On sharp stochastic zeroth-order Hessian estimators over Riemannian manifolds

Tianyu Wang

Abstract:

We study Hessian estimators for functions defined over an $n$-dimensional complete analytic Riemannian manifold. We introduce new stochastic zeroth-order Hessian estimators using $O (1)$ function evaluations. We show that, for an analytic real-valued function $f$, our estimator achieves a bias bound of order $ O \left( \gamma \delta^2 \right) $, where $ \gamma $ depends on both the Levi-Civita connection and function $f$, and $\delta$ is the finite difference step size. To the best of our knowledge, our results provide the first bias bound for Hessian estimators that explicitly depends on the geometry of the underlying Riemannian manifold.
Also, this improves best previously known bias bound for $O(1)$-evaluation Hessian estimators over Riemannian manifolds, which is of order $O (n^2 \delta)$ over an $n$-dimensional space. We also study downstream computations based on our Hessian estimators. The supremacy of our method is evidenced by empirical evaluations.