MILP-Evo: Closed-Loop Fully Automatic Design of MILP Solvers
Machine learning methods have shown that data-driven policies can accelerate mixed-integer linear programming (MILP) solvers, but many such approaches remain difficult to inspect, adapt, and deploy because the learned policy is represented as an external predictor or other opaque model. By contrast, explicit solver logic is easier to understand and integrate, but is usually hand-designed rather than learned from solver feedback. We study whether the automatic design of MILP solver logic can instead be cast as LLM-guided closed-loop search over executable white-box components evaluated directly by end-to-end solver behavior. To this end, we propose a closed-loop program evolution framework...
| Authors | Jinbiao Nie, Kewei Feng, Xiaoyuan Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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