A neural-network model for monotone linear asymmetric variational inequalities

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.