Innovation compression for communication-efficient distributed optimization with linear convergence
Information compression is essential to reduce communication cost in distributed optimization over peer-to-peer networks. This paper proposes a communication-efficient linearly convergent distributed (COLD) algorithm to solve strongly convex optimization problems. By compressing innovation vectors,...
Saved in:
Main Authors: | Zhang, Jiaqi, You, Keyou, Xie, Lihua |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/170700 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Convergence of asynchronous distributed gradient methods over stochastic networks
by: Xu, Jinming, et al.
Published: (2020) -
Exponential convergence of distributed optimization for heterogeneous linear multi-agent systems over unbalanced digraphs
by: Li, Li, et al.
Published: (2022) -
The convergence to normal distribution of random sums of independent random variables with finite variances
by: Petcharat Rattanawong
Published: (2009) -
Convergence rates to the Marchenko-Pastur type distribution
by: Bai, Z., et al.
Published: (2014) -
A non-interior continuation algorithm for the P0 or P * LCP with strong global and local convergence properties
by: Huang, Z.-H., et al.
Published: (2013)