Robust optimization: Difference between revisions

Revision as of 13:32, 2 December 2006

Revision as of 13:36, 2 December 2006

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where S is some set of scenarios (like parameter values). The robust optimization model (according to this more recent definition) is:

minx<math>\min_x {maxs\max_{s \in S} f(x; s)} x \in X(t)\, for all\forall t \in S,</math>

The policy (x) is required to be feasible no matter what parameter value (scenario) occurs; hence, it is requied 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).