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 {\sl 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.
Citation
The paper underlies the plenary lecture given by the second author at ISMP 2006 and will be published in the ISMP 2006 Special Issue of Mathematical Programming Series B