期刊
MATHEMATICAL PROGRAMMING
卷 112, 期 1, 页码 125-158出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s10107-006-0092-2
关键词
optimization under uncertainty; Robust Optimization; convex programming; chance constraints; Robust Linear Control
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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