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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In mathematics, robust optimization is an approach in optimization to deal with uncertainty. It is similar to the recourse model of stochastic programming, in that some of the parameters are random variables, 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.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).