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SLiSeS

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dc.contributor.authorBellavia, Stefania
dc.contributor.authorKrejić, Nataša
dc.contributor.authorKrklec Jerinkić, Nataša
dc.contributor.authorRaydan, Marcos
dc.contributor.institutionCMA - Centro de Matemática e Aplicações
dc.contributor.pblTaylor & Francis
dc.date.accessioned2025-02-18T21:23:15Z
dc.date.available2025-02-18T21:23:15Z
dc.date.issued2026
dc.descriptionPublisher Copyright: © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
dc.description.abstractThe spectral gradient method is known to be a powerful low-cost tool for solving large-scale optimization problems. In this paper, our goal is to exploit its advantages in the stochastic optimization framework, especially in the case of mini-batch subsampling that is often used in big data settings. To allow the spectral coefficient to properly explore the underlying approximate Hessian spectrum, we keep the same subsample for a prefixed number of iterations before subsampling again. We analyse the required algorithmic features and the conditions for almost sure convergence, and present initial numerical results that show the advantages of the proposed method.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent26
dc.format.extent3316486
dc.identifier.doi10.1080/10556788.2024.2426620
dc.identifier.issn1055-6788
dc.identifier.otherPURE: 110761265
dc.identifier.otherPURE UUID: 2892d5df-dd43-499d-a13a-55105c7f6668
dc.identifier.otherScopus: 85211448291
dc.identifier.otherWOS: 001372956300001
dc.identifier.urihttp://hdl.handle.net/10362/179297
dc.identifier.urlhttps://www.scopus.com/pages/publications/85211448291
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001372956300001
dc.language.isoeng
dc.peerreviewedyes
dc.relationCenter for Mathematics and Applications
dc.relationCenter for Mathematics and Applications
dc.subjectFinite sum minimization
dc.subjectLine search
dc.subjectSpectral gradient methods
dc.subjectSubsampling
dc.subjectSoftware
dc.subjectControl and Optimization
dc.subjectApplied Mathematics
dc.titleSLiSeSen
dc.title.subtitlesubsampled line search spectral gradient method for finite sumsen
dc.typejournal article
degois.publication.firstPage524
degois.publication.issue2
degois.publication.lastPage549
degois.publication.titleOptimization Methods and Software
degois.publication.volume41
dspace.entity.typePublication
oaire.awardNumberUIDB/00297/2020
oaire.awardNumberUIDP/00297/2020
oaire.awardTitleCenter for Mathematics and Applications
oaire.awardTitleCenter for Mathematics and Applications
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00297%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00297%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublicationd00ae22f-ec2b-47b2-935e-60cb44493cc6
relation.isProjectOfPublication65d392f7-8781-4d70-b9f3-069b07d4a311
relation.isProjectOfPublication.latestForDiscoveryd00ae22f-ec2b-47b2-935e-60cb44493cc6

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