Alternating Direction Method of Multiplier

The Alternating Direction Method of Multipliers (ADMM) is an optimization algorithm used to solve large-scale problems by decomposing them into smaller, more manageable subproblems. Current research focuses on extending ADMM's capabilities to diverse applications, including federated learning (where privacy and heterogeneity are key challenges), deep learning (accelerating training and improving convergence), and inverse problems (like image reconstruction and deconvolution). This versatility makes ADMM a powerful tool with significant impact across various fields, offering improved efficiency and robustness in solving complex optimization tasks.