arXiv: cs.AI preprintsInternational6 November 2026
Learning Scenario Reduction for Two-Stage Robust Optimization with Discrete Uncertainty
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arXiv:2605.14494v3 Announce Type: replace Abstract: 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 recourse structure. In this paper, we introduce PRISE, a problem-driven sequential lookahead heuristic that constructs reduced scenario sets by evaluating t
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