Proximal gradient method

Known as: Proximal Gradient Methods 
Proximal gradient methods are a generalized form of projection used to solve non-differentiable convex optimization problems. Many interesting… (More)
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Topic mentions per year

1996-2017
010203019962017

Papers overview

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2016
2016
In this paper, we propose an alternating proximal gradient method that solves convex minimization problems with three or more… (More)
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2015
2015
Nonconvex and nonsmooth problems have recently received considerable attention in signal/image processing, statistics and machine… (More)
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2015
2015
Multi-way data arises inmany applications such as electroencephalography classification, face recognition, text mining and… (More)
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2014
2014
This paper presents a new incremental gradient algorithm for minimizing the average of a large number of smooth component… (More)
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2013
2013
We analyze distributed optimization algorithms where parts of data and variables are distributed over several machines and… (More)
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2013
2013
We consider solving the 1-regularized least-squares ( 1-LS) problem in the context of sparse recovery for applications such as… (More)
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Highly Cited
2011
Highly Cited
2011
We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty that… (More)
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2011
2011
Nonnegative matrix factorization has been widely applied in face recognition, text mining, as well as spectral analysis. This… (More)
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Highly Cited
2010
Highly Cited
2010
We study the problem of estimating high dimensional regression models regularized by a structured-sparsity-inducing penalty that… (More)
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2010
2010
We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty that… (More)
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