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DTSTART;VALUE=DATE:20110920T171500
DTEND;VALUE=DATE:20110920T171500
UID:5634@agenda.unifr.ch
DESCRIPTION:Robust optimization is a twofold challenge. First, the solution of the\nproblem should be optimal; this requires mathematical programming to\ndevelop methods for optimization solvers. Second, the solution should\nbe robust, i.e., safeguarded against uncertain perturbations.\n<br>\nBased on the potential clouds formalism one can overcome several\nproblems of traditional uncertainty modeling. Clouds have already\nsucceeded to deal with higher dimensional uncertainties, even in case\nof lack of some statistical information. Moreover, they can be easily\ncombined with standard constrained optimization problems towards a\nrobust optimization problem formulation.\n<br>\nHowever, in several real-life applications, the number of objective\nfunction evaluations available to propagate the uncertainties is too\nlimited. Inspired by the Cauchy deviates method, we propose a\nsimulation based method for optimization over a polyhedron that is\nable to meet the limits.\n<br>\nTo perform numerical tests of the methods a test environment is being\ndeveloped. Real world test cases are given by aerospace design\noptimization problems.
SUMMARY:Martin Fuchs: Robust optimization and applications
CATEGORIES:Colloque / Congrès / Forum
LOCATION:PER 08\, Phys 2.52\, Chemin du Musée 3\, 1700 Fribourg
URL;VALUE=URI:https://agenda.unifr.ch/e/fr/5634
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