Stochastic Second-order Methods for Non-convex Optimization with Inexact Hessian and Gradient
arXiv: Optimization and Control, Volume abs/1809.09853, 2018.
Trust region and cubic regularization methods have demonstrated good performance in small scale non-convex optimization, showing the ability to escape from saddle points. Each iteration of these methods involves computation of gradient, Hessian and function value in order to obtain the search direction and adjust the radius or cubic regul...More
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