Solving stochastic programming problems with recourse including error bounds

Abstract

Under suitable convexity and integrability assumptions, for the stochastic programming problem with recourse statements are proved very easily, which have been shown until now only for stochastic linear programming. In particular, this includes lower bounds for approximations using discrete random vectors. Until now unpublished, even for the linear ease, are error bounds, which are proved here under different assumptions. Computational experiences are reported. Finally, some improvements are suggested which may reduce the computation time.