Semidefinite relaxations of the quadratic assignment problem in a Lagrangian framework
نویسندگان
چکیده
منابع مشابه
Semidefinite relaxations of the quadratic assignment problem in a Lagrangian framework
In this paper, we consider partial Lagrangian relaxations of continuous quadratic formulations of the Quadratic Assignment Problem (QAP) where the assignment constraints are not relaxed. These relaxations are a theoretical limit for semidefinite relaxations of the QAP using any linearized quadratic equalities made from the assignment constraints. Using this framework, we survey and compare stan...
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Semidefinite relaxations of the quadratic assignment problem (QAP ) have recently turned out to provide good approximations to the optimal value of QAP . We take a systematic look at various conic relaxations of QAP . We first show that QAP can equivalently be formulated as a linear program over the cone of completely positive matrices. Since it is hard to optimize over this cone, we also look ...
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It was recently demonstrated that a well-known eigenvalue bound for the Quadratic Assignment Problem (QAP) actually corresponds to a semideenite programming (SDP) relaxation. However, for this bound to be computationally useful the assignment constraints of the QAP must rst be eliminated, and the bound then applied to a lower-dimensional problem. The resulting \projected eigenvalue bound" is on...
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We consider semidefinite programming relaxations of the quadratic assignment problem, and show how to exploit group symmetry in the problem data. Thus we are able to compute the best known lower bounds for several instances of quadratic assignment problems from the problem library: [R.E. Burkard, S.E. Karisch, F. Rendl. QAPLIB — a quadratic assignment problem library. Journal on Global Optimiza...
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ژورنال
عنوان ژورنال: International Journal of Mathematics in Operational Research
سال: 2009
ISSN: 1757-5850,1757-5869
DOI: 10.1504/ijmor.2009.022879