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In this paper, we propose a novel accelerated gradient method called ANITA for solving the fundamental finite-sum optimization problems.
Problem complexity and method efficiency in optimization
Arkadi Nemirovski and David Yudin · 1983
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A method for unconstrained convex minimization problem with the rate of convergence o ( 1 / k 2 ) o(1/k^{2})
Yurii Nesterov · 1983
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Introductory Lectures on Convex Optimization: A Basic Course
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Robust stochastic approximation approach to stochastic programming
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Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization i: A generic algorithmic framework
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Linear coupling: An ultimate unification of gradient and mirror descent
Zeyuan Allen-Zhu and Lorenzo Orecchia · 2014
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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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Understanding machine learning: from theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
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A differential equation for modeling Nesterov’s accelerated gradient method: Theory and insights
Weijie Su, Stephen P Boyd, and Emmanuel J Candès · 2014
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Lin Xiao and Tong Zhang · 2014
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Improved SVRG for non-strongly-convex or sum-of-non-convex objectives
Zeyuan Allen-Zhu and Yang Yuan · 2015
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An optimal randomized incremental gradient method
Guanghui Lan and Yi Zhou · 2015
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Random gradient extrapolation for distributed and stochastic optimization
Guanghui Lan and Yi Zhou · 2018
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Zhize Li and Jian Li · 2018
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Spiderboost: A class of faster variance-reduced algorithms for nonconvex optimization
Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, and Vahid Tarokh · 2018
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Dongruo Zhou, Pan Xu, and Quanquan Gu · 2018
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Rong Ge, Zhize Li, Weiyao Wang, and Xiang Wang · 2019
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