Gram-Schmidt Orthogonalizationh

  • Thread starter happyg1
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In summary: I think you might be missing an equation in the book. Maybe it's equation (2)?In summary, the author attempted to solve for the r11 entry in their R matrix using the norm of the first column of A and got incorrect results. They then attempted to solve for the r12 entry in their R matrix using the norm of the first column of A and got incorrect results. They then attempted to solve for the r22 entry in their R matrix using the norm of the first column of A and got incorrect results. They then attempted to solve for the r23 entry in their R matrix using the norm of the first column of A and got incorrect results. They then attempted to solve for the r33 entry in their R
  • #1
happyg1
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Homework Statement


Use the Gram-Schmidt method to find an orthogonal matrix Q and an upper triangular matrix R so that A=QR, where
[tex]A=\begin{bmatrix}16 & -4 & 8\\-4 & 5 & -4\\8 & -4 & 14\end{bmatrix}[/tex]

Homework Equations



[tex]A^{(1)}=Q^{(1)}r_{11}[/tex]
[tex]A^{(2)}=Q^{(1)}r_{12}+Q^{(2)}r_{22}[/tex]
[tex]A^{(3)}=Q^{(1)}r_{13}+Q^{(2)}r_{23}+Q^{(3)}r_{33}[/tex]
[tex]r_{11}=||A^{(1)}||[/tex]
[tex]r_{12}=(Q^{(1)})^H A^{(2)}[/tex]
[tex]r_{22}=}||A^{(2)}-r_{12}Q^{(1)}||[/tex]

Those are the formulas we derived form the information in our book.
The [tex]A^{(1)}[/tex] represents the first column of the matrix A, and the same goes for the Q's. The [tex]r_{11}[/tex] is the 1,1 entry in the R matrix and so forth. The H is Hermetian.

The Attempt at a Solution


OK.
In order to find the r11 entry in my R matrix, I took the norm of the first column of A:
[tex]r_{11}=\sqrt{16^2+-4^2+8^2}=4\sqrt{21}[/tex]
Then I divide the first column of A by [tex]4\sqrt{21}[/tex] to gert my first column of Q.

That gives [tex]Q^{(1)}=
\left( \begin{matrix}\frac{4}{\sqrt{21}}\\ \frac{-1}{\sqrt{21}}\\ \frac{2}{\sqrt{21}}\end{matrix}\right)[/tex]Then I need my r12 entry of my R matrix. So,

[tex]r_{12}=(Q^{(1)})^HA^{(2)}[/tex]
which is:
[tex]\left( \begin{matrix}\frac{4}{\sqrt{21}}&\frac{-1}{\sqrt{21}}& \frac{2}{\sqrt{21}}\end{matrix}\right)
\left( \begin{matrix}-4\\5\\-4\end{matrix}\right) =\frac{-29}{\sqrt{21}}[/tex]

Then I need my second column of my Q matrix, which is supposed to be given by
[tex]r^{22}Q^{(2)}=A^{(2)}-r_{12}Q^{(1)}[/tex]
according to the equation. I just solved it for r22Q2.
Anyway, this is where my trouble starts.
I got:
[tex]\left(\begin{matrix}-4\\5\\-4\end{matrix}\right)
- (-\frac{29}{\sqrt{21}})
\left(\begin{matrix}\frac{4}{\sqrt{21}}\\ \frac{-1}{\sqrt{21}}\\ \frac{2}{\sqrt{21}}\end{matrix}\right) =
\left( \begin{matrix}\frac{-200}{24}\\ \frac{134}{21}\\ \frac{-142}{21}\end{matrix}\right)[/tex]

Which is totally wrong. I know I'm missing something, but I can't see it.
The norm of that thing is SUPPOSED to be 1, but it's not.

I hoipe my latex came out OK. Bear with me while I try to edit it.
CC
 
Last edited:
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  • #2
No, as you said, that's r22Q2, not Q2. You pick r22 so that Q2 has norm 1. Also, by "Hermetian", I'm pretty sure you mean to spell "Hermitian", and you mean to use the word "transpose".
 
  • #3
Ok, so I need to solve for the norm of[tex]\left( \begin{matrix}\frac{-200}{24}\\ \frac{134}{21}\\ \frac{-142}{21}\end{matrix}\right)[/tex]
so that's
[tex]\sqrt{(\frac{-200}{24})^2 (\frac{134}{21})^2 (\frac{-142}{21})^2}=\frac{2\sqrt{2170}}{7}[/tex]

so
[tex]r_{22}=\frac{2\sqrt{2170}}{7}[/tex]
so then I multiply r22Q2 by 1 over r22 to get Q2:

[tex]Q^{(2)}=\frac{7}{2 \sqrt{2170} \left( \begin{matrix}\frac{-200}{24}\\ \frac{134}{21}\\ \frac{-142}{21}\end{matrix}\right)=[/tex]

[tex]\left( \begin{matrix}\frac{(10)*\sqrt{2170}}{651}\\ \frac{(67)*\sqrt{2170}}{6510}\\ \frac{(71)*\sqrt{2170}}{6510}\end{matrix} \right)[/tex]

and the norm of that IS 1. but it doesn't match what the TI89 coughed out. I don't know if I'm right or not here.
TI89 got
[tex]Q^{(2)}=\left[ \begin{matrix}\frac{16}{\sqrt{1869}}\\ \frac{38}{\sqrt{1869}}\\ \frac{-13}{\sqrt{1869}}\end{matrix} \right][/tex]

They're not the same at all. I just don't know what's wrong here.
HELP
CC
 
Last edited:

What is Gram-Schmidt Orthogonalization?

Gram-Schmidt Orthogonalization is a mathematical process used to convert a set of linearly independent vectors into a set of orthogonal vectors. This process is commonly used in linear algebra and is named after mathematicians Jørgen Pedersen Gram and Erhard Schmidt.

What is the purpose of Gram-Schmidt Orthogonalization?

The purpose of Gram-Schmidt Orthogonalization is to simplify vector calculations by creating a set of orthogonal vectors. This makes it easier to solve systems of linear equations, calculate projections, and perform other vector operations.

How does Gram-Schmidt Orthogonalization work?

The process involves taking a set of linearly independent vectors and transforming them into a set of orthogonal vectors. This is done by iteratively subtracting the projection of each vector onto the previous vectors from itself, resulting in a perpendicular vector. The final set of orthogonal vectors is then normalized to have a unit length.

What are the benefits of using Gram-Schmidt Orthogonalization?

One of the main benefits of Gram-Schmidt Orthogonalization is that it simplifies vector operations by creating a set of orthogonal vectors. This can also help to reduce errors in calculations and make it easier to solve systems of linear equations.

What is the difference between Gram-Schmidt Orthogonalization and Gram-Schmidt Process?

Gram-Schmidt Orthogonalization and Gram-Schmidt Process are often used interchangeably, but there is a slight difference between the two. Gram-Schmidt Process refers to the initial step of converting a set of linearly independent vectors into a set of orthogonal vectors, while Gram-Schmidt Orthogonalization refers to the entire process, including the normalization step.

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