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We develop a new family of variance reduced stochastic gradient descent methods for minimizing the average of a very large number of smooth functions.
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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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“Introductory Lectures on Convex Optimization: A Basic Course”
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“Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function”
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“Randomized iterative methods for linear systems”
Robert Gower and Peter Richt“’arik · 2015
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“Variance reduced stochastic gradient descent with neighbors.”
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“Randomized Block Kaczmarz Method with Projection for Solving Least Squares”
“Parallel coordinate descent methods for big data optimization problems”
Peter Richt“’arik and Martin Tak“’ac · 2016
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“SDCA without Duality, Regularization, and Individual Convexity”
Shai Shalev-Shwartz · 2016
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“Randomized quasi-Newton updates are linearly convergent matrix inversion algorithms”
Robert Gower and Peter Richt“’arik · 2017
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“Semi-stochastic gradient descent methods”
Jakub Konecn“’y and Peter Richt“’arik · 2017
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Nicolas Loizou and Peter Richt“’arik · 2017
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Mark Schmidt, Reza Babanezhad, Mohamed Ahmed, Aaron Defazio, Ann Clifton and Anoop Sarkar · 2015
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“SARAH: A novel method for machine learning problems using stochastic recursive gradient”
Lam. Nguyen, Jie Liu, Katya Scheinberg and Martin Tak“’ac · 2017
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“Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods”
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