Proximal gradient methods are a generalized form of projection used to solve non-differentiable convex optimization problems. Many interestingâ€¦Â (More)

Semantic Scholar uses AI to extract papers important to this topic.

2016

2016

- Shiqian Ma
- J. Sci. Comput.
- 2016

In this paper, we propose an alternating proximal gradient method that solves convex minimization problems with three or moreâ€¦Â (More)

Is this relevant?

2015

2015

- Huan Li, Zhouchen Lin
- NIPS
- 2015

Nonconvex and nonsmooth problems have recently received considerable attention in signal/image processing, statistics and machineâ€¦Â (More)

Is this relevant?

2015

2015

- Yangyang Xu
- Math. Program. Comput.
- 2015

Multi-way data arises inmany applications such as electroencephalography classification, face recognition, text mining andâ€¦Â (More)

Is this relevant?

2014

2014

- Hamid Reza Feyzmahdavian, Arda Aytekin, Mikael Johansson
- 2014 IEEE International Workshop on Machineâ€¦
- 2014

This paper presents a new incremental gradient algorithm for minimizing the average of a large number of smooth componentâ€¦Â (More)

Is this relevant?

2013

2013

We analyze distributed optimization algorithms where parts of data and variables are distributed over several machines andâ€¦Â (More)

Is this relevant?

2013

2013

- Lin Xiao, Tong Zhang
- SIAM Journal on Optimization
- 2013

We consider solving the 1-regularized least-squares ( 1-LS) problem in the context of sparse recovery for applications such asâ€¦Â (More)

Is this relevant?

Highly Cited

2011

Highly Cited

2011

- Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell, Eric P. Xing
- UAI
- 2011

We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty thatâ€¦Â (More)

Is this relevant?

2011

2011

- Yangyang Xu
- ArXiv
- 2011

Nonnegative matrix factorization has been widely applied in face recognition, text mining, as well as spectral analysis. Thisâ€¦Â (More)

Is this relevant?

Highly Cited

2010

Highly Cited

2010

We study the problem of estimating high dimensional regression models regularized by a structured-sparsity-inducing penalty thatâ€¦Â (More)

Is this relevant?

2010

2010

We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty thatâ€¦Â (More)

Is this relevant?