Riemannian Stochastic Recursive Momentum Method for non-Convex Optimization
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 2505-2511.
https://doi.org/10.24963/ijcai.2021/345
Keywords
- Machine Learning: Online Learning
Abstract
We propose a stochastic recursive momentum method for Riemannian non-convex optimization that achieves a nearly-optimal complexity to find epsilon-approximate solution with one sample. The new algorithm requires one-sample gradient evaluations per iteration and does not require restarting with a large batch gradient, which is commonly used to obtain a faster rate. Extensive experiment results demonstrate the superiority of the proposed algorithm. Extensions to nonsmooth and constrained optimization settings are also discussed.
@inproceedings{ijcai2021p345,
title = {Riemannian Stochastic Recursive Momentum Method for non-Convex Optimization},
author = {Han, Andi and Gao, Junbin},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Prof. Zhi-Hua Zhou},
pages = {2505--2511},
year = {2021},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2021/345},
url = {https://doi.org/10.24963/ijcai.2021/345},
}
TY - CPAPER TI - Riemannian Stochastic Recursive Momentum Method for non-Convex Optimization AU - Han, Andi AU - Gao, Junbin T2 - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2021 SP - 2505 EP - 2511 DO - 10.24963/ijcai.2021/345 UR - https://doi.org/10.24963/ijcai.2021/345 ER -Copied
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