Uniform exponential convergence of sample average random functions under general sampling with applications in stochastic programming

Abstract

Sample average approximation (SAA) is one of the most popular methods for solving stochastic optimization and equilibrium problems. Research on SAA has been mostly
focused on the case when sampling is independent and identically distributed (iid) with exceptions \cite{dcb00,Hom08}. In this paper we study SAA with general sampling (including iid sampling and non-iid sampling) for
solving nonsmooth stochastic optimization problems, stochastic Nash equilibrium problems and stochastic generalized equations. To this end, we first derive the uniform exponential convergence of the sample average of a class of lower semicontinuous random functions and then apply it to a nonsmooth stochastic minimization problem.
Exponential convergence of estimators of both optimal solutions and M-stationary points (characterized by Mordukhovich limiting subgradients) are established under mild conditions. We also use the unform convergence result to establish the exponential rate of convergence of statistical estimators of a stochastic Nash equilibrium problem and estimators of the solutions to a stochastic generalized equation problem.

More information

Identifiers

ORCID for Huifu Xu: orcid.org/0000-0001-8307-2920

Catalogue record

Export record