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On Factorized Approximate Inverses
by
Miroslav Tuma
Institute of Computer Science, Czech Academy of Sciences, 182 07 Prague 8, Czech Republic
Coauthors: Michele Benzi
Keywords: sparse linear systems, preconditioned iterative methods, approximate inverses, parallel processing
In the last few years there has been considerable interest in explicit preconditioning techniques based on directly approximating the inverse of the coefficient matrix with a sparse matrix. Sparse approximate inverses have been shown to result in good rates of convergence of the preconditioned iteration (comparable to those obtained with incomplete factorization methods) while being well-suited for implementation on vector and parallel architectures. In the talk we will concentrate on some issues concerning construction and implementation of approximate inverses with a special emphasis on factorized approximate inverse techniques. We will explain more in detail natural bottlenecks of various implementational approaches.
M. Benzi, C. D. Meyer and M. T23 uma. A sparse approximate inverse preconditioner for the conjugate gradient method, SIAM J. Sci. Comput., 17:1135-1149, 1996.
R. Bridson, Wei Pai Tang: Ordering, Anisotropy, and Factored Sparse Approximate Inverses, SIAM J. Sci. Comput., 21:867-882, 1999.
Date received: February 18, 2001
Copyright © 2001 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Mathematical Conference Abstracts. Document # cagm-17.