On data-driven chance constraint learning for mixed-integer optimization problems

DOI10.1016/j.apm.2023.04.032zbMath1525.90286arXiv2207.03844OpenAlexW4367187014MaRDI QIDQ6072771

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Publication date: 13 October 2023

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2207.03844

zbMATH Keywords

machine learningquantile estimationchance constraintconstraint learningdata-driven optimization

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Stochastic programming (90C15)

Cites Work

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• Learning Multiple Quantiles With Neural Networks
• Solving Chance-Constrained Problems via a Smooth Sample-Based Nonlinear Approximation
• Strong mixed-integer programming formulations for trained neural networks
• Pyomo -- optimization modeling in Python
• Stochastic gradient boosting.

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