- Optimal parameter selection for the alternating direction method of multipliers (ADMM): quadratic problems Euhanna Ghadimi (euhannakth.se) André Teixeira (andreteikth.se) Iman Shames (iman.shamesunimleb.edu.au) Mikael Johansson (mikaeljkth.se) Abstract: The alternating direction method of multipliers (ADMM) has emerged as a powerful technique for large-scale structured optimization. Despite many recent results on the convergence properties of ADMM, a quantitative characterization of the impact of the algorithm parameters on the convergence times of the method is still lacking. In this paper we find the optimal algorithm parameters that minimize the convergence factor of the ADMM iterates in the context of $\ell_2$-regularized minimization and constrained quadratic programming. Numerical examples show that our parameter selection rules significantly outperform existing alternatives in the literature. Keywords: ADMM, Convergence Rate, Convergence Factor Category 1: Convex and Nonsmooth Optimization (Convex Optimization ) Category 2: Nonlinear Optimization (Quadratic Programming ) Citation: DOI 10.1109/TAC.2014.2354892, IEEE Transactions on Automatic Control Download: [PDF]Entry Submitted: 06/10/2013Entry Accepted: 06/10/2013Entry Last Modified: 12/11/2014Modify/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.