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Branching and bounds tighteningtechniques for non-convex MINLP
- DOI:
- 10.1080/10556780903087124
pages 597-634
Available online: 07 Aug 2009Abstract
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.
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- Citation information:
- Available online: 07 Aug 2009
Author affiliations
- a Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, PA, USA
- b IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
- c LIX, École Polytechnique, 91128, Palaiseau, France
- d Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA, USA
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