Riemannian stochastic recursive momentum method for non-convex optimization

• URL: http://arxiv.org/abs/2008.04555v1
• Date: Tue, 11 Aug 2020 07:05:58 GMT
• Title: Riemannian stochastic recursive momentum method for non-convex optimization
• Authors: Andi Han, Junbin Gao
• Abstract summary: We propose atildeOepsilon$-approximate gradient evaluations evaluation method for $mathcalO gradient evaluations with one iteration. Proposed experiment demonstrate the superiority of the algorithm.
• Score: 36.79189106909088
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a stochastic recursive momentum method for Riemannian non-convex optimization that achieves a near-optimal complexity of $\tilde{\mathcal{O}}(\epsilon^{-3})$ to find $\epsilon$-approximate solution with one sample. That is, our method requires $\mathcal{O}(1)$ gradient evaluations per iteration and does not require restarting with a large batch gradient, which is commonly used to obtain the faster rate. Extensive experiment results demonstrate the superiority of our proposed algorithm.

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