A proximal neurodynamic model for solving inverse mixed variational inequalities
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 1-9 |
| Journal / Publication | Neural Networks |
| Volume | 138 |
| Online published | 27 Jan 2021 |
| Publication status | Published - Jun 2021 |
Link(s)
Abstract
This paper proposes a proximal neurodynamic model (PNDM) for solving inverse mixed variational inequalities (IMVIs) based on the proximal operator. It is shown that the PNDM has a unique continuous solution under the condition of Lipschitz continuity (L-continuity). It is also shown that the equilibrium point of the proposed PNDM is asymptotically stable or exponentially stable under some mild conditions. Finally, three numerical examples are presented to illustrate effectiveness of the proposed PNDM.
Research Area(s)
- Exponential stability, Inverse mixed variational inequalities, Lipschitz continuous, Proximal neurodynamic model, Strong monotonicity
Citation Format(s)
A proximal neurodynamic model for solving inverse mixed variational inequalities. / Ju, Xingxing; Li, Chuandong; He, Xing et al.
In: Neural Networks, Vol. 138, 06.2021, p. 1-9.
In: Neural Networks, Vol. 138, 06.2021, p. 1-9.
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
