An effective inertial-relaxed CGPM for nonlinear monotone equations

In this paper, we propose an effective inertial-relaxed CGPM by considering the Armijo line search technique proposed by Amini and Kamandi (Numer Algorithms 70:559–570, 2015). The search direction designed possesses good properties. Furthermore, the global convergence is proved in the absence of the...

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Bibliographic Details
Published in:Journal of applied mathematics & computing 2024-02, Vol.70 (1), p.689-710
Main Authors: Jian, Jinbao, Ren, Zhiwen, Yin, Jianghua, Han, Daolan, Wu, Xiaodi
Format: Article
Language:English
Subjects:
Algorithms
Computational Mathematics and Numerical Analysis
Conjugate gradient method
Continuity (mathematics)
Convergence
Lipschitz condition
Mathematical analysis
Mathematical and Computational Engineering
Mathematics
Mathematics and Statistics
Mathematics of Computing
Monotone functions
Original Research
Sparse matrices
Theory of Computation
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Summary:In this paper, we propose an effective inertial-relaxed CGPM by considering the Armijo line search technique proposed by Amini and Kamandi (Numer Algorithms 70:559–570, 2015). The search direction designed possesses good properties. Furthermore, the global convergence is proved in the absence of the Lipschitz continuity. The numerical results on large-scale equations verify that our proposed method is efficient with the lower computational cost. In particular, applying it to sparse signal restoration indicates that the proposed algorithm is promising and competitive.
ISSN:1598-5865
1865-2085
DOI:10.1007/s12190-024-01991-y