用低成本数据加速优化，同时保证约束条件，自动判断何时停止。
Constrained multi-fidelity Bayesian optimization with automatic stop condition
- 融合高低精度数据，降低采样成本
- 支持变化的约束条件，可处理黑箱函数
- 自动停止机制，解决收敛难判断问题
贝叶斯优化（BO）在关键应用中用于以最少成本寻找最优设计。尽管BO具备高样本效率，但仅依赖昂贵的高精度数据仍会导致高成本，尤其在约束搜索空间中，需同时优化并保证可行性。现有文献缺乏系统性的停止标准。为此，我们提出一种约束感知的多精度贝叶斯优化框架（CMFBO），通过利用低成本低精度数据来最小化总体采样成本，同时确保可行性。本方法中，约束可随数据源变化，甚至为黑箱函数。我们还引入一种系统性停止准则，解决了长期存在的收敛评估难题。框架已开源于GitHub，基于GP+ Python包，并在多个基准问题上验证了其有效性。
原文摘要 · Abstract (English)
Bayesian optimization (BO) is increasingly employed in critical applications to find the optimal design with minimal cost. While BO is known for its sample efficiency, relying solely on costly high-fidelity data can still result in high costs. This is especially the case in constrained search spaces where BO must not only optimize but also ensure feasibility. A related issue in the BO literature is the lack of a systematic stopping criterion. To solve these challenges, we develop a constrained cost-aware multi-fidelity BO (CMFBO) framework whose goal is to minimize overall sampling costs by utilizing inexpensive low-fidelity sources while ensuring feasibility. In our case, the constraints can change across the data sources and may be even black-box functions. We also introduce a systematic stopping criterion that addresses the long-lasting issue associated with BO's convergence assessment. Our framework is publicly available on GitHub through the GP+ Python package and herein we validate it's efficacy on multiple benchmark problems.
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