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A General Iterative Method for Solving Constrained Convex Minimization Problems

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  • Ming Tian

    (Civil Aviation University of China)

  • Min-Min Li

    (Civil Aviation University of China)

Abstract

It is well known that the gradient-projection algorithm plays an important role in solving minimization problems. In this paper, we will use the idea of regularization to establish a general method so that the sequence generated by the general method can be strongly convergent to a minimizer of constrained convex minimization problems, which solves a variational inequality under suitable conditions.

Suggested Citation

  • Ming Tian & Min-Min Li, 2014. "A General Iterative Method for Solving Constrained Convex Minimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 162(1), pages 202-207, July.
    • Handle: RePEc:spr:joptap:v:162:y:2014:i:1:d:10.1007_s10957-013-0413-6
    DOI: 10.1007/s10957-013-0413-6
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    References listed on IDEAS

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    1. Hong-Kun Xu, 2011. "Averaged Mappings and the Gradient-Projection Algorithm," Journal of Optimization Theory and Applications, Springer, vol. 150(2), pages 360-378, August.
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    Cited by:

    1. Taksaporn Sirirut & Pattanapong Tianchai, 2018. "On Solving of Constrained Convex Minimize Problem Using Gradient Projection Method," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2018, pages 1-10, October.

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