Combining Constraint Solving and Bayesian Techniques for System Optimization

Franz Brauße; Zurab Khasidashvili; Konstantin Korovin

IJCAI 2022poster1 citations

Combining Constraint Solving and Bayesian Techniques for System Optimization

Franz Brauße, Zurab Khasidashvili, Konstantin Korovin

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.

Constraint Satisfaction and Optimization: Constraint OptimizationConstraint Satisfaction and Optimization: Constraints and Machine LearningConstraint Satisfaction and Optimization: SatisfiabiltyConstraint Satisfaction and Optimization: Solvers and ToolsMachine Learning: Optimisation

BibTeX

@inproceedings{ijcai2022p249,
  title     = {Combining Constraint Solving and Bayesian Techniques for System Optimization},
  author    = {Brauße, Franz and Khasidashvili, Zurab and Korovin, Konstantin},
  booktitle = {Proceedings of the Thirty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-22}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Lud De Raedt},
  pages     = {1788--1794},
  year      = {2022},
  month     = {7},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2022/249},
  url       = {https://doi.org/10.24963/ijcai.2022/249},
}