Description: Bilevel optimization is an optimization approach that involves two hierarchical levels of decisions, where the solution of one level depends on the solution of the other. This type of problem is common in scenarios where strategic and tactical decisions must be made simultaneously. In the context of optimization problems, bilevel optimization allows solutions to be derived from two levels of decision-making, where the outcome of one level influences the other. In this framework, the upper level is responsible for the global optimization of the model, while the lower level focuses on the local adaptation of the model to the specific information or conditions of each scenario. This hierarchical structure not only improves efficiency but also ensures that particular constraints or preferences are taken into account in both decision levels. Bilevel optimization is characterized by its complexity, as the interaction between the two levels can lead to non-trivial solutions and requires advanced optimization techniques to solve them. Its relevance lies in its ability to balance conflicting objectives while accommodating specific requirements, which is essential in various applications ranging from economics to engineering and machine learning.
