An edge of a flow network is called critical if decreasing the capacity of this edge results in a decrease in the maximum flow. Give an efficient algorithm that finds a critical edge in a network

Short Answer

Expert verified

The overall running time of the above algorithm is O(VE2)

Step by step solution

01

Introduction

Edge of network is having flow which is help to create some critically decreasing of maximum flow of connectivity. Even though an edges is critical towards the algorithm, it really should be completely filled.

02

Algorithm to find the critical edge in a network

Graph: G (V, E)

Output: FindCriticalEdge (G)

define getCriticalEdge (G):

residualGraph = fordFulkerson (G)

for edge u, v in residualGraph such that u, v is not in

residualGraph and u,v is not in

DFS ( residualGraph, u )

If has no path to v

Return u,v

return null

03

FordFulkerson Method

FordFulkerson approach is being used in the above procedure to obtain the maximum circulation inside a flow network. Even when an edge is crucial in the algorithm, it should have been filled to the brim; otherwise, the capacity will be reduced without compromising the maximum current.

All of this checks for a path from u to v using the DFS algorithm.

The for loop checks for the presence of something like a path every time DFS is run; if there isn't one, this edge seems to be a critical edge.

Therefore, the efficient algorithm to find the critical edge in a network has been obtained.

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Most popular questions from this chapter

A cargo plane can carry a maximum weight of 100 tons and a maximum volume of 60 cubic meters. There are three materials to be transported, and the cargo company may choose to carry any amount of each, up to the maximum available limits given below.

  • Material 1 has density 2tons/cubicmeters, maximum available amount 40 cubic meters, and revenue \(1,000 per cubic meter.
  • Material 2 has density 1ton/cubicmeters,maximum available amount 30 cubic meters, and revenue \)1,200 per cubic meter.
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Write a linear program that optimizes revenue within the constraints.

Give an example of a linear program in two variables whose feasible region is infinite, but such that there is an optimum solution of bounded cost.

There are many common variations of the maximum flow problem. Here are four of them.

(a) There are many sources and many sinks, and we wish to maximize the total flow from all sources to all sinks.

(b) Each vertex also has a capacity on the maximum flow that can enter it.

(c) Each edge has not only a capacity, but also a lower bound on the flow it must carry.

(d) The outgoing flow from each node u is not the same as the incoming flow, but is smaller by a factor of (1-U), whererole="math" localid="1659789093525" u is a loss coefficient associated with node u.

Each of these can be solved efficiently. Show this by reducing (a) and (b) to the original max-flow problem, and reducing (c) and (d) to linear programming.

A vertex cover of an undirected graph G = (V,E) is a subset of the vertices which touches every edge—that is, a subset SVsuch that for each edge {U,V}E, one or both of u, v are in S. Show that the problem of finding the minimum vertex cover in a bipartite graph reduces to maximum flow. (Hint: Can you relate this problem to the minimum cut in an appropriate network?)

Consider the following network (the numbers are edge capacities).

(a)Find the maximum flow fand a minimum cut.

(b)Draw the residual graphGf (along with its edge capacities). In this residual network, mark the vertices reachable fromS and the vertices from whichT is reachable.

(c)An edge of a network is called a bottleneck edge if increasing its capacity results in an increase in the maximum flow. List all bottleneck edges in the above network.

(d)Give a very simple example (containing at most four nodes) of a network which has no bottleneck edges.

(e)Give an efficient algorithm to identify all bottleneck edges in a network.

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