We consider cooperative multi-agent consensus optimization problems over both static and time-varying communication networks, where only local communications are allowed. The objective is to minimize the sum of agent-specific possibly non-smooth composite convex functions over agent-specific private conic constraint sets; hence, the optimal consensus decision should lie in the intersection of these private sets. Assuming the sum function is strongly convex, we provide convergence rates in suboptimality, infeasibility and consensus violation; examine the effect of underlying network topology on the convergence rates of the proposed decentralized algorithms.
@article{arxiv.1706.07907,
title = {Multi-agent constrained optimization of a strongly convex function over time-varying directed networks},
author = {Erfan Yazdandoost Hamedani and Necdet Serhat Aybat},
journal= {arXiv preprint arXiv:1706.07907},
year = {2017}
}