In this paper, we review the parallel and distributed optimization algorithms based on the alternating direction method of multipliers (ADMM) for solving "big data" optimization problems in modern communication networks. We first introduce the canonical formulation of the large-scale optimization problem. Next, we describe the general form of ADMM and then focus on several direct extensions and sophisticated modifications of ADMM from 2-block to N-block settings to deal with the optimization problem. The iterative schemes and convergence properties of each extension/modification are given, and the implementation on large-scale computing facilities is also illustrated. Finally, we numerate several applications in communication networks, such as the security constrained optimal power flow problem in smart grid networks and mobile data offloading problem in software defined networks (SDNs).
@article{arxiv.1504.01809,
title = {Multi-Block ADMM for Big Data Optimization in Modern Communication Networks},
author = {Lanchao Liu and Zhu Han},
journal= {arXiv preprint arXiv:1504.01809},
year = {2015}
}