  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.
