Faster Riemannian Newton-type optimization by subsampling and cubic regularization

This work is on constrained large-scale non-convex optimization where the constraint set implies a manifold structure. Solving such problems is important in a multitude of fundamental machine learning tasks. Recent advances on Riemannian optimization have enabled the convenient recovery of solutions by adapting unconstrained optimization algorithms over manifolds. However, it remains challenging to scale up and meanwhile maintain stable convergence rates and handle saddle points. We propose a new second-order Riemannian optimization algorithm, aiming at improving convergence rate and reducing computational cost. It enhances the Riemannian trust-region algorithm that explores curvature information to escape saddle points through a mixture of subsampling and cubic regularization techniques. We conduct rigorous analysis to study the convergence behavior of the proposed algorithm. We also perform extensive experiments to evaluate it based on two general machine learning tasks using multiple datasets. The proposed algorithm exhibits improved computational speed, e.g., a speed improvement from 12% to 227% , and improved convergence behavior, e.g., an iteration number reduction from 𝒪(max(ϵ_g^-2ϵ_H^-1,ϵ_H^-3)) to 𝒪(max(ϵ_g^-2,ϵ_H^-3)) , compared to a large set of state-of-the-art Riemannian optimization algorithms.