Title
Branching and bounds tighteningtechniques for non-convex MINLP
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.
Year
DOI
Venue
2009
10.1080/10556780903087124
Optimization Methods and Software
Keywords
DocType
Volume
non-convex minlp,non-linear estimation,convex over-and under-envelopes,extensive test,sbb software package,sbb method,real-world minlp instance,state-of-the-art minlp solver,minlp case,real-world minlp problem,important part,branch and bound
Journal
24
Issue
ISSN
Citations 
4-5
1055-6788
200
PageRank 
References 
Authors
6.57
32
5
Search Limit
100200
Name
Order
Citations
PageRank
Pietro Belotti139321.19
Jon Lee285658.60
Leo Liberti31280105.20
Francois Margot42329.62
Andreas Wachter52006.57