The global optimization of a high-dimensional black-box function under black-box constraints is a pervasive task in machine learning, control, and engineering. These problems are challenging since the feasible set is typically non-convex and hard to find, in addition to the curses of dimensionality and the heterogeneity of the underlying functions. In particular, these characteristics dramatically impact the performance of Bayesian optimization methods, that otherwise have become the de facto standard for sample-efficient optimization in unconstrained settings, leaving practitioners with evolutionary strategies or heuristics. We propose the scalable constrained Bayesian optimization (SCBO) algorithm that overcomes the above challenges and pushes the applicability of Bayesian optimization far beyond the state-of-the-art. A comprehensive experimental evaluation demonstrates that SCBO achieves excellent results on a variety of benchmarks. To this end, we propose two new control problems that we expect to be of independent value for the scientific community.

翻译：黑盒限制下高维黑盒功能的全球优化是机器学习、控制和工程方面的一项普遍任务。 这些问题具有挑战性。 因为这些问题具有挑战性,因为可行的成套方法一般是非曲线的,很难找到,除了维度的诅咒和基本功能的异质外,还很难找到。 特别是,这些特征对巴耶斯优化方法的性能产生了巨大影响,而巴耶斯优化方法实际上已成为在不受限制的环境中进行抽样高效优化的标准,使从业人员处于进化战略或超常状态。 我们提出了克服上述挑战、将巴耶斯优化的应用推向远超出最新技术的可缩缩放的巴耶斯优化(SCBO)算法(SCBO ) 。 一项全面的实验评估表明,SCBO在各种基准上取得了极好的结果。 为此,我们提出了两个新的控制问题,我们希望这些问题对科学界具有独立价值。