Stochastic optimization problems with nondifferentiable cost functionals with an application in stochastic programming

Abstract

In this paper we examine a class of stochastic optimization problems characterized by nondifferentiability of the objective function. It is shown that in many cases the expected value of the objective function is differentiable and thus the resulting optimization problem can be analyzed and solved by using classical analytical or numerical methods. The results are subsequently applied to the solution of a class of stochastic programming problems.