Linearized alternating direction method of multipliers for sparse group and fused LASSO models

Xinxin Li, Lili Mo, Xiaoming YUAN*, Jianzhong Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)203-221
Number of pages19
JournalComputational Statistics and Data Analysis
Volume79
DOIs
Publication statusPublished - Nov 2014

Scopus Subject Areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Alternating direction method of multipliers
  • Convex optimization
  • Least absolute shrinkage and selection operator
  • Linear regression
  • Variable selection

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