Alternating direction method for covariance selection models

Xiaoming YUAN*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

60 Citations (Scopus)

Abstract

The covariance selection problem captures many applications in various fields, and it has been well studied in the literature. Recently, an l 1-norm penalized log-likelihood model has been developed for the covariance selection problem, and this novel model is capable of completing the model selection and parameter estimation simultaneously. With the rapidly increasing magnitude of data, it is urged to consider efficient numerical algorithms for large-scale cases of the l1-norm penalized log-likelihood model. For this purpose, this paper develops the alternating direction method (ADM). Some preliminary numerical results show that the ADM approach is very efficient for large-scale cases of the l1-norm penalized log-likelihood model.

Original languageEnglish
Pages (from-to)261-273
Number of pages13
JournalJournal of Scientific Computing
Volume51
Issue number2
DOIs
Publication statusPublished - May 2012

Scopus Subject Areas

  • Software
  • Theoretical Computer Science
  • Numerical Analysis
  • Engineering(all)
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Alternating direction method
  • Covariance selection problem
  • Log-likelihood

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