Discrete Optimization Problem

Discrete optimization problems, which involve finding the best solution from a finite set of possibilities, are central to many areas of science and engineering, particularly in machine learning where they arise in tasks like neural network training and pruning. Current research focuses on developing efficient algorithms to solve these problems, especially for large-scale applications, exploring techniques like warm-starting with machine-learned predictions, score-based approximations, and improved branch-and-bound methods. These advancements are crucial for improving the scalability and performance of machine learning models and optimizing resource allocation in various applications, such as advertisement and distributed computing.