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A spectral three-term Hestenes–Stiefel conjugate gradient method

Author

Listed:
  • Parvaneh Faramarzi

    (Razi University)

  • Keyvan Amini

    (Razi University)

Abstract

In this paper, according to some suitable features of three-term conjugate gradient methods and excellent theoretical properties of the quasi-Newton methods, a new spectral three-term conjugate gradient is proposed. A modified secant condition is used to compute a suitable spectral parameter. The new search direction ensures the sufficient descent condition without any line search. It is established that the new scheme possesses the global convergence, under the strong Wolfe conditions. Preliminary numerical experiments show the efficiency of the new method, dealing with unconstrained optimization problems.

Suggested Citation

  • Parvaneh Faramarzi & Keyvan Amini, 2021. "A spectral three-term Hestenes–Stiefel conjugate gradient method," 4OR, Springer, vol. 19(1), pages 71-92, March.
    • Handle: RePEc:spr:aqjoor:v:19:y:2021:i:1:d:10.1007_s10288-020-00432-3
    DOI: 10.1007/s10288-020-00432-3
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    References listed on IDEAS

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    1. Parvaneh Faramarzi & Keyvan Amini, 2019. "A Modified Spectral Conjugate Gradient Method with Global Convergence," Journal of Optimization Theory and Applications, Springer, vol. 182(2), pages 667-690, August.
    2. C. X. Kou & Y. H. Dai, 2015. "A Modified Self-Scaling Memoryless Broyden–Fletcher–Goldfarb–Shanno Method for Unconstrained Optimization," Journal of Optimization Theory and Applications, Springer, vol. 165(1), pages 209-224, April.
    3. Saman Babaie-Kafaki & Reza Ghanbari, 2017. "A class of adaptive Dai–Liao conjugate gradient methods based on the scaled memoryless BFGS update," 4OR, Springer, vol. 15(1), pages 85-92, March.
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