A trust-region derivative-free algorithm for constrained optimization

Autor:	Lucas G. Pedroso, Elizabeth W. Karas, P. D. Conejo
Rok vydání:	2015
Předmět:	Trust region Mathematical optimization Control and Optimization Inverse quadratic interpolation Augmented Lagrangian method Applied Mathematics Feasible region Constrained optimization BOBYQA Derivative-free optimization Algorithm Software Interpolation Mathematics
Zdroj:	Optimization Methods and Software. 30:1126-1145
ISSN:	1029-4937 1055-6788
DOI:	10.1080/10556788.2015.1026968
Popis:	We propose a trust-region algorithm for constrained optimization problems in which the derivatives of the objective function are not available. In each iteration, the objective function is approximated by a model obtained by quadratic interpolation, which is then minimized within the intersection of the feasible set with the trust region. Since the constraints are handled in the trust-region subproblems, all the iterates are feasible even if some interpolation points are not. The rules for constructing and updating the quadratic model and the interpolation set use ideas from the BOBYQA software, a largely used algorithm for box-constrained problems. The subproblems are solved by ALGENCAN , a competitive implementation of an Augmented Lagrangian approach for general-constrained problems. Some numerical results for the Hock–Schittkowski collection are presented, followed by a performance comparison between our proposal and three derivative-free algorithms found in the literature.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::18017f59bf83181944083903d929a871 https://doi.org/10.1080/10556788.2015.1026968 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.