Learning Scenario Reduction for Two-Stage Robust Optimization with Discrete Uncertainty

ArXi:2605.14494v1 Announce Type: cross Two-Stage Robust Optimization (2RO) with discrete uncertainty is challenging, often rendering exact solutions prohibitive. Scenario reduction alleviates this issue by selecting a small, representative subset of scenarios to enable tractable computation. However, existing methods are largely problem-agnostic, operating solely on the uncertainty set without consulting the feasible region or re