Paper Info
Title
A geometrically converging dual method for distributed optimization over time-varying graphs.
Abstract
In this article, we consider a distributed convex optimization problem over time-varying undirected networks. We propose a dual method, primarily averaged network dual ascent (PANDA), that is proven to converge R-linearly to the optimal point given that the agents’ objective functions are strongly convex and have Lipschitz continuous gradients. Like dual decomposition, PANDA requires half the amou...
Year
DOI
Venue
2021
10.1109/TAC.2020.3018743
IEEE Transactions on Automatic Control
Keywords
DocType
Volume
Optimization,Convergence,Linear programming,Convex functions,Symmetric matrices,Europe,Information science
Journal
66
Issue
ISSN
Citations 
6
0018-9286
2
PageRank 
References 
Authors
0.35
8
2
Name
Order
Citations
PageRank
Marie Maros1111.82
Joakim Jalden224321.59