  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.
