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So the circuit partitioning problem is closely related to the minimum cut problem.
The max flow algorithm cleverly transfers the minimum cut problem into a max flow problem, with low computational complexity.
In this way, the model solution can be converted to the minimum cut problem with constraints, and we can employ the graph theory to solve the problem.
Based on the objective function generator grouping which is mentioned in (9) with the graph theory in section 2.1, the disturbed generators of coherent grouping problem could be converted into the minimum cut problem.
For instance, the bipartition algorithm for the well-known minimum cut problem is defined with the goal of separating the graph into S and such that is the minimum among all possible non-empty subsets S and.
In what follows, we formulate the network-guided SNP selection problem as a minimum cut problem on a graph derived from the SNP network in Section 2 and evaluate the performance of our solution both in simulations and on actual Arabidopsis thaliana data in Section 3. Let n be the number of SNPs and m the number of individuals.
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According to (17), the global minimum cut of an undirected graph can be converted into a minimal cut problem with a source point and a sink point.
Based on an algorithm developed by Fujishige and Zhang the problem is reduced to a sequence of O(n4) minimum cut problems.
Boykov and Kolmogrov [23] borrowed algorithms for network flows to search the minimum cut of graph-cuts problem.
The CNDP is related to a variety of other graph partitioning problems in the literature; for instance, the minimum multi-cut problem, which aims to separate a set of source-sink pairs by removing a subset of minimum weighted edges.
To prove Theorem 3, we do a polynomial time reduction from the Minimum K-Cut problem.
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minimal cut problem
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