Robust optimization: Difference between revisions

Revision as of 13:36, 2 December 2006

Revision as of 13:40, 2 December 2006

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The above makes robust optimization similar (at least in the model) to a [[goal program]]. Recently, the robust optimization community defines it differently – it optimizes for the worst-case scenario. Let the uncertain MP be given by

<math>\min f(x; s): x \in X(s),</math>

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

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

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