Numerical experience with a derivative-free trust-funnel method for nonlinear optimization problems with general nonlinear constraints
| Autor: | Ph. R. Sampaio, Ph. L. Toint |
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| Rok vydání: | 2016 |
| Předmět: |
Mathematical optimization
021103 operations research Control and Optimization business.product_category Optimization problem Applied Mathematics 0211 other engineering and technologies 010103 numerical & computational mathematics 02 engineering and technology Filter (signal processing) 01 natural sciences Nonlinear programming Nonlinear system Simple (abstract algebra) Derivative-free optimization Penalty method Funnel 0101 mathematics business Software Mathematics |
| Zdroj: | Optimization Methods and Software. 31:511-534 |
| ISSN: | 1029-4937 1055-6788 |
| DOI: | 10.1080/10556788.2015.1135919 |
| Popis: | A trust-funnel method is proposed for solving nonlinear optimization problems with general nonlinear constraints. It extends the one presented by Gould and Toint [Nonlinear programming without a penalty function or a filter. Math. Prog. 1221:155–196, 2010], originally proposed for equality-constrained optimization problems only, to problems with both equality and inequality constraints and where simple bounds are also considered. As the original one, our method makes use of neither filter nor penalty functions and considers the objective function and the constraints as independently as possible. To handle the bounds, an active-set approach is employed. We then exploit techniques developed for derivative-free optimization DFO to obtain a method that can also be used to solve problems where the derivatives are unavailable or are available at a prohibitive cost. The resulting approach extends the DEFT-FUNNEL algorithm presented by Sampaio and Toint [A derivative-free trust-funnel method for equality-constrained nonlinear optimization. Comput. Optim. Appl. 611:25–49, 2015], which implements a derivative-free trust-funnel method for equality-constrained problems. Numerical experiments with the extended algorithm show that our approach compares favourably to other well-known model-based algorithms for DFO. |
| Databáze: | OpenAIRE |
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