Is simultaneous row operation allowed in reducing a matrix to row echelon form?

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In summary: In general, the operations can be performed simultaneously if the "new" R1 is not used in the subsequent operations. I hope that this is clear enough.
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MathewsMD
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For any given 2 x 3 matrix,

Why are only elementary steps allowed (i.e. aR1, R1 +/- R2, R1 <--> R2) and not any other operation (e.g. 3R1 + 2R2) when reducing the matrix to row echelon form?

Also, is the operation R1 + R2 and R2 + R1 allowed simultaneously? I realize this would just output the exact same equation, which isn't incredibly useful, but is it allowed?
 
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The operations plus and times are closed over linear spaces that are mapped by matrices.
 
  • #3
j
MathewsMD said:
For any given 2 x 3 matrix,

Why are only elementary steps allowed (i.e. aR1, R1 +/- R2, R1 <--> R2) and not any other operation (e.g. 3R1 + 2R2) when reducing the matrix to row echelon form?
The way I've usually seen these elementary row operations is like so (http://en.wikipedia.org/wiki/Elementary_matrix#Operations):
Ri <--> Rj Switch two rows
Ri <-- kRi (k a nonzero scalar) Replace a row with a nonzero multiple of itself
Ri <-- Ri +kRj (k a nonzero scalar) Replace a row by itself plus a nonzero multiple of another row

3R1 + 2R2 could be effected by replacing R1 by itself plus 2/3 R2, followed by replacing R1 by 3 times itself.
MathewsMD said:
Also, is the operation R1 + R2 and R2 + R1 allowed simultaneously?
I don't understand what your notation means. Which row gets changed?
MathewsMD said:
I realize this would just output the exact same equation, which isn't incredibly useful, but is it allowed?
 
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  • #4
If you apply the "row operations", "multiply a row by a constant", "swap two rows", and "add a multiple of one row to another" to the identity matrix, you get an "elementary matrix". Applying that row operation to any matrix, A, is the same as multiplying the corresponding elementary matrix by A. For example, if you "add 4 times the first row to the third row" of the identity matrix you get
[tex]\begin{bmatrix}1 & 0 & 0 \\ 0 & 1 & 0 \\ 4 & 0 & 1\end{bmatrix}[/tex]
and adding four times the first row to the second row of
[tex]\begin{bmatrix}a_{11} & a_{12} & a_{13} \\ a_{21} & a_{22} & a_{23} \\ a_{31} & a_{32} & a_{33} \end{bmatrix}[/tex]
gives
[tex]\begin{bmatrix}a_{11} & a_{12} & a_{13} \\ a_{21} & a_{22} & a_{23} \\ a_{31}+ 4a_{11} & a_{32}+ 4a_{12} & a_{33}+ a_{13} \end{bmatrix}[/tex]

That is exactly the same as
[tex]\begin{bmatrix}1 & 0 & 0 \\ 0 & 1 & 0 \\ 4 & 0 & 1\end{bmatrix}\begin{bmatrix}a_{11} & a_{12} & a_{13} \\ a_{21} & a_{22} & a_{23} \\ a_{31} & a_{32} & a_{33}\end{bmatrix}[/tex]

In other words, those row operations are exactly the same as multiplying by matrices. If I apply row operations to reduce matrix A to the identity matrix, then multiplying together the corresponding matrices would give the inverse matrix. Applying those row operations to the identity matrix is the same as multiplying all those elementary matrices together, which gives the inverse matrix.
 
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MathewsMD said:
For any given 2 x 3 matrix,

Why are only elementary steps allowed (i.e. aR1, R1 +/- R2, R1 <--> R2) and not any other operation (e.g. 3R1 + 2R2) when reducing the matrix to row echelon form?

Also, is the operation R1 + R2 and R2 + R1 allowed simultaneously? I realize this would just output the exact same equation, which isn't incredibly useful, but is it allowed?

Because these operations are the only ones that preserve the solution to the system. This is obvious for swaps and scaling, but a bit harder for linear combinations. It is also hard (at least for me ; can't think of a way of doing it) to show these are the only operations with those properties.
Still, you can get ##3R_1+2R_2## as a combination of elementary operations.
 
  • #6
MathewsMD said:
Also, is the operation R1 + R2 and R2 + R1 allowed simultaneously? I realize this would just output the exact same equation, which isn't incredibly useful, but is it allowed?

No, the operations

1. R1 <-- R1 + R2
2. R2 <-- R2 + R1
(I assume that this was what you meant)
cannot be performed simultaneously.

This is easily seen for the matrix

[tex]\begin{bmatrix}1 & 1 \\ -1 & -1 \end{bmatrix}[/tex]
which is then transformed into the zero matrix

[tex]\begin{bmatrix}0 & 0 \\ 0& 0 \end{bmatrix}[/tex]
and these two matrices are clearly not row equivalent: the corresponding homogeneous systems have different solution sets, the latter is satisfied by any pair (x,y), which the first one is not.

So, row operations must in principle be performed sequentially. But in many cases, the operations are independent of the operations immediately before, and then they can be performed simultaneously, for example:

1. R2 <-- R2 + R1
2. R3 <-- R3 + R1

Since 1 does not change R1, 1 and 2 can be performed simultaneously. This is a difference from the previous case.
 

1. What are matrix operations?

Matrix operations are mathematical operations that are performed on matrices, which are rectangular arrays of numbers. These operations include addition, subtraction, multiplication, and division.

2. What is the purpose of matrix operations?

The purpose of matrix operations is to manipulate data in a more efficient and organized manner. Matrix operations are commonly used in fields such as engineering, physics, and computer science to solve complex problems and analyze data.

3. What are the different types of matrix operations?

The different types of matrix operations include addition, subtraction, multiplication, division, transpose, and inverse. Addition and subtraction are performed by adding or subtracting corresponding elements in the matrices. Multiplication is done by multiplying elements in rows and columns to get a new element. Division involves dividing one matrix by another. Transpose switches the rows and columns of a matrix, while inverse finds the matrix that, when multiplied by the original matrix, equals the identity matrix.

4. How are matrix operations performed?

Matrix operations are performed by following specific rules and properties for each operation. Addition and subtraction can only be done on matrices of the same size, while multiplication and division have specific rules for determining the resulting matrix. Transpose and inverse operations have their own unique processes as well.

5. What are some real-life applications of matrix operations?

Matrix operations have various real-life applications, such as in computer graphics, image processing, and machine learning. They are also used in solving systems of linear equations, calculating transformations in 3D space, and analyzing data in fields such as finance, economics, and statistics.

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