(linear algebra) is this rref? or did I solve it wrong?

In summary, to check if a solution is in reduced row echelon form, ensure that the first non-zero entry in each row is a 1, the leading 1s are to the right of the leading 1 in the row above, all other entries in the same column as a leading 1 are 0, and any rows of all 0s are at the bottom of the matrix. Solving a linear algebra problem in rref has benefits such as identifying important information and simplifying the matrix. A linear algebra problem can have multiple solutions in rref if it has infinitely many solutions. Some common mistakes when solving a problem include forgetting to perform the same operations on both sides of an equation, incorrectly reducing fractions, and not
  • #1
kira137
16
0

Homework Statement


I was given
| 1 2 -1 2 |
| 2 4 1 -2 | = A
| 3 6 0 -6 |

and reduce it to rref.

Also I had to find all four fundamental subspaces of A.

2. The attempt at a solution

so I did

R2 - 2R1
R3 - 3R1
R3 - R2

and got

| 1 2 -1 2 |
| 0 0 3 -6 |
| 0 0 0 -6 |

... Arent rref suppose to have all zero row at the bottom? I don't think this is rref because of that -6 in row 3...

and I'm not very sure about how to go about four fundamental subspaces. Am I suppose to solve for C(A), N(A), C(A^T), N(A^T)? and am I suppose to solve for dim for each of them or special solutions?

thank you for replies in adavance
 
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  • #2
No, you don't always end up with rows of zeros at the bottom. By the way, you're not done reducing the matrix. The first non-zero entry in a row should be equal to 1.

Your textbook should tell you what the four fundamental subspaces are and how to find them. Have you looked in there for the answer to your question?
 
  • #3
Since last row has -6 you can divide it by -6, i.e R3 = -1/6 * R3

Row 2 is clearly divisible by 3, so R2 = 1/3 * R2, you get [0 0 1 -2]. By adding 2*R3 + R2 = R2 you eliminate the -2. Long story short you can check your results by plugging it back into the original equation. Your system of linear equations is inconsistent and has NO solution, because you have a row of zeroes and 1 at the end, i.e

0*x1 + 0*x2 + 0*x3 = 1

If you had a row of all zeroes at the end, your system has infinitely many solutions, i.e
0*x1 + 0*x2 + 0*x3 = 0

**Make sure you take a minute to re-read what I just wrote, tattoo it on your hand somewhere and don't ever forget it**

This is the result of reduced row echelon formation performed on your matrix:
| 1 2 0 0 |
| 0 0 1 0 |
| 0 0 0 1 |
 
  • #4
cronxeh said:
Your system of linear equations is inconsistent and has NO solution, because you have a row of zeroes and 1 at the end
This is only true if A is the augmented matrix for a system of linear equations. It's not true if A is a mapping from R4 to R3.
 
  • #5
vela said:
This is only true if A is the augmented matrix for a system of linear equations. It's not true if A is a mapping from R4 to R3.

I'm sorry. I wasn't aware there exists a reason why one would want to rref a transformation matrix?
 
  • #6
cronxeh said:
I'm sorry. I wasn't aware there exists a reason why one would want to rref a transformation matrix?
One reason would be to find the four fundamental subspaces. :) You can also reduce it to determine the rank and nullity of the mapping.
 

1. How do I know if I have correctly solved a linear algebra problem and if my solution is in reduced row echelon form?

The easiest way to check if your solution is in reduced row echelon form (rref) is to look at the matrix and make sure it satisfies the following criteria:

  • The first non-zero entry in each row is a 1, also known as a leading 1.
  • The leading 1 in each row is to the right of the leading 1 in the row above it.
  • All other entries in the same column as a leading 1 are 0.
  • Any rows of all 0s are at the bottom of the matrix.

2. What are the benefits of solving a linear algebra problem in reduced row echelon form?

Solving a linear algebra problem in rref makes it easier to identify important information, such as the rank and nullity of the matrix, and determine whether a system of equations has a unique solution, infinitely many solutions, or no solution at all. It also allows for easier computation and simplification of the matrix.

3. Can a linear algebra problem have multiple solutions in reduced row echelon form?

Yes, a linear algebra problem can have multiple solutions in rref if it has infinitely many solutions. This typically occurs when the last row of the rref matrix contains all 0s, indicating a free variable that can take on any value. In this case, the solution would be represented as a parametric vector form.

4. What are common mistakes when solving a linear algebra problem and getting it into reduced row echelon form?

Some common mistakes when solving a linear algebra problem and getting it into rref include forgetting to perform the same operations on both sides of an equation, incorrectly reducing fractions, and forgetting to eliminate all non-zero entries in the same column as a leading 1. It is also important to double check each step and make sure the final solution satisfies the criteria for rref.

5. Are there any tips for efficiently solving a linear algebra problem and getting it into reduced row echelon form?

One tip for efficiently solving a linear algebra problem and getting it into rref is to use the most appropriate row operations to simplify the matrix. This may involve swapping rows, multiplying a row by a non-zero constant, or adding/subtracting a multiple of one row from another. It is also helpful to keep track of the leading 1s and make sure they are in the correct position in each row. Lastly, it is important to practice and familiarize yourself with the process to become more efficient over time.

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