Revisiting Sub-sampled Newton Methods

Many machine learning models depend on solving a large scale optimization problem. Recently, sub-sampled Newton methods have emerged to attract much attention for optimization due to their efficiency at each iteration, rectified a weakness in the ordinary Newton method of suffering a high cost at each iteration while commanding a high convergence rate. In this work we propose two new efficient Newton-type methods, Refined Sub-sampled Newton and Refined Sketch Newton. Our methods exhibit a great advantage over existing sub-sampled Newton methods, especially when Hessian-vector multiplication can be calculated efficiently and Hessian matrix is ill-conditioned. Specifically, the proposed methods are shown to converge superlinearly in general case and quadratically under a little stronger assumption. The proposed methods can be generalized to a unifying framework for the convergence proof of several existing sub-sampled Newton methods, revealing new convergence properties. Finally, we empirically evaluate the performance of our methods on several standard datasets and the results show consistent improvement in computational efficiency.