Distributed Proximal Splitting Algorithms with Rates and Acceleration
Optimization and Control
2022-01-28 v3 Machine Learning
Numerical Analysis
Numerical Analysis
Abstract
We analyze several generic proximal splitting algorithms well suited for large-scale convex nonsmooth optimization. We derive sublinear and linear convergence results with new rates on the function value suboptimality or distance to the solution, as well as new accelerated versions, using varying stepsizes. In addition, we propose distributed variants of these algorithms, which can be accelerated as well. While most existing results are ergodic, our nonergodic results significantly broaden our understanding of primal-dual optimization algorithms.
Cite
@article{arxiv.2010.00952,
title = {Distributed Proximal Splitting Algorithms with Rates and Acceleration},
author = {Laurent Condat and Grigory Malinovsky and Peter Richtárik},
journal= {arXiv preprint arXiv:2010.00952},
year = {2022}
}