Sequential and parallel preconditioners for large-scale integral-equation problems

buir.contributor.authorGürel, Levent
buir.contributor.authorErgül, Özgür
dc.citation.epage43en_US
dc.citation.spage35en_US
dc.contributor.authorMalas, Tahiren_US
dc.contributor.authorErgül, Özgüren_US
dc.contributor.authorGürel, Leventen_US
dc.coverage.spatialİzmir, Turkeyen_US
dc.date.accessioned2016-02-08T11:42:34Zen_US
dc.date.available2016-02-08T11:42:34Zen_US
dc.date.issued2007en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.departmentComputational Electromagnetics Research Center (BiLCEM)en_US
dc.descriptionDate of Conference: 30-31 August 2007en_US
dc.descriptionConference Name: Computational Electromagnetics Workshop, IEEE 2007en_US
dc.description.abstractFor efficient solutions of integral-equation methods via the multilevel fast multipole algorithm (MLFMA), effective preconditioners are required. In this paper we review appropriate preconditioners that have been used for sparse systems and developed specially in the context of MLFMA. First we review the ILU-type preconditioners that are suitable for sequential implementations. We can make these preconditioners robust and efficient for integral-equation methods by making appropriate selections and by employing pivoting to suppress the instability problem. For parallel implementations, the sparse approximate inverse or the iterative solution of the near-field system enables fast convergence up to certain problem sizes. However, for very large problems, the near-field matrix itself becomes insufficient to approximate the dense system matrix and preconditioners generated from the near-field interactions cannot be effective. Therefore, we propose an approximation strategy to MLFMA to be used as an effective preconditioner. Our numerical experiments reveal that this scheme significantly outperforms other preconditioners. With the combined effort of effective preconditioners and an efficiently parallelized MLFMA, we are able to solve problems with tens of millions of unknowns in a few hours. We report the solution of integral-equation problems that are among the largest in their classes. © 2007 IEEE.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T11:42:34Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2007en
dc.identifier.doi10.1109/CEM.2007.4387648en_US
dc.identifier.urihttp://hdl.handle.net/11693/27034en_US
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/CEM.2007.4387648en_US
dc.source.titleProceedings of the Computational Electromagnetics Workshop, IEEE 2007en_US
dc.subjectLarge-scale systemsen_US
dc.subjectIntegral equationsen_US
dc.subjectVirtual manufacturingen_US
dc.subjectMLFMAen_US
dc.subjectLinear systemsen_US
dc.subjectSparse matricesen_US
dc.subjectTinen_US
dc.subjectComputational electromagneticsen_US
dc.subjectRobustnessen_US
dc.subjectBoundary conditionsen_US
dc.titleSequential and parallel preconditioners for large-scale integral-equation problemsen_US
dc.typeConference Paperen_US

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