A Branch and Bound algorithm for general mixed-integer quadratic programs based on quadratic convex relaxation

Let be a general mixed-integer quadratic program that consists of minimizing a quadratic function subject to linear constraints. Our approach to solve is first to consider an equivalent general mixed-integer quadratic problem. This equivalent problem has additional variables , additional quadratic c...

Full description

Saved in:
Bibliographic Details
Published inJournal of combinatorial optimization Vol. 28; no. 2; pp. 376 - 399
Main Authors Billionnet, Alain, Elloumi, Sourour, Lambert, Amélie
Format Journal Article
LanguageEnglish
Published Boston Springer US 01.08.2014
Springer Verlag
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:Let be a general mixed-integer quadratic program that consists of minimizing a quadratic function subject to linear constraints. Our approach to solve is first to consider an equivalent general mixed-integer quadratic problem. This equivalent problem has additional variables , additional quadratic constraints , a convex objective function, and a set of valid inequalities. Contrarily to the reformulation proposed in Billionnet et al. (Math Program 131(1):381–401, 2012 ), the equivalent problem cannot be directly solved by a standard solver. Here, we propose a new Branch and Bound process based on the relaxation of the non-convex constraints to solve . Computational experiences are carried out on pure- and mixed-integer quadratic instances. The results show that the solution time of most of the considered instances with up to 60 variables is improved by our Branch and Bound algorithm in comparison with the approach of Billionnet et al. ( 2012 ) and with the general mixed-integer nonlinear solver BARON (Sahinidis and Tawarmalani, Global optimization of mixed-integer nonlinear programs, user’s manual, 2010 ).
ISSN:1382-6905
1573-2886
DOI:10.1007/s10878-012-9560-1