Show simple item record

dc.contributor.authorAbramson, Mark A.
Audet, Charles
Dennis, J.E. Jr.
dc.date.accessioned 2018-06-18T17:52:01Z
dc.date.available 2018-06-18T17:52:01Z
dc.date.issued 2004-06
dc.identifier.citation Abramson, Mark A., Audet, Charles and Dennis, J.E. Jr.. "Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems." (2004) https://hdl.handle.net/1911/102021.
dc.identifier.urihttps://hdl.handle.net/1911/102021
dc.description.abstract A 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.
dc.format.extent 29 pp
dc.title Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems
dc.type Technical report
dc.date.note June 2004
dc.identifier.digital TR04-09
dc.type.dcmi Text


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record