An active-set trust-region method for derivative-free nonlinear bound-constrained optimization

Serge Gratton, Philippe Toint, Anke Tröltzsch

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Résumé

We consider an implementation of a recursive model-based active-set trust-region method for solving bound-constrained nonlinear non-convex optimization problems without derivatives using the technique of self-correcting geometry proposed in K. Scheinberg and Ph.L. Toint [Self-correcting geometry in model-based algorithms for derivative-free unconstrained optimization. SIAM Journal on Optimization, (to appear), 2010]. Considering an active-set method in bound-constrained model-based optimization creates the opportunity of saving a substantial amount of function evaluations. It allows US to maintain much smaller interpolation sets while proceeding optimization in lower-dimensional subspaces. The resulting algorithm is shown to be numerically competitive. © 2011 Taylor & Francis.
langue originaleAnglais
Pages (de - à)873-894
Nombre de pages22
journalOptimization Methods and Software
Volume26
Numéro de publication4-5
Les DOIs
Etat de la publicationPublié - 1 août 2011

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