Linear variational inequality is a uniform approach for some important prob
lems in optimization and equilibrium problems. In this paper, we give a neu
ral-network model for solving asymmetric linear variational inequalities. T
he model is based on a simple projection and contraction method. Computer s
imulation is performed for linear programming (LP) and linear complementari
ty problems (LCP), The test results for LP problem demonstrate that our mod
el converges significantly faster than the three existing neural-network mo
dels examined in a recent comparative study paper.