- Global Convergence of Unmodified 3-Block ADMM for a Class of Convex Minimization Problems Tianyi Lin (tylinse.cuhk.edu.hk) Shiqian Ma (sqmase.cuhk.edu.hk) Shuzhong Zhang (zhangsumn.edu) Abstract: The alternating direction method of multipliers (ADMM) has been successfully applied to solve structured convex optimization problems due to its superior practical performance. The convergence properties of the 2-block ADMM have been studied extensively in the literature. Specifically, it has been proven that the 2-block ADMM globally converges for any penalty parameter $\gamma>0$. In this sense, the 2-block ADMM allows the parameter to be free, i.e., there is no need to restrict the value for the parameter when implementing this algorithm in order to ensure convergence. However, for the 3-block ADMM, Chen et al. recently constructed a counter-example showing that it can diverge if no further condition is imposed. The existing results on studying further sufficient conditions on guaranteeing the convergence of the 3-block ADMM usually require $\gamma$ to be smaller than a certain bound, which is usually either difficult to compute or too small to make it a practical algorithm. In this paper, we show that the 3-block ADMM still globally converges with any penalty parameter $\gamma>0$ when applied to solve a class of commonly encountered problems to be called regularized least squares decomposition (RLSD) in this paper, which covers many important applications in practice. Keywords: ADMM, Global Convergence, Convex Minimization, Regularized Least Squares Decomposition Category 1: Convex and Nonsmooth Optimization Citation: Download: [PDF]Entry Submitted: 05/16/2015Entry Accepted: 05/16/2015Entry Last Modified: 05/27/2015Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Optmization Society.