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Bayesian optimisation for constrained problems
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Ungredda, Juan and Branke, Juergen (2024) Bayesian optimisation for constrained problems. ACM Transactions on Modeling and Computer Simulation . doi:10.1145/3641544 ISSN 1558-1195. (In Press)
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Official URL: https://doi.org/10.1145/3641544
Abstract
Many real-world optimisation problems such as hyperparameter tuning in machine learning or simulation-based optimisation can be formulated as expensive-to-evaluate black-box functions. A popular approach to tackle such problems is Bayesian optimisation, which builds a response surface model based on the data collected so far, and uses the mean and uncertainty predicted by the model to decide what information to collect next. In this paper, we propose a generalisation of the well-known Knowledge Gradient acquisition function that allows it to handle constraints. We empirically compare the new algorithm with four other state-of-the-art constrained Bayesian optimisation algorithms and demonstrate its superior performance. We also prove theoretical convergence in the infinite budget limit.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > QA Mathematics | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Mathematics Faculty of Social Sciences > Warwick Business School |
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Library of Congress Subject Headings (LCSH): | Mathematical optimization, Bayesian statistical decision theory, Gaussian processes | ||||||
Journal or Publication Title: | ACM Transactions on Modeling and Computer Simulation | ||||||
Publisher: | Association for Computing Machinery (ACM) | ||||||
ISSN: | 1558-1195 | ||||||
Official Date: | 22 January 2024 | ||||||
Dates: |
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DOI: | 10.1145/3641544 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | In Press | ||||||
Re-use Statement: | © ACM, 2024 This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published inACM Transactions on Modeling and Computer Simulation, {VOL#, ISS#2024 http://doi.acm.org/10.1145/3641544 | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 22 January 2024 | ||||||
Date of first compliant Open Access: | 31 January 2024 | ||||||
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