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The global convergence of spectral RMIL conjugate gradient method for unconstrained optimization with applications to robotic model and image recovery

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  • Nasiru Salihu
  • Poom Kumam
  • Aliyu Muhammed Awwal
  • Ibrahim Mohammed Sulaiman
  • Thidaporn Seangwattana

Abstract

In 2012, Rivaie et al. introduced RMIL conjugate gradient (CG) method which is globally convergent under the exact line search. Later, Dai (2016) pointed out abnormality in the convergence result and thus, imposed certain restricted RMIL CG parameter as a remedy. In this paper, we suggest an efficient RMIL spectral CG method. The remarkable feature of this method is that, the convergence result is free from additional condition usually imposed on RMIL. Subsequently, the search direction is sufficiently descent independent of any line search technique. Thus, numerical experiments on some set of benchmark problems indicate that the method is promising and efficient. Furthermore, the efficiency of the proposed method is demonstrated on applications arising from arm robotic model and image restoration problems.

Suggested Citation

  • Nasiru Salihu & Poom Kumam & Aliyu Muhammed Awwal & Ibrahim Mohammed Sulaiman & Thidaporn Seangwattana, 2023. "The global convergence of spectral RMIL conjugate gradient method for unconstrained optimization with applications to robotic model and image recovery," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-19, March.
    • Handle: RePEc:plo:pone00:0281250
    DOI: 10.1371/journal.pone.0281250
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    References listed on IDEAS

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    5. Neculai Andrei, 2020. "Nonlinear Conjugate Gradient Methods for Unconstrained Optimization," Springer Optimization and Its Applications, Springer, number 978-3-030-42950-8, December.
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    Cited by:

    1. Jamilu Yahaya & Poom Kumam & Sani Salisu & Kanokwan Sitthithakerngkiet, 2024. "Spectral-like conjugate gradient methods with sufficient descent property for vector optimization," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-22, May.

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