Mathematical Programming: Series A and B
A general descent framework for the monotone variational inequality problem
Mathematical Programming: Series A and B
Unconstrained optimization reformulations of variational inequality problems
Journal of Optimization Theory and Applications
Equivalent Unconstrained Minimization and Global Error Bounds for Variational Inequality Problems
SIAM Journal on Control and Optimization
Mathematical Programming: Series A and B
Collusive game solutions via optimization
Mathematical Programming: Series A and B
Error Bounds of Regularized Gap Functions for Nonsmooth Variational Inequality Problems
Mathematical Programming: Series A and B
SIAM Journal on Optimization
On the Asymptotically Well Behaved Functions and Global Error Bound for Convex Polynomials
SIAM Journal on Optimization
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Solving a variational inequality problem can be equivalently reformulated into solving a unconstraint optimization problem where the corresponding objective function is called a merit function. An important class of merit function is the generalized D-gap function introduced in [N. Yamashita, K. Taji, M. Fukushima, Unconstrained optimization reformulations of variational inequality problems, J. Optim. Theory Appl. 92 (1997) 439-456] and Yamashita and Fukushima (1997) [17]. In this paper, we present new fractional local/global error bound results for the generalized D-gap functions of nonsmooth variational inequality problems, which gives an effective estimate on the distance between a specific point to the solution set, in terms of the corresponding function value of the generalized D-gap function. Numerical examples and a simple application to the free boundary problem are also presented to illustrate the significance of our error bound results.