DC FieldValueLanguage
dc.contributor.authorGonçalves-E-Silva, Kayoen
dc.contributor.authorAloise, Danielen
dc.contributor.authorXavier-De-Souza, Samuelen
dc.contributor.authorMladenović, Nenaden
dc.date.accessioned2020-05-02T16:41:54Z-
dc.date.available2020-05-02T16:41:54Z-
dc.date.issued2018-06-05en
dc.identifier.issn0354-0243en
dc.identifier.urihttp://researchrepository.mi.sanu.ac.rs/handle/123456789/2386-
dc.description.abstractNelder-Mead method (NM) for solving continuous non-linear optimization problem is probably the most cited and the most used method in the optimization literature and in practical applications, too. It belongs to the direct search methods, those which do not use the first and the second order derivatives. The popularity of NM is based on its simplicity. In this paper we propose even more simple algorithm for larger instances that follows NM idea. We call it Simplified NM (SNM): instead of generating all n + 1 simplex points in Rn, we perform search using just q + 1 vertices, where q is usually much smaller than n. Though the results cannot be better than after performing calculations in n+1 points as in NM, significant speed-up allows to run many times SNM from different starting solutions, usually getting better results than those obtained by NM within the same cpu time. Computational analysis is performed on 10 classical convex and non-convex instances, where the number of variables n can be arbitrarily large. The obtained results show that SNM is more effective than the original NM, confirming that LIMA yields good results when solving a continuous optimization problem.en
dc.publisherFaculty of Organizational Sciences, University of Belgrade-
dc.relationCNPq-Brazil grants 308887/2014-0 and 400350/2014-9-
dc.relationRSF grant 14-41-00039-
dc.relation.ispartofYugoslav Journal of Operations Researchen
dc.subjectContinuous optimization | Direct search methods | Less is more approach | Nelder mead methoden
dc.titleLess is more: Simplified nelder-mead method for large unconstrained optimizationen
dc.typeArticleen
dc.identifier.doi10.2298/YJOR180120014Gen
dc.identifier.scopus2-s2.0-85049141437en
dc.relation.firstpage153en
dc.relation.lastpage169en
dc.relation.issue2en
dc.relation.volume28en
dc.description.rankM51-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeArticle-
item.grantfulltextnone-
crisitem.author.orcid0000-0001-6655-0409-
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