Convex Concave

Convex-concave optimization focuses on finding saddle points in functions that are convex in one set of variables and concave in another, a problem arising frequently in machine learning and other fields. Current research emphasizes developing efficient algorithms, particularly parameter-free methods and those leveraging second-order information, to solve both convex-concave and more challenging nonconvex-concave minimax problems, often within distributed or stochastic settings. These advancements improve the scalability and robustness of solutions for applications like federated learning, distributionally robust optimization, and adversarial training, impacting various areas of data science and optimization.