 Robust optimization

Robust optimization chooses a decision while accounting for every parameter in a prescribed uncertainty set. A worst-case maximization takes the form ${\sup }_x{\inf }_{r\in \mathcal{U}}F(x,r)$ with the decision chosen before the uncertain parameter. With affine dependence on the uncertainty, the support function and linear programming duality often replace the inner optimization by explicit constraints.