Analysis of Distributed ADMM Algorithm for Consensus Optimization in Presence of Node Error

Abstract

Alternating direction method of multipliers (ADMM) is a popular convex optimization algorithm, which can be employed for solving distributed consensus optimization problems. In this setting, agents locally estimate the optimal solution of an optimization problem and exchange messages with their neighbors over a connected network. The distributed algorithms are typically exposed to different types of errors in practice, e.g., due to quantization or communication noise or loss. We here focus on analyzing the convergence of distributed ADMM for consensus optimization in the presence of additive random node error, in which case the nodes communicate a noisy version of their latest estimate of the solution to their neighbors in each iteration. We present analytical upper and lower bounds on the mean-squared steady-state error of the algorithm in case the local objective functions are strongly convex and have Lipschitz continuous gradients. In addition, we show that when the local objective functions are convex and the additive node error is bounded, the estimation error of the noisy ADMM for consensus optimization is also bounded. Numerical results are provided, which demonstrates the effectiveness of the presented analyses and shed light on the role of the system and network parameters on performance.