Exact penalty functions in constrained optimization
SIAM Journal on Control and Optimization
An exact penalization viewpoint of constrained optimization
SIAM Journal on Control and Optimization
Asymptotic analysis of stochastic programs
Annals of Operations Research
Analysis of sample-path optimization
Mathematics of Operations Research
Optimality Conditions for a Class of Mathematical Programs with Equilibrium Constraints
Mathematics of Operations Research
SIAM Journal on Optimization
Exact Penalization and Necessary Optimality Conditions for Generalized Bilevel Programming Problems
SIAM Journal on Optimization
SIAM Journal on Control and Optimization
Quantitative Stability in Stochastic Programming: The Method of Probability Metrics
Mathematics of Operations Research
Solving Stochastic Mathematical Programs with Complementarity Constraints Using Simulation
Mathematics of Operations Research
Mathematical Programming: Series A and B - Nonlinear convex optimization and variational inequalities
Mathematics of Operations Research
Stochastic mathematical programs with equilibrium constraints
Operations Research Letters
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This paper considers a one-stage stochastic mathematical program with a complementarity constraint (SMPCC), where uncertainties appear in both the objective function and the complementarity constraint, and an optimal decision on both upper-and lower-level decision variables must be made before the realization of the uncertainties. A partially exactly penalized sample average approximation (SAA) scheme is proposed to solve the problem. Asymptotic convergence of optimal solutions and stationary points of the penalized SAA problem is carried out. It is shown under some moderate conditions that the statistical estimators obtained from solving the penalized SAA problems converge almost surely to its true counterpart as the sample size increases. Exponential rate of convergence of estimators is also established under some additional conditions.