Gradient Descent Ascent

Gradient descent ascent (GDA) methods address minimax optimization problems, aiming to find saddle points where one function is minimized and another is maximized simultaneously. Current research focuses on improving GDA's convergence speed and stability, particularly by exploring algorithmic variations like alternating updates, smoothing techniques, and optimistic gradient methods, often within the context of generative adversarial networks (GANs) and federated learning. These advancements are significant because efficient and stable solutions to minimax problems are crucial for various machine learning applications, including GAN training, reinforcement learning, and robust optimization.