Numerical Evaluation of Approximation Methods in Stochastic Programming

We study an approach for the evaluation of approximation and solution methodsfor multistage linear stochastic programs by measuring the performance of the obtained solutions on a set of out-of-sample scenarios. The main point of the approachis to restore the feasibility of solutions to an approximated problem along the out-of-sample scenarios. For this purpose, we consider and compare different feasibilityand optimality based projection methods. With this at hand, we study the quality of solutions to different test models based on classical as well as recombiningscenario trees