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Heuristics for convex mixed integer nonlinear programs

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Abstract

In this paper, we describe the implementation of some heuristics for convex mixed integer nonlinear programs. The work focuses on three families of heuristics that have been successfully used for mixed integer linear programs: diving heuristics, the Feasibility Pump, and Relaxation Induced Neighborhood Search (RINS). We show how these heuristics can be adapted in the context of mixed integer nonlinear programming. We present results from computational experiments on a set of instances that show how the heuristics implemented help finding feasible solutions faster than the traditional branch-and-bound algorithm and how they help in reducing the total solution time of the branch-and-bound algorithm.

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Authors and Affiliations

  1. Laboratoire d’Informatique Fondamentale de Marseille, CNRS-Aix-Marseille Universités, Marseille, France

    Pierre Bonami

  2. IBM T.J. Watson Research Center, Yorktown Heights, NY, 10598, USA

    João P. M. Gonçalves

Authors
  1. Pierre Bonami
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  2. João P. M. Gonçalves
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Corresponding author

Correspondence to João P. M. Gonçalves.

Additional information

Supported by ANR grant BLAN06-1-138894.

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Bonami, P., Gonçalves, J.P.M. Heuristics for convex mixed integer nonlinear programs. Comput Optim Appl 51, 729–747 (2012). https://doi.org/10.1007/s10589-010-9350-6

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