Mathematical Programming: Series A and B
USSR Computational Mathematics and Mathematical Physics
A globally convergent Newton method for solving strongly monotone variational inequalities
Mathematical Programming: Series A and B
Journal of Optimization Theory and Applications
A note on a globally convergent Newton method for solving monotone variational inequalities
Operations Research Letters
A self-adaptive projection method with improved step-size for solving variational inequalities
Computers & Mathematics with Applications
A new class of projection and contraction methods for solving variational inequality problems
Computers & Mathematics with Applications
A self-adaptive gradient projection algorithm for the nonadditive traffic equilibrium problem
Computers and Operations Research
Some projection methods with the BB step sizes for variational inequalities
Journal of Computational and Applied Mathematics
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In this paper, we first show that the adjustment parameter in the step size choice strategy of the modified Goldstein-Levitin-Polyak projection method proposed by He et al. for asymmetric strongly monotone variational inequality problems can be bounded away from zero by a positive constant. Under this observation, we propose a new step size rule which seems to be more practical and robust than the original one. We show that the new modified method is globally convergent under the same conditions and report some computational results to illustrate the method.