Search schemes for random optimization algorithms that preserve the asymptotic distribution

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorDorea, CCY
dc.creatorGoncalves, C. R.
dc.date2014-05-20T13:23:29Z
dc.date2016-10-25T16:44:29Z
dc.date2014-05-20T13:23:29Z
dc.date2016-10-25T16:44:29Z
dc.date1999-09-01
dc.date.accessioned2017-04-05T19:57:42Z
dc.date.available2017-04-05T19:57:42Z
dc.identifierJournal of Applied Probability. Sheffield: Applied Probability Trust, v. 36, n. 3, p. 825-836, 1999.
dc.identifier0021-9002
dc.identifierhttp://hdl.handle.net/11449/7086
dc.identifierhttp://acervodigital.unesp.br/handle/11449/7086
dc.identifierWOS:000084090500017
dc.identifierhttp://projecteuclid.org/euclid.jap/1032374637
dc.identifierhttp://www.jstor.org/stable/3215444
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/855864
dc.descriptionMarkovian algorithms for estimating the global maximum or minimum of real valued functions defined on some domain Omega subset of R-d are presented. Conditions on the search schemes that preserve the asymptotic distribution are derived. Global and local search schemes satisfying these conditions are analysed and shown to yield sharper confidence intervals when compared to the i.i.d. case.
dc.languageeng
dc.publisherApplied Probability Trust
dc.relationJournal of Applied Probability
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectrandom search algorithms
dc.subjectglobal optimization
dc.subjectsearch schemes
dc.subjectasymptotic distribution
dc.titleSearch schemes for random optimization algorithms that preserve the asymptotic distribution
dc.typeOtro


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