Stopping rules for a class of sampling-based stochastic programming algorithms

David P. Morton*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)710-718
Number of pages9
JournalOperations Research
Issue number5
StatePublished - 1998

ASJC Scopus subject areas

  • Computer Science Applications
  • Management Science and Operations Research


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