On learning and branching: a survey

Andrea Lodi () and Giulia Zarpellon ()
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Andrea Lodi: École Polytechnique de Montréal
Giulia Zarpellon: École Polytechnique de Montréal

TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, 2017, vol. 25, issue 2, No 1, 207-236

Abstract: 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; 90-02; 68R-02; 68T-02 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (27)

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DOI: 10.1007/s11750-017-0451-6

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