用于解决平衡问题和常见固定点问题的双惯性子梯度外算法，并将其应用于图像修复

IF 2.1 2区数学 Q1 MATHEMATICS, APPLIED

Journal of Computational and Applied Mathematics Pub Date : 2024-11-20 DOI:10.1016/j.cam.2024.116396

Prasit Cholamjiak , Zhongbing Xie , Min Li , Papinwich Paimsang

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引用次数: 0

摘要

本文提出了一种解决希尔伯特空间中平衡问题和普通定点问题的双惯性方法。在子梯度外梯度法的基础上，我们修改了自适应规则，并使用额外参数来选择适当的步长。在合理的假设条件下，我们建立了所提算法的弱收敛性和线性收敛性。最后，通过数值实验验证了所提方法相对于现有文献的合理性和有效性。

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Double inertial subgradient extragradient algorithm for solving equilibrium problems and common fixed point problems with application to image restoration

This paper presents a double inertial method for solving equilibrium problems and common fixed point problems in Hilbert spaces. On the basis of the subgradient extragradient method, we modify the self adaptive rule and use an additional parameter to select appropriate step size. Under reasonable assumptions, we establish both weak and linear convergence properties for the proposed algorithm. Finally, numerical experiments are conducted to validate the rationality and effectiveness of the proposed method over the existing ones in the literature.

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来源期刊

Journal of Computational and Applied Mathematics 数学-应用数学

CiteScore

5.40

自引率

4.20%

发文量

437

审稿时长

3.0 months

期刊介绍： The Journal of Computational and Applied Mathematics publishes original papers of high scientific value in all areas of computational and applied mathematics. The main interest of the Journal is in papers that describe and analyze new computational techniques for solving scientific or engineering problems. Also the improved analysis, including the effectiveness and applicability, of existing methods and algorithms is of importance. The computational efficiency (e.g. the convergence, stability, accuracy, ...) should be proved and illustrated by nontrivial numerical examples. Papers describing only variants of existing methods, without adding significant new computational properties are not of interest. The audience consists of: applied mathematicians, numerical analysts, computational scientists and engineers.