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dc.contributor.authorBergmann, Ronny
dc.contributor.authorFerreira, Orizon P.
dc.contributor.authorM. Santos, Elianderson
dc.contributor.authorSouza, João Carlos O.
dc.date.accessioned2024-04-10T13:36:47Z
dc.date.available2024-04-10T13:36:47Z
dc.date.created2024-02-26T16:40:47Z
dc.date.issued2024
dc.identifier.citationJ Optim Theory Appl (2024)en_US
dc.identifier.issn0022-3239
dc.identifier.urihttps://hdl.handle.net/11250/3125859
dc.description.abstractIn this paper, we propose a Riemannian version of the difference of convex algorithm (DCA) to solve a minimization problem involving the difference of convex (DC) function. The equivalence between the classical and simplified Riemannian versions of the DCA is established. We also prove that under mild assumptions the Riemannian version of the DCA is well defined and every cluster point of the sequence generated by the proposed method, if any, is a critical point of the objective DC function. Some duality relations between the DC problem and its dual are also established. To illustrate the algorithm’s effectiveness, some numerical experiments are presented.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.titleThe Difference of Convex Algorithm on Hadamard Manifoldsen_US
dc.title.alternativeThe Difference of Convex Algorithm on Hadamard Manifoldsen_US
dc.typePreprinten_US
dc.description.versionsubmittedVersionen_US
dc.source.journalJournal of Optimization Theory and Applicationsen_US
dc.identifier.doi10.1007/s10957-024-02392-8
dc.identifier.cristin2249967
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode1


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