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dc.contributor.authorWatson, Layne T.en_US
dc.date.accessioned2013-06-19T14:36:33Z
dc.date.available2013-06-19T14:36:33Z
dc.date.issued1999-09-01
dc.identifierhttp://eprints.cs.vt.edu/archive/00000508/en_US
dc.identifier.urihttp://hdl.handle.net/10919/20016
dc.descriptionFor many years, globally convergent probability-one homotopy methods have been remarkably successful on difficult realistic engineering optimization problems, most of which were attacked by homotopy methods because other optimization algorithms failed or were ineffective. Convergence theory has been derived for a few particular problems, and considerable fixed point theory exists, but generally convergence theory for the homotopy maps used in practice for nonlinear constrained optimization has been lacking. This paper derives some probability-one homotopy convergence theorems for unconstrained and constrained optimization, for linear and nonlinear constraints, and with and without convexity. Some insight is provided into why the homotopies used in engineering practice are so successful, and why this success is more than dumb luck. By presenting the theory as variations on a prototype probability-one homotopy convergence theorem, the essence of such convergence theory is elucidated.en_US
dc.format.mimetypeapplication/postscripten_US
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen_US
dc.subjectNumerical analysisen_US
dc.subjectHistorical Collection(Till Dec 2001)en_US
dc.titleTheory of Globally Convergent Probability-One Homotopies for Nonlinear Programmingen_US
dc.typeTechnical reporten_US
dc.identifier.trnumberTR-99-04en_US
dc.type.dcmitypeTexten_US
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00000508/01/TR-99-04.ps


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