On the Convergence of Measurable Selections and an Application to Approximations in Stochastic Optimization

Abstract

Conditions are given that guarantee that a sequence (of sets) of measurable selections converges almost surely, in probability and in mean. These conditions are related to the convergence of the underlying sequence of measurable multifunctions. The results are applied to approximations for the so-called "distribution problem" of stochastic optimization.