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dc.contributor.authorTsoulos, I. G.en
dc.contributor.authorLagaris, I. E.en
dc.date.accessioned2015-11-24T17:01:26Z-
dc.date.available2015-11-24T17:01:26Z-
dc.identifier.issn0010-4655-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/10924-
dc.rightsDefault Licence-
dc.subjectglobal optimizationen
dc.subjectstochastic methodsen
dc.subjectmonte carloen
dc.subjectclusteringen
dc.subjectregion of attractionen
dc.subjectglobal optimizationen
dc.subjectalgorithmen
dc.titleMinFinder: Locating all the local minima of a functionen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.identifier.primaryDOI 10.1016/j.cpc.2005.10.001-
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
heal.publicationDate2006-
heal.abstractA new stochastic clustering algorithm is introduced that aims to locate all the local minima of a multidimensional continuous and differentiable function inside a bounded domain. The accompanying software (MinFinder) is written in ANSI C++. However, the user may code his objective function either in C++, C or Fortran 77. We compare the performance of this new method to the performance of Multistart and Topographical Multilevel Single Linkage Clustering on a set of benchmark problems.en
heal.journalNameComputer Physics Communicationsen
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)

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