The GUS-Property and Modulus-Based Methods for Tensor Complementarity Problems

Ping-Fan Dai () and Shi-Liang Wu ()
Additional contact information
Ping-Fan Dai: Hanshan Normal University
Shi-Liang Wu: Yunnan Normal University

Journal of Optimization Theory and Applications, 2022, vol. 195, issue 3, No 10, 976-1006

Abstract: Abstract In this paper, we consider the global uniqueness and solvability (GUS) of tensor complementarity problems for special power Lipschitz tensors (SPL-tensors). It is shown that a SPL-tensor is a P-tensor, but not necessarily an H-tensor. And it is also proved that tensor complementarity problems of SPL-tensors have the GUS-property. In addition, we propose modulus-based tensor splitting methods to solve the tensor complementarity problem. We consider both stand and accelerated tensor splitting methods for solving the reformulated fixed-point equations of tensor complementarity problems. The convergence analysis of two classes of modulus-based iterative methods is discussed when the system tensors are power Lipschitz tensors. Numerical examples are given to illustrate the effectiveness and efficiency of the presented approaches.

Keywords: Tensor complementarity problems; GUS-property; Modulus-based methods; Power Lipschitz tensors; 90C33; 90C30; 65H10 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10957-022-02089-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joptap:v:195:y:2022:i:3:d:10.1007_s10957-022-02089-w

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10957/PS2

DOI: 10.1007/s10957-022-02089-w

Access Statistics for this article

Journal of Optimization Theory and Applications is currently edited by Franco Giannessi and David G. Hull

More articles in Journal of Optimization Theory and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().