This repository contains a set of optimization algorithms and objective functions, and all code needed to reproduce experiments in:
-
"DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization" [PDF]. (code is in this file [link])
-
"Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction" [PDF]. (code is in the previous version of this repo [link])
Due to the random data generation procedure, resulting graphs may be slightly different from those appeared in the paper, but conclusions remain the same.
If you find this code useful, please cite our papers:
@article{li2021destress,
title={DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization},
author={Li, Boyue and Li, Zhize and Chi, Yuejie},
journal={arXiv preprint arXiv:2110.01165},
year={2021}
}
@article{li2020communication,
title={Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction},
author={Li, Boyue and Cen, Shicong and Chen, Yuxin and Chi, Yuejie},
journal={Journal of Machine Learning Research},
volume={21},
pages={1--51},
year={2020}
}
The gradient implementations of all objective functions are checked numerically.
Linear regression with random generated data.
The objective function is
Logistic regression with libsvmtools
.
The objective function is
One-hidden-layer fully-connected neural netowrk
One-hidden-layer fully-connected neural network with softmax loss on the MNIST dataset.
- Gradient descent
- Stochastic gradient descent
- Nesterov's accelerated gradient descent
- SVRG
- SARAH
- ADMM
- DANE
- Decentralized gradient descent
- Decentralized stochastic gradient descent
- Decentralized gradient descent with gradient tracking
- EXTRA
- NIDS
- Network-DANE/SARAH/SVRG
- GT-SARAH
- DESTRESS