A Quasi-Newton Proximal Splitting Method
Abstract
We describe efficient implementations of the proximity calculation for a useful class of functions; the implementations exploit the piece-wise linear nature of the dual problem. The second part of the paper applies the previous result to acceleration of convex minimization problems, and leads to an elegant quasi-Newton method. The optimization method compares favorably against state-of-the-art alternatives. The algorithm has extensive applications including signal processing, sparse regression and recovery, and machine learning and classification.
Cite
Text
Becker and Fadili. "A Quasi-Newton Proximal Splitting Method." Neural Information Processing Systems, 2012.Markdown
[Becker and Fadili. "A Quasi-Newton Proximal Splitting Method." Neural Information Processing Systems, 2012.](https://mlanthology.org/neurips/2012/becker2012neurips-quasinewton/)BibTeX
@inproceedings{becker2012neurips-quasinewton,
title = {{A Quasi-Newton Proximal Splitting Method}},
author = {Becker, Stephen and Fadili, Jalal},
booktitle = {Neural Information Processing Systems},
year = {2012},
pages = {2618-2626},
url = {https://mlanthology.org/neurips/2012/becker2012neurips-quasinewton/}
}