Network Optimization

Network optimization aims to efficiently allocate resources and manage the performance of complex systems, such as wireless and data center networks, and multi-agent systems. Current research heavily utilizes machine learning, particularly deep reinforcement learning, graph neural networks, and generative models like diffusion models, to address challenges in resource allocation, traffic engineering, and observer placement in dynamic environments. These advancements are crucial for improving the efficiency, scalability, and adaptability of next-generation networks and enabling autonomous network management, impacting both the theoretical understanding of network dynamics and the practical deployment of advanced communication technologies. The integration of domain knowledge into these data-driven approaches is also a growing area of focus.