Random Postprocessing for Combinatorial Bayesian Optimization

• URL: http://arxiv.org/abs/2309.02842v2
• Date: Thu, 28 Dec 2023 01:46:26 GMT
• Title: Random Postprocessing for Combinatorial Bayesian Optimization
• Authors: Keisuke Morita, Yoshihiko Nishikawa, Masayuki Ohzeki
• Abstract summary: We numerically study the effect of a postprocessing method on Bayesian optimization. We find the postprocessing method significantly reduces the number of sequential steps to find the global optimum.
• Score: 0.552480439325792
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model-based sequential approaches to discrete "black-box" optimization, including Bayesian optimization techniques, often access the same points multiple times for a given objective function in interest, resulting in many steps to find the global optimum. Here, we numerically study the effect of a postprocessing method on Bayesian optimization that strictly prohibits duplicated samples in the dataset. We find the postprocessing method significantly reduces the number of sequential steps to find the global optimum, especially when the acquisition function is of maximum a posterior estimation. Our results provide a simple but general strategy to solve the slow convergence of Bayesian optimization for high-dimensional problems.

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