Clustering improves the Goemans–Williamson approximation for the max-cut problem

Angel E. Rodriguez-Fernandez, Bernardo Gonzalez-Torres, Ricardo Menchaca-Mendez, Peter F. Stadler

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva


MAX-CUT is one of the well-studied NP-hard combinatorial optimization problems. It can be formulated as an Integer Quadratic Programming problem and admits a simple relaxation obtained by replacing the integer “spin” variables xi by unitary vectors ⃗vi . The Goemans–Williamson rounding algorithm assigns the solution vectors of the relaxed quadratic program to a corresponding integer spin depending on the sign of the scalar product ⃗vi ·⃗r with a random vector⃗r. Here, we investigate whether better graph cuts can be obtained by instead using a more sophisticated clustering algorithm. We answer this question affirmatively. Different initializations of k-means and k-medoids clustering produce better cuts for the graph instances of the most well known benchmark for MAX-CUT. In particular, we found a strong correlation of cluster quality and cut weights during the evolution of the clustering algorithms. Finally, since in general the maximal cut weight of a graph is not known beforehand, we derived instance-specific lower bounds for the approximation ratio, which give information of how close a solution is to the global optima for a particular instance. For the graphs in our benchmark, the instance specific lower bounds significantly exceed the Goemans–Williamson guarantee.

Idioma originalInglés
Número de artículo75
Páginas (desde-hasta)1-12
Número de páginas12
EstadoPublicada - sep 2020


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