Interior point filter method for semi-infinite programming problems
Semi-infinite programming (SIP) problems can be efficiently solved by reduction-type methods. Here, we present a new reduction method for SIP, where the multi-local optimization is carried out with a stretched simulated annealing algorithm, the reduced (finite) problem is approximately solved by a N...
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| Outros Autores: | , |
| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2012
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10198/7531 |
| País: | Portugal |
| Oai: | oai:bibliotecadigital.ipb.pt:10198/7531 |
| Resumo: | Semi-infinite programming (SIP) problems can be efficiently solved by reduction-type methods. Here, we present a new reduction method for SIP, where the multi-local optimization is carried out with a stretched simulated annealing algorithm, the reduced (finite) problem is approximately solved by a Newton’s primal–dual interior point method that uses a novel twodimensional filter line search strategy to guarantee the convergence to a KKT point that is a minimizer, and the global convergence of the overall reduction method is promoted through the implementation of a classical two-dimensional filter line search. Numerical experiments with a set of well-known problems are shown. |
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