  In this paper, we propose a simple acceleration scheme for Riemannian
gradient methods by extrapolating iterates on manifolds. We show when the
iterates are generated from Riemannian gradient descent method, the accelerated
scheme achieves the optimal convergence rate asymptotically and is
computationally more favorable than the recently proposed Riemannian Nesterov
accelerated gradient methods. Our experiments verify the practical benefit of
the novel acceleration strategy.
