Abstract
We extend the on-demand accuracy approach of Oliveira and Sagastizabal to constrained convex optimization. The resulting method is applied to risk-averse two-stage stochastic programming problems. We present a survey of risk-averse models. The appropriate oracle is formulated for the case of a conditional value-at-risk constraint. We discuss computational aspects and compare different approaches in a study.
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