On data-driven chance constraint learning for mixed-integer optimization problems
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- Rockafellar, R.T. & Royset, J.O., 2010. "On buffered failure probability in design and optimization of structures," Reliability Engineering and System Safety, Elsevier, vol. 95(5), pages 499-510.
- Dimitris Bertsimas & Nathan Kallus, 2020. "From Predictive to Prescriptive Analytics," Management Science, INFORMS, vol. 66(3), pages 1025-1044, March.
- William E. Hart & Carl D. Laird & Jean-Paul Watson & David L. Woodruff & Gabriel A. Hackebeil & Bethany L. Nicholson & John D. Siirola, 2017. "Pyomo — Optimization Modeling in Python," Springer Optimization and Its Applications, Springer, edition 2, number 978-3-319-58821-6, June.
- Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
- R. Tyrrell Rockafellar & Stan Uryasev & Michael Zabarankin, 2008. "Risk Tuning with Generalized Linear Regression," Mathematics of Operations Research, INFORMS, vol. 33(3), pages 712-729, August.
- So Yeon Chun & Alexander Shapiro & Stan Uryasev, 2012.
"Conditional Value-at-Risk and Average Value-at-Risk: Estimation and Asymptotics,"
Operations Research, INFORMS, vol. 60(4), pages 739-756, August.
- Chun, So Yeon & Shapiro, Alexander & Uryasev, Stan, 2011. "Conditional Value-at-Risk and Average Value-at-Risk: Estimation and Asymptotics," MPRA Paper 30132, University Library of Munich, Germany.
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Keywords
Chance Constraint;NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2022-08-15 (Big Data)
- NEP-CMP-2022-08-15 (Computational Economics)
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