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Article: A smoothing Newton's method for the construction of a damped vibrating system from noisy test eigendata

TitleA smoothing Newton's method for the construction of a damped vibrating system from noisy test eigendata
Authors
KeywordsDamped Vibrating System
Eigendata
Inverse Quadratic Eigenvalue Problem
Newton's Method
Quadratic Eigenvalue Problem
Issue Date2009
Citation
Numerical Linear Algebra With Applications, 2009, v. 16 n. 2, p. 109-128 How to Cite?
AbstractIn this paper we consider an inverse problem for a damped vibration system from the noisy measured eigendata, where the mass, damping, and stiffness matrices are all symmetric positive-definite matrices with the mass matrix being diagonal and the damping and stiffness matrices being tridiagonal. To take into consideration the noise in the data, the problem is formulated as a convex optimization problem involving quadratic constraints on the unknown mass, damping, and stiffness parameters. Then we propose a smoothing Newton-type algorithm for the optimization problem, which improves a pre-existing estimate of a solution to the inverse problem. We show that the proposed method converges both globally and quadratically. Numerical examples arc also given to demonstrate the efficiency of our method. ©2008 John Wiley & Sons, Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/156238
ISSN
2015 Impact Factor: 1.431
2015 SCImago Journal Rankings: 1.250
ISI Accession Number ID
Funding AgencyGrant Number
National Natural Science Foundation of China10601043
Program for New Century Excellent Talents in Xiamen University
RGC7017/07P
HKU CRCG
HKU Strategic Research Theme Fund on Computational Science
Funding Information:

Contract/grant sponsor: HKU Strategic Research Theme Fund on Computational Science

References

 

DC FieldValueLanguage
dc.contributor.authorBai, ZJen_US
dc.contributor.authorChing, WKen_US
dc.date.accessioned2012-08-08T08:40:59Z-
dc.date.available2012-08-08T08:40:59Z-
dc.date.issued2009en_US
dc.identifier.citationNumerical Linear Algebra With Applications, 2009, v. 16 n. 2, p. 109-128en_US
dc.identifier.issn1070-5325en_US
dc.identifier.urihttp://hdl.handle.net/10722/156238-
dc.description.abstractIn this paper we consider an inverse problem for a damped vibration system from the noisy measured eigendata, where the mass, damping, and stiffness matrices are all symmetric positive-definite matrices with the mass matrix being diagonal and the damping and stiffness matrices being tridiagonal. To take into consideration the noise in the data, the problem is formulated as a convex optimization problem involving quadratic constraints on the unknown mass, damping, and stiffness parameters. Then we propose a smoothing Newton-type algorithm for the optimization problem, which improves a pre-existing estimate of a solution to the inverse problem. We show that the proposed method converges both globally and quadratically. Numerical examples arc also given to demonstrate the efficiency of our method. ©2008 John Wiley & Sons, Ltd.en_US
dc.languageengen_US
dc.relation.ispartofNumerical Linear Algebra with Applicationsen_US
dc.subjectDamped Vibrating Systemen_US
dc.subjectEigendataen_US
dc.subjectInverse Quadratic Eigenvalue Problemen_US
dc.subjectNewton's Methoden_US
dc.subjectQuadratic Eigenvalue Problemen_US
dc.titleA smoothing Newton's method for the construction of a damped vibrating system from noisy test eigendataen_US
dc.typeArticleen_US
dc.identifier.emailChing, WK:wching@hku.hken_US
dc.identifier.authorityChing, WK=rp00679en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1002/nla.608en_US
dc.identifier.scopuseid_2-s2.0-60749090313en_US
dc.identifier.hkuros154203-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-60749090313&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume16en_US
dc.identifier.issue2en_US
dc.identifier.spage109en_US
dc.identifier.epage128en_US
dc.identifier.isiWOS:000262765500002-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridBai, ZJ=7202524302en_US
dc.identifier.scopusauthoridChing, WK=13310265500en_US

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