A regularized decomposition method for minimizing a sum of polyhedral functions
Mathematical Programming: Series A and B
Parallel processors for planning under uncertainty
Annals of Operations Research
Statistical verification of optimality conditions for stochastic programs with recourse
Annals of Operations Research
Annals of Operations Research - Special issue on sensitivity analysis and optimization of discrete event systems
Duality and statistical tests of optimality for two stage stochastic programs
Mathematical Programming: Series A and B
A branch and bound method for stochastic global optimization
Mathematical Programming: Series A and B
A fully sequential procedure for indifference-zone selection in simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Computational Optimization and Applications
Stopping Rules for a Class of Sampling-Based Stochastic Programming Algorithms
Operations Research
Variable-sample methods for stochastic optimization
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Assessing solution quality in stochastic programs
Mathematical Programming: Series A and B
Convergence theory for nonconvex stochastic programming with an application to mixed logit
Mathematical Programming: Series A and B
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Discrete Optimization via Simulation Using COMPASS
Operations Research
Sequential sampling for solving stochastic programs
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Efficient sample sizes in stochastic nonlinear programming
Journal of Computational and Applied Mathematics
Monte Carlo bounding techniques for determining solution quality in stochastic programs
Operations Research Letters
The stochastic root-finding problem: Overview, solutions, and open questions
ACM Transactions on Modeling and Computer Simulation (TOMACS)
An introspective on the retrospective-approximation paradigm
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
A combined deterministic and sampling-based sequential bounding method for stochastic programming
Proceedings of the Winter Simulation Conference
Convergence properties of direct search methods for stochastic optimization
Proceedings of the Winter Simulation Conference
On sample size control in sample average approximations for solving smooth stochastic programs
Computational Optimization and Applications
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We develop a sequential sampling procedure for a class of stochastic programs. We assume that a sequence of feasible solutions with an optimal limit point is given as input to our procedure. Such a sequence can be generated by solving a series of sampling problems with increasing sample size, or it can be found by any other viable method. Our procedure estimates the optimality gap of a candidate solution from this sequence. If the point estimate of the optimality gap is sufficiently small according to our termination criterion, then we stop. Otherwise, we repeat with the next candidate solution from the sequence under an increased sample size. We provide conditions under which this procedure (i) terminates with probability one and (ii) terminates with a solution that has a small optimality gap with a prespecified probability.