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Minimum k-cut
Combinatorial optimization graph problem From Wikipedia, the free encyclopedia
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In mathematics, the minimum k-cut is a combinatorial optimization problem that requires finding a set of edges whose removal would partition the graph to at least k connected components. These edges are referred to as k-cut. The goal is to find the minimum-weight k-cut. This partitioning can have applications in VLSI design, data-mining, finite elements and communication in parallel computing.

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Formal definition
Given an undirected graph G = (V, E) with an assignment of weights to the edges w: E → N and an integer partition V into k disjoint sets while minimizing
For a fixed k, the problem is polynomial time solvable in [1] However, the problem is NP-complete if k is part of the input.[2] It is also NP-complete if we specify k vertices and ask for the minimum k-cut which separates these vertices among each of the sets.[3]
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Approximations
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Perspective
Several approximation algorithms exist with an approximation of A simple greedy algorithm that achieves this approximation factor computes a minimum cut in each of the connected components and removes the lightest one. This algorithm requires a total of n − 1 max flow computations. Another algorithm achieving the same guarantee uses the Gomory–Hu tree representation of minimum cuts. Constructing the Gomory–Hu tree requires n − 1 max flow computations, but the algorithm requires an overall O(kn) max flow computations. Yet, it is easier to analyze the approximation factor of the second algorithm.[4][5] Moreover, under the small set expansion hypothesis (a conjecture closely related to the unique games conjecture), the problem is NP-hard to approximate to within (2 − ε) factor for every constant ε > 0,[6] meaning that the aforementioned approximation algorithms are essentially tight for large k.
A variant of the problem asks for a minimum weight k-cut where the output partitions have pre-specified sizes. This problem variant is approximable to within a factor of 3 for any fixed k if one restricts the graph to a metric space, meaning a complete graph that satisfies the triangle inequality.[7] More recently, polynomial time approximation schemes (PTAS) were discovered for those problems.[8]
While the minimum k-cut problem is W[1]-hard parameterized by k,[9] a parameterized approximation scheme can be obtained for this parameter.[10]
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References
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