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Robust solutions of affected conic optimization problems

We survey the main developments in Robust Optimization (RO), a methodology aimed at solving optimization problems (static and dynamic) affected by uncertainty. We focus primarily on issues of computational tractability of the robust counterparts emerging from conic optimization problems (linear, conic quadratic and semidefinite ). Results pertaining to the latter issues are then used to process efficiently probabilistic constraints under partial stochastic information. Finally we discuss the synthesis of uncertainty affected discrete-time linear control systems by the RO methodology and illustrate the results by treating a supply-chain problem.
Type of Seminar:
Public Seminar
Prof. Aharon Ben-Tal
Minerva Optimization Center, Technion, Haifa, Israel
Apr 30, 2007   16:30-18:00 (Coffee: 16:00-16:30)

ETH Zentrum, Main Building, Room: HG E41
Contact Person:

Dr. Colin Jones
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