A neural network model for monotone linear asymmetric variational inequalities

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.