Strong duality for a trust-region type relaxation of the quadratic assignment problem

Lagrangian duality underlies many efficient algorithms for convex minimizat ion problems. A key ingredient is strong duality. Lagrangian relaxation als o provides lower bounds for non-convex problems, where the quality of the l ower bound depends on the duality gap. Quadratically constrained quadratic programs (QQPs) provide important examples of non-convex programs. For the simple case of one quadratic constraint (the trust-region subproblem) stron g duality holds. In addition, necessary and sufficient (strengthened) secon d-order optimality conditions exist. However, these duality results already fail for the two trust-region subproblem. Surprisingly, there are classes of more complex, non-convex QQPs where strong duality holds. One example is the special case of orthogonality constraints, which arise naturally in re laxations for the quadratic assignment problem (QAP). In this paper we show that strong duality also holds for a relaxation of QAP where the orthogona lity constraint is replaced by a semidefinite inequality constraint. Using this strong duality result, and semidefinite duality, we develop new trust- region type necessary and sufficient optimality conditions for these proble ms. Our proof of strong duality introduces and uses a generalization of the Hoffman-Wielandt inequality. (C) 1999 Published by Elsevier Science Inc. A ll rights reserved.