A modified three-term of Dai–Yuan type derivative-free algorithm for nonlinear monotone equations with signal recovery problems

A lot of researchers have introduced iterative schemes for solving convex constrained nonlinear monotone equations. This paper aims to propose a modified three-term of Dai–Yuan type (TTDY) derivative-free algorithm for nonlinear monotone equations. The presented method has a low storage requirement, it can therefore easily solve large scale nonlinear equations. At every iteration, it generates a descent search direction independent of the line search. We established the global convergence of the propose approach under standard conditions. Numerical examples are given to illustrate the effectiveness of the algorithm when solving large-scale nonlinear monotone equations. Finally, the capacity of the proposed algorithm has been tested to solve nonlinear monotone equations equivalent to the ℓ 1 -norm regularized minimization problem.

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Publication Details

Journal
Franklin Open
Published
2025-04-08
DOI
https://doi.org/10.1016/j.fraope.2025.100255
Primary Topic
Numerical methods in inverse problems
Type
article
Field-Weighted Citation Impact
0.00

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article

A modified three-term of Dai–Yuan type derivative-free algorithm for nonlinear monotone equations with signal recovery problems

A. M. Kwami, Nibron Haggai Manjak, Aliyu Yusuf, Mohammed Abdulhameed
Franklin Open
Numerical methods in inverse problems
article

A modified three-term of Dai–Yuan type derivative-free algorithm for nonlinear monotone equations with signal recovery problems

A. M. Kwami, Nibron Haggai Manjak, Aliyu Yusuf, Mohammed Abdulhameed
article en

Abstract

A lot of researchers have introduced iterative schemes for solving convex constrained nonlinear monotone equations. This paper aims to propose a modified three-term of Dai–Yuan type (TTDY) derivative-free algorithm for nonlinear monotone equations. The presented method has a low storage requirement, it can therefore easily solve large scale nonlinear equations. At every iteration, it generates a descent search direction independent of the line search. We established the global convergence of the propose approach under standard conditions. Numerical examples are given to illustrate the effectiveness of the algorithm when solving large-scale nonlinear monotone equations. Finally, the capacity of the proposed algorithm has been tested to solve nonlinear monotone equations equivalent to the ℓ 1 -norm regularized minimization problem.

Franklin OpenVol. 11
Abubakar Tafawa Balewa University (NG), Sa'adu Zungur University (NG), The Federal Polytechnic, Ado-Ekiti (NG), Bayero University Kano (NG)
Tertiary Education Trust Fund
Openalex Percentile: Top 88%
Numerical methods in inverse problems
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