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

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

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Article ID: iaor2000527
Country: United States
Volume: 46
Issue: 5
Start Page Number: 710
End Page Number: 718
Publication Date: Sep 1998
Journal: Operations Research
Authors:
Keywords: statistics: sampling
Abstract:

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.

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