Bayesian Optimisation: Which Constraints Matter?
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908722534547456 |
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| author | Lin, Xietao Wang Ungredda, Juan Butler, Max Town, James Rahat, Alma Singh, Hemant Branke, Juergen |
| author_facet | Lin, Xietao Wang Ungredda, Juan Butler, Max Town, James Rahat, Alma Singh, Hemant Branke, Juergen |
| contents | Bayesian optimisation has proven to be a powerful tool for expensive global black-box optimisation problems. In this paper, we propose new Bayesian optimisation variants of the popular Knowledge Gradient acquisition functions for problems with \emph{decoupled} black-box constraints, in which subsets of the objective and constraint functions may be evaluated independently. In particular, our methods aim to take into account that often only a handful of the constraints may be binding at the optimum, and hence we should evaluate only relevant constraints when trying to optimise a function. We empirically benchmark these methods against existing methods and demonstrate their superiority over the state-of-the-art. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_17569 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian Optimisation: Which Constraints Matter? Lin, Xietao Wang Ungredda, Juan Butler, Max Town, James Rahat, Alma Singh, Hemant Branke, Juergen Machine Learning Optimization and Control Bayesian optimisation has proven to be a powerful tool for expensive global black-box optimisation problems. In this paper, we propose new Bayesian optimisation variants of the popular Knowledge Gradient acquisition functions for problems with \emph{decoupled} black-box constraints, in which subsets of the objective and constraint functions may be evaluated independently. In particular, our methods aim to take into account that often only a handful of the constraints may be binding at the optimum, and hence we should evaluate only relevant constraints when trying to optimise a function. We empirically benchmark these methods against existing methods and demonstrate their superiority over the state-of-the-art. |
| title | Bayesian Optimisation: Which Constraints Matter? |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2512.17569 |