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Scalable Relaxation Two-Sweep Modulus-Based Matrix Splitting Methods for Vertical LCP.

Authors :
Yu, Dongmei
Wei, Huiling
Chen, Cairong
Han, Deren
Source :
Journal of Optimization Theory & Applications. Oct2024, Vol. 203 Issue 1, p714-744. 31p.
Publication Year :
2024

Abstract

Based on a new equivalent reformulation, a scalable modulus-based matrix splitting (SMMS) method is proposed to solve the vertical linear complementarity problem (VLCP). By introducing a relaxation parameter and employing the two-sweep technique, we further enhance the scalability of the method, leading to a framework of the scalable relaxation two-sweep modulus-based matrix splitting (SRTMMS) method. To theoretically demonstrate the acceleration of the convergence provided by the SMMS method, we present a comparison theorem for the case of s = 2 . Furthermore, we establish the convergence of the SRTMMS method for arbitrary s. Preliminary numerical results indicate promising performance of the SRTMMS method. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00223239
Volume :
203
Issue :
1
Database :
Academic Search Index
Journal :
Journal of Optimization Theory & Applications
Publication Type :
Academic Journal
Accession number :
180628882
Full Text :
https://doi.org/10.1007/s10957-024-02529-9