用可学习的代理模型高效求解优化问题，保证可行性和质量。
Optimization Learning
- 构建可微分的优化代理，融合深度学习与修复层生成可行解。
- 支持端到端自监督训练，提供解的质量保证。
- 适用于电力系统实时风险评估等大规模优化场景。
本文提出优化学习（optimization learning）方法，设计能学习参数化优化问题输入输出映射的优化代理。这些代理模型具备天然可信性：能计算原问题的可行解，提供解的质量保障，并可扩展至大规模实例。优化代理是可微分程序，结合传统深度学习技术与修复或补全层，生成可行解。文章展示了优化代理可通过自监督方式端到端训练，并提出了性能保障机制与大规模扩展方法。其潜力在电力系统应用中得到验证，尤其在实时风险评估与安全约束最优潮流问题中表现突出。
原文摘要 · Abstract (English)
This article introduces the concept of optimization learning, a methodology to design optimization proxies that learn the input/output mapping of parametric optimization problems. These optimization proxies are trustworthy by design: they compute feasible solutions to the underlying optimization problems, provide quality guarantees on the returned solutions, and scale to large instances. Optimization proxies are differentiable programs that combine traditional deep learning technology with repair or completion layers to produce feasible solutions. The article shows that optimization proxies can be trained end-to-end in a self-supervised way. It presents methodologies to provide performance guarantees and to scale optimization proxies to large-scale optimization problems. The potential of optimization proxies is highlighted through applications in power systems and, in particular, real-time risk assessment and security-constrained optimal power flow.
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