ADMM Algorithm

The Alternating Direction Method of Multipliers (ADMM) algorithm is a powerful optimization technique used to solve large-scale problems by decomposing them into smaller, more manageable subproblems. Current research focuses on extending ADMM's applicability to non-convex problems, distributed and federated learning settings, and incorporating deep learning models, often within frameworks like Plug-and-Play ADMM or unfolded ADMM networks. This versatility makes ADMM increasingly significant for diverse applications, including machine learning, robotics, signal processing, and distributed systems, offering improved efficiency and scalability in solving complex optimization tasks.