Robust optimization: Difference between revisions

Revision as of 02:52, 5 December 2006

Revision as of 18:07, 1 March 2007

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In mathematics, '''robust optimization''' is an approach in [[optimization (mathematics)|optimization]] to deal with uncertainty. It is similar to the recourse model of [[stochastic programming]], in that some of the parameters are [[random variable]]s, except that feasibility for all possible realizations (called scenarios) is replaced by a [[penalty function]] in the objective. As such, the approach integrates [[goal programming]] with a scenario-based description of problem data. To illustrate, consider the LP:

:<math>\min cx + dy: Ax=b, Bx + Cy = e, x, y \le 0,</math>