Software for Data-Based Stochastic Programming Using Bootstrap Estimation

INFORMS Journal on Computing(2023)

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摘要
We describe software for stochastic programming that uses only sampled data to obtain both a consistent sample-average solution and a consistent estimate of confidence intervals for the optimality gap using bootstrap and bagging. The underlying distribution whence the samples come is not required. History: Accepted by Ted Ralphs, Area Editor for Software Tools. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0253 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0253 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
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关键词
stochastic programming,estimation,data-based
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