  Accelerator physics relies on numerical algorithms to solve optimization
problems in online accelerator control and tasks such as experimental design
and model calibration in simulations. The effectiveness of optimization
algorithms in discovering ideal solutions for complex challenges with limited
resources often determines the problem complexity these methods can address.
The accelerator physics community has recognized the advantages of Bayesian
optimization algorithms, which leverage statistical surrogate models of
objective functions to effectively address complex optimization challenges,
especially in the presence of noise during accelerator operation and in
resource-intensive physics simulations. In this review article, we offer a
conceptual overview of applying Bayesian optimization techniques towards
solving optimization problems in accelerator physics. We begin by providing a
straightforward explanation of the essential components that make up Bayesian
optimization techniques. We then give an overview of current and previous work
applying and modifying these techniques to solve accelerator physics
challenges. Finally, we explore practical implementation strategies for
Bayesian optimization algorithms to maximize their performance, enabling users
to effectively address complex optimization challenges in real-time beam
control and accelerator design.
