Oktay Günlük, Jayant R. Kalagnanam, et al.
Journal of Global Optimization
In this paper we prove global convergence for first- and second-order stationary points of a class of derivative-free trust-region methods for unconstrained optimization. These methods are based on the sequential minimization of quadratic (or linear) 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. © 2009 Society for Industrial and Applied Mathematics.
Oktay Günlük, Jayant R. Kalagnanam, et al.
Journal of Global Optimization
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Future Generation Computer Systems
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