On the safe side of stochastic programming: bounds and approximations

Abstract

There will always be stochastic programs that are too large or complex to be solved in their basic form. In this article, we review, discuss, and compare different ways such stochastic programs can be handled using bounds and approximations, all based on manipulations of the random variables. We are particularly interested in how methods based on different underlying ideas can be combined or possibly are the same.