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Zbl 1007.90047
Ben-Tal, Aharon; Nemirovski, Arkadi
Robust optimization-methodology and applications. (English)
[J] Math. Program. 92, No.3 (B), 453-480 (2002). ISSN 0025-5610; ISSN 1436-4646/e

Summary: Robust Optimization (RO) is a modeling methodology, combined with computational tools, to process optimization problems in which the data are uncertain and is only known to belong to some uncertainty set. The paper surveys the main results of RO as applied to uncertain linear, conic quadratic and semidefinite programming. For these cases, computationally tractable robust counterparts of uncertain problems are explicitly obtained, or good approximations of these counterparts are proposed, making RO a useful tool for real-world applications. We discuss some of these applications, specifically: antenna design, truss topology design and stability analysis/synthesis in uncertain dynamic systems. We also describe a case study of 90 LPs from the NETLIB collection. The study reveals that the feasibility properties of the usual solutions of real world LPs can be severely affected by small perturbations of the data and that the RO methodology can be successfully used to overcome this phenomenon.

MSC 2000:: *90C25 Convex programming
90C05 Linear programming
90C40 Markov decision processes, etc.

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