Sequential sampling for solving stochastic programs
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
A Sequential Sampling Procedure for Stochastic Programming
Operations Research
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Monte Carlo sampling-based algorithms hold much promise for solving stochastic programs with many scenarios. A critical component of such algorithms is a stopping criterion to ensure the quality of the solution. In this paper, we develop a stopping rule theory for a class of algorithms that estimate bounds on the optimal objective function value by sampling. We provide rules for selecting sample sizes and terminating the algorithm under which asymptotic validity of confidence intervals for the quality of the proposed solution can be verified. Empirical coverage results are given for a simple example.