Abstract
Solving systems of nonlinear equations has evolved into an active research field, with numerous iterative methods being proposed. Notably, iterative methods characterized by fast convergence remain of interest. In this paper, based on the modified line search scheme by Ou and Li, we introduce a derivative-free algorithm with a relaxed-inertial technique for approximating solutions of nonlinear systems involving pseudo-monotone mappings in Euclidean space. The global convergence of the proposed algorithm is established without Lipschitz continuity of the underlying mapping. Moreover, our approach allows flexibility in selecting the inertial extrapolation step length within the interval [0, 1]. To show the efficiency of the proposed method, we embed a derivative-free search direction into the scheme. Numerical experiments are given to illustrate the efficiency of the proposed algorithm for large-scale systems and sparse signal reconstruction.
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Acknowledgements
The authors are very much indebted and grateful to the editors and anonymous referees for their valuable comments and suggestions which improved the quality of this paper.
Funding
This research was supported by the Postdoctoral Fellowship program at King Mongkut’s University of Technology Thonburi. Poom Kumam would like to acknowledge the financial support received from two sources: (i) the Centre of Excellence in Theoretical and Computational Science (TaCS-CoE) at KMUTT, and (ii) the “Mid-Career Research Grant” (Grant No. N41A640089). Additionally, this research received support from the Science Fund of the Republic of Serbia under Grant No. 7359, for the project titled “LASCADO.”
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Ibrahim, A.H., Rapajić, S., Kamandi, A. et al. Relaxed-inertial derivative-free algorithm for systems of nonlinear pseudo-monotone equations. Comp. Appl. Math. 43, 239 (2024). https://doi.org/10.1007/s40314-024-02673-y
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DOI: https://doi.org/10.1007/s40314-024-02673-y
Keywords
- Iterative method
- Derivative-free method
- Nonlinear equations
- Inertial extrapolation method
- Sparse signal reconstruction