Distributed inexact dual consensus ADMM for network resource allocation

This paper investigates two novel distributed algorithms based on alternating direction method of multipliers (ADMM) for network resource allocation of N agents. The main objective is to derive an optimal allocation that minimizes a global objective expressed as a sum of locally known separable convex objective functions. Based on a communication matrix, the dual resource allocation problem is changed into a consensus optimization problem, in which each agent broadcasts the outcome of its local processing to all his neighbors. In this paper, we first propose a new distributed dual consensus ADMM (DC-ADMM) algorithm to address this consensus problem. Moreover, by applying an inexact step for each ADMM update, a distributed inexact DC-ADMM (IDC-ADMM) is also developed, which enables agents to perform cheap computation at each iteration. Finally, numerical simulations are delivered to illustrate and validate the proposed algorithm.