Prove that a matrix can be reduced to RRE and CRE

In summary: No I did not get what you are saying. Isn't statements like " You can pass from from A to a row/column reduced form in finite steps " is proven to be true.
  • #1
Buffu
849
146

Homework Statement



Let ##A## be an ##m \times n## matrix. Show that by means of a finite number of elementary row/column operations ##A## can be reduced to both "row reduced echelon" and "column reduced echelon" matrix ##R##. i.e ##R_{ij} = 0## if ##i \ne j##, ##R_{ii} = 1 ##, ##1 \le i \le r##, ##R_{ii} = 0## if ##i > r##. Also show that ##R = PAQ## where ##P## and ##Q## are invertible ##m\times m## and ##n \times n## matrices respectively.

Homework Equations

The Attempt at a Solution



Since I know I can pass ##A## to a row reduced echelon matrix in finite number of operations.
Lets say the row reduced echelon form of ##A## is ##R^{\prime}##. Then ##R^\prime = PA##.

Also since nothing is special about rows, therefore I can say that a matrix can be passed on to column reduced echelon in finite number of steps. Therefore I can pass ##R^\prime## to a column reduced form ##R## in finite number of steps. Let's say ##R = QR^\prime##

From above I can say ##A## can be passed to a column and row reduced echelon form in finite number of steps and ##R = QPA##.

Is this correct ? I think it is wrong since I used a lot of words and also I got ##R = QPA## not ##R = PAQ##.
 
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  • #2
Buffu said:

Homework Statement



Let ##A## be an ##m \times n## matrix. Show that by means of a finite number of elementary row/column operations ##A## can be reduced to both "row reduced echelon" and "column reduced echelon" matrix ##R##. i.e ##R_{ij} = 0## if ##i \ne j##, ##R_{ii} = 1 ##, ##1 \le i \le r##, ##R_{ii} = 0## if ##i > r##. Also show that ##R = PAQ## where ##P## and ##Q## are invertible ##m\times m## and ##n \times n## matrices respectively.

Homework Equations

The Attempt at a Solution



Since I know I can pass ##A## to a row reduced echelon matrix in finite number of operations.
Lets say the row reduced echelon form of ##A## is ##R^{\prime}##. Then ##R^\prime = PA##.

Also since nothing is special about rows, therefore I can say that a matrix can be passed on to column reduced echelon in finite number of steps. Therefore I can pass ##R^\prime## to a column reduced form ##R## in finite number of steps. Let's say ##R = QR^\prime##

From above I can say ##A## can be passed to a column and row reduced echelon form in finite number of steps and ##R = QPA##.

Is this correct ? I think it is wrong since I used a lot of words and also I got ##R = QPA## not ##R = PAQ##.

So, you can have ##R = QPA,## and this can be written as ##R = P_1 A Q_1##, where ##P_1 = QP## and ##Q_1 = I## (the ##n \times n## identity matrix).
 
  • #3
Ray Vickson said:
So, you can have ##R = QPA,## and this can be written as ##R = P_1 A Q_1##, where ##P_1 = QP## and ##Q_1 = I## (the ##n \times n## identity matrix).

Then the proof is correct ?
 
  • #4
Buffu said:
Then the proof is correct ?

What proof? All you did was make statements; you did not really "prove" anything.
 
  • #5
Ray Vickson said:
What proof? All you did was make statements; you did not really "prove" anything.

No I did not get what you are saying. Isn't statements like " You can pass from from A to a row/column reduced form in finite steps " is proven to be true.
So I just need to combine these types of statements to form a proof.
 

1. What is the difference between RRE and CRE?

The Reduced Row Echelon Form (RRE) and Canonical Row Echelon Form (CRE) are two different ways of representing a matrix after performing Gaussian elimination. RRE is a simplified form where the leading coefficient of each row is 1 and all the other entries in that column are 0. CRE is a more generalized form where the leading coefficient can be any non-zero number and the other entries in that column can be any real number.

2. Why is it important to reduce a matrix to RRE or CRE?

Reducing a matrix to RRE or CRE is important because it simplifies the matrix and makes it easier to solve equations involving the matrix. It also helps in finding the inverse of a matrix and performing other operations on it.

3. How do you prove that a matrix can be reduced to RRE?

To prove that a matrix can be reduced to RRE, we need to show that it satisfies the three conditions for RRE: 1) All rows with only zeros are placed at the bottom, 2) The leading coefficient of each row is to the right of the leading coefficient of the row above it, and 3) The leading coefficient of each row is 1. We can use Gaussian elimination to show that these conditions are satisfied.

4. Is there a specific method to reduce a matrix to RRE or CRE?

Yes, there is a specific method called Gaussian elimination to reduce a matrix to RRE or CRE. This method involves performing row operations on the matrix such as swapping rows, multiplying a row by a non-zero constant, and adding a multiple of one row to another row. By performing these operations, we can obtain a matrix in either RRE or CRE form.

5. Can any matrix be reduced to RRE and CRE?

Yes, any matrix can be reduced to RRE and CRE. This is because Gaussian elimination is an algorithm that can be applied to any matrix to reduce it to one of these forms. However, the resulting form may not be unique as there can be multiple ways to reduce a matrix to RRE or CRE.

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