Multi-Block ADMM for Big Data Optimization in Modern Communication Networks

TL;DR

This paper reviews parallel and distributed ADMM algorithms for large-scale optimization in modern communication networks, discussing extensions, convergence, implementation, and applications like smart grids and SDNs.

Contribution

It introduces multi-block ADMM extensions for big data problems in communication networks, analyzing their convergence and practical implementation.

Findings

Extended ADMM to N-block settings for large-scale problems

Analyzed convergence properties of multi-block ADMM variants

Demonstrated applications in smart grids and SDN data offloading

Abstract

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).