Software for Data-Based Stochastic Programming Using Bootstrap Estimation.

Bibliographic Details
Title:	Software for Data-Based Stochastic Programming Using Bootstrap Estimation.
Authors:	Chen, Xiaotie¹ (AUTHOR) xtjchen@ucdavis.edu, Woodruff, David L.² (AUTHOR) dlwoodruff@ucdavis.edu
Source:	INFORMS Journal on Computing. Nov/Dec2023, Vol. 35 Issue 6, p1218-1224. 7p.
Subject Terms:	Data libraries, Computer software, Software development tools, Confidence intervals, Stochastic programming
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. 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/. [ABSTRACT FROM AUTHOR]
	Copyright of INFORMS Journal on Computing is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Business Source Index

Be the first to leave a comment!