Primal Dual Algorithm

Primal-dual algorithms are optimization methods solving problems by iteratively updating both primal and dual variables to find a saddle point, efficiently handling constrained optimization problems. Current research focuses on extending these algorithms to handle non-convex, non-smooth, and stochastic settings, particularly within reinforcement learning, federated learning, and distributionally robust optimization, often employing techniques like actor-critic methods, stochastic gradient methods, and adaptive learning rates. This work is significant because it enables efficient solutions to complex optimization problems arising in diverse machine learning applications, improving model accuracy and training efficiency while addressing constraints like safety and privacy.