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dc.contributor.authorConn, Andrew R.-
dc.contributor.authorScheinberg, Katya-
dc.contributor.authorVicente, Luís Nunes-
dc.identifier.citationPré-Publicações DMUC. 06-49 (2006)en_US
dc.description.abstractIn 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.en_US
dc.description.sponsorshipCentro de Matemática da Universidade de Coimbra; FCT under grant POCI/59442/MAT/2004en_US
dc.publisherCentro de Matemática da Universidade de Coimbraen_US
dc.subjectTrust-region methodsen_US
dc.subjectDerivative-free optimizationen_US
dc.subjectNonlinear optimizationen_US
dc.subjectGlobal convergenceen_US
dc.titleGlobal convergence of general derivative-free trust-region algorithms to first and second order critical pointsen_US
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