COFADMM: A computational features selection with alternating Direction Method of Multipliers

Research output: Contribution to journalConference article

3 Citations (Scopus)


Due to the explosion in size and complexity of Big Data, it is increasingly important to be able to solve problems with very large number of features. Classical feature selection procedures involves combinatorial optimization, with computational time increasing exponentially with the number of features. During the last decade, penalized regression has emerged as an attractive alternative for regularization and high dimensional feature selection problems. Alternating Direction Method of Multipliers (ADMM) optimization is suited for distributed convex optimization and distributed computing for big data. The purpose of this paper is to propose a broader algorithm COFADMM which combines the strength of convex penalized techniques in feature selection for big data and the power of the ADMM for optimization. We show that combining the ADMM algorithm with COFADMM can provide a path of solutions efficiently and quickly. COFADMM is easy to use, is available in C, Matlab upon request from the corresponding author.

Original languageEnglish
Pages (from-to)821-830
Number of pages10
JournalProcedia Computer Science
Publication statusPublished - 1 Jan 2014
Event14th Annual International Conference on Computational Science, ICCS 2014 - Cairns, QLD, Australia
Duration: 10 Jun 201412 Jun 2014



  • ADMM algorithm
  • Coordinate descent algorithm
  • Features selection
  • Lasso
  • Least angle regression algorithm

ASJC Scopus subject areas

  • Computer Science(all)

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