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, Volume 25, Issue 2, pp 207–236 | Cite as

On learning and branching: a survey

Invited Paper

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.

Keywords

Branch and bound Machine learning 

Mathematics Subject Classification

90-02 68R-02 68T-02 

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Copyright information

© Sociedad de Estadística e Investigación Operativa 2017

Authors and Affiliations

  1. 1.Canada Excellence Research ChairÉcole Polytechnique de MontréalMontréalCanada

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