NeurIPS 2019poster19 citations
General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme
Tao Sun, Yuejiao Sun, Dongsheng Li, Qing Liao
Abstract
The incremental aggregated gradient algorithm is popular in network optimization and machine learning research. However, the current convergence results require the objective function to be strongly convex. And the existing convergence rates are also limited to linear convergence. Due to the mathematical techniques, the stepsize in the algorithm is restricted by the strongly convex constant, which may make the stepsize be very small (the strongly convex constant may be small).
BibTeX
@inproceedings{NEURIPS2019_46922a08,
author = {Sun, Tao and Sun, Yuejiao and Li, Dongsheng and Liao, Qing},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/46922a0880a8f11f8f69cbb52b1396be-Paper.pdf},
volume = {32},
year = {2019}
}