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We develop and analyze a new family of {\em nonaccelerated and accelerated loopless variance-reduced methods} for finite sum optimization problems.
A stochastic approximation method
H. Robbins and S. Monro · 1951
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Robust stochastic approximation approach to stochastic programming
Arkadi Nemirovski, Anatoli Juditsky, Guanghui Lan, and Alexander Shapiro · 2009
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Accelerating stochastic gradient descent using predictive variance reduction
Rie Johnson and Tong Zhang · 2013
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Stochastic dual coordinate ascent methods for regularized loss
Shai Shalev-Shwartz and Tong Zhang · 2013
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SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives
Aaron Defazio, Francis Bach, and Simon Lacoste-Julien · 2014
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A proximal stochastic gradient method with progressive variance reduction
Lin Xiao and Tong Zhang · 2014
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Quartz: Randomized dual coordinate ascent with arbitrary sampling
Zheng Qu, Peter Richtárik, and Tong Zhang · 2015
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Coordinate descent with arbitrary sampling I: Algorithms and complexity
Z. Qu and P. Richtárik · 2016
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Coordinate descent with arbitrary sampling II: Expected separable overapproximation
Z. Qu and P. Richtárik · 2016
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On optimal probabilities in stochastic coordinate descent methods
P. Richtárik and M. Takáč · 2016
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Parallel coordinate descent methods for big data optimization
Peter Richtárik and Martin Takáč · 2016
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Katyusha: The first direct acceleration of stochastic gradient methods
Zeyuan Allen-Zhu · 2017
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Stochastic primal-dual hybrid gradient algorithm with arbitrary sampling and imaging applications
A. Chambolle, M. J. Ehrhardt, P. Richtárik, and C. B. Schönlieb · 2017
Stochastic primal-dual coordinate method for regularized empirical risk minimization
Yuchen Zhang and Lin Xiao · 2017
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Accelerated coordinate descent with arbitrary sampling and best rates for minibatches
F. Hanzely and P. Richtárik · 2018
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Vr-sgd: A simple stochastic variance reduction method for machine learning
Fanhua Shang, Kaiwen Zhou, James Cheng, Ivor Tsang, Lijun Zhang, and Dacheng Tao · 2018
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Sgd: General analysis and improved rates
Robert M. Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin, and Peter Richtárik · 2019
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Nonconvex variance reduced optimization with arbitrary sampling
Samuel Horváth and Peter Richtárik · 2019
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Cited alongside, same era.
Minimizing finite sums with the stochastic average gradient
M. Schmidt, N. Le Roux, and F. Bach · 2017
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Dmitry Kovalev, Samuel Horváth, and Peter Richtárik · 2019
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Saga with arbitrary sampling
Xun Qian, Zheng Qu, and Peter Richtárik · 2019
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