Optimization for One, Feasibility for Most: Decoupling Optimization from Feasibility in Chance-Constrained Programs
Chance-constrained stochastic programs (CCSPs) require solutions that satisfy uncertain constraints with high probability. Standard approaches either embed scenarios directly into optimization by considering sample average approximation (SAA)-based reformulations, growing prohibitively expensive and often failing to find feasible solutions; or employ conservative reformulations that guarantee feasibility but sacrifice objective quality by design. We propose ACCESS (Adaptive Chance-Constrained Exploration via Structured Sampling), a structured sampling framework that decouples optimization from feasibility evaluation. First, high-quality candidate solutions are generated by solving many in...
| Authors | Justin Dumouchelle, Shunyu Yao, Beste Basciftci et al. |
|---|---|
| Primary category | math.OC |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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