A Stochastic Tensor Method for Non-convex Optimization

Aurélien Lucchi, Jonas Köhler

Type: Preprint

Publication Date: 2019-11-23

Citations: 8

Locations

arXiv (Cornell University) - View

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Works Cited by This (46)

Action	Title	Year	Authors
+	Spectral norm of random tensors	2014	Ryota Tomioka Taiji Suzuki
+	Trust Region Methods	2000	Andrew R. Conn Nicholas I. M. Gould Philippe L. Toint
+ PDF Chat	A linear-time algorithm for trust region problems	2015	Elad Hazan Tomer Koren
+	Estimate sequence methods: extensions and approximations	2009	Michel Baes
+	Accelerating the cubic regularization of Newton’s method on convex problems	2007	Yu. Nesterov
+ PDF Chat	On the Use of Stochastic Hessian Information in Optimization Methods for Machine Learning	2011	Richard H. Byrd Gillian M. Chin Will Neveitt Jorge Nocedal
+	Adaptive cubic regularisation methods for unconstrained optimization. Part II: worst-case function- and derivative-evaluation complexity	2010	Coralia Cartis Nicholas I. M. Gould Philippe L. Toint
+	Cubic regularization of Newton method and its global performance	2006	Yurii Nesterov B. T. Polyak
+ PDF Chat	Most Tensor Problems Are NP-Hard	2013	Christopher J. Hillar Lek‐Heng Lim
+	A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets	2012	Nicolas Le Roux Mark Schmidt Francis Bach