8 Février - 14 Février

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Jeudi 11 Février
Heure: 10:30 - 11:30
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: Linearization techniques for MINLP
Description: Sandra Ulrich Ngueveu We review state-of-the-art linearization and approximation techniques for the solution of non-linear mixed-integer programs. We show in particular how to ensure an a priori guarantee on the quality/feasibility of the solution, a reduction of the size of the converted problem and a minimization of the computing time. We then present an iterative method for the solution of a class of non-linear mixed-integer programs to arbitrary numerical precision. By keeping the scope of the update local from one iteration to another, the computational burden is only slightly increased from iteration to iteration. As a consequence, our method presents very nice scalability properties and is little sensitive to the desired precision. We assess its efficiency for approximating the non-linear variants of three problems: the uncapacitated facility location problem, the multi-commodity network design problem, and the transportation problem. Our results indicate that, as the desired precision becomes smaller, our approach can lead to significant gains in computing times, often being orders of magnitude faster than a baseline method, and scales to approximate larger problems.