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Adaptive Hybrid Mixed Two-Point Step Size Gradient Algorithm for Solving Non-Linear Systems

Author

Listed:
  • Eltiyeb Ali

    (Department of Mathematics, College of Science and Arts—Sharourah, Najran University, P.O. Box 1988, Najran 68341, Saudi Arabia)

  • Salem Mahdi

    (Department of Mathematics & Computer Science, Faculty of Science, Alexandria University, Alexandria 5424041, Egypt
    Educational Research and Development Center Sanaa, Sanaa 31220, Yemen)

Abstract

In this paper, a two-point step-size gradient technique is proposed by which the approximate solutions of a non-linear system are found. The two-point step-size includes two types of parameters deterministic and random. A new adaptive backtracking line search is presented and combined with the two-point step-size gradient to make it globally convergent. The idea of the suggested method depends on imitating the forward difference method by using one point to estimate the values of the gradient vector per iteration where the number of the function evaluation is at most one for each iteration. The global convergence analysis of the proposed method is established under actual and limited conditions. The performance of the proposed method is examined by solving a set of non-linear systems containing high dimensions. The results of the proposed method is compared to the results of a derivative-free three-term conjugate gradient CG method that solves the same test problems. Fair, popular, and sensible evaluation criteria are used for comparisons. The numerical results show that the proposed method has merit and is competitive in all cases and superior in terms of efficiency, reliability, and effectiveness in finding the approximate solution of the non-linear systems.

Suggested Citation

  • Eltiyeb Ali & Salem Mahdi, 2023. "Adaptive Hybrid Mixed Two-Point Step Size Gradient Algorithm for Solving Non-Linear Systems," Mathematics, MDPI, vol. 11(9), pages 1-35, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2102-:d:1135796
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    References listed on IDEAS

    as
    1. Ahmad M. Alshamrani & Adel Fahad Alrasheedi & Khalid Abdulaziz Alnowibet & Salem Mahdi & Ali Wagdy Mohamed, 2022. "A Hybrid Stochastic Deterministic Algorithm for Solving Unconstrained Optimization Problems," Mathematics, MDPI, vol. 10(17), pages 1-26, August.
    2. G. Zhou & K. C. Toh, 2005. "Superlinear Convergence of a Newton-Type Algorithm for Monotone Equations," Journal of Optimization Theory and Applications, Springer, vol. 125(1), pages 205-221, April.
    3. Zhifeng Dai & Huan Zhu, 2020. "A Modified Hestenes-Stiefel-Type Derivative-Free Method for Large-Scale Nonlinear Monotone Equations," Mathematics, MDPI, vol. 8(2), pages 1-14, January.
    4. Khalid Abdulaziz Alnowibet & Salem Mahdi & Mahmoud El-Alem & Mohamed Abdelawwad & Ali Wagdy Mohamed, 2022. "Guided Hybrid Modified Simulated Annealing Algorithm for Solving Constrained Global Optimization Problems," Mathematics, MDPI, vol. 10(8), pages 1-25, April.
    5. Khalid Abdulaziz Alnowibet & Salem Mahdi & Ahmad M. Alshamrani & Karam M. Sallam & Ali Wagdy Mohamed, 2022. "A Family of Hybrid Stochastic Conjugate Gradient Algorithms for Local and Global Minimization Problems," Mathematics, MDPI, vol. 10(19), pages 1-37, October.
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    Cited by:

    1. Dandan Li & Yong Li & Songhua Wang, 2024. "An Improved Three-Term Conjugate Gradient Algorithm for Constrained Nonlinear Equations under Non-Lipschitz Conditions and Its Applications," Mathematics, MDPI, vol. 12(16), pages 1-22, August.

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