A partitioning algorithm for the mixed integer nonlinear programming problem

Biket Ergüneş, Linet Özdamar, Onur Demir and Nur Gülcan

International Journal of Operational Research, 2017, vol. 28, issue 2, 201-215

Abstract: An interval partitioning method (IPM) is proposed to solve the (non-convex) mixed integer nonlinear programming problem (MINLP). The MINLP is encountered in many application areas and solving this problem bears practical importance. This paper proposes an IPM where two tree search strategies (breadth first and mixed breadth/depth first) and three variable subdivision methods are implemented. Two proposed variable subdivision methods are novel and they prioritise variables hierarchically according to several features. The IPM is implemented on a set of non-convex MINLP instances extracted from the MINLP benchmarks and numerical results show that its performance is quite promising.

Keywords: mixed integer nonlinear programming; MINLP; interval partitioning; global optimisation; variable subdivision rules; tree search. (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=81469 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijores:v:28:y:2017:i:2:p:201-215

Access Statistics for this article

More articles in International Journal of Operational Research from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().