Bayesian Optimization (BO) is used to find the global optima of black box functions. In this work, we propose a practical BO method of function compositions where the form of the composition is known but the constituent functions are expensive to evaluate. By assuming an independent Gaussian process (GP) model for each of the constituent black-box function, we propose EI and UCB based BO algorithms and demonstrate their ability to outperform vanilla BO and the current state-of-art algorithms. We demonstrate a novel application of the proposed methods to dynamic pricing in revenue management when the underlying demand function is expensive to evaluate.

翻译：贝叶斯优化（BO）常用于寻找黑箱函数的全局最优解。本文提出一种针对函数组合的实用贝叶斯优化方法，其中组合形式已知，但各组成函数的评估代价高昂。通过为每个黑箱组成函数建立独立的高斯过程（GP）模型，我们分别提出了基于EI和UCB的贝叶斯优化算法，并证明了这些算法在性能上优于标准贝叶斯优化及现有最优方法。我们将所提方法创新性地应用于收益管理中的动态定价问题，其中底层需求函数评估代价高昂。