MILP Solver

Mixed-integer linear programming (MILP) solvers are crucial for tackling numerous real-world optimization problems, aiming to find optimal solutions within computationally feasible timeframes. Current research focuses on improving solver efficiency through machine learning, particularly by developing models that learn effective cut selection strategies (e.g., hierarchical sequence models, reinforcement learning approaches) and dynamically determine when to stop cut generation. These advancements leverage data-driven techniques to enhance the performance of existing solvers, leading to significant improvements in solving speed and scalability for various applications, including those in robotics and network verification.