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    A fast dual gradient method for separable convex optimization via smoothing

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    Fulltext not available
    Authors
    Li, J.
    Wu, Z.
    Wu, Changzhi
    Long, Q.
    Wang, X.
    Lee, J.
    Jung, K.
    Date
    2016
    Type
    Journal Article
    
    Metadata
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    Citation
    Li, J. and Wu, Z. and Wu, C. and Long, Q. and Wang, X. and Lee, J. and Jung, K. 2016. A fast dual gradient method for separable convex optimization via smoothing. Pacific Journal of Optimization. 12 (2): pp. 289-305.
    Source Title
    Pacific Journal of Optimization
    ISSN
    1348-9151
    School
    Department of Construction Management
    URI
    http://hdl.handle.net/20.500.11937/40067
    Collection
    • Curtin Research Publications
    Abstract

    This paper considers a class of separable convex optimization problems with linear coupled constraints arising in many applications. Based on a novel smoothing technique, a simple fast dual gradient method is presented to solve the class of problems. Then the convergence of the proposed method is proved and the computational complexity bound of the method for achieving an approximately optimal solution is given explicitly. An improved iteration complexity bound is obtained when the objective function of the problem is strongly convex. Our algorithm is simple and fast, which can be implemented in a parallel fashion. Numerical experiments on a network utility maximization problem to illustrate the effectiveness of the proposed algorithm.

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