To describe some issues involved in optimization under uncertainty, we start with a static optimization
problem. Suppose we want to maximize an objective function G(x, ω), where x denotes the decision to be
made, X denotes the set of all feasible decisions, ω denotes an outcome that is unknown at the time the
decision has to be made, and Ω denotes the set of all possible outcomes.
There are several approaches for dealing with optimization under uncertainty. Some of these approaches
are illustrated next in the context of an example.