The projected splitting iterative methods based on tensor splitting and its majorization matrix splitting for the tensor complementarity problem

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Accounts of Chemical Research Pub Date : 2024-03-24 DOI:10.1007/s11590-024-02104-1
Mengxiao Fan, Jicheng Li
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Abstract

In this paper, we develop two kinds of the projected iterative methods for the tensor complementarity problem combining two different splitting frameworks. The first method is on the basis of tensor splitting, and its monotone convergence is proved based on the \({\mathcal{L}}\)-tensor and the strongly monotone tensor. Meanwhile, an alternative method is in the light of majorization matrix splitting, the convergence of which is given and is particularly analyzed based on the power Lipschitz tensor. Some numerical examples are tested to illustrate the proposed methods.

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基于张量分裂及其大化矩阵分裂的张量互补问题的投影分裂迭代法
在本文中,我们结合两种不同的分裂框架,为张量互补问题开发了两种投影迭代法。第一种方法以张量分裂为基础,基于 \({\mathcal{L}}\)- 张量和强单调张量证明了其单调收敛性。同时,根据大化矩阵分裂给出了另一种方法,并基于幂 Lipschitz 张量对其收敛性进行了分析。为了说明所提出的方法,还测试了一些数值示例。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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