For the linear program

max x1−2x3x1−x2≤12x2−x3≤1x1,x2,x3≥0

Prove that the solution(x1,x2,x3)=(3/2,1/2,0) is optimal

Short Answer

Expert verified

The solution (x1,x2,x3)=(3/2,1/2,0)is the optimal solution.

Step by step solution

01

Introduction to problem

Consider the three variables namelyx1,x2,x3and the two linear inequalities as follows,

x1−x2≤12x2−x3≤1

with a condition, x1,x2,x3≥0

Assume the inequalities be an equation of the form,

[1] x1−x2=1[2] 2x2−x3=1[3] x1,x2,x3=0

Equation’s last line signifies those variablesx1,x2,x3 can never be negative.

Consider the three cases:

Case (I): x1=0

Case (II):x2=0

Case (III):x3=0

02

Evaluate case  x1=0

In this case, Substitute x1=0in Equation [1],

[1] x1−x2=10−x2=1          [∵x1=0]x2=−1

But no variable can be negative.

Hence, discard the Case (I)

03

Evaluate case  x2=0

In this case, substitutex2=0in Equation [1] as follows,

[1] x1−x2=1x1−0=1          [∵x2=0]x1=1

Substitutex2=0in Equation [2] as follows,

[2]2x2−x3=10−x3=1   [∵x2=0]x3=−1

But no variable can be negative.

Hence, discard the Case (II).

04

Evaluate case  x2=0

In this case, substitutex3=0in Equation as follows,

[2]2x2−x3=12x2−0=1    [∵x3=0]2x2=1x2=12

Substitutex2=12in Equation [1] as follows,

[1]x1−x2=1x1−12=1​   ​​ [∵x2=12]x1=1+12x1=32

Therefore, this case is possible.

05

Finding  max x1−2x3

The maximum of x1−2x3 can be calculated as follows,

x1−2x3=32−2×0=32

Therefore, the optimal solution at(x1,x2,x3) is (3/2,1/2,0).

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Most popular questions from this chapter

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?

Hall’s theorem. Returning to the matchmaking scenario of Section 7.3, suppose we have a bipartite graph with boys on the left and an equal number of girls on the right. Hall’s theorem says that there is a perfect matching if and only if the following condition holds: any subset sof boys is connected to at least |s|girls.

Prove this theorem. (Hint: The max-flow min-cut theorem should be helpful.)

In a satisfiable system of linear inequalities

a11x1+···+a1nxn≤b1:am1x1+···+amnxn≤bm

we describe the inequality as forced-equal if it is satisfied with equality by every solution x = (x1,...,xn)of the system. Equivalently,Piajixi≤bj is not forced-equal if there exists an x that satisfies the whole system and such that Piajixi≤bj.

For example, in

x1+x2≤2-x1-x2≤-2x1≤1-x2≤0

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.

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.

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