Second-Order Stochastic Optimization for Machine Learning in Linear Time
Naman Agarwal, Brian Bullins, Elad Hazan; 18(116):1−40, 2017.
First-order stochastic methods are the state-of-the-art in large-scale machine learning optimization owing to efficient per-iteration complexity. Second-order methods, while able to provide faster convergence, have been much less explored due to the high cost of computing the second-order information. In this paper we develop second-order stochastic methods for optimization problems in machine learning that match the per- iteration cost of gradient based methods, and in certain settings improve upon the overall running time over popular first-order methods. Furthermore, our algorithm has the desirable property of being implementable in time linear in the sparsity of the input data.
|© JMLR 2017. (edit)|