Upper bounding in inner regions for global optimization under inequality constraints

Ignacio Araya, Gilles Trombettoni, Bertrand Neveu, Gilles Chabert

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32 Citas (Scopus)


In deterministic continuous constrained global optimization, upper bounding the objective function generally resorts to local minimization at several nodes/iterations of the branch and bound. We propose in this paper an alternative approach when the constraints are inequalities and the feasible space has a non-null volume. First, we extract an inner region, i.e., an entirely feasible convex polyhedron or box in which all points satisfy the constraints. Second, we select a point inside the extracted inner region and update the upper bound with its cost. We describe in this paper two original inner region extraction algorithms implemented in our interval B&B called IbexOpt (AAAI, pp 99–104, 2011). They apply to nonconvex constraints involving mathematical operators like , (Formula presented.). This upper bounding shows very good performance obtained on medium-sized systems proposed in the COCONUT suite.

Idioma originalInglés
Páginas (desde-hasta)145-164
Número de páginas20
PublicaciónJournal of Global Optimization
EstadoPublicada - oct. 2014
Publicado de forma externa


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