A Predefined-Time Neurodynamic Approach with Time-Varying Coefficients for Mixed Variational Inequalities and Applications
Abstract
This paper proposes a predefined-time (PDT) neurodynamic approach with time-varying coefficients for solving mixed variational inequality problems (MVIs). A class of first-order proximal neurodynamic models is developed to guarantee convergence within a user-prescribed time from arbitrary initial conditions. PDT stability is rigorously established via Lyapunov analysis under strong pseudomonotonicity and Lipschitz continuity assumptions, and explicit relationships between convergence time and system parameters are derived. The robustness of the proposed method against bounded disturbances is also analyzed. Applications to composite and minimax optimization problems, together with numerical simulations, demonstrate the effectiveness and fast convergence performance of the proposed framework.
Keywords
Cite
@article{arxiv.2605.02893,
title = {A Predefined-Time Neurodynamic Approach with Time-Varying Coefficients for Mixed Variational Inequalities and Applications},
author = {Vajahat Karim Khan and Md. Kalimuddin Ahmad},
journal= {arXiv preprint arXiv:2605.02893},
year = {2026}
}
Comments
The authors have identified the need for substantial revisions in the theoretical analysis, proofs, and numerical experiments presented in the manuscript. To avoid possible confusion arising from the current preliminary version, the authors request withdrawal of this version