Scalable Constrained Bayesian Optimization
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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 difficult 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. Due to the lack of sample-efficient methods, practitioners usually fall back to evolutionary strategies or heuristics. We propose the scalable constrained Bayesian optimization (SCBO) algorithm that addresses the above challenges by data-independent transformations of the functions and follows the recent theme of local Bayesian optimization. A comprehensive experimental evaluation demonstrates that SCBO achieves excellent results and outperforms the state-of-the-art methods.
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