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Constrained Global Optimization with Radial Basis Functions
Mälardalen University, Department of Mathematics and Physics.
Mälardalen University, Department of Mathematics and Physics.
2003 (English)Report (Other academic)
Abstract [en]

Response surface methods show promising results for global optimization of costly non convex objective functions, i.e. the problem of finding the global minimum when there are several local minima and each function value takes considerable CPU time to compute. Such problems often arise in industrial and financial applications, where a function value could be a result of a time-consuming computer simulation or optimization. Derivatives are most often hard to obtain. The problem is here extended with linear and nonlinear constraints, and the nonlinear constraints can be costly or not. A new algorithm that handles the constraints, based on radial basis functions (RBF), and that preserves the convergence proof of the original RBF algorithm is presented. The algorithm takes advantage of the optimization algorithms in the Tomlab optimization environment (www.tomlab.biz). Numerical results are presented for standard test problems.

Place, publisher, year, edition, pages
Västerås: Department of Mathematics and Physics, Mälardalen University , 2003.
Series
Technical Report Research Report, MdH-IMa-2004
National Category
Mathematics
Identifiers
URN: urn:nbn:se:mdh:diva-3169OAI: oai:DiVA.org:mdh-3169DiVA: diva2:115833
Note

Presented at the 18th International Symposium on Mathematical Programming, Copenhagen, Denmark aug 24-29

Available from: 2007-04-12 Created: 2007-04-12 Last updated: 2015-07-30Bibliographically approved

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CiteExportLink to record
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