. Hence, it can be reordered using nested dissection, to allow a parallel computation of the preconditioner and of the iterative process. We show the efficiency of our preconditioner through a set of numerical experiments on symmetric and nonsymmetric matrices. Copyright © 2014 John Wiley & Sons, Ltd.