2004
DOI: 10.1137/s1052623403421486
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Weak Sharp Solutions of Variational Inequalities in Hilbert Spaces

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Cited by 39 publications
(23 citation statements)
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“…They characterized the weak sharp solutions for variational inequalities by minimum principle sufficiency property under the condition that the underlying mapping is pseudomonotone + and continuous on the compact polyhedral set. Wu and Wu [8] extended the results of Marcotte and Zhu [7] under a different assumption. They also introduced the concept of maximum principle sufficiency property for variational inequalities.…”
Section: Introductionmentioning
confidence: 73%
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“…They characterized the weak sharp solutions for variational inequalities by minimum principle sufficiency property under the condition that the underlying mapping is pseudomonotone + and continuous on the compact polyhedral set. Wu and Wu [8] extended the results of Marcotte and Zhu [7] under a different assumption. They also introduced the concept of maximum principle sufficiency property for variational inequalities.…”
Section: Introductionmentioning
confidence: 73%
“…We consider the following condition which is the extension of a similar condition considered by Wu and Wu [8].…”
Section: Formulations and Preliminariesmentioning
confidence: 99%
See 1 more Smart Citation
“…Zhang et al [16] have also obtained relevant results by studying the dual gap function for a variational inequality. Wu and Wu [13] have further presented several equivalent conditions for the weak sharpness of C * in terms of an error bound of G in a Hilbert space. Hu and Song [11] have extended the results of weak sharpness for the solutions of VIP(C, F) under the conditions that F is pseudomonotone and continuous and G is Gâteaux differentiable and Lipschitz in a Banach space.…”
Section: Introductionmentioning
confidence: 99%
“…We note that several results of [8,11,13,15] have been obtained by utilizing the dual gap function G while g is seldom used. Although G is convex, it is more complicated to compute its values than that of g since for a fixed point x, G(x) is the maximum of a nonlinear program usually while g(x) is that of a linear program.…”
Section: Introductionmentioning
confidence: 99%