Optimal Flow

Optimal flow research focuses on efficiently managing the movement of various entities, from data packets in networks to vehicles in traffic systems and even color information in images. Current research employs diverse approaches, including reinforcement learning algorithms, graph neural networks, and adaptations of classical algorithms like Push-Relabel, often incorporating prediction models to accelerate computation. These advancements aim to optimize resource utilization, minimize costs (e.g., travel time, energy consumption), and improve the performance of various systems, impacting fields ranging from telecommunications and transportation to computer vision.