Approximation to optimization problems: an elementary review
Mathematics of Operations Research
Mathematical Programming: Series A and B
Sample-path optimization of convex stochastic performance functions
Mathematical Programming: Series A and B
Sample-path solution of stochastic variational inequalities, with applications to option pricing
WSC '96 Proceedings of the 28th conference on Winter simulation
Analysis of sample-path optimization
Mathematics of Operations Research
A simulation-based approach to two-stage stochastic programming with recourse
Mathematical Programming: Series A and B
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Evaluating derivatives: principles and techniques of algorithmic differentiation
Evaluating derivatives: principles and techniques of algorithmic differentiation
Mathematical Programs with Complementarity Constraints: Stationarity, Optimality, and Sensitivity
Mathematics of Operations Research
The Sample Average Approximation Method for Stochastic Discrete Optimization
SIAM Journal on Optimization
Stochastic mathematical programs with equilibrium constraints
Operations Research Letters
Solving Stochastic Mathematical Programs with Complementarity Constraints Using Simulation
Mathematics of Operations Research
A hybrid electromagnetism-like algorithm for single machine scheduling problem
Expert Systems with Applications: An International Journal
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Recently, simulation-based methods have been successfully used for solving challenging stochastic optimization problems and equilibrium models. Here we report some of the recent progress we had in broadening the applicability of so-called the sample-path method to include the solution of certain stochastic mathematical programs with equilibrium constraints. We first describe the method and the class of stochastic mathematical programs with complementarity constraints that we are interested in solving and then outline a set of sufficient conditions for its almost-sure convergence. We also illustrate an application of the method to solving a toll pricing problem in transportation networks. These developments also make it possible to solve certain stochastic bilevel optimization problems and Stackelberg games, involving expectations or steady-state functions, using simulation.