 Solving mathematical programs with vanishing constraints using a gradient-based neural network modelAnjali Rawat, Vinay Singh and Alireza Nazemi Mathematics and Computers in Simulation (MATCOM), 2026, vol. 239, issue C, 1097-1116 Abstract: In this paper, a gradient-based neural network model is applied to successfully determine the solution of a class of nonlinear optimization problems known as Mathematical Programs with Vanishing Constraints (MPVC). First, a smoothing and regularization technique is used to transform the original MPVC problem into a smooth and relaxed nonlinear optimization problem. Then, by utilizing the penalty function method and a gradient-based neural network model, the optimal solution of the transformed nonlinear problem is estimated. The proposed model has a straightforward structure and low computing cost. Theoretical investigation of the neural network has shown that the equilibrium of the suggested network is asymptotically stable and converges to the optimal solution of the original MPVC. Simulation results provide additional evidence that supports the theoretical analysis and confirms the computational efficiency of the suggested network. Keywords: Vanishing constraints; Nonlinear dynamic scheme; Penalty method; Gradient neural network; Stationary points; Smoothing method; Asymptotic stability; 4-bar truss problem (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:239:y:2026:i:c:p:1097-1116 DOI: 10.1016/j.matcom.2025.07.065 Access Statistics for this article Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier |
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