Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems

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dc.contributor.authorAbramson, Mark A.en_US
dc.contributor.authorAudet, Charlesen_US
dc.contributor.authorDennis, J.E. Jr.en_US
dc.date.accessioned2018-06-18T17:52:01Zen_US
dc.date.available2018-06-18T17:52:01Zen_US
dc.date.issued2004-06en_US
dc.date.noteJune 2004en_US
dc.description.abstractA new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented. This class combines and extends the Audet-Dennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPS-filter algorithms for general nonlinear constraints. In generalizing existing algorithms, new theoretical convergence results are presented that reduce seamlessly to existing results for more specific classes of problems. While no local continuity or smoothness assumptions are required to apply the algorithm, a hierarchy of theoretical convergence results based on the Clarke calculus is given, in which local smoothness dictate what can be proved about certain limit points generated by the algorithm. To demonstrate the usefulness of the algorithm, the algorithm is applied to the design of a load-bearing thermal insulation system. We believe this is the first algorithm with provable convergence results to directly target this class of problems.en_US
dc.format.extent29 ppen_US
dc.identifier.citationAbramson, Mark A., Audet, Charles and Dennis, J.E. Jr.. "Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems." (2004) <a href="https://hdl.handle.net/1911/102021">https://hdl.handle.net/1911/102021</a>.en_US
dc.identifier.digitalTR04-09en_US
dc.identifier.urihttps://hdl.handle.net/1911/102021en_US
dc.language.isoengen_US
dc.titleFilter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problemsen_US
dc.typeTechnical reporten_US
dc.type.dcmiTexten_US
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