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In this paper we analyze a known relaxation for the Sparsest Cut
problem based on positive semidefinite constraints, and we present a
branch and bound algorithm and heuristics based on this relaxation.
The relaxed formulation and the algorithms were tested on small and moderate
sized instances. It leads to values very close to the
optimum solution values. The exact algorithm could obtain solutions
for small and moderate sized instances, and the best heuristics
obtained optimum or near optimum...
In this paper we analyze a known relaxation for the Sparsest Cut
problem based on positive semidefinite constraints, and we present a
branch and bound algorithm and heuristics based on this relaxation.
The relaxed formulation and the algorithms were tested on small and moderate
sized instances. It leads to values very close to the
optimum solution values. The exact algorithm could obtain solutions
for small and moderate sized instances, and the best heuristics
obtained optimum or near optimum...
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