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Selected topics in robust convex optimization

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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.

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Authors and Affiliations

  1. Faculty of Industrial Engineering and Management, Technion – Israel Institute of Technology, Technion city, Haifa, 32000, Israel

    Aharon Ben-Tal

  2. School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0205, USA

    Arkadi Nemirovski

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  1. Aharon Ben-Tal
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  2. Arkadi Nemirovski
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Correspondence to Arkadi Nemirovski.

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Ben-Tal, A., Nemirovski, A. Selected topics in robust convex optimization. Math. Program. 112, 125–158 (2008). https://doi.org/10.1007/s10107-006-0092-2

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