针对可独立评估的约束条件，提出更高效的贝叶斯优化方法。
Bayesian Optimisation: Which Constraints Matter?
- 基于知识梯度，设计可区分关键约束的新型优化算法
- 实验显示优于现有最先进方法，尤其在少量约束起作用时表现突出
- 适合高成本优化中存在多个但少数关键约束的场景
贝叶斯优化在昂贵的全局黑箱优化问题中表现出强大能力。本文针对目标函数与约束函数可解耦（即部分函数可独立评估）的问题，提出新的知识梯度型贝叶斯优化变体。其核心思想是：最优解通常仅受少数约束影响，因此应优先评估相关约束以提升效率。通过实证对比，新方法在多个基准上均优于现有最先进方法，验证了其有效性。
原文摘要 · Abstract (English)
Bayesian optimisation has proven to be a powerful tool for expensive global black-box optimisation problems. In this paper, we propose new Bayesian optimisation variants of the popular Knowledge Gradient acquisition functions for problems with \emph{decoupled} black-box constraints, in which subsets of the objective and constraint functions may be evaluated independently. In particular, our methods aim to take into account that often only a handful of the constraints may be binding at the optimum, and hence we should evaluate only relevant constraints when trying to optimise a function. We empirically benchmark these methods against existing methods and demonstrate their superiority over the state-of-the-art.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。