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We present a simple scheme for restarting first-order methods for convex optimization problems.
Une propriété topologique des sous-ensembles analytiques réels
Stanislaw Lojasiewicz · 1963
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Gradient methods for the minimisation of functionals
Boris T Polyak · 1963
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On convergence rates of subgradient optimization methods
Jean-Louis Goffin · 1977
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Subgradient methods: a survey of Soviet research
Boris T Polyak · 1977
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Problem Complexity and Method Efficiency in Optimization
Arkadi Nemirovski and David Yudin · 1983
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A method of solving a convex programming problem with convergence rate O ( 1 / k 2 ) O(1/k^{2})
Yurii Nesterov · 1983
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Optimal methods of smooth convex minimization
Arkadi Nemirovski and Yurii Nesterov · 1985
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Minimization Methods for Non-Differentiable Functions
Naum Z Shor · 1985
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Introduction to optimization. translations series in mathematics and engineering
Boris T Polyak · 1987
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Sur la géométrie semi-et sous-analytique
Stanislas Łojasiewicz · 1993
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Smooth minimization of non-smooth functions
Yu Nesterov · 2005
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The Łojasiewicz inequality for nonsmooth subanalytic functions with applications to subgradient dynamical systems
Jérôme Bolte, Aris Daniilidis, and Adrian Lewis · 2007
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Smoothing technique and its applications in semidefinite optimization
Yurii Nesterov · 2007
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On accelerated proximal gradient methods for convex-concave optimization
Paul Tseng · 2008
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A fast iterative shrinkage-thresholding algorithm for linear inverse problems
Amir Beck and Marc Teboulle · 2009
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First-order algorithm with 𝒪 ( ln ( 1 / ϵ ) ) {\mathcal{O}({\rm ln}(1{/}\epsilon))} convergence for ϵ {\epsilon} -equilibrium in two-person zero-sum games
Andrew Gilpin, Javier Pena, and Tuomas Sandholm · 2010
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Smoothing and first order methods: A unified framework
Amir Beck and Marc Teboulle · 2012
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Gradient methods for minimizing composite functions
Yu Nesterov · 2013
Restarting accelerated gradient methods with a rough strong convexity estimate
Olivier Fercoq and Zheng Qu · 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
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Linear convergence of first order methods for non-strongly convex optimization
Ion Necoara, Yurii Nesterov, and Francois Glineur · 2016
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“Efficient” subgradient methods for general convex optimization
James Renegar · 2016
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From error bounds to the complexity of first-order descent methods for convex functions
Jérôme Bolte, Trong Phong Nguyen, Juan Peypouquet, and Bruce W Suter · 2017
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Monotonicity and restart in fast gradient methods
Pontus Giselsson and Stephen Boyd · 2014
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Primal-dual subgradient methods for minimizing uniformly convex functions
Anatoli Iouditski and Yurii Nesterov · 2014
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An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization
Qihang Lin and Lin Xiao · 2014
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Universal gradient methods for convex optimization problems
Yurii Nesterov · 2015
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Adaptive restart for accelerated gradient schemes
Brendan ODonoghue and Emmanuel Candes · 2015
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Accelerated first-order methods for hyperbolic programming
James Renegar · 2017
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Sharpness, restart and acceleration
Vincent Roulet and Alexandre d’Aspremont · 2017
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Adaptive accelerated gradient converging methods under Hölderian error bound condition
Tianbao Yang · 2017
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RSG: Beating subgradient method without smoothness and strong convexity
Tianbao Yang and Qihang Lin · 2018
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Adaptive restart of accelerated gradient methods under local quadratic growth condition
Olivier Fercoq and Zheng Qu · 2019
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Faster subgradient methods for functions with hölderian growth
Patrick R Johnstone and Pierre Moulin · 2020
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