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Conference Paper: Passivity-preserving model reduction via a computationally efficient project-and-balance scheme

TitlePassivity-preserving model reduction via a computationally efficient project-and-balance scheme
Authors
KeywordsDominant Eigenspace
Model Reduction
Projection
Riccati Equation
SR Algorithm
Stochastic Balanced Truncation
Issue Date2004
Citation
Proceedings - Design Automation Conference, 2004, p. 369-374 How to Cite?
AbstractThis paper presents an efficient two-stage project-and-balance scheme for passivity-preserving model order reduction. Orthogonal dominant eigenspace projection is implemented by integrating the Smith method and Krylov subspace iteration. It is followed by stochastic balanced truncation wherein a novel method, based on the complete separation of stable and unstable invariant subspaces of a Hamiltonian matrix, is used for solving two dual algebraic Riccati equations at the cost of essentially one. A fast-converging quadruple-shift bulge-chasing SR algorithm is also introduced for this purpose. Numerical examples confirm the quality of the reduced-order models over those from conventional schemes.
Persistent Identifierhttp://hdl.handle.net/10722/98846
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorWong, Nen_HK
dc.contributor.authorBalakrishnan, Ven_HK
dc.contributor.authorKoh, CKen_HK
dc.date.accessioned2010-09-25T18:04:41Z-
dc.date.available2010-09-25T18:04:41Z-
dc.date.issued2004en_HK
dc.identifier.citationProceedings - Design Automation Conference, 2004, p. 369-374en_HK
dc.identifier.issn0738-100Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/98846-
dc.description.abstractThis paper presents an efficient two-stage project-and-balance scheme for passivity-preserving model order reduction. Orthogonal dominant eigenspace projection is implemented by integrating the Smith method and Krylov subspace iteration. It is followed by stochastic balanced truncation wherein a novel method, based on the complete separation of stable and unstable invariant subspaces of a Hamiltonian matrix, is used for solving two dual algebraic Riccati equations at the cost of essentially one. A fast-converging quadruple-shift bulge-chasing SR algorithm is also introduced for this purpose. Numerical examples confirm the quality of the reduced-order models over those from conventional schemes.en_HK
dc.languageengen_HK
dc.relation.ispartofProceedings - Design Automation Conferenceen_HK
dc.subjectDominant Eigenspaceen_HK
dc.subjectModel Reductionen_HK
dc.subjectProjectionen_HK
dc.subjectRiccati Equationen_HK
dc.subjectSR Algorithmen_HK
dc.subjectStochastic Balanced Truncationen_HK
dc.titlePassivity-preserving model reduction via a computationally efficient project-and-balance schemeen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailWong, N:nwong@eee.hku.hken_HK
dc.identifier.authorityWong, N=rp00190en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-4444296154en_HK
dc.identifier.hkuros89933en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-4444296154&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage369en_HK
dc.identifier.epage374en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridWong, N=35235551600en_HK
dc.identifier.scopusauthoridBalakrishnan, V=7102659847en_HK
dc.identifier.scopusauthoridKoh, CK=7201749804en_HK

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