Extra Gradient Method

The extra-gradient method is a powerful algorithm for solving minimax optimization problems, which arise frequently in machine learning and other fields. Current research focuses on improving its convergence rates, particularly for non-convex settings, and exploring variations like alternating updates and second-order methods to enhance efficiency. These advancements are significant because they enable faster and more robust solutions to challenging problems such as training generative adversarial networks and robust optimization, impacting both theoretical understanding and practical applications. Furthermore, research is actively addressing distributed and robust versions of the algorithm to handle large-scale data and adversarial agents.