New Bounding

"New bounding" research focuses on developing tighter and more efficient bounds for various optimization problems, aiming to improve the speed and accuracy of algorithms. Current efforts concentrate on refining branch-and-bound methods, leveraging dynamic programming and novel bounding techniques (e.g., partition-based bounds, continuity-based relaxations) across diverse applications like decision tree learning, graph problems (maximum s-bundle), and the traveling salesman problem. These advancements lead to improved solution quality and computational efficiency, impacting fields ranging from machine learning and computer graphics to robotics and network optimization.