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dc.contributor.authorRingholm, Torbjørn
dc.contributor.authorLazic, Jasmina
dc.contributor.authorSchönlieb, Carola-Bibiane
dc.date.accessioned2019-06-27T08:51:30Z
dc.date.available2019-06-27T08:51:30Z
dc.date.created2018-09-21T13:09:13Z
dc.date.issued2018
dc.identifier.issn1936-4954
dc.identifier.urihttp://hdl.handle.net/11250/2602488
dc.description.abstractThis paper concerns an optimization algorithm for unconstrained nonconvex problems where the objective function has sparse connections between the unknowns. The algorithm is based on applying a dissipation preserving numerical integrator, the Itoh--Abe discrete gradient scheme, to the gradient flow of an objective function, guaranteeing energy decrease regardless of step size. We introduce the algorithm, prove a convergence rate estimate for nonconvex problems with Lipschitz continuous gradients, and show an improved convergence rate if the objective function has sparse connections between unknowns. The algorithm is presented in serial and parallel versions. Numerical tests show its use in Euler's elastica regularized imaging problems and its convergence rate and compare the execution time of the method to that of the iPiano algorithm and the gradient descent and heavy-ball algorithms.nb_NO
dc.language.isoengnb_NO
dc.publisherSociety for Industrial and Applied Mathematicsnb_NO
dc.titleVariational image regularization with Euler's elastica using a discrete gradient schemenb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.journalSIAM Journal of Imaging Sciencesnb_NO
dc.identifier.doi10.1137/17M1162354
dc.identifier.cristin1612081
dc.relation.projectNorges forskningsråd: 231632nb_NO
dc.description.localcode© 2018, Society for Industrial and Applied Mathematics Read More: https://epubs.siam.org/doi/10.1137/17M1162354nb_NO
cristin.unitcode194,63,15,0
cristin.unitnameInstitutt for matematiske fag
cristin.ispublishedfalse
cristin.fulltextpostprint
cristin.qualitycode1


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