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https://hdl.handle.net/10316/11218
Título: | A globally convergent primal-dual interior-point filter method for nonlinear programming: new filter optimality measures and computational results | Autor: | Silva, Renata Ulbrich, Michael Ulbrich, Stefan Vicente, Luís Nunes |
Palavras-chave: | Interior-point methods; Primal-dual; Filter; Global convergence; Largescale NLP | Data: | 2008 | Editora: | Centro de Matemática da Universidade de Coimbra | Citação: | Pré-Publicações DMUC. 08-49 (2008) | Resumo: | In this paper we prove global convergence for first and second-order stationarity points of a class of derivative-free trust-region methods for unconstrained optimization. These methods are based on the sequential minimization of linear or quadratic models built from evaluating the objective function at sample sets. The derivative-free models are required to satisfy Taylor-type bounds but, apart from that, the analysis is independent of the sampling techniques. A number of new issues are addressed, including global convergence when acceptance of iterates is based on simple decrease of the objective function, trust-region radius maintenance at the criticality step, and global convergence for second-order critical points. | URI: | https://hdl.handle.net/10316/11218 | Direitos: | openAccess |
Aparece nas coleções: | FCTUC Matemática - Vários |
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Ficheiro | Descrição | Tamanho | Formato | |
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A globally convergent primal-dual interior-point filter method.pdf | 321.5 kB | Adobe PDF | Ver/Abrir |
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