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Regularization Using a Parameterized Trust Region Subproblem

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dc.contributor.authorGrodzevich, Olegen
dc.date.accessioned2006-08-22T14:29:56Z
dc.date.available2006-08-22T14:29:56Z
dc.date.issued2004en
dc.date.submitted2004en
dc.description.abstractWe present a new method for regularization of ill-conditioned problems that extends the traditional trust-region approach. Ill-conditioned problems arise, for example, in image restoration or mathematical processing of medical data, and involve matrices that are very ill-conditioned. The method makes use of the L-curve and L-curve maximum curvature criterion as a strategy recently proposed to find a good regularization parameter. We describe the method and show its application to an image restoration problem. We also provide a MATLAB code for the algorithm. Finally, a comparison to the CGLS approach is given and analyzed, and future research directions are proposed.en
dc.formatapplication/pdfen
dc.format.extent653697 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10012/1159
dc.language.isoenen
dc.pendingfalseen
dc.publisherUniversity of Waterlooen
dc.rightsCopyright: 2004, Grodzevich, Oleg. All rights reserved.en
dc.subjectMathematicsen
dc.subjectregularizationen
dc.subjectill-poseden
dc.subjectinverse imaging problemen
dc.subjectnumerically harden
dc.subjectrobustnessen
dc.subjectalgorithmsen
dc.subjectprogrammingen
dc.subjectefficiencyen
dc.subjectconjugate gradienten
dc.titleRegularization Using a Parameterized Trust Region Subproblemen
dc.typeMaster Thesisen
uws-etd.degreeMaster of Mathematicsen
uws-etd.degree.departmentCombinatorics and Optimizationen
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen
uws.typeOfResourceTexten

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Combinatorics and Optimization