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An Optimal Parameter for Dai–Liao Family of Conjugate Gradient Methods

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  • M. Fatemi

    (K. N. Toosi University of Technology
    K. N. Toosi University of Technology)

Abstract

We introduce a new efficient nonlinear conjugate gradient method for unconstrained optimization, based on minimizing a penalty function. Our penalty function combines the good properties of the linear conjugate gradient method using some penalty parameters. We show that the new method is a member of Dai–Liao family and, more importantly, propose an efficient Dai–Liao parameter by closely analyzing the penalty function. Numerical experiments show that the proposed parameter is promising.

Suggested Citation

  • M. Fatemi, 2016. "An Optimal Parameter for Dai–Liao Family of Conjugate Gradient Methods," Journal of Optimization Theory and Applications, Springer, vol. 169(2), pages 587-605, May.
    • Handle: RePEc:spr:joptap:v:169:y:2016:i:2:d:10.1007_s10957-015-0786-9
    DOI: 10.1007/s10957-015-0786-9
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    References listed on IDEAS

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    1. Avinoam Perry, 1977. "A Class of Conjugate Gradient Algorithms with a Two-Step Variable Metric Memory," Discussion Papers 269, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
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    Cited by:

    1. Zohre Aminifard & Saman Babaie-Kafaki, 2019. "An optimal parameter choice for the Dai–Liao family of conjugate gradient methods by avoiding a direction of the maximum magnification by the search direction matrix," 4OR, Springer, vol. 17(3), pages 317-330, September.

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