Bayesian Optimisation with Formal Guarantees
This addresses the need for reliable optimization in industrial applications, offering a novel approach to ensure safety and guarantees, though it appears incremental as it builds on existing Bayesian optimization and SMT techniques.
The paper tackles the problem of optimizing complex real-world functions where standard methods fail to provide validated solutions and correctness guarantees, by combining Bayesian optimization with SMT-based constraint solving to achieve safe, stable solutions with optimality guarantees.
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 optimisation and SMT-based constraint solving to achieve safe and stable solutions with optimality guarantees.