Bayesian optimization

Method for optimizing expensive black-box functions using a surrogate model and an acquisition function to iteratively select the most promising points to evaluate. It balances exploration and exploitation to efficiently find the global optimum, making it particularly useful in scenarios where function evaluations are costly or time-consuming. Bayesian optimization is primarily used by researchers and practitioners in fields such as machine learning, engineering, and scientific research where optimization of complex functions is required.