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hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorAUJOL, Jean-François
hal.structure.identifierMorphologie et Images [MORPHEME]
dc.contributor.authorCALATRONI, Luca
hal.structure.identifierInstitut National des Sciences Appliquées - Toulouse [INSA Toulouse]
dc.contributor.authorDOSSAL, Charles
hal.structure.identifierInstitut National des Sciences Appliquées - Toulouse [INSA Toulouse]
dc.contributor.authorLABARRIÈRE, Hippolyte
hal.structure.identifierInstitut National des Sciences Appliquées - Toulouse [INSA Toulouse]
hal.structure.identifierÉquipe Recherche Opérationnelle, Optimisation Combinatoire et Contraintes [LAAS-ROC]
hal.structure.identifierInstitut de Mathématiques de Toulouse UMR5219 [IMT]
dc.contributor.authorRONDEPIERRE, Aude
dc.date.accessioned2024-04-04T02:33:32Z
dc.date.available2024-04-04T02:33:32Z
dc.date.created2023-07
dc.date.issued2023-07-27
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/190489
dc.description.abstractEnWe consider a combined restarting and adaptive backtracking strategy for the popular Fast IterativeShrinking-Thresholding Algorithm frequently employed for accelerating the convergence speed of large-scale structured convex optimization problems. Several variants of FISTA enjoy a provable linear convergence rate for the function values $F(x_n)$ of the form $\mathcal{O}( e^{-K\sqrt{\mu/L}~n})$ under the prior knowledge of problem conditioning, i.e. of the ratio between the (\L ojasiewicz) parameter $\mu$ determining the growth of the objective function and the Lipschitz constant $L$ of its smooth component. These parameters are nonetheless hard to estimate in many practical cases. Recent works address the problem by estimating either parameter via suitable adaptive strategies. In our work both parameters can be estimated at the same time by means of an algorithmic restarting scheme where, at each restart, a non-monotone estimation of $L$ is performed. For this scheme, theoretical convergence results are proved, showing that a $\mathcal{O}( e^{-K\sqrt{\mu/L}n})$ convergence speed can still be achieved along with quantitative estimates of the conditioning. The resulting Free-FISTA algorithm is therefore parameter-free. Several numerical results are reported to confirm the practical interest of its use in many exemplar problems.
dc.description.sponsorshipMathématiques de l'optimisation déterministe et stochastique liées à l'apprentissage profond - ANR-19-CE23-0017
dc.language.isoen
dc.subject.enComposite optimization
dc.subject.enRestart
dc.subject.enBacktracking
dc.subject.enLojasiewicz property
dc.subject.enLipschitz constant
dc.subject.enAcceleration
dc.title.enParameter-Free FISTA by Adaptive Restart and Backtracking
dc.typeDocument de travail - Pré-publication
dc.subject.halMathématiques [math]/Optimisation et contrôle [math.OC]
dc.identifier.arxiv2206.06853
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
hal.identifierhal-04172497
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04172497v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2023-07-27&rft.au=AUJOL,%20Jean-Fran%C3%A7ois&CALATRONI,%20Luca&DOSSAL,%20Charles&LABARRI%C3%88RE,%20Hippolyte&RONDEPIERRE,%20Aude&rft.genre=preprint


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