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A customized proximal point algorithm for convex minimization with linear constraints

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  • Bingsheng He
  • Xiaoming Yuan
  • Wenxing Zhang

Abstract

This paper demonstrates a customized application of the classical proximal point algorithm (PPA) to the convex minimization problem with linear constraints. We show that if the proximal parameter in metric form is chosen appropriately, the application of PPA could be effective to exploit the simplicity of the objective function. The resulting subproblems could be easier than those of the augmented Lagrangian method (ALM), a benchmark method for the model under our consideration. The efficiency of the customized application of PPA is demonstrated by some image processing problems. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Bingsheng He & Xiaoming Yuan & Wenxing Zhang, 2013. "A customized proximal point algorithm for convex minimization with linear constraints," Computational Optimization and Applications, Springer, vol. 56(3), pages 559-572, December.
    • Handle: RePEc:spr:coopap:v:56:y:2013:i:3:p:559-572
    DOI: 10.1007/s10589-013-9564-5
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    References listed on IDEAS

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    1. R. T. Rockafellar, 1976. "Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming," Mathematics of Operations Research, INFORMS, vol. 1(2), pages 97-116, May.
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    Cited by:

    1. Jianlin Jiang & Liyun Ling & Yan Gu & Su Zhang & Yibing Lv, 2023. "Customized Alternating Direction Methods of Multipliers for Generalized Multi-facility Weber Problem," Journal of Optimization Theory and Applications, Springer, vol. 196(1), pages 362-389, January.
    2. Ying Gao & Wenxing Zhang, 2023. "An alternative extrapolation scheme of PDHGM for saddle point problem with nonlinear function," Computational Optimization and Applications, Springer, vol. 85(1), pages 263-291, May.
    3. Jiawei Chen & Qamrul Hasan Ansari & Yeong-Cheng Liou & Jen-Chih Yao, 2016. "A proximal point algorithm based on decomposition method for cone constrained multiobjective optimization problems," Computational Optimization and Applications, Springer, vol. 65(1), pages 289-308, September.

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