DOE OSTI · 2328584
Learning to Branch with Interpretable Machine Learning Models
Abstract
The data consists of a slide deck that was presented at the INFORMS 2023 conference. The presentation summarizes our approach to learning how to branch and compares our approach to the popular solver SCIP and a state-of-the-art ML-based branching rule.
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Bayramoglu, Selin, Nemhauser, George, Sahinidis, Nick. 2023-10-15. Learning to Branch with Interpretable Machine Learning Models. https://www.osti.gov/biblio/2328584
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