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    An Inexact Dual Fast Gradient-Projection Method for Separable Convex Optimization with Linear Coupled Constraints

    Access Status
    Fulltext not available
    Authors
    Li, J.
    Wu, Z.
    Wu, Changzhi
    Long, Q.
    Wang, X.
    Date
    2015
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Li, J. and Wu, Z. and Wu, C. and Long, Q. and Wang, X. 2015. An Inexact Dual Fast Gradient-Projection Method for Separable Convex Optimization with Linear Coupled Constraints. Journal of Optimization Theory and Applications.
    Source Title
    Journal of Optimization Theory and Applications
    DOI
    10.1007/s10957-015-0757-1
    ISSN
    0022-3239
    School
    Department of Construction Management
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/LP130100451
    URI
    http://hdl.handle.net/20.500.11937/36258
    Collection
    • Curtin Research Publications
    Abstract

    © 2015 Springer Science+Business Media New York In this paper, a class of separable convex optimization problems with linear coupled constraints is studied. According to the Lagrangian duality, the linear coupled constraints are appended to the objective function. Then, a fast gradient-projection method is introduced to update the Lagrangian multiplier, and an inexact solution method is proposed to solve the inner problems. The advantage of our proposed method is that the inner problems can be solved in an inexact and parallel manner. The established convergence results show that our proposed algorithm still achieves optimal convergence rate even though the inner problems are solved inexactly. Finally, several numerical experiments are presented to illustrate the efficiency and effectiveness of our proposed algorithm.

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