Proceedings

Riemannian Stochastic Recursive Momentum Method for non-Convex Optimization

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 2505-2511.

https://doi.org/10.24963/ijcai.2021/345

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.

Andi Han, and Junbin Gao. 2021. Riemannian Stochastic Recursive Momentum Method for non-Convex Optimization. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 2505–2511. https://doi.org/10.24963/ijcai.2021/345
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