An Extended Validity Domain for Constraint Learning

Yilin Zhu
Corresponding Author
Yilin Zhu
[email protected]
https://orcid.org/0000-0003-0745-5182
Department of Mathematics, University of Iowa, Iowa City, Iowa 52242
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,
Samuel Burer
Samuel Burer
[email protected]
Department of Business Analytics, University of Iowa, Iowa City, Iowa 52242
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Corresponding Author

Yilin Zhu

[email protected]

https://orcid.org/0000-0003-0745-5182

Department of Mathematics, University of Iowa, Iowa City, Iowa 52242

Search for more papers by this author

,

Samuel Burer

[email protected]

Department of Business Analytics, University of Iowa, Iowa City, Iowa 52242

Search for more papers by this author

Published Online:27 Jan 2026https://doi.org/10.1287/ijoc.2024.0815

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ijoc.2024.0815.sm1.pdf

ijoc.2024.0815.cd.zip

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Received:June 20, 2024
Accepted:December 01, 2025
Published Online:January 27, 2026

Copyright © 2026, INFORMS

Cite as

Yilin Zhu, Samuel Burer (2026) An Extended Validity Domain for Constraint Learning. INFORMS Journal on Computing 0(0).

https://doi.org/10.1287/ijoc.2024.0815

Keywords

Acknowledgments

The authors thank Paul Grigas for discussions on the relationship between constraint learning and contextual optimization. The authors are also in debt to the anonymous associate editor and three referees for invaluable suggestions that have greatly improved the paper.