On learning and branching: a survey

On learning and branching: a survey

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Article ID: iaor20172949
Volume: 25
Issue: 2
Start Page Number: 207
End Page Number: 236
Publication Date: Jul 2017
Journal: TOP
Authors: ,
Keywords: heuristics, programming: branch and bound, learning, decision, artificial intelligence
Abstract:

This paper surveys learning techniques to deal with the two most crucial decisions in the branch‐and‐bound algorithm for Mixed‐Integer Linear Programming, namely variable and node selections. Because of the lack of deep mathematical understanding on those decisions, the classical and vast literature in the field is inherently based on computational studies and heuristic, often problem‐specific, strategies. We will both interpret some of those early contributions in the light of modern (machine) learning techniques, and give the details of the recent algorithms that instead explicitly incorporate machine learning paradigms.

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