A Supervised Machine Learning Approach to Variable Branching in Branch-And-Bound

A new approach that uses supervised machine learning techniques to improve the performances of optimization algorithms in the context of mixed-integer programming (MIP) by imitating the decisions taken by a supposedly good branching strategy with a fast approximation.