Factor the matrix into the form QR where Q is orthogonal

In summary, the conversation discusses how to factor a given matrix into the form QR, where Q is orthogonal and R is upper triangular. The conversation also mentions some conditions that need to be met for Q to be orthogonal, including having unit column vectors. The main takeaway is that such factorizations are not unique and there can be multiple ways to arrive at the same result.
  • #1
Dustinsfl
2,281
5
Factor the matrix into the form QR where Q is orthogonal and R is upper triangular.

[tex]\begin{bmatrix}
a & b\\
c & d
\end{bmatrix}*\begin{bmatrix}
e & f\\
0 & g
\end{bmatrix}=\begin{bmatrix}
-1 & 3\\
1 & 5
\end{bmatrix}[/tex]

[tex]\begin{bmatrix}
a & c
\end{bmatrix}*\begin{bmatrix}
b\\
d
\end{bmatrix}=0[/tex]

[tex]ae=-1[/tex]

[tex]af+bg=3[/tex]

[tex]ce=1[/tex]

[tex]cf+dg=5[/tex]

Skipping some steps but I arrive at:[tex]\begin{bmatrix}
1 & \frac{4}{g}\\
-1 & \frac{4}{g}
\end{bmatrix}*\begin{bmatrix}
-1 & -1\\
0 & g
\end{bmatrix}=\begin{bmatrix}
-1 & 3\\
1 & 5
\end{bmatrix}[/tex]

So as long as [tex]g \neq 0[/tex] it is all good?
 
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  • #2


Works for me. You have a matrix that is orthogonal and another that is upper triangular, and they multiply to make the matrix on the right. The moral of the story seems to be that such factorizations aren't unique.
 
  • #3


"Q is orthogonal" consists of three conditions, not one...
 
  • #4


I forgot about the part where the columns have to be unit vectors...
 
  • #5


Ok so the column vectors also have to be unit vectors and what is the other stipulation?
 
  • #6


Dustinsfl said:
Ok so the column vectors also have to be unit vectors and what is the other stipulation?
I was counting polynomial equations -- so what you just said counts as 2 conditions.
 

1. What is the purpose of factoring a matrix into the form QR?

Factoring a matrix into the form QR allows for quicker and more efficient computation of certain matrix operations, such as finding the inverse or solving systems of linear equations.

2. What does it mean for a matrix to be orthogonal?

An orthogonal matrix is a square matrix where the rows and columns are orthogonal unit vectors. This means that the dot product between any two rows or columns is equal to 0, and the magnitude of each row or column is equal to 1.

3. How is the QR factorization different from other matrix factorizations?

The QR factorization is unique in that it decomposes a matrix into an orthogonal matrix (Q) and an upper triangular matrix (R). This is different from other factorizations, such as LU or Cholesky, which result in two lower triangular matrices.

4. Can any matrix be factored into the form QR?

Yes, any square matrix can be factored into the form QR. However, if the matrix is not full rank (meaning it has linearly dependent rows or columns), the factorization may not be unique.

5. How is the QR factorization used in applications?

The QR factorization is commonly used in applications that involve solving systems of linear equations, such as least squares regression or optimization problems. It is also used in numerical methods for computing eigenvalues and eigenvectors of a matrix.

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