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    Inertial accelerated algorithms for solving a split feasibility problem

    254802.pdf (302.5Kb)
    Access Status
    Open access
    Authors
    Dang, Y.
    Sun, Jie
    Xu, Honglei
    Date
    2017
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Dang, Y. and Sun, J. and Xu, H. 2017. Inertial accelerated algorithms for solving a split feasibility problem. Journal of Industrial and management optimization. 13 (3): pp. 1383-1394.
    Source Title
    Journal of Industrial and Management Optimization
    DOI
    10.3934/jimo.2016078
    ISSN
    1547-5816
    School
    Department of Mathematics and Statistics
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP160102819
    Remarks

    This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Journal of Industrial and Management Optimization following peer review. The definitive publisher-authenticated version cited above is available online at: http://doi.org/10.3934/jimo.2016078

    URI
    http://hdl.handle.net/20.500.11937/56140
    Collection
    • Curtin Research Publications
    Abstract

    Inspired by the inertial proximal algorithms for finding a zero of a maximal monotone operator, in this paper, we propose two inertial accel erated algorithms to solve the split feasibility problem. One is an inertial relaxed-CQ algorithm constructed by applying inertial technique to a relaxed- CQ algorithm, the other is a modified inertial relaxed-CQ algorithm which combines the KM method with the inertial relaxed-CQ algorithm. We prove their asymptotical convergence under some suitable conditions. Numerical re sults are reported to show the effectiveness of the proposed algorithms.

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