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A General Iterative Method for Solving Constrained Convex Minimization Problems

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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.

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Acknowledgements

The authors thank the referees for their helpful comments, which notably im- proved the presentation of the proofs.

Thanks for your work for my paper.

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Authors and Affiliations

  1. College of Science, Civil Aviation University of China, Tianjin, 300300, China

    Ming Tian & Min-Min Li

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  1. Ming Tian
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  2. Min-Min Li
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Correspondence to Ming Tian.

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Tian, M., Li, MM. A General Iterative Method for Solving Constrained Convex Minimization Problems. J Optim Theory Appl 162, 202–207 (2014). https://doi.org/10.1007/s10957-013-0413-6

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  • DOI: https://doi.org/10.1007/s10957-013-0413-6

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