The reformulation-based αGO algorithm for solving nonconvex MINLP problems - some improvements

A4 Conference proceedings

Internal Authors/Editors

Publication Details

List of Authors: Andreas Lundell, Tapio Westerlund
Editors: Sauro Pierucci, Jiří J. Klemeš
Place: Milano
Publication year: 2013
Journal: Chemical Engineering Transactions
Publisher: Associazione Italiana di Ingegneria Chimica
Book title: 11th International Conference on Chemical and Process Engineering - selected papers of ICheaP11
Journal acronym: CHEM ENGINEER TRANS
Volume number: 32
Start page: 1321
End page: 1326
Number of pages: 6
ISBN: 978-88-95608-23-5
ISSN: 1974-9791
eISSN: 2283-9216


The alpha-reformulation (alpha R) technique can be used to transform any nonconvex twice-differentiable mixed-integer nonlinear programming problem to a convex relaxed form. By adding a quadratic function to the nonconvex function it is possible to convexify it, and by subtracting a piecewise linearization of the added function a convex underestimator will be obtained. This reformulation technique is implemented in the a global optimization (alpha GO) algorithm solving the specified problem type to global optimality as a sequence of reformulated subproblems where the piecewise linear functions are refined in each step. The tightness of the underestimator has a large impact on the efficiency of the solution process, and in this paper it is shown how it is possible to reduce the approximation error by utilizing a piecewise quadratic spline function defined on smaller subintervals. The improved underestimator is also applied to test problems illustrating its performance.

Last updated on 2019-16-06 at 04:30