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dc.contributor.authorWang, Ningchuan 12:41:19 (GMT) 12:41:19 (GMT)
dc.description.abstractThe Graph Partitioning problems are hard combinatorial optimization problems. We are interested in both lower bounds and upper bounds. We introduce several methods including basic eigenvalue and projected eigenvalue techniques, convex quadratic programming techniques, and semidefinite programming (SDP). In particular, we show that the SDP relaxation is equivalent to and arises from the Lagrangian relaxation for a particular quadratically constrained quadratic model. Moreover, the bounds obtained by the eigenvalue techniques are good and cheap.en
dc.publisherUniversity of Waterlooen
dc.subjectGraph Partitioningen
dc.subjectSemidefinite Programmingen
dc.subjecteigenvalue boundsen
dc.titleEigenvalue, Quadratic Programming and Semidefinite Programming Bounds for Graph Partitioning Problemsen
dc.typeMaster Thesisen
dc.subject.programCombinatorics and Optimizationen and Optimizationen
uws-etd.degreeMaster of Mathematicsen

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