Bilevel optimization by conditional Bayesian optimization
| dc.contributor.author | Dogan, Vedat | en |
| dc.contributor.author | Prestwich, Steven D. | en |
| dc.contributor.funder | Science Foundation Ireland | en |
| dc.date.accessioned | 2024-01-26T11:45:52Z | |
| dc.date.available | 2024-01-26T11:45:52Z | |
| dc.date.issued | 2023-09-22 | en |
| dc.description.abstract | Bilevel optimization problems have two decision-makers: a leader and a follower (sometimes more than one of either, or both). The leader must solve a constrained optimization problem in which some decisions are made by the follower. These problems are much harder to solve than those with a single decision-maker, and efficient optimal algorithms are known only for special cases. A recent heuristic approach is to treat the leader as an expensive black-box function, to be estimated by Bayesian optimization. We propose a novel approach called ConBaBo to solve bilevel problems, using a new conditional Bayesian optimization algorithm to condition previous decisions in the bilevel decision-making process. This allows it to extract knowledge from earlier decisions by both the leader and follower. We present empirical results showing that this enhances search performance and that ConBaBo outperforms some top-performing algorithms in the literature on two commonly used benchmark datasets. | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Accepted Version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Dogan, V. and Prestwich, S. (2023) 'Bilevel optimization by conditional Bayesian optimization', In: Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P.M., Umeton, R. (eds) Machine Learning, Optimization, and Data Science. LOD 2023. Lecture Notes in Computer Science, vol 14505. Springer, Cham. pp. 243–258. https://doi.org/10.1007/978-3-031-53969-5_19 | en |
| dc.identifier.doi | https://doi.org/10.1007/978-3-031-53969-5_19 | en |
| dc.identifier.endpage | 258 | en |
| dc.identifier.journaltitle | Lecture Notes in Computer Science | |
| dc.identifier.startpage | 243 | en |
| dc.identifier.uri | https://hdl.handle.net/10468/15437 | |
| dc.identifier.volume | 14505 | |
| dc.language.iso | en | en |
| dc.relation.ispartof | The 9th International Conference on Machine Learning, Optimization, and Data Science | en |
| dc.relation.project | info:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3918/IE/Confirm Centre for Smart Manufacturing/ | en |
| dc.relation.project | info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ | en |
| dc.rights | © the authors 2024. This is a post-peer-review, pre-copyedit version of a paper published as: Dogan, V., Prestwich, S. (2024). Bilevel Optimization by Conditional Bayesian Optimization. In: Machine Learning, Optimization, and Data Science. LOD 2023. Lecture Notes in Computer Science, vol 14505. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-53969-5_19 | en |
| dc.subject | Bilevel optimization | en |
| dc.subject | Conditional Bayesian optimization | en |
| dc.subject | Stackelberg games | en |
| dc.subject | Gaussian process | en |
| dc.title | Bilevel optimization by conditional Bayesian optimization | en |
| dc.type | Conference item | en |
| dc.type | Article (peer-reviewed) | en |
| dc.type | book-chapter | en |
