Quadratic 0-1 programming : tightening linear or quadratic convex reformulation by use of relaxations
RAIRO - Operations Research - Recherche Opérationnelle, Volume 42 (2008) no. 2, pp. 103-121.

Many combinatorial optimization problems can be formulated as the minimization of a 0-1 quadratic function subject to linear constraints. In this paper, we are interested in the exact solution of this problem through a two-phase general scheme. The first phase consists in reformulating the initial problem either into a compact mixed integer linear program or into a 0-1 quadratic convex program. The second phase simply consists in submitting the reformulated problem to a standard solver. The efficiency of this scheme strongly depends on the quality of the reformulation obtained in phase 1. We show that a good compact linear reformulation can be obtained by solving a continuous linear relaxation of the initial problem. We also show that a good quadratic convex reformulation can be obtained by solving a semidefinite relaxation. In both cases, the obtained reformulation profits from the quality of the underlying relaxation. Hence, the proposed scheme gets around, in a sense, the difficulty to incorporate these costly relaxations in a branch-and-bound algorithm.

Le problème de la minimisation d'une fonction quadratique en variables 0-1 sous contraintes linéaires permet de modéliser de nombreux problèmes d'Optimisation Combinatoire. Nous nous intéressons à sa résolution exacte par un schéma général en deux phases. La première phase permet de reformuler le problème de départ soit en un programme linéaire compact en variables mixtes soit en un programme quadratique convexe en variables 0-1. La deuxième phase consiste simplement à soumettre le problème reformulé à un solveur standard. L'efficacité de ce schéma est étroitement liée à la qualité de la reformulation obtenue à la fin de la phase 1. Nous montrons qu'une bonne reformulation linéaire compacte peut être obtenue par la résolution d'une relaxation linéaire. De même, une bonne reformulation quadratique convexe peut être obtenue par une relaxation semi-définie positive. Dans les deux cas, la reformulation obtenue tire profit de la qualité de la relaxation sur laquelle elle se base. Ainsi, le schéma proposé contourne, d'une certaine façon, la difficulté d'intégrer des relaxations, coûteuses en temps de calcul, dans un algorithme de branch-and-bound.

DOI: 10.1051/ro:2008011
Classification: 90C10, 90C11, 90C20
Keywords: combinatorial optimization, quadratic 0-1 programming, linear reformulation, quadratic convex reformulation
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     title = {Quadratic 0-1 programming : tightening linear or quadratic convex reformulation by use of relaxations},
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Billionnet, Alain; Elloumi, Sourour; Plateau, Marie-Christine. Quadratic 0-1 programming : tightening linear or quadratic convex reformulation by use of relaxations. RAIRO - Operations Research - Recherche Opérationnelle, Volume 42 (2008) no. 2, pp. 103-121. doi : 10.1051/ro:2008011. http://archive.numdam.org/articles/10.1051/ro:2008011/

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