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• W2167345674 abstract "We derive a new semidefinite programming relaxation for the general graph partition problem (GPP). Our relaxation is based on matrix lifting with matrix variable having order equal to the number of vertices of the graph. We show that this relaxation is equivalent to the Frieze-Jerrum relaxation for the maximum k-cut problem with an additional constraint that involves the restrictions on the subset sizes. Because the new relaxation does not depend on the number of subsets k into which the graph should be partitioned we are able to compute bounds for large k. We compare theoretically and numerically the new relaxation with other semide-finite programming (SDP) relaxations for the GPP. The results show that our relaxation provides competitive bounds and is solved significantly faster than any other known SDP bound for the general GPP." @default.
• W2167345674 created "2016-06-24" @default.
• W2167345674 creator A5082314475 @default.
• W2167345674 date "2014-02-01" @default.
• W2167345674 modified "2023-10-18" @default.
• W2167345674 title "An Efficient Semidefinite Programming Relaxation for the Graph Partition Problem" @default.
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• W2167345674 doi "https://doi.org/10.1287/ijoc.1120.0542" @default.
• W2167345674 hasPublicationYear "2014" @default.
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