An acquisition function is a mathematical tool used in optimization, particularly in the field of machine learning and statistics. It helps determine the most informative points to sample next in a process, guiding the search for the best solution. This is especially useful in scenarios where evaluating a function is expensive or time-consuming. In the context of Bayesian optimization, the acquisition function balances exploration and exploitation. It evaluates potential points based on their expected improvement or uncertainty, allowing for efficient decision-making. By strategically selecting points, it aims to minimize the number of evaluations needed to find the optimal solution.