A derivative-free RMIL conjugate gradient method for constrained nonlinear systems of monotone equations

In this paper, we introduce a derivative-free RMIL conjugate gradient method designed for nonlinear systems of monotone equations with convex constraints. The proposed method represents a refinement of the RMIL method through its integration with projection techniques and a derivative-free line search strategy. When certain reasonable assumptions are satisfied, we can prove the global convergence of the proposed method. Numerical experiments demonstrate that the method is highly effective. Moreover, we employ the method to solve sparse signal restoration problems.

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Publication Details

Journal
AIMS Mathematics
Published
2025-01-01
DOI
https://doi.org/10.3934/math.2025528
Citations
1
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
1.32
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article

A derivative-free RMIL conjugate gradient method for constrained nonlinear systems of monotone equations

Xiaowei Fang
1 citations
AIMS Mathematics
Model Reduction and Neural Networks
1.32
article

A derivative-free RMIL conjugate gradient method for constrained nonlinear systems of monotone equations

Xiaowei Fang
article en
1 citations

Abstract

In this paper, we introduce a derivative-free RMIL conjugate gradient method designed for nonlinear systems of monotone equations with convex constraints. The proposed method represents a refinement of the RMIL method through its integration with projection techniques and a derivative-free line search strategy. When certain reasonable assumptions are satisfied, we can prove the global convergence of the proposed method. Numerical experiments demonstrate that the method is highly effective. Moreover, we employ the method to solve sparse signal restoration problems.

AIMS MathematicsVol. 10(5)
Huzhou Normal University (CN)
Openalex Percentile: Top 20%
Model Reduction and Neural Networks
1.32
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