Branch and Cut

Branch and cut is a powerful algorithm for solving integer programming problems, aiming to find optimal solutions by iteratively adding constraints (cuts) to reduce the search space within a branch-and-bound framework. Current research emphasizes data-driven approaches, employing machine learning models like neural networks to intelligently select cuts and configure the algorithm's parameters (e.g., node selection, branching strategies), leading to improved efficiency. This focus on learning-augmented branch and cut significantly impacts the speed and scalability of solving complex optimization problems across diverse fields, from operations research to machine learning applications.