Iterative near-field preconditioner for the multilevel fast multipole algorithm

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buir.contributor.authorGürel, Levent
dc.citation.epage1949en_US
dc.citation.issueNumber4en_US
dc.citation.spage1929en_US
dc.citation.volumeNumber32en_US
dc.contributor.authorGürel, Leventen_US
dc.contributor.authorMalas, T.en_US
dc.date.accessioned2016-02-08T09:57:34Z
dc.date.available2016-02-08T09:57:34Z
dc.date.issued2010-07-06en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.departmentComputational Electromagnetics Research Center (BiLCEM)en_US
dc.description.abstractFor 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.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T09:57:34Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2010en
dc.identifier.doi10.1137/09076101Xen_US
dc.identifier.issn1064-8275
dc.identifier.urihttp://hdl.handle.net/11693/22253
dc.language.isoEnglishen_US
dc.publisherSociety for Industrial and Applied Mathematicsen_US
dc.relation.isversionofhttp://dx.doi.org/10.1137/09076101Xen_US
dc.source.titleSIAM Journal on Scientific Computingen_US
dc.subjectComputational electromagneticsen_US
dc.subjectFast multipole method (FMM)en_US
dc.subjectFlexible solversen_US
dc.subjectIntegral equationsen_US
dc.subjectMultilevel fast multipole algorithm (MLFMA)en_US
dc.subjectPreconditioningen_US
dc.subjectSparse approximate inverse preconditionersen_US
dc.subjectVariable preconditioningen_US
dc.titleIterative near-field preconditioner for the multilevel fast multipole algorithmen_US
dc.typeArticleen_US

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