Extending Scope of Robust Optimization: Comprehensive Robust Counterparts of Uncertain Problems

Aharon Ben-Tal (abentalie.technion.ac.il)
Stephen Boyd (boydstanford.edu)
Arkadi Nemirovski (nemirovsie.technion.ac.il)

Abstract: In this paper, we propose a new methodology for handling optimization problems with uncertain data. With the usual Robust Optimization paradigm, one looks for the decisions ensuring a required performance for all realizations of the data from a given bounded uncertainty set, whereas with the proposed approach, we require also a controlled deterioration in performance when the data is outside the uncertainty set. The extension of Robust Optimization methodology developed in this paper opens up new possibilities to solve e±ciently multi-stage finite-horizon uncertain optimization problems, in particular, to analyze and to synthesize linear controllers for discrete time dynamical systems.

Keywords: Robust optimization, decision making under uncertainty, linear control

Category 1: Robust Optimization

Category 2: Applications -- Science and Engineering (Control Applications )

Citation: Research Report 02/2005, May 2005, Minerva Optimization Center, Faculty of Industrial Engineering and Management, Technion - Israel Institute of Technology, Technion City, Haifa 32000, Israel

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Entry Submitted: 05/25/2005
Entry Accepted: 05/25/2005
Entry Last Modified: 11/25/2005

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