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This paper considers the problems of unconstrained minimization of large scale smooth convex functions having block-coordinate-wise Lipschitz continuous gradients.
Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
Peter Richtarik and Martin Takac · 2011
Earlier work this paper cites.
Performance of first-order methods for smooth convex minimization: a novel approach
Yoel Drori and Marc Teboulle · 2012
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Efficiency of coordinate descent methods on huge-scale optimization problems
Yu Nesterov · 2012
Earlier work this paper cites.
On the convergence of block coordinate descent type methods
Amir Beck and Luba Tetruashvili · 2013
Cited alongside, same era.
On the complexity analysis of randomized block-coordinate descent methods
Zhaosong Lu and Lin Xiao · 2015
Cited alongside, same era.
Coordinate descent converges faster with the gauss-southwell rule than random selection
Julie Nutini, Mark Schmidt, Issam H Laradji, Michael P Friedlander, and Hoyt Koepke · 2015
Cited alongside, same era.
Coordinate descent algorithms
Stephen J Wright · 2015
Later among the works it cites.
An improved convergence analysis of cyclic block coordinate descent-type methods for strongly convex minimization
Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, and Mingyi Hong · 2016
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