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This paper studies the lower bound complexity for the optimization problem whose objective function is the average of $n$ individual smooth convex functions.
A method for solving the convex programming problem with convergence rate o(1/kˆ2)
Yurii Nesterov · 1983
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Rie Johnson and Tong Zhang · 2013
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Introductory lectures on convex optimization: A basic course , volume 87
Yurii Nesterov · 2013
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Linear convergence with condition number independent access of full gradients
Lijun Zhang, Mehrdad Mahdavi, and Rong Jin · 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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A lower bound for the optimization of finite sums
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Aaron Defazio · 2016
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Blake Woodworth and Nathan Srebro · 2016
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Zeyuan Allen-Zhu · 2017
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Lower bounds for finding stationary points I
Yair Carmon, John C. Duchi, Oliver Hinder, and Aaron Sidford · 2017
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An optimal randomized incremental gradient method
Guanghui Lan and Yi Zhou · 2017
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Minimizing finite sums with the stochastic average gradient
Mark Schmidt, Nicolas Le Roux, and Francis Bach · 2017
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Katyusha X: Practical momentum method for stochastic sum-of-nonconvex optimization
Zeyuan Allen-Zhu · 2018
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Lower bounds for smooth nonconvex finite-sum optimization
Dongruo Zhou and Quanquan Gu · 2019
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