Bilevel optimization is a hierarchical optimization framework where one problem, known as the upper-level problem, is nested within another, called the lower-level problem. This structure is particularly useful for modeling scenarios where decision-making processes are interdependent, such as in economics, engineering design, and machine learning hyperparameter tuning.