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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.

Short Answer

Expert verified

Ford-Fulkerson algorithm is the linear time algorithm that determines if the solution obtains a maximum flow.

Step by step solution

01

Explain Maximum flow

Consider a network that consists of a directed graph with source and sink nodes. Each edge of the directed graph has its capacity denoted by c. The value of the edge capacity must be greater than zero.The maximum flow aims to send as much data as possible from source to sink. The maximum flow should not exceed the capacity of any of the edges, and the amount of entering flow must be equal to leaving flow.

02

Give a linear time algorithm to determine the maximum flow. 

The Linear time algorithm works sequentially for each edge to find the flow. The flow begins with the initial value of zero. Augmented path is the path that satisfies the maximum flow constraints. For each augmented path, flow is added sequentially path-wise.

Ford-Fulkerson algorithm:

Source s,

Sink t,

initialโ€‰flowโ†’0

While augmented path

Add path

Return flow

The above algorithm runs in linear time to find the maximum flow.

Therefore, the Ford-Fulkerson algorithm is the linear time algorithm that determines whether the solution gives a maximum flow.

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Most popular questions from this chapter

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-โˆˆU), whererole="math" localid="1659789093525" โˆˆu 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.

Find the value of the game specified by the following payoff matrix.

00โ€1โ€101โ€2โ€1โ€1โ€111โ€10011โ€20โ€31โ€1โ€1โ€10โ€32โ€10โ€21โ€1

(Hint: Consider the mixed strategies (13,0,0,12,16,0,0,0)and )(23,0,0,13))

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.

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.

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

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