Feb 21, 2014 · In this paper, we study a gradient-based neural network method for solving strictly convex quadratic programming (SCQP) problems.

In this paper, we study a gradient-based neural network method for solving strictly convex quadratic programming (SCQP) problems. By converting the SCQP problem ...

Oct 22, 2024 · In this paper, we study a gradient-based neural network method for solving strictly convex quadratic programming (SCQP) problems.

A new gradient-based neural network is constructed on the basis of the duality theory, optimization theory, convex analysis theory, Lyapunov stability ...

This paper presents a capable neural network for solving strictly convex quadratic programming (SCQP) problems with general linear constraints.

A Gradient-Based Neural Network Method for Solving Strictly Convex Quadratic Programming Problems. Alireza Nazemi. Alireza Nazemi. Masoomeh Nazemi.

Ai W, Song YJ, Chen YP. An improved neural network for solving optimization of quadratic programming problems. In: Proceedings of the fifth international ...

In this paper, we apply a gradient neural network model to efficiently solve the convex second-order cone constrained variational inequality problem.

A new gradient-based neural network is constructed on the basis of the duality theory, optimization theory, convex analysis theory, Lyapunov stability ...

This paper proposes a neural network model for solving convex quadratic programming (CQP) problems, whose equilibrium points coincide with ...