Multi-Agent Distributed Optimization via Inexact Consensus ADMM

Understand

Multi-agent distributed consensus optimization problems arise in many signal processing applications.

• Recently, the alternating direction method of multipliers (ADMM) has been used for solving this family of problems.
• ADMM based distributed optimization method is shown to have faster convergence rate compared with classic methods based on consensus subgradient, but can be computationally expensive, especially for problems with complicated structures or large dimensions.
• In this paper, we propose low-complexity algorithms that can reduce the overall computational cost of consensus ADMM by an order of magnitude for certain large-scale problems.