Distributed Multi-Agent Systems

Distributed in-network data processing and parallel optimization algorithms

To fully realize the blessing offered by increasingly availability of data, we face a few computational challenges. First, the sheer volume and the spatial-temporal availability of data makes it impossible to run analytics using central processors and storage. This happens, for instance, when the sheer volume of the data overwhelms the storage capacity of any single computer.

Another example is when data are collected in a massively distributed manner, and sharing local information with central processors is either infeasible or not economical, owing to the large size of the network and volume of data, energy constraints, and/or privacy concerns. Thus, there is an urgent need of developing distributed in-network data processing and parallel optimization algorithms.

Selected Publications

  • Tsung-Hui Chang, Mingyi Hong and Xiangfeng Wang, “Asynchronous Distributed ADMM for Large-Scale Optimization- Part I: Algorithm and Convergence Analysis”, IEEE Transactions on Signal Processing, Vol. 64, No 12, pages 3118 - 3130, 2016
  • Tsung-Hui Chang, Mingyi Hong and Xiangfeng Wang, “Multi-Agent Distributed Optimization via Inexact Consensus ADMM”, IEEE Transactions on Signal Processing, vol.63, no.2, pp.482,497, Jan.15, 2015
  • Mingyi Hong and Tsung-Hui Chang, “Stochastic Proximal Gradient Consensus Over Random Networks”