Min Max

Min-max optimization, encompassing saddle point problems, focuses on finding equilibrium solutions where one function is minimized while another is maximized. Current research emphasizes developing robust and efficient algorithms, including gradient descent-ascent methods (with variations like optimistic and dissipative approaches), and exploring their application in diverse areas such as generative adversarial networks (GANs) and robust optimization. These advancements are crucial for improving the performance and stability of machine learning models and addressing challenges in multi-agent systems and adversarial settings, impacting fields ranging from computer vision to reinforcement learning.