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Measuring the Impact of Branching Rules for Mixed-Integer Programming

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Operations Research Proceedings 2017

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Abstract

Branching rules are an integral component of the branch-and-bound algorithm typically used to solve mixed-integer programs and subject to intense research. Different approaches for branching are typically compared based on the solving time as well as the size of the branch-and-bound tree needed to prove optimality. The latter, however, has some flaws when it comes to sophisticated branching rules that do not only try to take a good branching decision, but have additional side-effects. We propose a new measure for the quality of a branching rule that distinguishes tree size reductions obtained by better branching decisions from those obtained by such side-effects. It is evaluated for common branching rules providing new insights in the importance of strong branching.

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References

  1. Achterberg, T. (2007). Constraint Integer Programming. Ph.D. thesis. Technische Universität, Berlin.

    Google Scholar 

  2. Achterberg, T., & Berthold, T. (2009). Hybrid Branching. In W. J. van Hoeve & J. N. Hooker (Eds.), CPAIOR 2009 (Vol. 5547, pp. 309–311). LNCS, Springer.

    Chapter  Google Scholar 

  3. Achterberg, T., Koch, T., & Martin, A. (2005). Branching rules revisited. Operations Research Letters, 33, 42–54.

    Article  Google Scholar 

  4. Benichou, M., et al. (1971). Experiments in mixed-integer linear programming. Mathematical Programming, 1, 76–94.

    Article  Google Scholar 

  5. COR@L MIP Instances. Accessed June 2017. http://coral.ise.lehigh.edu/data-sets/mixedinteger-instances/.

  6. Gamrath, G. (2014). Improving strong branching by domain propagation. EURO Journal on Computational Optimization, 2(3), 99–122.

    Article  Google Scholar 

  7. Gauthier, J. -M., & Ribière, G. (1977). Experiments in mixed-integer linear programmingusing pseudo-costs. Math Prog, 12(1), 26–47.

    Article  Google Scholar 

  8. Koch, T., et al. (2011). MIPLIB 2010. Mathematical Programming Computation, 3(2), 103–163.

    Article  Google Scholar 

  9. Land, A. H., & Doig, A. G. (1960). An automatic method of solving discrete programming problems. Econometrica, 28(3), 497–520.

    Article  Google Scholar 

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Acknowledgements

The work for this article has been conducted within the Research Campus Modal funded by the German Federal Ministry of Education and Research (fund number 05M14ZAM).

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Correspondence to Gerald Gamrath .

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Gamrath, G., Schubert, C. (2018). Measuring the Impact of Branching Rules for Mixed-Integer Programming. In: Kliewer, N., Ehmke, J., Borndörfer, R. (eds) Operations Research Proceedings 2017. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-89920-6_23

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