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,

initialflow0

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

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.
  • Material 3 has density 3tons/cubicmeters, maximum available amount 20 cubic meters, and revenue $12,000 per cubic meter.

Write a linear program that optimizes revenue within the constraints.

You are given the following points in the plane:

(1,3),(2,5),(3,7),(5,11),(7,14),(8,15),(10,19)

.You want to find a lineax+by=c that approximately passes through these points (no line is a perfect fit). Write a linear program (you don’t need to solve it) to find the line that minimizes the maximum absolute error,max1i7|axi+byic|

Consider the following generalization of the maximum flow problem.

You are given a directed network G=(V,E)with edge capacities {ce}. Instead of a single (s,t)pair, you are given multiple pairs (s1,t1),(s2,t2),,(sk,tk), where the siare sources of Gand tithe are sinks of G. You are also given kdemands d1,,dk. The goal is to find kflows f(1),,f(k)with the following properties:

  • f(i)is a valid flow fromSi toti .
  • For each edge e, the total flowfe(1)+fe(2)++fe(k) does not exceed the capacityce .
  • The size of each flowf(i) is at least the demand di.
  • The size of the total flow (the sum of the flows) is as large as possible.

How would you solve this problem?

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?

Consider the following linear program.

maximize 5x+3y

5x-2y0x+y7x5x0y0

Plot the feasible region and identify the optimal solution.

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