Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.1304
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Title: Semidefinite relaxation approaches for the quadratic assignment problem
Language: English (United States)
Authors: Lange, Marko 
Keywords: QAP;SDP;Relaxation;Facial Reduction;Verification
Issue Date: 2016
Abstract (german): Diese Doktorarbeit behandelt bekannte und neue Relaxationstechniken für das quadratische Zuordnungsproblem, eines der schwierigsten zu lösenden NP-schweren Probleme der Kombinatorik. Der Schwerpunkt der Arbeit liegt auf neuen Ansätzen zur Approximation durch Semidefinite Optimierungsprobleme.
Abstract (english): This thesis deals with known and new relaxation techniques for the quadratic assignment problem; a fundamental combinatorial optimization problem which is often considered as one of the hardest of NP-hard problems. The focus of this thesis is on techniques for the construction of semidefinite programming relaxations.
URI: http://hdl.handle.net/11420/1307
DOI: 10.15480/882.1304
Institute: Zuverlässiges Rechnen E-19 
Type: Dissertation
Advisor: Rump, Siegfried M. 
Thesis grantor: Technische Universität Hamburg
Appears in Collections:Publications (tub.dok)

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