Combining Constraint Solving and Bayesian Techniques for System Optimization

Franz Brauße; Zurab Khasidashvili; Konstantin Korovin

doi:10.24963/ijcai.2022/249

Combining Constraint Solving and Bayesian Techniques for System Optimization

Franz Brauße, Zurab Khasidashvili, Konstantin Korovin

Formal Methods

Intel

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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 SMTbased constraint solving to achieve safe and stable solutions with optimality guarantees.

Original language	English
Title of host publication	Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22)
Pages	1788-1794
DOIs	https://doi.org/10.24963/ijcai.2022/249
Publication status	E-pub ahead of print - 1 Nov 2022
Event	Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22} - Vienna, Austria Duration: 23 Jul 2022 → 29 Jul 2022

Conference

Conference	Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}
Period	23/07/22 → 29/07/22

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10.24963/ijcai.2022/249Licence: CC BY

https://www.ijcai.org/proceedings/2022/249

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title = "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 acombination of Bayesian optimization and SMTbased constraint solving to achieve safe and stable solutions with optimality guarantees.",

author = "Franz Brau{\ss}e and Zurab Khasidashvili and Konstantin Korovin",

year = "2022",

month = nov,

day = "1",

doi = "10.24963/ijcai.2022/249",

language = "English",

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booktitle = "Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22)",

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Brauße, F, Khasidashvili, Z & Korovin, K 2022, Combining Constraint Solving and Bayesian Techniques for System Optimization. in Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22). pp. 1788-1794, Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}, 23/07/22. https://doi.org/10.24963/ijcai.2022/249

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AU - Brauße, Franz

AU - Khasidashvili, Zurab

AU - Korovin, Konstantin

PY - 2022/11/1

Y1 - 2022/11/1

N2 - 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 acombination of Bayesian optimization and SMTbased constraint solving to achieve safe and stable solutions with optimality guarantees.

AB - 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 acombination of Bayesian optimization and SMTbased constraint solving to achieve safe and stable solutions with optimality guarantees.

U2 - 10.24963/ijcai.2022/249

DO - 10.24963/ijcai.2022/249

M3 - Conference contribution

SP - 1788

EP - 1794

BT - Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22)

T2 - Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}

Y2 - 23 July 2022 through 29 July 2022

ER -

Combining Constraint Solving and Bayesian Techniques for System Optimization

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Conference

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