Large Scale Combinatorial

Large-scale combinatorial optimization (CO) focuses on efficiently finding optimal solutions within extremely large search spaces, a challenge arising in numerous real-world applications. Current research emphasizes developing scalable neural network architectures, including graph neural networks and diffusion models, often coupled with divide-and-conquer strategies or self-improving learning mechanisms to handle the complexity of massive problem instances. These advancements aim to improve the speed and solution quality of CO solvers, impacting fields like logistics, resource allocation, and even fantasy sports management by providing faster and more effective decision-making tools.