Mathematical Programming: Series A and B
A new method for a class of linear variational inequalities
Mathematical Programming: Series A and B
Modified Projection-Type Methods for Monotone Variational Inequalities
SIAM Journal on Control and Optimization
A class of iterative methods for solving nonlinear projection equations
Journal of Optimization Theory and Applications
A New Projection Method for Variational Inequality Problems
SIAM Journal on Control and Optimization
A Nonlinear Conjugate Gradient Method with a Strong Global Convergence Property
SIAM Journal on Optimization
Journal of Optimization Theory and Applications
A new modified Goldstein-Levitin-Polyakprojection method for variational inequality problems
Computers & Mathematics with Applications
A neural network model for monotone linear asymmetric variational inequalities
IEEE Transactions on Neural Networks
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This paper presents a new class of projection and contraction methods for solving monotone variational inequality problems. The methods can be viewed as combinations of some existing projection and contraction methods and the method of shortest residuals, a special case of conjugate gradient methods for solving unconstrained nonlinear programming problems. Under mild assumptions, we show the global convergence of the methods. Some preliminary computational results are reported to show the efficiency of the methods.