We propose a stochastic variance-reduced cubic regularized Newton algorithm to optimize the finite-sum problem over Riemannian submanifold of Euclidean space. The proposed requires full gradient and Hessian update at beginning each epoch while it performs updates in iterations within epoch. iteration complexity $$O(\epsilon ^{-3/2})$$ obtain an $$(\epsilon ,\sqrt{\epsilon })$$ -second-order sta...
