Document Type
Journal Article
Department/Unit
Department of Mathematics
Title
Linearized alternating direction method of multipliers for sparse group and fused LASSO models
Language
English
Abstract
The least absolute shrinkage and selection operator (LASSO) has been playing an important role in variable selection and dimensionality reduction for linear regression. In this paper we focus on two general LASSO models: Sparse Group LASSO and Fused LASSO, and apply the linearized alternating direction method of multipliers (LADMM for short) to solve them. The LADMM approach is shown to be a very simple and efficient approach to numerically solve these general LASSO models. We compare it with some benchmark approaches on both synthetic and real datasets. © 2014 Elsevier B.V. All rights reserved.
Keywords
Alternating direction method of multipliers, Convex optimization, Least absolute shrinkage and selection operator, Linear regression, Variable selection
Publication Date
2014
Source Publication Title
Computational Statistics and Data Analysis
Volume
79
Start Page
203
End Page
221
Publisher
Elsevier
DOI
10.1016/j.csda.2014.05.017
Link to Publisher's Edition
ISSN (print)
01679473
Recommended Citation
Li, Xinxin, Lili Mo, Xiaoming Yuan, and Jianzhong Zhang. "Linearized alternating direction method of multipliers for sparse group and fused LASSO models." Computational Statistics and Data Analysis 79 (2014): 203-221.