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Global Convergence and Asymptotic Optimality of the Heavy Ball Method for a Class of Nonconvex Optimization Problems

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dc.contributor.authorOugrinovski, Valeri
dc.contributor.authorPetersen, Ian
dc.contributor.authorShames, Iman
dc.date.accessioned2024-05-06T01:52:51Z
dc.date.issued2022
dc.date.updated2023-01-08T07:17:03Z
dc.description.abstractIn this letter we revisit the famous heavy ball method and study its global convergence for a class of non-convex problems with sector-bounded gradient. We characterize the parameters that render the method globally convergent and yield the best R-convergence factor. We show that for this family of functions, this convergence factor is superior to the factor obtained from the triple momentum method.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn2475-1456en_AU
dc.identifier.urihttp://hdl.handle.net/1885/317297
dc.language.isoen_AUen_AU
dc.publisherIEEE Control Systems Societyen_AU
dc.rights© 2022 IEEEen_AU
dc.sourceIEEE Control Systems Lettersen_AU
dc.subjectOptimization algorithmsen_AU
dc.subjectrobust controlen_AU
dc.titleGlobal Convergence and Asymptotic Optimality of the Heavy Ball Method for a Class of Nonconvex Optimization Problemsen_AU
dc.typeJournal articleen_AU
local.bibliographicCitation.lastpage2454en_AU
local.bibliographicCitation.startpage2449en_AU
local.contributor.affiliationOugrinovski, Valeri, College of Engineering, Computing and Cybernetics, ANUen_AU
local.contributor.affiliationPetersen, Ian, College of Engineering, Computing and Cybernetics, ANUen_AU
local.contributor.affiliationShames, Iman, College of Engineering, Computing and Cybernetics, ANUen_AU
local.contributor.authoruidOugrinovski, Valeri, u5258088en_AU
local.contributor.authoruidPetersen, Ian, u4036493en_AU
local.contributor.authoruidShames, Iman, u4353999en_AU
local.description.embargo2099-12-31
local.description.notesImported from ARIESen_AU
local.identifier.absfor400705 - Control engineeringen_AU
local.identifier.absseo280110 - Expanding knowledge in engineeringen_AU
local.identifier.ariespublicationa383154xPUB30049en_AU
local.identifier.citationvolume6en_AU
local.identifier.doi10.1109/LCSYS.2022.3163408en_AU
local.identifier.scopusID2-s2.0-85127504811
local.publisher.urlhttps://www.ieee.org/en_AU
local.type.statusPublished Versionen_AU

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