Distributed, scalable and gossip-free consensus optimization with application to data analysis

arXiv: Optimization and Control, 2017.

Cited by: 0|Views7

Abstract:

Distributed algorithms for solving additive or consensus optimization problems commonly rely on first-order or proximal splitting methods. These algorithms generally come with restrictive assumptions and at best enjoy a linear convergence rate. Hence, they can require many iterations or communications among agents to converge. In many c...More

Code:

Data:

Get fulltext within 24h
Bibtex
Your rating :
0

 

Tags
Comments