Stability of Stochastic Programming Problems

Abstract

Abstract The behaviour of stochastic programming problems is studied in case of the underlying probability distribution being perturbed and approximated, respectively. Most of the theoretical results provide continuity properties of optimal values and solution sets relative to changes of the original probability distribution, varying in some space of probability measures equipped with some convergence and metric, respectively. We start by discussing relevant notions of convergence and distances for probability measures. Then we associate a distance with a stochastic program in a natural way and derive (quantitative) continuity properties of values and solutions by appealing to general perturbation results for optimization problems. Later we show how these results relate to stability with respect to weak convergence and how certain ideal probability metrics may be associated with more specific stochastic programs. In particular, we establish stability results for two-stage and chance constrained models. Finally, we present some consequences for the asymptotics of empirical approximations and for the construction of scenario-based approximations of stochastic programs.