Integrating nonlinear branch-and-bound and outer approximation for convex Mixed Integer Nonlinear Programming

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 approximatio...

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Vydané v:Journal of global optimization Ročník 60; číslo 2; s. 373 - 389
Hlavní autori: Melo, Wendel, Fampa, Marcia, Raupp, Fernanda
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Boston Springer US 01.10.2014
Springer
Predmet:
Algorithms
Comparative analysis
Computer Science
Mathematics
Mathematics and Statistics
Operations Research/Decision Theory
Optimization
Real Functions
Branch-and-bound
Mixed Integer Nonlinear Programming
Outer approximation
Hybrid algorithm
ISSN:0925-5001, 1573-2916
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Popis
Shrnutí: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.
ISSN:0925-5001
1573-2916
DOI:10.1007/s10898-014-0217-8