A family of second-order methods for convex -regularized optimization

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dc.authorwosidNocedal, Jorge/B-7255-2009
dc.authorwosidOztoprak, Figen/ABH-1969-2021
dc.contributor.authorByrd, Richard H.
dc.contributor.authorChin, Gillian M.
dc.contributor.authorNocedal, Jorge
dc.contributor.authorOztoprak, Figen
dc.date.accessioned2024-07-18T20:40:38Z
dc.date.available2024-07-18T20:40:38Z
dc.date.issued2016
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractThis paper is concerned with the minimization of an objective that is the sum of a convex function f and an regularization term. Our interest is in active-set methods that incorporate second-order information about the function f to accelerate convergence. We describe a semismooth Newton framework that can be used to generate a variety of second-order methods, including block active set methods, orthant-based methods and a second-order iterative soft-thresholding method. The paper proposes a new active set method that performs multiple changes in the active manifold estimate at every iteration, and employs a mechanism for correcting these estimates, when needed. This corrective mechanism is also evaluated in an orthant-based method. Numerical tests comparing the performance of three active set methods are presented.en_US
dc.description.sponsorshipNational Science Foundation [DMS-1216554, DMS-1216567]; Department of Energy [DE-SC0001774, DE-FG02-87ER25047]; NSERC; Google Inc.; Scientific and Technological Research Council of Turkey [113M500]; Division Of Mathematical Sciences; Direct For Mathematical & Physical Scien [1216554, 1216567] Funding Source: National Science Foundation; U.S. Department of Energy (DOE) [DE-SC0001774, DE-FG02-87ER25047] Funding Source: U.S. Department of Energy (DOE)en_US
dc.description.sponsorshipRichard H. Byrd was supported by National Science Foundation Grant DMS-1216554 and Department of Energy Grant DE-SC0001774. Gillian M. Chin was supported by an NSERC fellowship and a grant from Google Inc. Jorge Nocedal was supported by National Science Foundation Grant DMS-1216567, and by Department of Energy Grant DE-FG02-87ER25047. Figen Oztoprak was supported by Department of Energy Grant DE-SC0001774 and by Scientific and Technological Research Council of Turkey Grant 113M500.en_US
dc.identifier.doi10.1007/s10107-015-0965-3
dc.identifier.endpage467en_US
dc.identifier.issn0025-5610
dc.identifier.issn1436-4646
dc.identifier.issue1.Şuben_US
dc.identifier.scopus2-s2.0-84949008991en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage435en_US
dc.identifier.urihttps://doi.org/10.1007/s10107-015-0965-3
dc.identifier.urihttps://hdl.handle.net/11411/7150
dc.identifier.volume159en_US
dc.identifier.wosWOS:000382053900014en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofMathematical Programmingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectThresholding Algorithmen_US
dc.subjectNewtonen_US
dc.subjectShrinkageen_US
dc.subjectStrategyen_US
dc.subjectOnlineen_US
dc.titleA family of second-order methods for convex -regularized optimization
dc.typeArticle

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