Journal of Global Optimization
Matrix games in the multicast networks: maximum information flows with network switching
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
A novel neural network for a class of convex quadratic minimax problems
Neural Computation
A new class of projection and contraction methods for solving variational inequality problems
Computers & Mathematics with Applications
Neural networks for a class of bi-level variational inequalities
Journal of Global Optimization
A delayed projection neural network for solving linear variational inequalities
IEEE Transactions on Neural Networks
A discrete-time neural network for optimization problems with hybrid constraints
IEEE Transactions on Neural Networks
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
Robotics and Computer-Integrated Manufacturing
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A linear variational inequality is a uniform approach for some important problems in optimization and equilibrium problems. We give a neural network model for solving asymmetric linear variational inequalities. The model is based on a simple projection and contraction method. Computer simulation is performed for linear programming (LP) and linear complementarity problems (LCP). The test results for the LP problem demonstrate that our model converges significantly faster than the three existing neural network models examined in a comparative study paper