On solving MINLP problems with nonconvex signomials in SHOT

Andreas Lundell, Jan Kronqvist

Research output: Chapter in Book/Conference proceedingConference contributionScientific


In this abstract, we briefly explain how the mixed-integer nonlinear programming solver SHOT is to be extended with a reformulation framework for nonconvex signomial functions. In the framework, nonconvex terms are convexified using lifting single-variable power and exponential transformations in combination with piecewise linear approximations. This gives a reformulated problem with a convex feasible region that overestimates that of the original nonconvex problem in an extended variable space. The reformulations are implemented using SHOT’s current reformulation functionality. Additionally, an updating mechanism is added to SHOT, which iteratively updates the piecewise linear approximations until the global optimal solution is found.
Original languageEnglish
Title of host publicationJournal of Global Optimization
Subtitle of host publicationSpecial Issue on Global Optimization: HUGO
Number of pages4
Publication statusPublished - Sept 2022
MoE publication typeB3 Non-refereed article in conference proceedings
EventHungarian Global Optimization Workshop HUGO 2022 - Szeged, Hungary
Duration: 5 Sept 20228 Sept 2022

Publication series

NameJournal of global optimization
ISSN (Print)0925-5001
ISSN (Electronic)1573-2916


ConferenceHungarian Global Optimization Workshop HUGO 2022
Abbreviated titleHUGO


  • Supporting hyperplane optimization toolkit (SHOT)
  • Mixed-integer nonlinear programming (MINLP)
  • lifting reformulations
  • signomial functions


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