Integrality gap minimization heuristics for binary mixed integer nonlinear programming

Wendel Melo (), Marcia Fampa () and Fernanda Raupp ()
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Wendel Melo: Federal University of Uberlandia
Marcia Fampa: Federal University of Rio de Janeiro
Fernanda Raupp: National Laboratory for Scientific Computing (LNCC) of the Ministry of Science, Technology and Innovation

Journal of Global Optimization, 2018, vol. 71, issue 3, No 10, 593-612

Abstract: 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.

Keywords: Binary mixed integer nonlinear programming; Heuristics; Integrality gap minimization; Solver (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10898-018-0623-4

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