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- Publisher
- Taylor & Francis
- Copyright
- Copyright Taylor & Francis Group, LLC
- ISSN
- 1029-4937
- eISSN
- 1055-6788
- DOI
- 10.1080/10556780903087124
- Publisher site
- See Article on Publisher Site
Abstract
Many industrial problems can be naturally formulated using mixed integer non-linear programming (MINLP) models and can be solved by spatial Branch&Bound (sBB) techniques. We study the impact of two important parts of sBB methods: bounds tightening (BT) and branching strategies. We extend a branching technique originally developed for MILP, reliability branching, to the MINLP case. Motivated by the demand for open-source solvers for real-world MINLP problems, we have developed an sBB software package named couenne (Convex Over- and Under-ENvelopes for Non-linear Estimation) and used it for extensive tests on several combinations of BT and branching techniques on a set of publicly available and real-world MINLP instances. We also compare the performance of couenne with a state-of-the-art MINLP solver.
Journal
Optimization Methods and Software – Taylor & Francis
Published: Oct 1, 2009
Keywords: mixed-integer non-linear programming; Couenne; branching rules; bounds tightening; 90C11; 90C26; 90C57