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Some improved Dai–Yuan conjugate gradient methods for large-scale unconstrained optimization problems

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Abstract

In this paper, we introduce some modifications of the classic conjugate gradient method Dai–Yuan, to solve large-scale unconstrained optimization problems. Our modifications are based on four improved secant conditions. We indicate that the presented methods inherit the appropriate global convergence property of the Dai–Yuan method. Furthermore, we illustrate the amazing numerical behavior of these modifications in two sets of numerical experiments.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their helpful suggestions and valuable comments.

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Authors and Affiliations

  1. Department of Basic Science, Hamedan University of Technology, Hamedan, 65155-579, Iran

    S. Bojari

  2. Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, 14115-134, Iran

    M. R. Eslahchi

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  1. S. Bojari
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Correspondence to M. R. Eslahchi.

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Bojari, S., Eslahchi, M.R. Some improved Dai–Yuan conjugate gradient methods for large-scale unconstrained optimization problems. J. Appl. Math. Comput. 69, 4213–4228 (2023). https://doi.org/10.1007/s12190-023-01918-z

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