Fast AltGDA Type Algorithm

Fast AltGDA-type algorithms aim to efficiently solve nonconvex minimax optimization problems, a common challenge in machine learning, particularly in adversarial training and federated learning. Current research focuses on improving convergence speed and reducing computational complexity through techniques like momentum acceleration, proximal gradient updates, and adaptive learning rates, often within decentralized or privacy-preserving frameworks. These advancements are significant because they enable the training of more complex models and improve the scalability of machine learning applications across diverse settings. The resulting algorithms show promise for enhancing the performance and efficiency of various machine learning tasks.