A Primal-Dual Method for Conic Constrained Distributed Optimization Problems
Abstract
We consider cooperative multi-agent consensus optimization problems over an undirected network of agents, where only those agents connected by an edge can directly communicate. The objective is to minimize the sum of agent-specific composite convex functions over agent-specific private conic constraint sets; hence, the optimal consensus decision should lie in the intersection of these private sets. We provide convergence rates in sub-optimality, infeasibility and consensus violation; examine the effect of underlying network topology on the convergence rates of the proposed decentralized algorithms; and show how to extend these methods to handle time-varying communication networks.
Cite
Text
Aybat and Hamedani. "A Primal-Dual Method for Conic Constrained Distributed Optimization Problems." Neural Information Processing Systems, 2016.Markdown
[Aybat and Hamedani. "A Primal-Dual Method for Conic Constrained Distributed Optimization Problems." Neural Information Processing Systems, 2016.](https://mlanthology.org/neurips/2016/aybat2016neurips-primaldual/)BibTeX
@inproceedings{aybat2016neurips-primaldual,
title = {{A Primal-Dual Method for Conic Constrained Distributed Optimization Problems}},
author = {Aybat, Necdet Serhat and Hamedani, Erfan Yazdandoost},
booktitle = {Neural Information Processing Systems},
year = {2016},
pages = {5049-5057},
url = {https://mlanthology.org/neurips/2016/aybat2016neurips-primaldual/}
}