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The methods based on minimum cuts in a graph are designed to minimize the similarity between pixels that are being split [22, 23, 26].
Similar to the proof that the MCDCM problem is NP-hard in "Minimum cuts in correlated networks" section, the MC-SRLG problem in the form of Fig. 6 can be reduced to the NP-hard SP-SRLG problem.
Recently, two very fast algorithms for computing minimum cuts in graphs were reported (Nagamochi and Ibaraki; SIAM J. Discrete Math. 5 (19921992) 54; Stoer and Wagner, J. ACM 44 (4) (1997) 585).
Consequently, based on these link weight assignments, the MC-NSRLG problem in Fig. 8 can be reduced to the SP-SRLG problem in Fig. 7, similar to the proof of the MCDCM problem in "Minimum cuts in correlated networks" section.
In "Shortest paths in correlated networks" and "Minimum cuts in correlated networks" sections, we study the shortest path problem and min-cut problem, respectively, for the proposed models and devise algorithms to solve them exactly.
It is important to mention that the performance of the branch-and-cut algorithm can further be improved if we permute the order in which the minimum cuts between r and i ∈ F, z i > 0, in CUT F case, and between r and j, j ∈ R, in CUT R case, are calculated.
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Officials said the minimum cut would result in the reduction of about 28,000 permits.
Since Cut 1 satisfies the constraint of coherence/separation, it is a probable minimum cut.
In VLSI circuit partitioning, the problem of obtaining a minimum cut is of prime importance.
Moreover, Minimum Cut Sets (MCS) and importance calculation are applied to analyze the fault tree quantitatively.
So the circuit partitioning problem is closely related to the minimum cut problem.
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