Matheuristics pp 231-244 | Cite as

A Good Recipe for Solving MINLPs

  • Leo Liberti
  • Giacomo Nannicini
  • Nenad Mladenović
Part of the Annals of Information Systems book series (AOIS, volume 10)


Finding good (or even just feasible) solutions for Mixed-Integer Nonlinear Programming problems independently of the specific problem structure is a very hard but practically useful task, especially when the objective and/or the constraints are nonconvex. We present a general-purpose heuristic based on Variable Neighbourhood Search, Local Bran-ching, Sequential Quadratic Programming and Branch-and-Bound. We test the proposed approach on the MINLPLib, discussing optimality, reliability and speed.


Sequential Quadratic Programming Variable Neighbourhood Mixed Integer Nonlinear Program Sequential Quadratic Programming Method Sequential Quadratic Programming Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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We are very grateful to Prof. Tapio Westerlund for carefully checking all the computational results and informing us of some misprints on the MINLPLib website.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Leo Liberti
    • 1
  • Giacomo Nannicini
    • 1
  • Nenad Mladenović
    • 2
    • 3
  1. 1.LIX, École PolytechniquePalaiseauFrance
  2. 2.Brunel UniversityLondonUK
  3. 3.Institute of Mathematics, Academy of SciencesBelgradeSerbia

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