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In this work we establish the first linear convergence result for the stochastic heavy ball method.
Some methods of speeding up the convergence of iteration methods
B.T. Polyak · 1964
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Introduction to optimization. translations series in mathematics and engineering
B.T. Polyak · 1987
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A randomized Kaczmarz algorithm with exponential convergence
T. Strohmer and R. Vershynin · 2009
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Libsvm: a library for support vector machines
Chih-Chung Chang and Chih-Jen Lin · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G.E. Hinton · 2012
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Accelerating stochastic gradient descent using predictive variance reduction
R. Johnson and T. Zhang · 2013
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On the importance of initialization and momentum in deep learning
I. Sutskever, J. Martens, G.E. Dahl, and G.E. Hinton · 2013
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SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives
A. Defazio, F. Bach, and S. Lacoste-Julien · 2014
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Randomized iterative methods for linear systems
R.M. Gower and P. Richtárik · 2015
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Going deeper with convolutions
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S. Gadat, F. Panloup, and S. Saadane · 2016
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Mini-batch semi-stochastic gradient descent in the proximal setting
J. Konečný, J. Liu, P. Richtárik, and M. Takáč · 2016
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Unified convergence analysis of stochastic momentum methods for convex and non-convex optimization
T. Yang, Q. Lin, and Z. Li · 2016
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Semi-stochastic gradient descent methods
J. Konečný and P. Richtárik · 2017
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Stochastic reformulations of linear systems: algorithms and convergence theory
P. Richtárik and M. Takáč · 2017
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Minimizing finite sums with the stochastic average gradient
M. Schmidt, N. Le Roux, and F. Bach · 2017
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