Strongly Concave Minimax

Strongly concave minimax optimization focuses on efficiently solving problems where a function is minimized with respect to one set of variables and simultaneously maximized with respect to another, with the maximization problem exhibiting strong concavity. Current research emphasizes developing accelerated algorithms, often incorporating variance reduction and momentum techniques, to improve convergence rates for both centralized and decentralized settings, particularly handling non-smooth regularizers and nonconvexity in the minimization variables. These advancements are crucial for tackling challenging machine learning tasks like robust regression and AUC maximization, improving both the efficiency and scalability of these methods.