Stochastic convex optimization problems with expectation constraints (SOECs) are encountered in statistics and machine learning, business, and engineering. In data-rich environments, the SOEC objective and constraints contain expectations defined with respect to large datasets. Therefore,...

In this paper we consider stochastic discrete optimization problems (DOP) in which feasible solutions remain feasible irrespective of the randomness of the problem parameters. We introduce the concept of the risk associated with a solution and define optimal solution in terms of having least...

We introduce a numerical method to solve stochastic optimal control problems, which are linear in the control. We facilitate the idea of solving two-point boundary value problems with spline functions in order to solve the resulting dynamic programming equation. We then show how to effectively...