  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
l2-regularized minimization and constrained quadratic programming. Numerical
examples show that our parameter selection rules significantly outperform
existing alternatives in the literature.
