Scholarlink

<p>We study preconditioning techniques for the indefinite least squares problem and present the shift-splitting (SS) preconditioner. We prove that the shift-splitting method is unconditionally convergent, which leads to an excellent clustering property of the eigenvalues of the SS preconditioned matrix. Additionally, we propose a relaxed version of the SS preconditioner (RSS) that further improves computational efficiency. Numerical experiments demonstrate that our proposed SS and RSS preconditioners are superior to existing preconditioners in terms of CPU time and number of iterations, and the corresponding preconditioned matrices exhibit good spectral clustering properties.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A preconditioned shift-splitting iteration method for solving indefinite least squares problem

  • 期刊论文

  • 发表日期：

    2025年12月12日

查看全文
  • Kailiang Xin,
  • Lingsheng Meng,
  • Jun Li,
  • Yunying Huang

摘要

We study preconditioning techniques for the indefinite least squares problem and present the shift-splitting (SS) preconditioner. We prove that the shift-splitting method is unconditionally convergent, which leads to an excellent clustering property of the eigenvalues of the SS preconditioned matrix. Additionally, we propose a relaxed version of the SS preconditioner (RSS) that further improves computational efficiency. Numerical experiments demonstrate that our proposed SS and RSS preconditioners are superior to existing preconditioners in terms of CPU time and number of iterations, and the corresponding preconditioned matrices exhibit good spectral clustering properties.