Abstract
The design of strategies for branching in Mixed Integer Programming (MIP) is guided by cycles of parameter tuning and offline experimentation on an extremely heterogeneous testbed, using the average performance. Once devised, these strategies (and their parameter settings) are essentially input-agnostic. To address these issues, we propose a machine learning (ML) framework for variable branching in MIP. Our method observes the decisions made by Strong Branching (SB), a time-consuming strategy that produces small search trees, collecting features that characterize the candidate branching variables at each node of the tree. Based on the collected data, we learn an easy-to-valuate surrogate function that mimics the SB strategy, by means of solving a learning-to-rank problem, common in ML. The learned ranking function is then used for branching. The learning is instance-specific, and is performed on-the-fly while executing a branch-and-bound search to solve the instance. Experiments on benchmark instances indicate that our method produces significantly smaller search trees than existing heuristics, and is competitive with a state-of-the-art commercial solver.
Original language | English |
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Title of host publication | Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence |
Editors | Dale Schuurmans, Michael Wellman |
Place of Publication | Palo Alto, California |
Publisher | Association for the Advancement of Artificial Intelligence (AAAI) |
Pages | 724-731 |
Number of pages | 8 |
ISBN (Electronic) | 9781577357605 |
Publication status | Published - 2016 |
Event | AAAI Conference on Artificial Intelligence 2016 - Phoenix Convention Center, Phoenix, United States of America Duration: 12 Feb 2016 → 17 Feb 2016 Conference number: 30th http://www.aaai.org/Conferences/AAAI/aaai16.php |
Conference
Conference | AAAI Conference on Artificial Intelligence 2016 |
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Abbreviated title | AAAI 2016 |
Country/Territory | United States of America |
City | Phoenix |
Period | 12/02/16 → 17/02/16 |
Internet address |