Proximal gradient methods for learning

Known as: Proximal gradient 
Proximal gradient (forward backward splitting) methods for learning is an area of research in optimization and statistical learning theory which… (More)
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Topic mentions per year

Topic mentions per year

2000-2018
05020002018

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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2013
2013
We analyze distributed optimization algorithms where parts of data and variables are distributed over several machines and… (More)
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Highly Cited
2012
Highly Cited
2012
Recently sparse representation has been applied to visual tracker by modeling the target appearance using a sparse approximation… (More)
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2012
2012
We present a distributed proximal-gradient method for optimizing the average of convex functions, each of which is the private… (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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Highly Cited
2011
Highly Cited
2011
We consider the problem of optimizing the sum of a smooth convex function and a non-smooth convex function using proximal… (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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Highly Cited
2009
Highly Cited
2009
This paper proposes scalable and fast algorithms for solving the Robust PCA problem, namely recovering a low-rank matrix with an… (More)
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Highly Cited
2009
Highly Cited
2009
The affine rank minimization problem, which consists of finding a matrix of minimum rank subject to linear equality constraints… (More)
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