8 citations · 32 across the 42 of their papers we have counts for
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math.OC2020★ 3 cited
Escape saddle points faster on manifolds via perturbed Riemannian stochastic recursive gradient
Andi Han, Junbin Gao
In this paper, we propose a variant of Riemannian stochastic recursive gradient method that can achieve second-order convergence guarantee and escape saddle points using simple per…
math.OC2020★ 2 cited
Riemannian stochastic recursive momentum method for non-convex optimization
Andi Han, Junbin Gao
We propose a stochastic recursive momentum method for Riemannian non-convex optimization that achieves a near-optimal complexity of to find -approx…
math.OC2020★ 5 cited
Variance reduction for Riemannian non-convex optimization with batch size adaptation
Andi Han, Junbin Gao
Variance reduction techniques are popular in accelerating gradient descent and stochastic gradient descent for optimization problems defined on both Euclidean space and Riemannian…