Primal MINLP Heuristics in a Nutshell

Timo Berthold ()
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Timo Berthold: Zuse Institute Berlin

A chapter in Operations Research Proceedings 2013, 2014, pp 23-28 from Springer

Abstract: Abstract Primal heuristics are an important component of state-of-the-art codes for mixed integer nonlinear programming (MINLP). In this article we give a compact overview of primal heuristics for MINLP that have been suggested in the literature of recent years. We sketch the fundamental concepts of different classes of heuristics and discuss specific implementations. A brief computational experiment shows that primal heuristics play a key role in achieving feasibility and finding good primal bounds within a global MINLP solver.

Keywords: Variable Neighborhood Search; Outer Approximation; Large Neighborhood Search; Projection Step; Mixed Integer Nonlinear Programming (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-319-07001-8_4

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DOI: 10.1007/978-3-319-07001-8_4

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