@inproceedings{509450dde384475f8aba7950c6408298,
title = "On solving MINLP problems with nonconvex signomials in SHOT",
abstract = "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{\textquoteright}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.",
keywords = "Supporting hyperplane optimization toolkit (SHOT), Mixed-integer nonlinear programming (MINLP), lifting reformulations, signomial functions",
author = "Andreas Lundell and Jan Kronqvist",
note = "Ej {\"a}nnu publicerat?; Hungarian Global Optimization Workshop HUGO 2022, HUGO ; Conference date: 05-09-2022 Through 08-09-2022",
year = "2022",
month = sep,
language = "English",
series = "Journal of global optimization",
publisher = "Springer",
pages = "129--132",
booktitle = "Journal of Global Optimization",
}