Upper Bounding in Inner Regions for Global Optimization under Inequality Constraints

Abstract : In deterministic constrained global optimization, upper bounding the objective function generally resorts to local minimization at the nodes of the branch and bound. The local minimization process is sometimes costly when constraints must be respected. We propose in this paper an alternative approach when the constraints are inequalities or relaxed equalities so that 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 use two inner region extraction algorithms implemented in our interval B&B called IbexOpt [7]. This upper bounding shows good performance in medium-sized systems proposed in the COCONUT suite.
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Communication dans un congrès
GOW'12, Jun 2012, Natal, Brazil. pp.25-29, 2012
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Dernière modification le : samedi 11 février 2017 - 01:05:27
Document(s) archivé(s) le : jeudi 20 décembre 2012 - 03:48:14

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  • HAL Id : hal-00733860, version 1

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Ignacio Araya, Gilles Trombettoni, Bertrand Neveu, Gilles Chabert. Upper Bounding in Inner Regions for Global Optimization under Inequality Constraints. GOW'12, Jun 2012, Natal, Brazil. pp.25-29, 2012. <hal-00733860>

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