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[高分] When Is Two-Stage Sample Robust Optimization Asymptotically Optimal?
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何展鹏 (HO Chin Pang) City University of Hong Kong abstract：Two-stage sample robust optimization with linear decision rules is a standard data-driven approach to two-stage stochastic linear programs with unknown distributions. Existing literature has already established that this approach is asymptotically optimal under several conditions. While most conditions are mild, one is technical, and whether it follows from the others was left as an open question. We resolve this open question through a single geometric feature of the support set: simpleness, the property that every vertex is incident to exactly as many edges as the ambient dimension. When the support set is simple, the technical condition is automatic, and asymptotic optimality holds under the remaining standard assumptions. When the support set is not simple, there exists a problem instance on which the technical condition fails and asymptotic optimality breaks down. ... |
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(2025-6-4)
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