Critical sets in parametric optimization
Mathematical Programming: Series A and B
A nonsmooth version of Newton's method
Mathematical Programming: Series A and B
Mathematics of Operations Research
Semismooth Newton Methods for Solving Semi-Infinite Programming Problems
Journal of Global Optimization
BI-Level Strategies in Semi-Infinite Programming
BI-Level Strategies in Semi-Infinite Programming
Nonsmooth Equations in Optimization: Regularity, Calculus, Methods and Applications (Nonconvex Optimization and Its Applications)
The Adaptive Convexification Algorithm: A Feasible Point Method for Semi-Infinite Programming
SIAM Journal on Optimization
Generalized semi-infinite programming: A tutorial
Journal of Computational and Applied Mathematics
Robust solutions of uncertain linear programs
Operations Research Letters
A new smoothing Newton-type algorithm for semi-infinite programming
Journal of Global Optimization
SIAM Journal on Optimization
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We study convergence of a semismooth Newton method for generalized semi-infinite programming problems with convex lower level problems where, using NCP functions, the upper and lower level Karush-Kuhn-Tucker conditions of the optimization problem are reformulated as a semismooth system of equations. Nonsmoothness is caused by a possible violation of strict complementarity slackness. We show that the standard regularity condition for convergence of the semismooth Newton method is satisfied under natural assumptions for semi-infinite programs. In fact, under the Reduction Ansatz in the lower level and strong stability in the reduced upper level problem this regularity condition is satisfied. In particular, we do not have to assume strict complementary slackness in the upper level. Numerical examples from, among others, design centering and robust optimization illustrate the performance of the method.