Paper Info
Title
Distributed Prediction-Correction ADMM for Time-Varying Convex Optimization
Abstract
This paper introduces a dual-regularized ADMM approach to distributed, time-varying optimization. The proposed algorithm is designed in a prediction-correction framework, in which the computing nodes predict the future local costs based on past observations, and exploit this information to solve the time-varying problem more effectively. In order to guarantee linear convergence of the algorithm, a...
Year
DOI
Venue
2020
10.1109/IEEECONF51394.2020.9443280
2020 54th Asilomar Conference on Signals, Systems, and Computers
Keywords
DocType
ISBN
Computers,Prediction algorithms,Convex functions,Trajectory,Optimization,Convergence
Conference
978-0-7381-3126-9
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Nicola Bastianello113.40
Andrea Simonetto2144.35
Ruggero Carli389469.17