Unfolded D ADMM

Unfolded ADMM (Alternating Direction Method of Multipliers) represents a class of algorithms that improve the efficiency and robustness of the standard ADMM approach, primarily by integrating it with deep learning techniques. Current research focuses on applying unfolded ADMM to various machine learning tasks, including distributed learning, deep network training, and inverse problems like image reconstruction, often aiming to reduce communication overhead or improve convergence speed through learned hyperparameters or adaptive step sizes. This approach offers significant potential for accelerating large-scale optimization problems across diverse fields, leading to more efficient and scalable solutions in areas such as federated learning and medical imaging.