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For the following network, with edge capacities as shown, find the maximum flow from S to T, along with a matching cut.

Short Answer

Expert verified

The maximum flow is obtained with the residual capacity 13 and the matching cut is{S,C,F} and {A,B,D,E,G,T}

Step by step solution

01

Step 1:

Choose the augmenting path as S -> A -> D -> G -> T and the edge, which has the lowest capacity along the path, is A -> D andit has the capacity 4.

The following diagram represents it:

In the above diagram,

Left side is the current path and right side is the residual path.

The residual capacity is 4.

Subtract 4 from the capacity of the forward edge. Therefore, its capacity 6 now becomes 2 and the capacity of the reverse edge becomes 4.

Subtract 4 from the capacity of the forward edge. Therefore, its capacity 4 now becomes 0 and the capacity of the reverse edge becomes 4.

Subtract 4 from the capacity of the forward edge. Therefore, its capacity 5 now becomes 1 and the capacity of the reverse edge becomes 4.

Subtract 4 from the capacity of the forward edge. Therefore, its capacity 12 now becomes 8 and the capacity of the reverse edge becomes 4.

02

Step 2:

Choose the augmenting path as S -> A -> B -> E -> G -> T and the edge, which has the lowest capacity along the path, and it has the capacity 2.

The following diagram represents it:

In the above diagram,

Left side is the current path and right side is the residual path.

The residual capacity is 2.

Subtract 2 from the capacity of the forward edge. Therefore, its capacity 2 now becomes 0 and the capacity of the reverse edge becomes 6 from 4.

Subtract 2 from the capacity of the forward edge. Therefore, its capacity 2 i now becomes 0 and the capacity of the reverse edge becomes 2.

Subtract 2 from the capacity of the forward edge. Therefore, its capacity 20 now becomes 18 and the capacity of the reverse edge becomes 2.

Subtract 2 from the capacity of the forward edge. Therefore, its capacity 10 now becomes 8 and the capacity of the reverse edge becomes 2.

03

Step 3:

Subtract 2 from the capacity of the forward edge. Therefore, its capacity 8 now becomes 6 and the capacity of the reverse edge becomes 6 from 4.

Choose the augmenting path as S -> B -> E -> G -> T and the edge, which has the lowest capacity along the path, and it has the capacity 1.

The following diagram represents it:

In the above diagram,

Left side is the current path and right side is the residual path.

The residual capacity is 1.

Subtract 1 from the capacity of the forward edge. Therefore, its capacity 1 now becomes 0 and the capacity of the reverse edge becomes 1.

Subtract 1 from the capacity of the forward edge. Therefore, its capacity 18 now becomes 17 and the capacity of the reverse edge becomes 3 from 2.

Subtract 1 from the capacity of the forward edge. Therefore, its capacity 8 now becomes 7 and the capacity of the reverse edge becomes 3 from 2.

Subtract 1 from the capacity of the forward edge. Therefore, its capacity 6 now becomes 5 and the capacity of the reverse edge becomes 7 from 6.

04

Step 4:

Choose the augmenting path as S-> C -> F -> T and the edge, which has the lowest capacity along the path, and it has the capacity 4.

The following diagram represents it:

In the above diagram,

Left side is the current path and right side is the residual path.

The residual capacity is 4.

Subtract 4 from the capacity of the forward edge. Therefore, its capacity 10 now becomes 6 and the capacity of the reverse edge becomes 4.

Subtract 4 from the capacity of the forward edge. Therefore, its capacity 5 now becomes 1 and the capacity of the reverse edge becomes 4.

Subtract 4 from the capacity of the forward edge. Therefore, its capacity 4 now becomes 0 and the capacity of the reverse edge becomes 4.

05

Step 5:

Choose the augmenting path as S -> C -> B -> E -> G -> T and the edge, which has the lowest capacity along the path, and it has the capacity 2.

The following diagram represents it:

In the above diagram,

Left side is the current path and right side is the residual path.

The residual capacity is 2.

Subtract 2 from the capacity of the forward edge. Therefore, its capacity 6 now becomes 4 and the capacity of the reverse edge becomes 6 from 4.

Subtract 2 from the capacity of the forward edge. Therefore, its capacity 2 now becomes 0 and the capacity of the reverse edge becomes 2.

Subtract 2 from the capacity of the forward edge. Therefore, its capacity 17 now becomes 15 and the capacity of the reverse edge becomes 5 from 3.

06

Step 6:

Subtract 2 from the capacity of the forward edge. Therefore, its capacity 7 now becomes 5 and the capacity of the reverse edge becomes 5 from 3.

Subtract 2 from the capacity of the forward edge. Therefore, its capacity 5 now becomes 3 and the capacity of the reverse edge becomes 9 from 7.

The maximum flow is obtained with the residual capacity 4+2+1+4+2=13

Then cut the partition into two, let it be “M” and “N”. Both should be a disjoint group where “S” should be in “M” and “T” should be in “N”.

LetM={S,C,F}andN={A,B,D,E,G,T}

Cut “M” is shown below:

Cut “N” is shown below:

Therefore, the maximum flow is obtained with the residual capacity 13 and the matching cut is {S,C,F} and {A,B,D,E,G,T}

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Most popular questions from this chapter

The pizza business in Little Town is split between two rivals, Tony and Joey. They are each investigating strategies to steal business away from the other. Joey is considering either lowering prices or cutting bigger slices. Tony is looking into starting up a line of gourmet pizzas, or offering outdoor seating, or giving free sodas at lunchtime. The effects of these various strategies are summarized in the following payoff matrix (entries are dozens of pizzas, Joey’s gain and Tony’s loss).




TONY




Gourmet

Seating

Freesoda

JOEY

Lower price

+2

0

-3


BiggerSlices

_1

-2

+1

For instance, if Joey reduces prices and Tony goes with the gourmet option, then Tony will lose 2 dozen pizzas worth of nosiness to Joey.

What is the value of this game, and what are the optimal strategies for Tony and Joey?

Show that the change-making problem (Exercise) can be formulated as an integer linear program. Can we solve this program as an LP, in the certainty that the solution will turn out to be integral (as in the case of bipartite matching)? Either prove it or give a counterexample.

Suppose someone presents you with a solution to the max-flow problem on some network. Give a linear-time algorithm to determine whether the solution does indeed give a maximum flow.

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?)

Question: Consider the following simple network with edge capacities as shown.

a) Show that, if the Ford-Fulkerson algorithm is run on this graph, a careless choice of updates might cause it to take 1000iterations. Imagine if the capacities were a million instead of 1000.

We will now find a strategy for choosing paths under which the algorithm is guaranteed to terminate in a reasonable number of iterations.

Consider an arbitrary directed network (G=V,E,s,t,ce)in which we want to find the maximum flow.Assume for simplicity that all edge capacities are at least 1, and define the capacity of an s - t path to be the smallest capacity of its constituent edges. The fattest path from s to t is the path with the most capacity.

b) Show that the fattest s - t path in a graph can be computed by a variant of Dijkstra’s algorithm.

c) Show that the maximum flow in Gis the sum of individual flows along at most|E|paths from s to t.

d) Now show that if we always increase flow along the fattest path in the residual graph, then the Ford-Fulkerson algorithm will terminate in at mostO(ElogF) iterations, where F is the size of the maximum flow. (Hint: It might help to recall the proof for the greedy set cover algorithm in Section 5.4.)

In fact, an even simpler rule—finding a path in the residual graph using breadth-first search— guarantees that atO(V.E)most iterations will be needed.

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