You are not logged in.

On the proximal Landweber Newton method for a class of nonsmooth convex problems

Zhang, Hai-Bin, Jiang, Jiao-Jiao and Zhao, Yun-Bin 2015, On the proximal Landweber Newton method for a class of nonsmooth convex problems, Computational optimization and applications, vol. 61, no. 1, pp. 79-99, doi: 10.1007/s10589-014-9703-7.

Attached Files
Name Description MIMEType Size Downloads

Title On the proximal Landweber Newton method for a class of nonsmooth convex problems
Author(s) Zhang, Hai-Bin
Jiang, Jiao-Jiao
Zhao, Yun-Bin
Journal name Computational optimization and applications
Volume number 61
Issue number 1
Start page 79
End page 99
Total pages 21
Publisher Springer
Place of publication Berlin, Germany
Publication date 2015-05
ISSN 0926-6003
1573-2894
Keyword(s) Newton’s method
Nonsmooth convex optimization
Projected Landweber method
Proximal splitting method
Sparse group LASSO
Summary We consider a class of nonsmooth convex optimization problems where the objective function is a convex differentiable function regularized by the sum of the group reproducing kernel norm and (Formula presented.)-norm of the problem variables. This class of problems has many applications in variable selections such as the group LASSO and sparse group LASSO. In this paper, we propose a proximal Landweber Newton method for this class of convex optimization problems, and carry out the convergence and computational complexity analysis for this method. Theoretical analysis and numerical results show that the proposed algorithm is promising.
Language eng
DOI 10.1007/s10589-014-9703-7
Field of Research 080201 Analysis of Algorithms and Complexity
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30072678

Document type: Journal Article
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 13 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Thu, 14 Jul 2016, 14:40:28 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.