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In the history of first-order algorithms, Nesterov's accelerated gradient descent (NAG) is one of the milestones.
Prlnciple of the nonlocal search in the systems of automatic optimization
I. M. Gelfand and M. L. Tsetlin · 1961
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Some methods of speeding up the convergence of iteration methods
B. T. Polyak · 1964
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A method for solving the convex programming problem with convergence rate
Y. E. Nesterov · 1983
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On the minimizing property of a second order dissipative system in hilbert spaces
F. Alvarez · 2000
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Introductory lectures on convex optimization: A basic course , volume 87
Y. Nesterov · 2003
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Geometric Numerical integration: structure-preserving algorithms for ordinary differential equations
E. Haier, C. Lubich, and G. Wanner · 2006
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A fast iterative shrinkage-thresholding algorithm for linear inverse problems
A. Beck and M. Teboulle · 2009
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A second-order differential system with hessian-driven damping; application to non-elastic shock laws
H. Attouch, P.-E. Maingé, and P. Redont · 2012
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A dynamical approach to an inertial forward-backward algorithm for convex minimization
H. Attouch, J. Peypouquet, and P. Redont · 2014
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Convex optimization: Algorithms and complexity
S. Bubeck et al · 2015
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Accelerated mirror descent in continuous and discrete time
W. Krichene, A. Bayen, and P. L. Bartlett · 2015
Cited alongside, same era.
The rate of convergence of nesterov’s accelerated forward-backward method is actually faster than
H. Attouch and J. Peypouquet · 2016
Cited alongside, same era.
Fast convex optimization via inertial dynamics with hessian driven damping
H. Attouch, J. Peypouquet, and P. Redont · 2016
Cited alongside, same era.
Accelerated gradient methods for nonconvex nonlinear and stochastic programming
S. Ghadimi and G. Lan · 2016
Cited alongside, same era.
A differential equation for modeling Nesterov’s accelerated gradient method: Theory and insights
W. Su, S. Boyd, and E. J. Candes · 2016
A dynamical systems perspective on nesterov acceleration
M. Muehlebach and M. Jordan · 2019
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On learning rates and schrödinger operators
B. Shi, W. J. Su, and M. I. Jordan · 2020
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An extension of the second order dynamical system that models nesterov’s convex gradient method
C. D. Alecsa, S. C. László, and T. Pinţa · 2021
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On the self-penalization phenomenon in feature selection
M. I. Jordan, K. Liu, and F. Ruan · 2021
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On the hyperparameters in stochastic gradient descent with momentum
B. Shi · 2021
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Cited alongside, same era.
A variational perspective on accelerated methods in optimization
A. Wibisono, A. C. Wilson, and M. I. Jordan · 2016
Cited alongside, same era.
Dynamical, symplectic and stochastic perspectives on gradient-based optimization
M. I. Jordan · 2018
Cited alongside, same era.
Sharp convergence rates for langevin dynamics in the nonconvex setting
X. Cheng, N. S. Chatterji, Y. Abbasi-Yadkori, P. L. Bartlett, and M. I. Jordan
Cited in the paper.
Underdamped langevin mcmc: A non-asymptotic analysis
X. Cheng, N. S. Chatterji, P. L. Bartlett, and M. I. Jordan
Cited in the paper.
Understanding the acceleration phenomenon via high-resolution differential equations
B. Shi, S. S. Du, M. I. Jordan, and W. J. Su · 2021
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A lyapunov analysis of accelerated methods in optimization
A. C. Wilson, B. Recht, and M. I. Jordan · 2021
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Convergence of inertial dynamics driven by sums of potential and nonpotential operators and with implicit newton-like damping
S. Adly and H. Attouch · 2022
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