Technical Note—Two-Stage Sample Robust Optimization

In “Two-Stage Sample Robust Optimization,” Bertsimas, Shtern, and Sturt investigate a simple approximation scheme, based on overlapping linear decision rules, for solving data-driven two-stage distributionally robust optimization problems with the type-infinity Wasserstein ambiguity set. Their main result establishes that this scheme is asymptotically optimal stochastic problems; is, under mild assumptions, cost first-stage decisions obtained by approximating problem converge to those of underlying as number data points grows infinity. These guarantees notably apply do not have relatively complete recourse, which arise frequently in applications. context, authors show through numerical experiments practically tractable produces significantly outperform from state-of-the-art alternatives.