Distributed MultiAgent Systems and Applications

Synopsis
To fully realize the blessing offered by increasingly availability of data, we face a few computational challenges. First, the sheer volume and the
spatialtemporal 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 innetwork
data processing and parallel optimization algorithms.
(image source: rstreet)

Publications
TsungHui Chang, Mingyi Hong and Xiangfeng Wang, “Asynchronous Distributed ADMM for LargeScale Optimization Part I: Algorithm and Convergence Analysis”, IEEE Transactions on Signal Processing, Vol. 64, No 12, pages 3118  3130, 2016; available at [arXiv.org]
TsungHui Chang, Mingyi Hong and Xiangfeng Wang, “MultiAgent Distributed Optimization via Inexact Consensus ADM”, IEEE Transactions on Signal Processing, vol.63, no.2, pp.482,497, Jan.15, 2015; available at [arXiv.org]
TsungHui Chang, WeiCheng Liao, Mingyi Hong and Xiangfeng Wang, “Asynchronous Distributed ADMM for LargeScale Optimization Part II: Linear Convergence Analysis and Numerical Performance”, IEEE Transactions on Signal Processing, Vol. 64, No. 12, pages 3131  3144, 2016; available at [arXiv.org]
Mingyi Hong and TsungHui Chang, “Stochastic Proximal Gradient Consensus Over Random Networks” available at [arXiv.org], Nov, 201
