How are column operations used to obtain Q in elementary column operations?

In summary, the conversation is about a specific example in the book "Matrices and Linear Transformations" by Charles G. Cullens. The example demonstrates Theorem 1.30, which states that if a sequence of elementary operations on a given matrix yields a specific form, then the equation PAQ = B holds. The conversation discusses how to obtain the matrices P and Q using row and column equivalence, with the focus being on how to obtain Q. The steps for obtaining Q are listed, but the person is struggling to understand them and what exactly is meant by column reduced echelon form.
  • #1
QuantumP7
68
0
I'm reading Charles G. Cullens' "Matrices and Linear Transformations" and have a question about one of the examples. Don't worry- I'm self-studying this.

Homework Statement


This is the section about matrix equivalence- both row and column equivalence. It says "Theorem 1.30 If a sequence of elementary operations on A applied to:

[tex]\begin{bmatrix} A & I \\ I & 0 \end{bmatrix}[/tex] yields

[tex]\begin{bmatrix} B & P \\ Q & 0 \end{bmatrix}[/tex] then PAQ = B." This example works with this theorem.

So, it has an example A = [tex]\begin{bmatrix} 1 & 2 & -1 & 2 \\ -2 & -5 & 3 & 0 \\ 1 & 0 & 1 & 10 \end{bmatrix}[/tex]

with A being augmented: [tex]\begin{bmatrix} 1 & 2 & -1 & 2 & 1 & 0 & 0 \\ -2 & -5 & 3 & 0 & 0 & 1 & 0\\ 1 & 0 & 1 & 10 & 0 & 0 & 1 \\ 1 & 0 & 0 & 0 & & & \\ 0 & 1 & 0 & 0 & & &\\ 0 & 0 & 1 & 0 & & &\\ 0 & 0 & 0 & 1 & & &\end{bmatrix}[/tex]

Where the 3 x 3 identity matrix is for P and the 4 x 4 matrix is for Q. I understand the elementary row operations to obtain P, which turns out to be:
[tex]\begin{bmatrix} 1 & 0 & 0 \\ -2 & -1 & 0 \\ -5 & -2 & 1 \end{bmatrix}[/tex]
. This is just transforming A and P into a row-reduced echelon form.

What I do not understand is the algorithm for obtaining Q (which turns out to be:

[tex]\begin{bmatrix} 1 & -2 & -1 & -10 \\ 0 & 1 & 1 & 4 \\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \end{bmatrix}[/tex] )

How is this Q obtained? Are we trying to reduce the matrix for Q to a column reduced echelon form? If so, what exactly is column reduced echelon form? The book lists steps for obtaining Q, but does not explain them. Here are the steps:
(-2[tex]C_{1} + C_{2}[/tex])
([tex]C_{1} + C_{3}[/tex])
(-2[tex]C_{1} + C_{4}[/tex])

then

([tex]C_{2} + C_{3}[/tex])
(4[tex]C_{2} + C_4}[/tex])

Homework Equations


The Attempt at a Solution

 
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  • #2
I have read the book and attempted to apply the steps to the matrix, but I do not understand what is going on. I know that row/column equivalence has to do with elementary row/column operations, but I do not understand how these operations yield Q.
 

1. What are elementary column operations?

Elementary column operations are a set of mathematical operations used to manipulate the columns of a matrix. These operations include multiplying a column by a constant, swapping two columns, and adding a multiple of one column to another.

2. Why are elementary column operations useful?

Elementary column operations are useful because they allow us to transform a matrix into an equivalent matrix with simpler properties. This makes it easier to work with and solve systems of linear equations or perform other calculations.

3. How do elementary column operations affect the solution to a system of linear equations?

Elementary column operations do not change the solution to a system of linear equations. This is because they preserve the linear relationships between the equations and variables in the system.

4. Can elementary column operations be used on any type of matrix?

Yes, elementary column operations can be used on any type of matrix, including square matrices, rectangular matrices, and augmented matrices.

5. What is the difference between elementary column operations and elementary row operations?

The main difference between elementary column operations and elementary row operations is that column operations manipulate the columns of a matrix, while row operations manipulate the rows. Both types of operations are important in matrix manipulation and solving systems of linear equations.

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