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Showing 2017 · math.OCShow all
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math.OC2017★ 34 cited
Second-Order Optimization for Non-Convex Machine Learning: An Empirical Study
Peng Xu, Farbod Roosta-Khorasani, Michael W. Mahoney
While first-order optimization methods such as stochastic gradient descent (SGD) are popular in machine learning (ML), they come with well-known deficiencies, including relatively-…
math.OC2017★ 53 cited
Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information
Peng Xu, Fred Roosta, Michael W. Mahoney
We consider variants of trust-region and cubic regularization methods for non-convex optimization, in which the Hessian matrix is approximated. Under mild conditions on the inexact…