Global optimization requires global information

dc.contributor.author	Baritompa, William P.
dc.contributor.author	Stephens, Chris.
dc.date.accessioned	2015-12-15T22:12:22Z
dc.date.available	2015-12-15T22:12:22Z
dc.date.issued	1996	en
dc.description.abstract	There are many global optimization algorithms which do not use global information. We broaden previous results, showing limitations on such algorithms, even if allowed to run forever. We show deterministic algorithms must sample a dense set to find the global optimum value and can never be guaranteed to converge only to global optimizers. Further, analogous results show introducing a stochastic element does not overcome these limitations. An example is simulated annealing in practice. Our results show there are functions for which the probability of success is arbitrarily small.	en
dc.identifier.issn	1172-8531
dc.identifier.uri	http://hdl.handle.net/10092/11614
dc.language.iso	en
dc.publisher	University of Canterbury. Dept. of Mathematics	en
dc.relation.isreferencedby	NZCU
dc.rights	Copyright William P. Baritompa	en
dc.rights.uri	https://canterbury.libguides.com/rights/theses
dc.subject	Global optimization	en
dc.subject	convergence	en
dc.subject	stochastic algorithms	en
dc.subject	deterministic algorithms	en
dc.subject.anzsrc	Field of Research::01 - Mathematical Sciences	en
dc.title	Global optimization requires global information	en
dc.type	Discussion / Working Papers
thesis.degree.name	Research Report	en
uc.bibnumber	580236
uc.college	Faculty of Engineering

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