Acceleration in Hyperbolic and Spherical Spaces

David Martínez-Rubio

Keywords: Riemannian optimization, acceleration, first-order methods

Abstract:

We further research on the acceleration phenomenon on Riemannian manifolds by introducing the first global first-order method that achieves the same rates as accelerated gradient descent in the Euclidean space for the optimization of smooth and geodesically convex (g-convex) or strongly g-convex functions defined on the hyperbolic space or a subset of the sphere, up to constants and log factors. To the best of our knowledge, this is the first method that is proved to achieve these rates globally on functions defined on a Riemannian manifold $\mathcal{M}$ other than the Euclidean space.
Additionally, for any Riemannian manifold of bounded sectional curvature, we provide reductions from optimization methods for smooth and g-convex functions to methods for smooth and strongly g-convex functions and vice versa.

One-sentence Summary: We find the first provable accelerated first-order algorithm for g-convex function minimization in manifolds other than the Euclidean space.

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