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    Alternating direction method of multipliers for nonconvex fused regression problems

    Access Status
    Fulltext not available
    Authors
    Xiu, X.
    Liu, Wan-Quan
    Li, L.
    Kong, L.
    Date
    2019
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Xiu, X. and Liu, W. and Li, L. and Kong, L. 2019. Alternating direction method of multipliers for nonconvex fused regression problems. Computational Statistics and Data Analysis. 136: pp. 59-71.
    Source Title
    Computational Statistics and Data Analysis
    DOI
    10.1016/j.csda.2019.01.002
    ISSN
    0167-9473
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/74066
    Collection
    • Curtin Research Publications
    Abstract

    It is well-known that the fused least absolute shrinkage and selection operator (FLASSO) has been playing an important role in signal and image processing. Recently, the nonconvex penalty is extensively investigated due to its success in sparse learning. In this paper, a novel nonconvex fused regression model, which integrates FLASSO and the nonconvex penalty nicely, is proposed. The developed alternating direction method of multipliers (ADMM) approach is shown to be very efficient owing to the fact that each derived subproblem has a closed-form solution. In addition, the convergence is discussed and proved mathematically. This leads to a fast and convergent algorithm. Extensive numerical experiments show that our proposed nonconvex fused regression outperforms the state-of-the-art approach FLASSO.

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