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 regularization is applied to the dual, yielding a dual-regularized ADMM. We analyze the convergence properties of the time-varying algorithm, as well as the regularization error of the dual-regularized ADMM. Numerical results show that in time-varying settings, despite the regularization error, the performance of the dual-regularized ADMM can outperform inexact gradient-based methods, as well as exact dual decomposition techniques, in terms of asymptotical error and consensus constraint violation.
Keywords
Cite
@article{arxiv.2009.08335,
title = {Distributed Prediction-Correction ADMM for Time-Varying Convex Optimization},
author = {Nicola Bastianello and Andrea Simonetto and Ruggero Carli},
journal= {arXiv preprint arXiv:2009.08335},
year = {2024}
}
Comments
Presented at Asilomar Conference on Signals, Systems, and Computers 2020