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Software for Data-Based Stochastic Programming Using Bootstrap Estimation

Author

Listed:
  • Xiaotie Chen

    (Department of Mathematics, University of California Davis, Davis, California 95616)

  • David L. Woodruff

    (Graduate School of Management, University of Davis, Davis, California 95616)

Abstract

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.

Suggested Citation

  • Xiaotie Chen & David L. Woodruff, 2023. "Software for Data-Based Stochastic Programming Using Bootstrap Estimation," INFORMS Journal on Computing, INFORMS, vol. 35(6), pages 1218-1224, November.
    • Handle: RePEc:inm:orijoc:v:35:y:2023:i:6:p:1218-1224
    DOI: 10.1287/ijoc.2022.0253
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    References listed on IDEAS

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    1. Alexander Shapiro, 2003. "Inference of statistical bounds for multistage stochastic programming problems," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 58(1), pages 57-68, September.
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