  The alternating direction method of multipliers (ADMM) is commonly used for
distributed model fitting problems, but its performance and reliability depend
strongly on user-defined penalty parameters. We study distributed ADMM methods
that boost performance by using different fine-tuned algorithm parameters on
each worker node. We present a O(1/k) convergence rate for adaptive ADMM
methods with node-specific parameters, and propose adaptive consensus ADMM
(ACADMM), which automatically tunes parameters without user oversight.

Machine Learning;Numerical Analysis;Systems and Control