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Two New Dai–Liao-Type Conjugate Gradient Methods for Unconstrained Optimization Problems

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Abstract

In this paper, we present two new Dai–Liao-type conjugate gradient methods for unconstrained optimization problems. Their convergence under the strong Wolfe line search conditions is analysed for uniformly convex objective functions and general objective functions, respectively. Numerical experiments show that our methods can outperform some existing Dai–Liao-type methods by using Dolan and Moré’s performance profile.

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Acknowledgements

The authors are grateful to the anonymous referees for their constructive comments and useful suggestions. This work was supported by the National Science Foundation of China under Grant No. 11571004.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Lanzhou University, Lanzhou, 730000, People’s Republic of China

    Yutao Zheng & Bing Zheng

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  1. Yutao Zheng
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  2. Bing Zheng
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Correspondence to Bing Zheng.

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Zheng, Y., Zheng, B. Two New Dai–Liao-Type Conjugate Gradient Methods for Unconstrained Optimization Problems. J Optim Theory Appl 175, 502–509 (2017). https://doi.org/10.1007/s10957-017-1140-1

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  • DOI: https://doi.org/10.1007/s10957-017-1140-1

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