Compact Optimality Verification for Optimization Proxies
<|reference_start|>Compact Optimality Verification for Optimization Proxies: Recent years have witnessed increasing interest in optimization proxies, i.e., machine learning models that approximate the input-output mapping of parametric optimization problems and return near-optimal feasible solutions. Following recent work by (Nellikkath & Chatzivasileiadis, 2021), this paper reconsiders the optimality verification problem for optimization proxies, i.e., the determination of the worst-case optimality gap over the instance distribution. The paper proposes a compact formulation for optimality verification and a gradient-based primal heuristic that brings substantial computational benefits to the original formulation. The compact formulation is also more general and applies to non-convex optimization problems. The benefits of the compact formulation are demonstrated on large-scale DC Optimal Power Flow and knapsack problems.<|reference_end|>
@article{chen2024compact,
    title={Compact Optimality Verification for Optimization Proxies},
    author={Wenbo Chen, Haoruo Zhao, Mathieu Tanneau, Pascal Van Hentenryck},
    journal={arXiv preprint arXiv:2405.21023},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.21023},
    primaryClass={math.OC cs.AI}
}