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We consider empirical risk minimization of linear predictors with convex loss functions.
Incremental subgradient methods for nondifferentiable optimization
Nedic, Angelia and Bertsekas, Dimitri P · 2001
Earlier work this paper cites.
Introductory Lectures on Convex Optimization: A Basic Course
Nesterov, Y · 2004
Earlier work this paper cites.
A first-order primal-dual algorithm for convex problems with applications to imaging
Chambolle, Antonin and Pock, Thomas · 2011
Earlier work this paper cites.
Incremental gradient, subgradient, and proximal methods for convex optimization: A survey
Bertsekas, Dimitri P · 2012
Earlier work this paper cites.
Efficiency of coordinate descent methods on huge-scale optimization problems
Nesterov, Yu · 2012
Earlier work this paper cites.
A stochastic gradient method with an exponential convergence _rate for finite training sets
Roux, Nicolas L, Schmidt, Mark, and Bach, Francis · 2012
Earlier work this paper cites.
Adaptive primal-dual hybrid gradient methods for saddle-point problems
Goldstein, Tom, Li, Min, Yuan, Xiaoming, Esser, Ernie, and Baraniuk, Richard · 2013
Earlier work this paper cites.
Accelerating stochastic gradient descent using predictive variance reduction
Johnson, Rie and Zhang, Tong · 2013
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Stochastic dual coordinate ascent methods for regularized loss minimization
Shalev-Shwartz, Shai and Zhang, Tong · 2013
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Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression
Bach, Francis · 2014
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Saga: A fast incremental gradient method with support for non-strongly convex composite objectives
Defazio, Aaron, Bach, Francis, and Lacoste-Julien, Simon · 2014
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Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
Richtárik, Peter and Takáč, Martin · 2014
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A proximal stochastic gradient method with progressive variance reduction
Xiao, Lin and Zhang, Tong · 2014
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Accelerated, parallel, and proximal coordinate descent
Katyusha: Accelerated variance reduction for faster sgd
Allen-Zhu, Zeyuan · 2016
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Stochastic variance reduction methods for saddle-point problems
Balamurugan, Palaniappan and Bach, Francis · 2016
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On the ergodic convergence rates of a first-order primal–dual algorithm
Chambolle, Antonin and Pock, Thomas · 2016
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On the global and linear convergence of the generalized alternating direction method of multipliers
Deng, Wei and Yin, Wotao · 2016
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A first-order primal-dual algorithm with linesearch
Malitsky, Yura and Pock, Thomas · 2016
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Sdca without duality, regularization, and individual convexity
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Fercoq, Oliver and Richtárik, Peter · 2015
Cited alongside, same era.
An optimal randomized incremental gradient method
Lan, Guanghui and Zhou, Yi · 2015
Cited alongside, same era.
Stochastic primal-dual coordinate method for regularized empirical risk minimization
Zhang, Yuchen and Xiao, Lin · 2015
Cited alongside, same era.
A universal catalyst for first-order optimization
Lin, Hongzhou, Mairal, Julien, and Harchaoui, Zaid
Cited in the paper.
An accelerated randomized proximal coordinate gradient method and its application to regularized empirical risk minimization
Lin, Qihang, Lu, Zhaosong, and Xiao, Lin
Cited in the paper.
Shalev-Shwartz, Shai · 2016
Later among the works it cites.
Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization
Shalev-Shwartz, Shai and Zhang, Tong · 2016
Later among the works it cites.