A fast inertial self-adaptive projection based algorithm for solving large-scale nonlinear monotone equations

In this paper, we propose a fast inertial self-adaptive projection based algorithm for solving large-scale monotone equations with the relaxation factor. The modified hyperplane projection technique accelerates the computational performance of the proposed algorithm, and the self-adaptive parameter...

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Veröffentlicht in:Journal of computational and applied mathematics Jg. 426; S. 115087
Hauptverfasser: Zhang, N., Liu, J.K., Zhang, L.Q., Lu, Z.L.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.07.2023
Schlagworte:
Conjugate gradient algorithm
Global convergence
Hyperplane projection technique
Inertial term
Nonlinear equations
Nonlinear equations
Global convergence
Inertial term
Hyperplane projection technique
Conjugate gradient algorithm
ISSN:0377-0427, 1879-1778
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Zusammenfassung:In this paper, we propose a fast inertial self-adaptive projection based algorithm for solving large-scale monotone equations with the relaxation factor. The modified hyperplane projection technique accelerates the computational performance of the proposed algorithm, and the self-adaptive parameter enhances its stability. Under some appropriate assumptions, the global convergence of the proposed algorithm is established. Moreover, the proposed algorithm is suitable to solve large-scale problems because it does not need gradient information or Jacobian matrix. The numerical results indicate that the new algorithm is robust and efficient by comparing with other popular algorithms.
ISSN:0377-0427
1879-1778
DOI:10.1016/j.cam.2023.115087