Iterative near-field preconditioner for the multilevel fast multipole algorithm

Date
2010-07-06
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Source Title
SIAM Journal on Scientific Computing
Print ISSN
1064-8275
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Publisher
Society for Industrial and Applied Mathematics
Volume
32
Issue
4
Pages
1929 - 1949
Language
English
Type
Article
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Abstract

For iterative solutions of large and difficult integral-equation problems in computational electromagnetics using the multilevel fast multipole algorithm (MLFMA), preconditioners are usually built from the available sparse near-field matrix. The exact solution of the near-field system for the preconditioning operation is infeasible because the LU factors lose their sparsity during the factorization. To prevent this, incomplete factors or approximate inverses can be generated so that the sparsity is preserved, but at the expense of losing some information stored in the near-field matrix. As an alternative strategy, the entire near-field matrix can be used in an iterative solver for preconditioning purposes. This can be accomplished with low cost and complexity since Krylov subspace solvers merely require matrix-vector multiplications and the near-field matrix is sparse. Therefore, the preconditioning solution can be obtained by another iterative process, nested in the outer solver, provided that the outer Krylov subspace solver is flexible. With this strategy, we propose using the iterative solution of the near-field system as a preconditioner for the original system, which is also solved iteratively. Furthermore, we use a fixed preconditioner obtained from the near-field matrix as a preconditioner to the inner iterative solver. MLFMA solutions of several model problems establish the effectiveness of the proposed nested iterative near-field preconditioner, allowing us to report the efficient solution of electric-field and combined-field integral-equation problems involving difficult geometries and millions of unknowns.

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Keywords
Computational electromagnetics, Fast multipole method (FMM), Flexible solvers, Integral equations, Multilevel fast multipole algorithm (MLFMA), Preconditioning, Sparse approximate inverse preconditioners, Variable preconditioning
Citation
Published Version (Please cite this version)