Finding an optimized set of transformations for convexifying nonconvex MINLP problems

A4 Conference proceedings

Internal Authors/Editors

Publication Details

List of Authors: Lundell, Andreas, Westerlund, Tapio
Editors: Iftekhar A Karimi, Rajagopalan Srinivasan
Place: Amsterdam
Publication year: 2012
Journal: Computer Aided Chemical Engineering
Publisher: Elsevier
Book title: 11th International Symposium on Process Systems Engineering
Journal acronym: COMPUT-AIDED CHEM EN
Title of series: European Symposium on Computer Aided Process Engineering
Volume number: 31
Start page: 1497
End page: 1501
Number of pages: 5
ISBN: 978-0-444-59505-8
ISSN: 1570-7946


In this paper we describe a method for obtaining sets of transformations for reformulating a mixed integer nonlinear programming (MINLP) problem containing nonconvex twice-differentiable (C-2) functions to a convex MINLP problem in an extended variable space. The method for obtaining the transformations is based on solving a mixed integer linear programming (MILP) problem given the structure of the nonconvex MINLP problem. The solution of the MILP problem renders a minimal set of transformations convexifying the nonconvex problem. This technique is implemented as an part of the alpha signomial global optimization algorithm (alpha SGO), a global optimization algorithm for nonconvex MINLP problems.


global optimization, nonconvex MINLP problems, reformulation techniques, signomial functions

Last updated on 2020-23-02 at 04:33