A hybrid neural optimization scheme based on parallel updates

dc.contributor.authorPapageorgiou, G.en
dc.contributor.authorLikas, A.en
dc.contributor.authorStafylopatis, A.en
dc.date.accessioned2015-11-24T17:03:03Z
dc.date.available2015-11-24T17:03:03Z
dc.identifier.issn0020-7160-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/11126
dc.rightsDefault Licence-
dc.subjectoptimizationen
dc.subjectparallel computingen
dc.subjectboltzmann machineen
dc.subjectcauchy machineen
dc.subjectboltzmann machinesen
dc.subjectnetworksen
dc.subjectmodelsen
dc.titleA hybrid neural optimization scheme based on parallel updatesen
heal.abstractA synchronous Hopfield-type neural network model containing units with analog input and binary output, which is suitable for parallel implementation, is examined in the context of solving discrete optimization problems. A hybrid parallel update scheme concerning the stochastic input-output behaviour of each unit is presented. This parallel update scheme maintains the solution quality of the Boltzmann Machine optimizer, which is inherently sequential. Experimental results on the Maximum Independent Set problem demonstrate the benefit of using the proposed optimizer in terms of computation time. Excellent speedup has been obtained through parallel implementation on both shared memory and distributed memory architecures.en
heal.accesscampus-
heal.fullTextAvailabilityTRUE-
heal.journalNameInternational Journal of Computer Mathematicsen
heal.journalTypepeer reviewed-
heal.languageen-
heal.publicationDate1998-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
heal.typejournalArticle-
heal.type.elΆρθρο Περιοδικούel
heal.type.enJournal articleen

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