We propose a novel distributed algorithm for one of the most fundamental problems in networks: the average consensus. We view the average consensus as an optimization problem, which allows us to use recent techniques and results from the optimization area. Based on the assumption that a coloring scheme of the network is available, we derive a decentralized, asynchronous, and communication-efficient algorithm that is based on the Alternating Direction Method of Multipliers (ADMM). Our simulations with other state-of-the-art consensus algorithms show that the proposed algorithm is the one exhibiting the most stable performance across several network models.