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dc.contributor.authorLiao, Aipingen_US
dc.date.accessioned2007-04-04T13:07:19Z
dc.date.available2007-04-04T13:07:19Z
dc.date.issued1994-08en_US
dc.identifier.citationhttp://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.tc/94-190en_US
dc.identifier.urihttps://hdl.handle.net/1813/5525
dc.description.abstractIn this paper we present a simple algorithm for global optimization. This algorithm combines random searches with efficient local minimization algorithms. The proposed algorithm begins with an initial "local minimizer." In each iteration, a search direction is generated randomly, along which some points are chosen as the initial points for the local optimization algorithm and several "local minimizers" are obtained. The next iteration is determined by comparing these localminimizers. We will discuss the expected number of iterations for finding a global minimizer with this algorithm. Several variants of the algorithm that take advantage of the partially separable structure are proposed for the Lennard-Jones cluster problem and tested on the IBM SP1 parallel computer. Our numerical results show that our algorithms are promising.en_US
dc.format.extent246951 bytes
dc.format.extent298409 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.language.isoen_USen_US
dc.publisherCornell Universityen_US
dc.subjecttheory centeren_US
dc.subjectglobal optimizationen_US
dc.subjectpartially separable structureen_US
dc.subjectLennard-Jones potential functionen_US
dc.titleA New Parallel Algorithm for Global Optimization with Application to the Molecular Cluster Problemen_US
dc.typetechnical reporten_US


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