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Sparse approximate inverse preconditioning for dense linear systems arising in computational electromagnetics

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Abstract

We investigate the use of sparse approximate inverse preconditioners for the iterative solution of linear systems with dense complex coefficient matrices arising in industrial electromagnetic problems. An approximate inverse is computed via a Frobenius norm approach with a prescribed nonzero pattern. Some strategies for determining the nonzero pattern of an approximate inverse are described. The results of numerical experiments suggest that sparse approximate inverse preconditioning is a viable approach for the solution of large-scale dense linear systems on parallel computers.

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Authors and Affiliations

  1. Parallel Computing Group, Physics and Mathematics Department, Aerospatiale, 12 Rue Pasteur, BP 76, F-92150, Suresnes Cedex, France

    Guillaume Alléon

  2. Parallel Algorithms Project, CERFACS, 42 Ave. G. Coriolis, F-31057, Toulouse Cedex, France

    Michele Benzi & Luc Giraud

Authors
  1. Guillaume Alléon
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  2. Michele Benzi
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  3. Luc Giraud
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Alléon, G., Benzi, M. & Giraud, L. Sparse approximate inverse preconditioning for dense linear systems arising in computational electromagnetics. Numerical Algorithms 16, 1–15 (1997). https://doi.org/10.1023/A:1019170609950

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