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Integrality gap minimization heuristics for binary mixed integer nonlinear programming

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Abstract

We present two feasibility heuristics for binary mixed integer nonlinear programming. Called integrality gap minimization algorithm (IGMA)—versions 1 and 2, our heuristics are based on the solution of integrality gap minimization problems with a space partitioning scheme defined over the integer variables of the problem addressed. Computational results on a set of benchmark instances show that the proposed approaches present satisfactory results.

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Authors and Affiliations

  1. College of Computer Science, Federal University of Uberlandia, Uberlândia, Brazil

    Wendel Melo

  2. Institute of Mathematics and COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    Marcia Fampa

  3. National Laboratory for Scientific Computing (LNCC) of the Ministry of Science, Technology and Innovation, Petrópolis, Brazil

    Fernanda Raupp

Authors
  1. Wendel Melo
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  2. Marcia Fampa
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  3. Fernanda Raupp
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Correspondence to Wendel Melo.

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Melo, W., Fampa, M. & Raupp, F. Integrality gap minimization heuristics for binary mixed integer nonlinear programming. J Glob Optim 71, 593–612 (2018). https://doi.org/10.1007/s10898-018-0623-4

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