Combining Constraint Solving and Bayesian Techniques for System Optimization
Abstract
Application domains of Bayesian optimization include optimizing black-box functions or very complex functions. The functions we are interested in describe complex real-world systems applied in industrial settings. Even though they do have explicit representations, standard optimization techniques fail to provide validated solutions and correctness guarantees for them. In this paper we present a combination of Bayesian optimization and SMT-based constraint solving to achieve safe and stable solutions with optimality guarantees.
Cite
Text
Brauße et al. "Combining Constraint Solving and Bayesian Techniques for System Optimization." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/249Markdown
[Brauße et al. "Combining Constraint Solving and Bayesian Techniques for System Optimization." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/braue2022ijcai-combining/) doi:10.24963/IJCAI.2022/249BibTeX
@inproceedings{braue2022ijcai-combining,
title = {{Combining Constraint Solving and Bayesian Techniques for System Optimization}},
author = {Brauße, Franz and Khasidashvili, Zurab and Korovin, Konstantin},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {1788-1794},
doi = {10.24963/IJCAI.2022/249},
url = {https://mlanthology.org/ijcai/2022/braue2022ijcai-combining/}
}