Robust optimization: Difference between revisions

Revision as of 13:48, 2 December 2006

Revision as of 02:52, 5 December 2006

Line 1:

In mathematics, '''Robustrobust optimization'''. Ais terman givenapproach toin an[[optimization approach(mathematics)|optimization]] to deal with uncertainty,. It 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>

Line 19:

The policy (x) is required to be feasible no matter what parameter value (scenario) occurs; hence, it is required to be in the intersection of all possible X(s). The inner maximization yields the worst possible objective value among all scenarios. There are variations, such as "adjustability" (i.e., recourse).

{{category:optimization}}

== References==

H.J. Greenberg. Mathematical Programming Glossary. World Wide Web, http://glossary.computing.society.informs.org/, 1996-2006. Edited by the INFORMS Computing Society.

{{[[category:optimization}}]]