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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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