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An Infeasible Point Method for Minimizing the Lennard-Jones Potential
dc.contributor.authorGockenbach, Mark S.en_US
dc.contributor.authorKearsley, Anthony J.en_US
dc.contributor.authorSymes, William W.en_US
dc.date.accessioned2006-09-11T15:16:25Z
dc.date.available2006-09-11T15:16:25Z
dc.date.issued1997-11en_US
dc.identifier.citationGockenbach, Mark S.; Kearsley, Anthony J.; Symes, William W.; (1997). "An Infeasible Point Method for Minimizing the Lennard-Jones Potential." Computational Optimization and Applications 8(3): 273-286. <http://hdl.handle.net/2027.42/44788>en_US
dc.identifier.issn0926-6003en_US
dc.identifier.issn1573-2894en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/44788
dc.description.abstractMinimizing the Lennard-Jones potential, the most-studied modelproblem for molecular conformation, is an unconstrained globaloptimization problem with a large number of local minima. In thispaper, the problem is reformulated as an equality constrainednonlinear programming problem with only linear constraints. Thisformulation allows the solution to approached through infeasibleconfigurations, increasing the basin of attraction of the globalsolution. In this way the likelihood of finding a global minimizeris increased. An algorithm for solving this nonlinear program isdiscussed, and results of numerical tests are presented.en_US
dc.format.extent99758 bytes
dc.format.extent3115 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherKluwer Academic Publishers; Springer Science+Business Mediaen_US
dc.subject.otherMathematicsen_US
dc.subject.otherConvex and Discrete Geometryen_US
dc.subject.otherOptimizationen_US
dc.subject.otherOperations Research, Mathematical Programmingen_US
dc.subject.otherStatistics, Generalen_US
dc.subject.otherOperation Research/Decision Theoryen_US
dc.subject.otherGlobal Optimizationen_US
dc.subject.otherPenalty Methodsen_US
dc.subject.otherNon Linear Programmingen_US
dc.titleAn Infeasible Point Method for Minimizing the Lennard-Jones Potentialen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Mathematics, University of Michigan, Ann Arbor, MI, 48109-0001en_US
dc.contributor.affiliationotherApplied and Computational Mathematics Division, National Institute of Standards and Technology, Gaithersburg, MD, 20899en_US
dc.contributor.affiliationotherDepartment of Computational & Applied Mathematics, Rice University, Houston, Texas, 77251-1892en_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/44788/1/10589_2004_Article_140555.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1023/A:1008627606581en_US
dc.identifier.sourceComputational Optimization and Applicationsen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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