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An Outer-Inner Approximation for separable MINLPs

Hassan Hijazi(hijazi***at***lix.polytechnique.fr)
Pierre Bonami(pierre.bonami***at***lif.univ-mrs.fr)
Adam Ouorou(adam.ouorou***at***orange.com)

Abstract: A common structure in convex mixed-integer nonlinear programs is separable nonlinear functions. In the presence of such structures, we propose three improvements to the outer approximation algorithms. The first improvement is a simple extended formulation, the second is a refined outer approximation, and the third is a heuristic inner approximation of the feasible region. These methods have been implemented in the open source solver Bonmin and are available for download from the COIN-OR project website. We test the effectiveness of the approach on three real-world applications and on a larger set of models from an MINLP benchmark library. Finally, we show how the techniques can be extended to perspective formulations of several problems. The proposed tools lead to an important reduction in average computing time on most tested instances.

Keywords: Mixed-Integer Nonlinear Programming, Outer Approximation, Perspective cuts

Category 1: Integer Programming ((Mixed) Integer Nonlinear Programming )

Category 2: Optimization Software and Modeling Systems

Citation: LIX, Ecole Polytechnique, F-91128 Palaiseau, France 06/2012

Download: [PDF]

Entry Submitted: 08/27/2012
Entry Accepted: 08/27/2012
Entry Last Modified: 08/27/2012

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