Further results on an abstract model for branching and its application to mixed integer programming

Daniel Anderson, Pierre Le Bodic, Kerri Morgan

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Abstract

A key ingredient in branch and bound (B&B) solvers for mixed-integer programming (MIP) is the selection of branching variables since poor or arbitrary selection can affect the size of the resulting search trees by orders of magnitude. A recent article by Le Bodic and Nemhauser (Math Program 166(1–2):369–405, 2017) investigated variable selection rules by developing a theoretical model of B&B trees from which they developed some new, effective scoring functions for MIP solvers. In their work, Le Bodic and Nemhauser left several open theoretical problems, solutions to which could guide the future design of variable selection rules. In this article, we first solve many of these open theoretical problems. We then implement an improved version of the model-based branching rules in SCIP 6.0, a state-of-the-art academic MIP solver, in which we observe an 11 % geometric average time and node reduction on instances of the MIPLIB 2017 Benchmark Set that require large B&B trees.

Original languageEnglish
Pages (from-to)811-841
Number of pages31
JournalMathematical Programming
Volume190
DOIs
Publication statusPublished - Nov 2021

Keywords

  • Algorithm analysis
  • Branch and bound
  • Branching rules
  • Mixed-integer programming

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