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Abstract: The influence of reorderings on the performance of factorized sparse approximate inverse preconditioners is considered. Some theoretical results on the effect of orderings on the fill-in and decay behavior of the inverse factors of a sparse matrix are presented. It is shown experimentally that certain reorderings, like Minimum Degree and Nested Dissection, can be very beneficial both in the preconditioner construction phase and in terms of the rate of convergence of the preconditioned iteration. (Update)
Context of citations to this paper: More
...which are zero even without small entries dropped in the algorithm, i.e. when the true inverse factors are computed. Because the 3 In [3, 7] other orderings were considered for AINV, but as nested dissection is generally close to best in convergence, often best in...
...One important issue for factored approximate inverses is the ordering of the rows and columns of the matrix. As demonstrated in [8, 12, 13], performance can be significantly improved by an appropriate reordering e.g. nested dissection (we use the Metis routine[29] On...
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BibTeX entry: (Update)
Benzi, M. and Tuma, M., Orderings for factorized sparse approximate inverse preconditioners, SIAM J. Sci. Comput., to appear. http://citeseer.ist.psu.edu/article/benzi98orderings.html More
@article{ benzi00orderings,
author = "Michele Benzi and Miroslav Tuma",
title = "Orderings for Factorized Sparse Approximate Inverse Preconditioners",
journal = "SIAM Journal on Scientific Computing",
volume = "21",
number = "5",
pages = "1851--1868",
year = "2000",
url = "citeseer.ist.psu.edu/article/benzi98orderings.html" }
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