Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/11218
Title: A globally convergent primal-dual interior-point filter method for nonlinear programming: new filter optimality measures and computational results
Authors: Silva, Renata 
Ulbrich, Michael 
Ulbrich, Stefan 
Vicente, Luís Nunes 
Keywords: Interior-point methods; Primal-dual; Filter; Global convergence; Largescale NLP
Issue Date: 2008
Publisher: Centro de Matemática da Universidade de Coimbra
Citation: Pré-Publicações DMUC. 08-49 (2008)
Abstract: 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
Rights: openAccess
Appears in Collections:FCTUC Matemática - Vários

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