Branch and Bound

Branch and bound is a powerful algorithmic framework for solving complex optimization problems, aiming to find optimal solutions by systematically exploring a search space and pruning unpromising branches. Current research focuses on enhancing its efficiency through various strategies, including improved bounding techniques, novel branching heuristics informed by machine learning (e.g., graph neural networks, reinforcement learning), and the integration of machine learning for tasks like node selection and cut generation within branch-and-bound solvers. These advancements significantly impact diverse fields, improving the speed and scalability of solving problems in areas such as machine learning, neural network verification, and mixed-integer programming.