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We present a variant of accelerated gradient descent algorithms, adapted from Nesterov's optimal first-order methods, for weakly-quasi-convex and weakly-quasi-strongly-convex functions.
Gradient methods for the minimisation of functionals
Boris T Polyak · 1963
Earlier work this paper cites.
A method for solving the convex programming problem with convergence rate o ( 1 / k 2 ) o(1/k^{2})
Yurii Nesterov · 1983
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Introductory Lectures on Convex Optimization: A Basic Course
Yurii Nesterov · 2004
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Sequential Subspace Optimization Method for Large-scale Unconstrained Problems
Guy Narkiss and Michael Zibulevsky · 2005
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Gradient descent learns linear dynamical systems
Moritz Hardt, Tengyu Ma, and Benjamin Recht · 2016
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Linear convergence of gradient and proximal-gradient methods under the polyak-łojasiewicz condition
Hamed Karimi, Julie Nutini, and Mark Schmidt · 2016
Cited alongside, same era.
Accelerated methods for α \alpha -weakly-quasi-convex problems
Sergey Guminov and Alexander Gasnikov · 2017
Cited alongside, same era.
An optimal first order method based on optimal quadratic averaging
Dmitriy Drusvyatskiy, Maryam Fazel, and Scott Roy · 2018
Cited alongside, same era.
Global convergence of policy gradient methods for the linear quadratic regulator
Maryam Fazel, Rong Ge, Sham Kakade, and Mehran Mesbahi · 2018
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LQR through the lens of first order methods: Discrete-time case
Jingjing Bu, Afshin Mesbahi, Maryam Fazel, and Mehran Mesbahi · 2019
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Near-optimal methods for minimizing star-convex functions and beyond
Oliver Hinder, Aaron Sidford, and Nimit S. Sohoni · 2019
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Primal-dual accelerated gradient methods with small-dimensional relaxation oracle
Yurii Nesterov, Alexander Gasnikov, Sergey Guminov, and Pavel Dvurechensky · 2019
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