Selected topics in robust convex optimization

Authors:
Aharon Ben-Tal;Arkadi Nemirovski
Affiliations:
Technion – Israel Institute of Technology, Faculty of Industrial Engineering and Management, Technion city, 32000, Haifa, Israel;Georgia Institute of Technology, School of Industrial and Systems Engineering, Technion city, 30332-0205, Atlanta, GA, USA
Venue:
Mathematical Programming: Series A and B
Year:
2007

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Abstract

Robust Optimization is a rapidly developing methodology for handling optimization problems affected by non-stochastic “uncertain-but- bounded” data perturbations. In this paper, we overview several selected topics in this popular area, specifically, (1) recent extensions of the basic concept of robust counterpart of an optimization problem with uncertain data, (2) tractability of robust counterparts, (3) links between RO and traditional chance constrained settings of problems with stochastic data, and (4) a novel generic application of the RO methodology in Robust Linear Control.