Solve each linear system using row reduction

What he should have done was replace row 1 with "row1- 3*row2":3 3 3 | 30 0 0 | 6 Replace row 1 by row1- 3*row2: 3 3 3 | 3- 3(-1)= 3+ 3= 60 0 0 | 11 1 1 | 10 0 0 | 11 1 1 | 0 (R1-R2)0 0 0 | 1 (R2/1)1 1 1 | 0 (R1-R2
  • #1
ND3G
79
0
3x+3y+3z=3
x+y+z=-1

3 3 3 | 3
1 1 1 |-1

1 1 1 | 0
0 0 0 | 1

x = -y -z
y = 1

Substitute y into x, x = -1 -z
Let z = t, tER

Then x = -1 -t, y = 1

The corresponding vector equation of the line intersection is:

(x,y,z) = (-1, 1, 0) + t(-1, 0, 1)

Does that look right? This is my first time dealing with these.
 
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  • #2
No, it isn't right. It can easily be seen that this system has no solution.

Btw, when you obtain a "solution", it is useful to plug it back into your equation. For example, for t = 0, you have (x, y, z) = (-1, 1, 0), and it can easily be seen that it doesn't satisfy the equations.
 
  • #3
ND3G said:
3x+3y+3z=3
x+y+z=-1
If you divide the first equation by 3, that becomes x+ y+ z= 1! Obviously, you can't have x+ y+ z= 1 and x+ y+ z= -1 for the same x,y,z.

3 3 3 | 3
1 1 1 |-1

1 1 1 | 0
0 0 0 | 1

x = -y -z
y = 1
How did you get that first row? If you divide each number in the first row by 3, you get 1 1 1 | 1, not 1 1 1 | 0. And if you then subtract that new first row from the second row, you get 0 0 0 | -2. Of course, that is equivalent to 0x+ 0y+ 0z= -2 which is not true for any values of x, y, and z.
I don't see how you could get "y= 1". That would be 0 1 0 | 1

Substitute y into x, x = -1 -z
Let z = t, tER

Then x = -1 -t, y = 1

The corresponding vector equation of the line intersection is:

(x,y,z) = (-1, 1, 0) + t(-1, 0, 1)

Does that look right? This is my first time dealing with these.

These two planes are parallel and do not intersect.
 
  • #4
3 3 3 | 3
1 1 1 |-1

3 3 3 | 3
0 0 0 | 6

1 1 1 | 1
0 0 0 | 1

1 1 1 | 0 This last part is R1 - R2
0 0 0 | 1
 
  • #5
I suggest you go through your lecture notes on row reduction once again (i.e. what can be done and how can it be done) and read HallsofIvy's post once again carefully.
 
  • #6
ND3G said:
3 3 3 | 3
1 1 1 |-1

3 3 3 | 3
0 0 0 | 6
I will have to ask again how you got this. In order to get "0" in the first place of the second row by a row operation, you will have "subtract 1/3 the first row from the second row so as to get 1- (1/3)(3)= 1- 1= 0. That will give 0 for the next two places also but -1- (1/3)(3= -1-1= -2 as I said before. The second row becomes 0 0 0 | -2, not 0 0 0 | 6.

1 1 1 | 1
0 0 0 | 1

1 1 1 | 0 This last part is R1 - R2
0 0 0 | 1
 
  • #7
Here is what I did.

3 3 3 | 3
1 1 1 |-1

3 3 3 | 3
0 0 0 | 6 (R1-3R2) --> 3 - (-3) = 6

1 1 1 | 1 (R1/3)
0 0 0 | 1 (R2/6)

1 1 1 | 0 (R1-R2)
0 0 0 | 1

This matches exactly with the answer my TI-83 gives me.
 
Last edited:
  • #8
ND3G said:
3 3 3 | 3
0 0 0 | 6 (R1-3R2) --> 3 - (-3) = 6

This isn't "row 1 - 3*row2".
 
  • #9
He has replaced row 2 with "row1- 3*row2". Unfortunately that loses all the information in row1!
 

1. What is row reduction?

Row reduction, also known as Gaussian elimination, is a method used to solve a system of linear equations by manipulating the rows of a matrix to create a simpler form. This process involves eliminating variables and creating zeros in specific positions to make it easier to find the solution.

2. How do I perform row reduction?

To perform row reduction, you first need to write the system of linear equations in matrix form. Then, you can use elementary row operations such as multiplying a row by a nonzero constant, adding one row to another, or swapping rows to transform the matrix into an upper triangular form. Once the matrix is in this form, the solution can be easily obtained by back substitution.

3. What are the benefits of using row reduction to solve a linear system?

Row reduction is a systematic and efficient method for solving a system of linear equations. It allows you to break down a complex system into smaller, simpler equations, making it easier to find the solution. It also helps to identify inconsistent or dependent systems, where no unique solution exists.

4. Are there any limitations to using row reduction?

Although row reduction is a powerful tool for solving linear systems, it can become time-consuming and tedious for larger matrices. Additionally, it may not always be possible to perform row reduction if the matrix is not in a suitable form or if the system is inconsistent or dependent.

5. Can row reduction be used for systems with more than two variables?

Yes, row reduction can be applied to systems with any number of variables. The process remains the same, but the number of steps and computations may increase as the number of variables increases. However, the principles and techniques used are the same, making it a versatile method for solving linear systems of any size.

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