Constrained Bayesian Optimization with Lower Confidence Bound

In this article, we present a hybrid Bayesian optimization (BO) framework to solve constrained optimization problems by adopting a state-of-the-art acquisition function from the unconstrained BO literature, the well-known lower confidence bound acquisition function and propose a novel variant that analyzes the feasible and infeasible regions which ensure the theoretical convergence guarantee. The proposed variant is compared with the existing state-of-the-art approaches in the constrained BO literature via implementing these approaches on six different problems, including black-box, classical engineering, and hyperparameter tuning problems. Further, we demonstrate the effectiveness of our approach through graphical and statistical testing.

本文提出一种混合贝叶斯优化（Bayesian Optimization, BO）框架，用于求解约束优化问题。该框架从无约束贝叶斯优化领域的前沿研究中引入经典的置信下限获取函数（lower confidence bound acquisition function），并提出一种全新的改进变体，该变体针对可行域与不可行域进行分析，且具备理论收敛保证。为验证所提变体的性能，本文在六类不同问题（包括黑盒问题、经典工程问题与超参数调优问题）上实现了约束贝叶斯优化领域的现有前沿方法，并将本文所提变体与这些方法进行对比。此外，本文通过可视化图表与统计检验验证了所提方法的有效性。

搜集汇总

背景与挑战

背景概述

该数据集提出了一种新的约束贝叶斯优化方法，通过六类问题验证了其有效性，属于数学科学和计算生物学领域的研究数据。