Journal article
Strong duality for a trust-region type relaxation of the quadratic assignment problem
Linear algebra and its applications, Vol.301(1), pp.121-136
11/01/1999
DOI: 10.1016/S0024-3795(99)00205-0
Abstract
Lagrangian duality underlies many efficient algorithms for convex minimization problems. A key ingredient is strong duality. Lagrangian relaxation also provides lower bounds for non-convex problems, where the quality of the lower 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) strong duality holds. In addition, necessary and sufficient (strengthened) second-order optimality conditions exist. However, these duality results already fail for the two trust-region sub-problem. 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 relaxations for the quadratic assignment problem (QAP). In this paper we show that strong duality also holds for a relaxation of QAP where the orthogonality 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 problems. Our proof of strong duality introduces and uses a generalization of the Hoffman–Wielandt inequality.
Details
- Title: Subtitle
- Strong duality for a trust-region type relaxation of the quadratic assignment problem
- Creators
- Kurt Anstreicher - University of IowaXin Chen - Operations Research Center, MIT, 77 Mass Ave, Bldg E40-194,ctyCambridge, MA 02139 UKHenry Wolkowicz - University of WaterlooYa-Xiang Yuan - Institute of Computing Technology
- Resource Type
- Journal article
- Publication Details
- Linear algebra and its applications, Vol.301(1), pp.121-136
- DOI
- 10.1016/S0024-3795(99)00205-0
- ISSN
- 0024-3795
- eISSN
- 1873-1856
- Publisher
- Elsevier Inc
- Language
- English
- Date published
- 11/01/1999
- Academic Unit
- Industrial and Systems Engineering; Computer Science; Business Analytics
- Record Identifier
- 9984380460202771
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