Improving Hit-and-Run for global optimization
dc.contributor.author | Smith, Robert L. | en_US |
dc.contributor.author | Zabinsky, Zelda Barbara | en_US |
dc.contributor.author | McDonald, J. Fred | en_US |
dc.contributor.author | Romeijn, H. Edwin | en_US |
dc.contributor.author | Kaufman, David E. | en_US |
dc.date.accessioned | 2006-09-11T15:27:36Z | |
dc.date.available | 2006-09-11T15:27:36Z | |
dc.date.issued | 1993-06 | en_US |
dc.identifier.citation | Zabinsky, Zelda B.; Smith, Robert L.; McDonald, J. Fred; Romeijn, H. Edwin; Kaufman, David E.; (1993). "Improving Hit-and-Run for global optimization." Journal of Global Optimization 3(2): 171-192. <http://hdl.handle.net/2027.42/44932> | en_US |
dc.identifier.issn | 0925-5001 | en_US |
dc.identifier.issn | 1573-2916 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/44932 | |
dc.description.abstract | Improving Hit-and-Run is a random search algorithm for global optimization that at each iteration generates a candidate point for improvement that is uniformly distributed along a randomly chosen direction within the feasible region. The candidate point is accepted as the next iterate if it offers an improvement over the current iterate. We show that for positive definite quadratic programs, the expected number of function evaluations needed to arbitrarily well approximate the optimal solution is at most O(n 5/2 ) where n is the dimension of the problem. Improving Hit-and-Run when applied to global optimization problems can therefore be expected to converge polynomially fast as it approaches the global optimum. | en_US |
dc.format.extent | 944759 bytes | |
dc.format.extent | 3115 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Kluwer Academic Publishers; Springer Science+Business Media | en_US |
dc.subject.other | Global Optimization | en_US |
dc.subject.other | Monte Carlo Optimization | en_US |
dc.subject.other | Operation Research/Decision Theory | en_US |
dc.subject.other | Real Functions | en_US |
dc.subject.other | Economics / Management Science | en_US |
dc.subject.other | Computer Science, General | en_US |
dc.subject.other | Optimization | en_US |
dc.subject.other | Random Search | en_US |
dc.subject.other | Algorithm Complexity | en_US |
dc.title | Improving Hit-and-Run for global optimization | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Mathematics | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Industrial and Operations Engineering, The University of Michigan, 48109-2117, Ann Arbor, Michigan, USA | en_US |
dc.contributor.affiliationum | Department of Industrial and Operations Engineering, The University of Michigan, 48109-2117, Ann Arbor, Michigan, USA | en_US |
dc.contributor.affiliationother | Department of Operations Research & Tinbergen Institute, Erasmus University Rotterdam, P.O. Box 1738, NL-3000, DR Rotterdam, The Netherlands | en_US |
dc.contributor.affiliationother | Industrial Engineering Program, FU-20, University of Washington, 98195, Seattle, Washington, USA | en_US |
dc.contributor.affiliationother | Department of Mathematics and Statistics, University of Windsor, N9B 3P4, Windsor, Ontario, Canada | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/44932/1/10898_2005_Article_BF01096737.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1007/BF01096737 | en_US |
dc.identifier.source | Journal of Global Optimization | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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