Inexact Orthant-Wise Quasi-Newton Method
Authors
Faguo Wu, Wang Yao, Xiao Zhang, Chenxu Wang, Zhiming Zheng
Corresponding Author
Faguo Wu
Available Online May 2018.
- DOI
- 10.2991/ammsa-18.2018.31How to use a DOI?
- Keywords
- orthant-based; sparse optimization; inexact Newton; proximal gadient
- Abstract
The Orthant-Wise Limited-memory Quasi-Newton method (OWL-QN), based on the L-BFGS method, is an effective algorithm for solving the ‘1-regularized sparse learning problem. In order to deal with the ‘1-regularization, OWL-QN restrict the point to an orthant on which the quadratic model is valid and differentiable. In this paper, we propose an Inexact Orthant-Wise Limited-memory Quasi-Newton method (IOWL-QN). This method, at every iteration, compute an approximate solution satisfied the inexactness conditions to estimate the exact solution. We give brief proof to the convergence and report the numerical results.
- Copyright
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Faguo Wu AU - Wang Yao AU - Xiao Zhang AU - Chenxu Wang AU - Zhiming Zheng PY - 2018/05 DA - 2018/05 TI - Inexact Orthant-Wise Quasi-Newton Method BT - Proceedings of the 2018 2nd International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2018) PB - Atlantis Press SP - 149 EP - 153 SN - 1951-6851 UR - https://doi.org/10.2991/ammsa-18.2018.31 DO - 10.2991/ammsa-18.2018.31 ID - Wu2018/05 ER -