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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… Expand
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Papers overview

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2018
2018
This paper considers manifold optimization problems with nonsmooth and nonconvex objective function. Existing methods for solving… Expand
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2016
2016
In this paper, we apply the idea of alternating proximal gradient to solve separable convex minimization problems with three or… Expand
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Highly Cited
2015
Highly Cited
2015
Nonconvex and nonsmooth problems have recently received considerable attention in signal/image processing, statistics and machine… Expand
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Highly Cited
2015
Highly Cited
2015
In this paper, we show for the first time how gradient TD (GTD) reinforcement learning methods can be formally derived as true… Expand
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Highly Cited
2014
Highly Cited
2014
Proximal gradient descent (PGD) and stochastic proximal gradient descent (SPGD) are popular methods for solving regularized risk… Expand
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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… Expand
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Highly Cited
2012
Highly Cited
2012
We study the problem of estimating high-dimensional regression models regularized by a structured sparsity-inducing penalty that… Expand
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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… Expand
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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… Expand
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Highly Cited
2009
Highly Cited
2009
The a‐ne rank minimization problem, which consists of flnding a matrix of minimum rank subject to linear equality constraints… Expand
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