Fast Unconstrained Optimization via Hessian Averaging and Adaptive Gradient Sampling Methods
We consider minimizing finite-sum and expectation objective functions via Hessian-averaging based subsampled Newton methods. These methods allow for gradient inexactness and have fixed per-iteration Hessian approximation costs. The recent work (Na et al. 2023) demonstrated that Hessian averaging can be utilized to achieve fast \(\mathcal{O}\left(\sqrt{\frac{\log k}{k}}\right)\) local superlinear convergence for strongly convex functions in high … Read more