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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Algorithmica 11 (1994), S. 278-290 
    ISSN: 1432-0541
    Keywords: Network flow ; Cuts ; Parametric cuts ; Graph algorithms ; Layout
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Galloet al. [4] recently examined the problem of computing on line a sequence ofk maximum flows and minimum cuts in a network ofn nodes, where certain edge capacities change between each flow. They showed that for an important class of networks, thek maximum flows and minimum cuts can be computed simply in O(n3+kn2) total time, provided that the capacity changes are made “in order.” Using dynamic trees their time bound isO(nm log(n2/m)+km log(n2/m)). We show how to reduce the total time, using a simple algorithm, to O(n3+kn) for explicitly computing thek minimum cuts and implicitly representing thek flows. Using dynamic trees our bound is O(nm log(n2/m)+kn log(n2/m)). We further reduce the total time toO(n 2√m) ifk is at most O(n). We also apply the ideas from [10] to show that the faster bounds hold even when the capacity changes are not “in order,” provided we only need the minimum cuts; if the flows are required then the times are respectively O(n3+km) and O(n2√m). We illustrate the utility of these results by applying them to therectilinear layout problem.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Algorithmica 10 (1993), S. 64-89 
    ISSN: 1432-0541
    Keywords: Minimum cut ; Connectivity cut ; All-pairs max-flows ; Cut tree ; Equivalent flow tree ; DAG ; Edge-connectivity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract There are two well-known, elegant, compact, and efficiently computed representations of selected minimum edge cuts in a weighted, undirected graphG=(V, E) withn nodes andm edges: at one extreme, the Gomory-Hu cut tree [12] represents $$\left( {\begin{array}{*{20}c} n \\ 2 \\ \end{array} } \right)$$ minimum cuts, one for each pair of nodes inG; at the other extreme, the Picard-Queyranne DAG [24] represents all the minimum cuts between a single pair of nodes inG. The GH cut tree is constructed with onlyn−1 max-flow computations, and the PQ DAG is constructed with one max-flow computation, plusO(m) additional time. In this paper we show how to marry these two representations, getting the best features of both. We first show that we can construct all $$\left( {\begin{array}{*{20}c} n \\ 2 \\ \end{array} } \right)$$ DAGs, one for each fixed pair of nodes, using onlyn−1 max-flow computations as in [12], plusO(m) time per DAG as in [24]. This speeds up the obvious approach by a factor ofn. We then apply this approach to an unweighted graphG, to find all the edge-connectivity cuts inG, i.e., cuts with capacity equal to the connectivity ofG. Matula [22] gave a method to find one connectivity cut inO(nm) time; we show thatO(nm) time suffices to represent all connectivity cuts compactly, and to list all of them explicitly. This improves the previous best time bound ofO(n 2 m) [3] for listing the connectivity cuts. The connectivity cuts are central in network reliability calculations. We then show how to find all pairs of nodes that are separated by at least one connectivity cut inO(nm) time.
    Type of Medium: Electronic Resource
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