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In recent years it was proved that simple modifications of the classical Frank-Wolfe algorithm (aka conditional gradient algorithm) for smooth convex minimization over convex and compact polytopes, converge with linear rate, assuming the objective function has the quadratic growth property.
An algorithm for quadratic programming
M. Frank and P. Wolfe · 1956
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Constrained minimization methods
Evgeny S Levitin and Boris T Polyak · 1966
Earlier work this paper cites.
Integer and nonlinear programming
Philip Wolfe · 1970
Earlier work this paper cites.
Some comments on Wolfe’s ‘away step’
Jacques GuéLat and Patrice Marcotte · 1986
Earlier work this paper cites.
A conditional gradient method with linear rate of convergence for solving convex linear systems
Amir Beck and Marc Teboulle · 2004
Earlier work this paper cites.
Coresets, sparse greedy approximation, and the frank-wolfe algorithm
Kenneth L. Clarkson · 2008
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Projection-free online learning
Elad Hazan and Satyen Kale · 2012
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Playing non-linear games with linear oracles
Dan Garber and Elad Hazan · 2013
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Revisiting frank-wolfe: Projection-free sparse convex optimization
Martin Jaggi · 2013
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Block-coordinate frank-wolfe optimization for structural svms
Simon Lacoste-Julien, Martin Jaggi, Mark W. Schmidt, and Patrick Pletscher · 2013
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Efficient image and video co-localization with frank-wolfe algorithm
Armand Joulin, Kevin Tang, and Li Fei-Fei · 2014
Cited alongside, same era.
Faster rates for the frank-wolfe method over strongly-convex sets
Dan Garber and Elad Hazan · 2015
Cited alongside, same era.
On the global linear convergence of frank-wolfe optimization variants
Simon Lacoste-Julien and Martin Jaggi · 2015
Cited alongside, same era.
A linearly convergent variant of the conditional gradient algorithm under strong convexity, with applications to online and stochastic optimization
Dan Garber and Elad Hazan · 2016
Cited alongside, same era.
Linear-memory and decomposition-invariant linearly convergent conditional gradient algorithm for structured polytopes
Dan Garber and Ofer Meshi · 2016
Cited alongside, same era.
Frank-wolfe algorithms for saddle point problems
Gauthier Gidel, Tony Jebara, and Simon Lacoste-Julien · 2017
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Linear convergence of stochastic frank wolfe variants
Donald Goldfarb, Garud Iyengar, and Chaoxu Zhou · 2017
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A unified optimization view on generalized matching pursuit and frank-wolfe
Francesco Locatello, Rajiv Khanna, Michael Tschannen, and Martin Jaggi · 2017
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Frank-wolfe splitting via augmented lagrangian method
Gauthier Gidel, Fabian Pedregosa, and Simon Lacoste-Julien · 2018
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Blended conditonal gradients
Gábor Braun, Sebastian Pokutta, Dan Tu, and Stephen Wright · 2019
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Locally accelerated conditional gradients
Jelena Diakonikolas, Alejandro Carderera, and Sebastian Pokutta · 2019
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Anton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz, Puneet Dokania, and Simon Lacoste-Julien · 2016
Cited alongside, same era.
On the von neumann and frank–wolfe algorithms with away steps
Javier Pena, Daniel Rodríguez, and Negar Soheili · 2016
Cited alongside, same era.
Decomposition-invariant conditional gradient for general polytopes with line search
Mohammad Ali Bashiri and Xinhua Zhang · 2017
Cited alongside, same era.
Linearly convergent away-step conditional gradient for non-strongly convex functions
Amir Beck and Shimrit Shtern · 2017
Cited alongside, same era.
Lazifying conditional gradient algorithms
Gábor Braun, Sebastian Pokutta, and Daniel Zink · 2017
Cited alongside, same era.
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Logarithmic regret for online gradient descent beyond strong convexity
Dan Garber · 2019
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Primal-dual block generalized frank-wolfe
Qi Lei, Jiacheng Zhuo, Constantine Caramanis, Inderjit S Dhillon, and Alexandros G Dimakis · 2019
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Polytope conditioning and linear convergence of the frank–wolfe algorithm
Javier Pena and Daniel Rodriguez · 2019
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