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We introduce a generic scheme for accelerating first-order optimization methods in the sense of Nesterov, which builds upon a new analysis of the accelerated proximal point algorithm.
A method of solving a convex programming problem with convergence rate O O (1/ k 2 k^{2} )
Y. Nesterov · 1983
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New proximal point algorithms for convex minimization
O. Güler · 1992
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Convex Analysis and Minimization Algorithms I
J.-B. Hiriart-Urruty and C. Lemaréchal · 1996
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Introductory Lectures on Convex Optimization: A Basic Course
Y. Nesterov · 2004
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A fast iterative shrinkage-thresholding algorithm for linear inverse problems
A. Beck and M. Teboulle · 2009
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Robust stochastic approximation approach to stochastic programming
A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro · 2009
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Convex Analysis and Monotone Operator Theory in Hilbert Spaces
H. H. Bauschke and P. L. Combettes · 2011
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Convergence rates of inexact proximal-gradient methods for convex optimization
M. Schmidt, N. Le Roux, and F. Bach · 2011
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Optimization with sparsity-inducing penalties
F. Bach, R. Jenatton, J. Mairal, and G. Obozinski · 2012
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An accelerated inexact proximal point algorithm for convex minimization
B. He and X. Yuan · 2012
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First order methods for nonsmooth convex large-scale optimization
A. Juditsky and A. Nemirovski · 2012
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Efficiency of coordinate descent methods on huge-scale optimization problems
Y. Nesterov · 2012
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Inexact and accelerated proximal point algorithms
S. Salzo and S. Villa · 2012
Cited alongside, same era.
Proximal stochastic dual coordinate ascent
S. Shalev-Shwartz and T. Zhang · 2012
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Gradient methods for minimizing composite functions
Y. Nesterov · 2013
Cited alongside, same era.
Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
P. Richtárik and M. Takáč · 2014
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A proximal stochastic gradient method with progressive variance reduction
L. Xiao and T. Zhang · 2014
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A lower bound for the optimization of finite sums
A. Agarwal and L. Bottou · 2015
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Convex Optimization Algorithms
D. P. Bertsekas · 2015
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Un-regularizing: approximate proximal point algorithms for empirical risk minimization
R. Frostig, R. Ge, S. M. Kakade, and A. Sidford · 2015
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An optimal randomized incremental gradient method
G. Lan · 2015
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M. Schmidt, N. Le Roux, and F. Bach · 2013
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SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives
A. J. Defazio, F. Bach, and S. Lacoste-Julien · 2014
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Finito: A faster, permutable incremental gradient method for big data problems
A. J. Defazio, T. S. Caetano, and J. Domke · 2014
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Proximal algorithms
N. Parikh and S.P. Boyd · 2014
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Incremental majorization-minimization optimization with application to large-scale machine learning
J. Mairal · 2015
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Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization
S. Shalev-Shwartz and T. Zhang · 2015
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Stochastic primal-dual coordinate method for regularized empirical risk minimization
Y. Zhang and L. Xiao · 2015
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