Bayesian optimization (also known as black-box optimization) is an optimization technique that uses Bayesian inference to find the optimal value of an unknown function. It is a type of optimization algorithm that has become increasingly popular in machine learning and artificial intelligence.
In Bayesian optimization, the optimization process is carried out by making a series of informed choices to reach an optimal result. It focuses on the overall process of exploration and exploitation, with the goal of finding the best possible solution given a set of predetermined criteria.
Bayesian optimization works by creating a model of the unknown function which is then used to make informed choices about which parameter values to test. This model is updated after each iteration and provided with additional data, allowing it to be more accurate in predicting the best parameter values. This type of optimization can be applied to problems with continuous or binary input and output spaces, and has found great application in hyperparameter tuning.
Bayesian optimization is particularly well suited to problems that cannot be easily solved with gradient-based optimization techniques. Additionally, it can efficiently reduce the number of iterations needed to arrive at a solution, often leading to optimal values in much fewer iterations than classical approaches.
The Bayesian optimization procedure is one of the most powerful tools available for optimizing complex systems and can be used for a variety of tasks, such as design optimization, predictive modelling, and intelligent control. It is also easily adaptable to a variety of situations, since it requires little prior information about the system being optimized.
