Convergence Rate Analysis of Prediction Correction Methods for a Class of Variational Inequalities with Linear Constraints
This paper considers a class of variational inequalities with linear constraints:finding x∗∈Ω,such that F(x∗)T(x-x∗)≥0,∀x∈Ω,where Ω={x∈Rn|Ax≤b,x∈K},A∈Rm×n,b∈Rm,K is a simple nonempty closed convex subset of Rn,F is a continuous unknown mapping from Rn toRn,and satisfies the strong monotonicity.We study a new prediction correction method for this class of problems.Based on the previous convergence results,we further analyze the linear convergence by using the error bound condition.Finally,two numerical results in traffic equilibrium problems with linear constraints demonstrate the effectiveness of the algorithm.
linear constraintsvariational inequalitiesglobal linear convergenceprediction correction method
万方数据
维普
