Title
Integrating nonlinear branch-and-bound and outer approximation for convex Mixed Integer Nonlinear Programming
Abstract
In this paper, we present a new hybrid algorithm for convex Mixed Integer Nonlinear Programming (MINLP). The proposed hybrid algorithm is an improved version of the classical nonlinear branch-and-bound (BB) procedure, where the enhancements are obtained with the application of the outer approximation algorithm on some nodes of the enumeration tree. The two methods are combined in such a way that each one collaborates to the convergence of the other. Computational experiments with benchmark instances of the MINLP problem show the good performance of the proposed algorithm, which is compared to the outer approximation algorithm, the nonlinear BB algorithm and the hybrid algorithm implemented in the solver Bonmin.
Year
DOI
Venue
2014
10.1007/s10898-014-0217-8
Journal of Global Optimization
Keywords
Field
DocType
Mixed Integer Nonlinear Programming,Branch-and-bound,Outer approximation,Hybrid algorithm
Convergence (routing),Approximation algorithm,Branch and bound,Mathematical optimization,Nonlinear system,Hybrid algorithm,Enumeration,Regular polygon,Solver,Mathematics
Journal
Volume
Issue
ISSN
60
2
0925-5001
Citations 
PageRank 
References 
6
0.50
9
Authors
3
Name
Order
Citations
PageRank
Wendel Melo1133.02
M. Fampa2301.89
Fernanda M. P. Raupp3295.35