提出可自验证的混合求解器，实现超快且可信的电网调度优化。
Self-Certifying Primal-Dual Optimization Proxies for Large-Scale Batch Economic Dispatch
- 结合原问题与对偶问题训练代理模型，利用对偶理论实时估算解的误差
- 在大规模输电系统上实现超过1000倍提速，最大误差控制在2%以内
- 适合需要快速决策且对精度有严格要求的电力系统调度场景
近期研究显示，优化代理模型可在大规模问题上达到高保真度，平均最优性差距低于1%。然而最坏情况分析表明，存在分布内查询导致最优性差距高出数个数量级，使实际应用中难以信任预测结果。本文旨在平衡传统求解器与优化代理模型，基于用户定义的最优性阈值，实现可解释的速度-精度权衡，确保部署可信。为此，提出一种混合求解器，利用对偶理论高效界定预测解的最优性差距，并在无法认证时回退至经典求解器。为提升混合求解器的加速效果，提出一种融合原问题与对偶问题训练的替代训练方法。在大规模输电系统上的实验表明，该混合求解器具备高度可扩展性：相比并行单纯形法求解器，实现超过1000倍加速，同时保证最大最优性差距不超过2%。
原文摘要 · Abstract (English)
Recent research has shown that optimization proxies can be trained to high fidelity, achieving average optimality gaps under 1% for large-scale problems. However, worst-case analyses show that there exist in-distribution queries that result in orders of magnitude higher optimality gap, making it difficult to trust the predictions in practice. This paper aims at striking a balance between classical solvers and optimization proxies in order to enable trustworthy deployments with interpretable speed-optimality tradeoffs based on a user-defined optimality threshold. To this end, the paper proposes a hybrid solver that leverages duality theory to efficiently bound the optimality gap of predictions, falling back to a classical solver for queries where optimality cannot be certified. To improve the achieved speedup of the hybrid solver, the paper proposes an alternative training procedure that combines the primal and dual proxy training. Experiments on large-scale transmission systems show that the hybrid solver is highly scalable. The proposed hybrid solver achieves speedups of over 1000x compared to a parallelized simplex-based solver while guaranteeing a maximum optimality gap of 2%.
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