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We propose a new method for unconstrained optimization of a smooth and strongly convex function, which attains the optimal rate of convergence of Nesterov's accelerated gradient descent.
A method of solving a convex programming problem with convergence rate o( 1 / k 2 1/k^{2} )
Y. Nesterov · 1983
Earlier work this paper cites.
Introductory lectures on convex optimization: A basic course
Y. Nesterov · 2004
Earlier work this paper cites.
Libsvm: A library for support vector machines
Chih-Chung Chang and Chih-Jen Lin · 2011
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minfunc: unconstrained differentiable multivariate optimization in matlab
M Schmidt · 2012
Earlier work this paper cites.
Adaptive restart for accelerated gradient schemes
Brendan O’Donoghue and Emmanuel Candes · 2013
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Linear coupling: An ultimate unification of gradient and mirror descent
Z. Allen-Zhu and L. Orecchia · 2014
Cited alongside, same era.
Theory of convex optimization for machine learning
S. Bubeck · 2014
Cited alongside, same era.
Analysis and design of optimization algorithms via integral quadratic constraints
L. Lessard, B. Recht, and A. Packard · 2014
Later among the works it cites.
A differential equation for modeling nesterov’s accelerated gradient method: Theory and insights
W. Su, S. Boyd, and E. Candès · 2014
Later among the works it cites.
From averaging to acceleration, there is only a step-size
N. Flammarion and F. Bach · 2015
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