这篇论文提出DCL方法，在约束函数未知时用局部代理高效优化，解决了实际难题，值得关注。
Dynamic Constraint Learning (DCL) 提出一种数据驱动框架，用于在约束函数未知且无法查询时进行约束优化。该框架在每个迭代中从附近数据学习局部代理，并在数据支持的信任区域内求解子问题。与离线全局约束学习相比，DCL使用局部代理，适应优化过程中的数据分布。在合成测试问题和一个文献案例研究中，DCL实现了与全局模型相当的解决方案质量，同时使用更简单的局部模型和更小的优化子问题。
Optimization with Dynamic Constraint Learning (DCL)
We propose Dynamic Constraint Learning (DCL), a data-driven framework for constrained optimization when constraint functions are unknown and cannot be queried during optimization. At each iteration, the method learns a local surrogate from nearby data and solves a subproblem within a data-supported trust region. Compared with offline global constraint learning, the approach uses local surrogates that adapt to the data distribution during optimization and can achieve solution quality comparable to that of global models while using simpler local models and smaller optimization subproblems. We demonstrate the performance of DCL on a synthetic test problem and two case studies from the literature.