A novel neural network for a class of convex quadratic minimax problems

Authors:
Xing-Bao Gao;Li-Zhi Liao
Affiliations:
College of Mathematics and Information Science, Shaanxi Normal University, Xi'an, Shaanxi, China;Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
Venue:
Neural Computation
Year:
2006

Citing 14
Cited 10

Linear-quadratic programming and optimal control

SIAM Journal on Control and Optimization
Equivalent differentiable optimization problems and descent methods for asymmetric variational inequality problems

Mathematical Programming: Series A and B
Modified Projection-Type Methods for Monotone Variational Inequalities

SIAM Journal on Control and Optimization
A Modified Forward-Backward Splitting Method for Maximal Monotone Mappings

SIAM Journal on Control and Optimization
Iterative solution of nonlinear equations in several variables

Iterative solution of nonlinear equations in several variables
On the stability of globally projected dynamical systems

Journal of Optimization Theory and Applications
Stability Analysis of Gradient-Based Neural Networks for Optimization Problems

Journal of Global Optimization
A recurrent neural network with exponential convergence for solving convex quadratic program and related linear piecewise equations

Neural Networks
An Extended Projection Neural Network for Constrained Optimization

Neural Computation
A general methodology for designing globally convergent optimization neural networks

IEEE Transactions on Neural Networks
A neural network model for monotone linear asymmetric variational inequalities

IEEE Transactions on Neural Networks
Exponential stability of globally projected dynamic systems

IEEE Transactions on Neural Networks
A novel neural network for nonlinear convex programming

IEEE Transactions on Neural Networks
A neural network for a class of convex quadratic minimax problems with constraints

IEEE Transactions on Neural Networks

Robust Model Predictive Control Using a Discrete-Time Recurrent Neural Network

ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Applications of the general projection neural network in solving extended linear-quadratic programming problems with linear constraints

Neurocomputing
A project neural network for solving degenerate quadratic minimax problem with linear constraints

Neurocomputing
A new projection-based neural network for constrained variational inequalities

IEEE Transactions on Neural Networks
A new recurrent neural network for solving convex quadratic programming problems with an application to the k-winners-take-all problem

IEEE Transactions on Neural Networks
A new one-layer neural network for linear and quadratic programming

IEEE Transactions on Neural Networks
Design of recurrent neural networks for solving constrained least absolute deviation problems

IEEE Transactions on Neural Networks
A novel recurrent neural network with finite-time convergence for linear programming

Neural Computation
A dynamical model for solving degenerate quadratic minimax problems with constraints

Journal of Computational and Applied Mathematics
Efficient neural networks for solving variational inequalities

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Based on the inherent properties of convex quadratic minimax problems, this article presents a new neural network model for a class of convex quadratic minimax problems. We show that the new model is stable in the sense of Lyapunov and will converge to an exact saddle point in finite time by defining a proper convex energy function. Furthermore, global exponential stability of the new model is shown under mild conditions. Compared with the existing neural networks for the convex quadratic minimax problem, the proposed neural network has finite-time convergence, a simpler structure, and lower complexity. Thus, the proposed neural network is more suitable for parallel implementation by using simple hardware units. The validity and transient behavior of the proposed neural network are illustrated by some simulation results.