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Systems of linear equations – special cases

Section overview

In this section you will learn to:

  1. Determine the linear systems that have no solution.
  2. Solve the linear systems that have infinitely many solutions.

If we consider the intersection of two lines in a plane, three things can happen.

  1. The lines intersect in exactly one point. This is called an independent system .
  2. The lines are parallel, so they do not intersect. This is called an inconsistent system .
  3. The lines coincide, so they intersect at infinitely many points. This is a dependent system .

The figures below shows all three cases.

 The graph on the right depicts an independent system. The graph in the middle depicts an inconsistent system. The graph on the left depicts a dependent system.

Every system of equations has either one solution, no solution, or infinitely many solutions.

In the [link] , we used the Gauss-Jordan method to solve systems that had exactly one solution. In this section, we will determine the systems that have no solution, and solve the systems that have infinitely many solutions.

Solve the following system of equations.

x + y = 7 size 12{x+y=7} {}
x + y = 9 size 12{x+y=9} {}

Let us use the Gauss-Jordan method to solve this system. The augmented matrix is as follows.

1 1 7 1 1 9 size 12{ left [ matrix { 1 {} # 1 {} # \lline {} # 7 {} ##1 {} # 1 {} # \lline {} # 9{} } right ]} {} x + y = 7 x + y = 9 size 12{ left [ matrix { x+y=7 {} ##x+y=9 } right ]} {}

If we multiply the first row by – 1 and add to the second row, we get

1 1 7 0 0 2 size 12{ left [ matrix { 1 {} # 1 {} # \lline {} # 7 {} ##0 {} # 0 {} # \lline {} # 2{} } right ]} {} x + y = 7 0x + 0y = 2 size 12{ left [ matrix { x+y=7 {} ##0x+0y=2 } right ]} {}

Since 0 cannot equal 2, the last equation cannot be true for any choices of x size 12{x} {} and y size 12{y} {} .

Alternatively, it is clear that the two lines are parallel; therefore, they do not intersect.

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At this stage, we are going to start using a calculator to row reduce the augmented matrix.

Solve the following system of equations.

2x + 3y 4z = 7 size 12{2x+3y - 4z=7} {}
3x + 4y 2z = 9 size 12{3x+4y - 2z=9} {}
5x + 7y 6z = 20 size 12{5x+7y - 6z="20"} {}

We enter the following augmented matrix in the calculator.

2 3 4 7 3 4 2 9 5 7 6 20 size 12{ left [ matrix { 2 {} # 3 {} # - 4 {} # \lline {} # 7 {} ##3 {} # 4 {} # - 2 {} # \lline {} # 9 {} ## 5 {} # 7 {} # - 6 {} # \lline {} # "20"{}} right ]} {}

Now by pressing the key to obtain the reduced row-echelon form, we get

1 0 10 0 0 1 8 0 0 0 0 1 size 12{ left [ matrix { 1 {} # 0 {} # "10" {} # \lline {} # 0 {} ##0 {} # 1 {} # - 8 {} # \lline {} # 0 {} ## 0 {} # 0 {} # 0 {} # \lline {} # 1{}} right ]} {}

The last row indicates that the system is inconsistent; therefore, there is no solution.

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Solve the following system of equations.

x + y = 7 size 12{x+y=7} {}
x + y = 7 size 12{x+y=7} {}

The problem clearly asks for the intersection of two lines that are the same; that is, the lines coincide. This means the lines intersect at an infinite number of points.

A few intersection points are listed as follows: (3, 4), (5, 2), (–1, 8), (–6, 13) etc. However, when a system has an infinite number of solutions, the solution is often expressed in the parametric form. This can be accomplished by assigning an arbitrary constant, t size 12{t} {} , to one of the variables, and then solving for the remaining variables. Therefore, if we let y = t size 12{y=t} {} , then x = 7 t size 12{x=7 - t} {} . Or we can say all ordered pairs of the form ( 7 t size 12{7 - t} {} , t size 12{t} {} ) satisfy the given system of equations.

Alternatively, while solving the Gauss-Jordan method, we will get the reduced row-echelon form given below.

1 1 7 0 0 0 size 12{ left [ matrix { 1 {} # 1 {} # \lline {} # 7 {} ##0 {} # 0 {} # \lline {} # 0{} } right ]} {}

The row of all zeros, can simply be discarded in a manner that it never existed. This leaves us with only one equation but two variables. And whenever there are more variables than the equations, the solution must be expressed in terms of an arbitrary constant, as above. That is, x = 7 t size 12{x=7 - t} {} , y = t size 12{y=t} {} .

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Solve the following system of equations.

x + y + z = 2 size 12{x+y+z=2} {}
2x + y z = 3 size 12{2x+y - z=3} {}
3x + 2y = 5 size 12{3x+2y=5} {}

The augmented matrix and the reduced row-echelon form are given below.

1 1 1 2 2 1 1 3 3 2 0 5 size 12{ left [ matrix { 1 {} # 1 {} # 1 {} # \lline {} # 2 {} ##2 {} # 1 {} # - 1 {} # \lline {} # 3 {} ## 3 {} # 2 {} # 0 {} # \lline {} # 5{}} right ]} {}
1 0 2 1 0 1 3 1 0 0 0 0 size 12{ left [ matrix { 1 {} # 0 {} # - 2 {} # \lline {} # 1 {} ##0 {} # 1 {} # 3 {} # \lline {} # 1 {} ## 0 {} # 0 {} # 0 {} # \lline {} # 0{}} right ]} {}

Since the last equation dropped out, we are left with two equations and three variables. This means the system has infinite number of solutions. We express those solutions in the parametric form by letting the last variable z size 12{z} {} equal the parameter t size 12{t} {} .

The first equation reads x 2z = 1 size 12{x - 2z=1} {} , therefore, x = 1 + 2z size 12{x=1+2z} {} .

The second equation reads y + 3z = 1 size 12{y+3z=1} {} , therefore, y = 1 3z size 12{y=1 - 3z} {} .

And now if we let z = t size 12{z=t} {} , the solution is expressed as follows:

x = 1 + 2t size 12{x=1+2t} {} , y = 1 3t size 12{y=1 - 3t} {} , z = t size 12{z=t} {} .

The reader should note that particular solutions to the system can be obtained by assigning values to the parameter t size 12{t} {} . For example, if we let t = 2 size 12{t=2} {} , we have the solution (5, –5, 2).

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Source:  OpenStax, Applied finite mathematics. OpenStax CNX. Jul 16, 2011 Download for free at http://cnx.org/content/col10613/1.5
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