{"title":"求解H_+ -矩阵线性互补问题的一种预条件模矩阵分裂新方法","authors":"D. Yu, Yifei Yuan, Yiming Zhang","doi":"10.3934/era.2023007","DOIUrl":null,"url":null,"abstract":"For solving the linear complementarity problem (LCP), we propose a preconditioned new modulus-based matrix splitting (PNMMS) iteration method by extending the state-of-the-art new modulus-based matrix splitting (NMMS) iteration method to a more general framework with a customized preconditioner. We devise a generalized preconditioner that is associated with both $ H_+ $-matrix $ A $ and vector $ q $ of the LCP. The convergence analysis is conducted under some mild conditions. In particular, we provide a comparison theorem to theoretically show the PNMMS method accelerates the convergence rate. Numerical experiments further illustrate that the PNMMS method is efficient and has better performance for solving the large and sparse LCP.","PeriodicalId":48554,"journal":{"name":"Electronic Research Archive","volume":"365 1","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A preconditioned new modulus-based matrix splitting method for solving linear complementarity problem of $ H_+ $-matrices\",\"authors\":\"D. Yu, Yifei Yuan, Yiming Zhang\",\"doi\":\"10.3934/era.2023007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For solving the linear complementarity problem (LCP), we propose a preconditioned new modulus-based matrix splitting (PNMMS) iteration method by extending the state-of-the-art new modulus-based matrix splitting (NMMS) iteration method to a more general framework with a customized preconditioner. We devise a generalized preconditioner that is associated with both $ H_+ $-matrix $ A $ and vector $ q $ of the LCP. The convergence analysis is conducted under some mild conditions. In particular, we provide a comparison theorem to theoretically show the PNMMS method accelerates the convergence rate. Numerical experiments further illustrate that the PNMMS method is efficient and has better performance for solving the large and sparse LCP.\",\"PeriodicalId\":48554,\"journal\":{\"name\":\"Electronic Research Archive\",\"volume\":\"365 1\",\"pages\":\"\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Electronic Research Archive\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.3934/era.2023007\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electronic Research Archive","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.3934/era.2023007","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS","Score":null,"Total":0}
引用次数: 0
摘要
为了解决线性互补问题(LCP),我们提出了一种预条件的新模矩阵分裂(PNMMS)迭代方法,通过定制预条件将最新的新模矩阵分裂(NMMS)迭代方法扩展到更一般的框架。我们设计了一个与LCP的$ H_+ $-矩阵$ a $和向量$ q $相关联的广义预条件。在一些温和的条件下进行了收敛性分析。特别地,我们提供了一个比较定理,从理论上证明了PNMMS方法加快了收敛速度。数值实验进一步证明了PNMMS方法的有效性,对于求解大型稀疏LCP具有较好的性能。
A preconditioned new modulus-based matrix splitting method for solving linear complementarity problem of $ H_+ $-matrices
For solving the linear complementarity problem (LCP), we propose a preconditioned new modulus-based matrix splitting (PNMMS) iteration method by extending the state-of-the-art new modulus-based matrix splitting (NMMS) iteration method to a more general framework with a customized preconditioner. We devise a generalized preconditioner that is associated with both $ H_+ $-matrix $ A $ and vector $ q $ of the LCP. The convergence analysis is conducted under some mild conditions. In particular, we provide a comparison theorem to theoretically show the PNMMS method accelerates the convergence rate. Numerical experiments further illustrate that the PNMMS method is efficient and has better performance for solving the large and sparse LCP.