Global Exponential Stability of a Neural Network for Inverse Variational Inequalities

Phan Tu Vuong (), Xiaozheng He () and Duong Viet Thong ()
Additional contact information
Phan Tu Vuong: University of Southampton
Xiaozheng He: Rensselaer Polytechnic Institute
Duong Viet Thong: National Economics University

Journal of Optimization Theory and Applications, 2021, vol. 190, issue 3, No 9, 915-930

Abstract: Abstract We investigate the convergence properties of a projected neural network for solving inverse variational inequalities. Under standard assumptions, we establish the exponential stability of the proposed neural network. A discrete version of the proposed neural network is considered, leading to a new projection method for solving inverse variational inequalities, for which we obtain the linear convergence. We illustrate the effectiveness of the proposed neural network and its explicit discretization by considering applications in the road pricing problem arising in transportation science. The results obtained in this paper provide a positive answer to a recent open question and improve several recent results in the literature.

Keywords: Dynamic programming; Variational inequality; Neural network; Exponential stability; Road pricing problem; 47J20; 49J40; 65P40 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10957-021-01915-x 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:190:y:2021:i:3:d:10.1007_s10957-021-01915-x

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

DOI: 10.1007/s10957-021-01915-x

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 ().