Strengthening the sequential convex MINLP technique by perspective reformulations

  • Claudia D’AmbrosioEmail author
  • Antonio Frangioni
  • Claudio Gentile
Original Paper


The sequential convex MINLP (SC-MINLP) technique is a solution method for nonconvex mixed-integer nonlinear problems (MINLPs) where the nonconvexities are separable. It is based on solving a sequence of convex MINLPs which trade a better and better relaxation of the nonconvex part of the problem with the introduction of more and more piecewise-linear nonconvex terms, and therefore binary variables. The convex MINLPs are obtained by partitioning the domain of each separable nonconvex term in the intervals in which it is convex and those in which it is concave. In the former, the term is left in its original form, while in the latter it is piecewise-linearized. Since each interval corresponds to a semi-continuous variable, we propose to modify the convex terms using the Perspective Reformulation technique to strengthen the bounds. We show by means of experimental results on different classes of instances that doing so significantly decreases the solution time of the convex MINLPs, which is the most time consuming part of the approach, and has therefore the potential to improving the overall effectiveness of SC-MINLP.


Global optimization Nonconvex separable functions Sequential convex MINLP technique Perspective reformulation 



The second and the third authors are partially supported by the Project MIUR-PRIN 2015B5F27W. This paper has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 764759.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.LIX UMR 7161, École PolytechniquePalaiseauFrance
  2. 2.Dipartimento di InformaticaUniversità di PisaPisaItaly
  3. 3.Istituto di Analisi dei Sistemi ed Informatica “Antonio Ruberti”, Consiglio Nazionale delle RicercheRomeItaly

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