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Recently, research on accelerated stochastic gradient descent methods (e.g., SVRG) has made exciting progress (e.g., linear convergence for strongly convex problems).
A method of solving a convex programming problem with convergence rate O ( 1 / k 2 ) {O}(1/k^{2})
Y. Nesterov · 1983
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Introductory Lectures on Convex Optimization: A Basic Course
Y. Nesterov · 2004
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Solving large scale linear prediction problems using stochastic gradient descent algorithms
T. Zhang · 2004
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Smooth minimization of non-smooth functions
Y. Nesterov · 2005
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A fast iterative shrinkage-thresholding algorithm for linear inverse problems
A. Beck and M. Teboulle · 2009
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Pegasos: primal estimated sub-gradient solver for SVM
S. Shalev-Shwartz, Y. Singer, N. Srebro, and A. Cotter · 2011
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S. Lacoste-Julien, M. Schmidt, and F. Bach · 2012
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An optimal method for stochastic composite optimization
G. Lan · 2012
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Making gradient descent optimal for strongly convex stochastic optimization
A. Rakhlin, O. Shamir, and K. Sridharan · 2012
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A stochastic gradient method with an exponential convergence rate for finite training sets
N. L. Roux, M. Schmidt, and F. Bach · 2012
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Accelerating stochastic gradient descent using predictive variance reduction
R. Johnson and T. Zhang · 2013
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Mixed optimization for smooth functions
M. Mahdavi, L. Zhang, and R. Jin · 2013
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Minimizing finite sums with the stochastic average gradient
M. Schmidt, N. L. Roux, and F. Bach · 2013
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Stochastic dual coordinate ascent methods for regularized loss minimization
S. Shalev-Shwartz and T. Zhang · 2013
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Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes
O. Shamir and T. Zhang · 2013
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On the importance of initialization and momentum in deep learning
I. Sutskever, J. Martens, G. Dahl, and G. Hinton · 2013
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Linear convergence with condition number independent access of full gradients
L. Zhang, M. Mahdavi, and R. Jin · 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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Stochastic proximal gradient descent with acceleration techniques
A. Nitanda · 2014
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A differential equation for modeling Nesterov’s accelerated gradient method: Theory and insights
Improved SVRG for non-strongly-convex or sum-of-non-convex objectives
Z. Allen-Zhu and Y. Yuan · 2016
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Faster eigenvector computation via shift-and-invert preconditioning
D. Garber, E. Hazan, C. Jin, S. M. Kakade, C. Musco, P. Netrapalli, and A. Sidford · 2016
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Accelerated stochastic mirror descent algorithms for composite non-strongly convex optimization
L. T. K. Hien, C. Lu, H. Xu, and J. Feng · 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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Stochastic variance reduction for nonconvex optimization
S. J. Reddi, A. Hefny, S. Sra, B. Poczos, and A. Smola · 2016
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W. Su, S. P. Boyd, and E. J. Candes · 2014
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A proximal stochastic gradient method with progressive variance reduction
L. Xiao and T. Zhang · 2014
Cited alongside, same era.
An optimal randomized incremental gradient method
G. Lan and Y. Zhou · 2015
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A universal catalyst for first-order optimization
H. Lin, J. Mairal, and Z. Harchaoui · 2015
Cited alongside, same era.
A stochastic PCA and SVD algorithm with an exponential convergence rate
O. Shamir · 2015
Cited alongside, same era.
Stochastic optimization with importance sampling for regularized loss minimization
P. Zhao and T. Zhang · 2015
Cited alongside, same era.
Optimal black-box reductions between optimization objectives
Z. Allen-Zhu and E. Hazan · 2016
Cited alongside, same era.
S. Ruder · 2016
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Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization
S. Shalev-Shwartz and T. Zhang · 2016
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Tight complexity bounds for optimizing composite objectives
B. Woodworth and N. Srebro · 2016
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Homotopy smoothing for non-smooth problems with lower complexity than O ( 1 / ϵ ) {O}(1/\epsilon)
Y. Xu, Y. Yan, Q. Lin, and T. Yang · 2016
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Stochastic subgradient methods with linear convergence for polyhedral convex optimization
T. Yang and Q. Lin · 2016
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Katyusha: The first direct acceleration of stochastic gradient methods
Z. Allen-Zhu · 2017
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Accelerated variance reduced stochastic ADMM
Y. Liu, F. Shang, and J. Cheng · 2017
Closest in time.
Variance reduced stochastic gradient descent with sufficient decrease
F. Shang, Y. Liu, J. Cheng, K. W. Ng, and Y. Yoshida · 2017
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