Computational aspects of risk-averse optimizationin two-stage stochastic models

Abstract

Computational studies on two-stage stochastic programming problems indicate that aggregate models have better scale-up properties than disaggregate ones, though the threshold of breaking even may be high. In this paper we attempt to explain this phenomenon, and to lower this threshold.We present the on-demand accuracy approach of Oliveira and Sagastizábal in a form which shows that this approach, when applied to two-stage stochastic programming problems, combines the advantages of the disaggregate and the aggregate models.Moreover, we generalize the on-demand accuracy approach to constrained convex problems, and showhow to apply it to risk-averse two-stage stochastic programming problems.