Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7232-7241, 2019.
Recently, there has been a great deal of research attention on understanding the convergence behavior of first-order methods. One line of this research focuses on analyzing the convergence behavior of first-order methods using tools from continuous dynamical systems such as ordinary differential equations and differential inclusions. These research results shed lights on better understanding first-order methods from a non-optimization point of view. The alternating direction method of multipliers (ADMM) is a widely used first-order method for solving optimization problems arising from machine learning and statistics, and it is important to investigate its behavior using these new techniques from dynamical systems. Existing works along this line have been mainly focusing on problems with smooth objective functions, which exclude many important applications that are traditionally solved by ADMM variants. In this paper, we analyze some well-known and widely used ADMM variants for nonsmooth optimization problems using tools of differential inclusions. In particular, we analyze the convergence behavior of linearized ADMM, gradient-based ADMM, generalized ADMM and accelerated generalized ADMM for nonsmooth problems and show their connections with dynamical systems. We anticipate that these results will provide new insights on understanding ADMM for solving nonsmooth problems.