JOURNAL ARTICLE

On estimation of the optimal parameter of the modulus‐based matrix splitting algorithm for linear complementarity problems on second‐order cones.

  • Published In: Numerical Linear Algebra with Applications, 2023, v. 30, n. 4. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Li, Zhizhi; Zhang, Huai 3 of 3

Abstract

There are many studies on the well‐known modulus‐based matrix splitting (MMS) algorithm for solving complementarity problems, but very few studies on its optimal parameter, which is of theoretical and practical importance. Therefore and here, by introducing a novel mapping to explicitly cast the implicit fixed point equation and thus obtain the iteration matrix involved, we first present the estimation approach of the optimal parameter of each step of the MMS algorithm for solving linear complementarity problems on the direct product of second‐order cones (SOCLCPs). It also works on single second‐order cone and the non‐negative orthant. On this basis, we further propose an iteration‐independent optimal parameter selection strategy for practical usage. Finally, the practicability and effectiveness of the new proposal are verified by comparing with the experimental optimal parameter and the diagonal part of system matrix. In addition, with the optimal parameter, the effectiveness of the MMS algorithm can indeed be greatly improved, even better than the state‐of‐the‐art solvers SCS and SuperSCS that solve the equivalent SOC programming. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Numerical Linear Algebra with Applications. 2023/08, Vol. 30, Issue 4, p1
  • Document Type:Article
  • Subject Area:Mathematics
  • Publication Date:2023
  • ISSN:1070-5325
  • DOI:10.1002/nla.2480
  • Accession Number:164657666
  • Copyright Statement:Copyright of Numerical Linear Algebra with Applications is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.