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Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization

Mark Schmidt 1, 2, * Nicolas Le Roux 1, 2 Francis Bach 1, 2 
* Corresponding author
1 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique - ENS Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : We consider the problem of optimizing the sum of a smooth convex function and a non-smooth convex function using proximal-gradient methods, where an error is present in the calculation of the gradient of the smooth term or in the proximity operator with respect to the non-smooth term. We show that both the basic proximal-gradient method and the accelerated proximal-gradient method achieve the same convergence rate as in the error-free case, provided that the errors decrease at appropriate rates.Using these rates, we perform as well as or better than a carefully chosen fixed error level on a set of structured sparsity problems.
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Submitted on : Thursday, December 1, 2011 - 4:03:15 PM
Last modification on : Thursday, March 17, 2022 - 10:08:43 AM
Long-term archiving on: : Monday, December 5, 2016 - 12:57:43 AM


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  • HAL Id : inria-00618152, version 3
  • ARXIV : 1109.2415



Mark Schmidt, Nicolas Le Roux, Francis Bach. Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization. NIPS'11 - 25 th Annual Conference on Neural Information Processing Systems, Dec 2011, Grenada, Spain. ⟨inria-00618152v3⟩



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