Semidefinite Programming Relaxations for the Quadratic Assignment Problem

Semideenite programming (SDP) relaxations for the quadratic assignment problem (QAP) are derived using the dual of the (homogenized) Lagrangian dual of appropriate equivalent representations of QAP. These relaxations result in the interesting, special, case where only the dual problem of the SDP relaxation has strict interior, i.e. the Slater constraint qualii-cation always fails for the primal problem. Although there is no duality gap in theory, this indicates that the relaxation cannot be solved in a numerically stable way. By exploring the geometrical structure of the relaxation, we are able to nd projected SDP relaxations. These new relaxations, and their duals, satisfy the Slater constraint qualiication, and so can be solved numerically using primal-dual interior-point methods. For one of our models, a preconditioned conjugate gradient method is used for solving the large linear systems which arise when nding the Newton direction. The preconditioner is found by exploiting the special structure of the relaxation. See e.g. 41] for a similar approach for solving SDP problems arised from the control applications. Numerical results are presented which indicate that the described methods yield at least competitive lower bounds.