Robustness and Convergence Analysis of First-Order Distributed Optimization Algorithms over Subspace Constraints
Optimization and Control
2022-10-31 v1
Abstract
This paper extends algorithms that remove the fixed point bias of decentralized gradient descent to solve the more general problem of distributed optimization over subspace constraints. Leveraging the integral quadratic constraint framework, we analyze the performance of these generalized algorithms in terms of worst-case robustness and convergence rate. The utility of our framework is demonstrated by showing how one of the extended algorithms, originally designed for consensus, is now able to solve a multitask inference problem.
Cite
@article{arxiv.2210.16277,
title = {Robustness and Convergence Analysis of First-Order Distributed Optimization Algorithms over Subspace Constraints},
author = {Dennis J. Marquis and Dany Abou Jaoude and Mazen Farhood and Craig A. Woolsey},
journal= {arXiv preprint arXiv:2210.16277},
year = {2022}
}