Normalization & value of Eigenvectors

In summary: Hi,Thanks for your reply. I am sorry for my writing.I got it.So we can have (x, -x, 0) only.In summary, We have a matrix A with eigenvalues λ1 = 1, λ2 = 0, and λ3 = 2. The eigenvectors corresponding to λ1 and λ3 are [0, 0, 1] and [-1, 1, 0] respectively. However, for λ2, we have the equations x + y = 0 and z = 0, which results in the eigenvector [x, -x, 0] where x is an arbitrary number. This vector can take on any
  • #1
zak100
462
11

Homework Statement


I have got the following matrix. I have found the eigen values but in some eq x, y & z terms are vanishing, so how to find the value of eigen vector? Also why we have to do normalization??

A__=__[1__1__0]
______[1__1__0]
______[0__0__1]

Homework Equations


A-λI=0
Ax = -λIx

The Attempt at a Solution


A-λI=0
[1__1__0]_-_[λ__0__0]_____=0____________________
[1__1__0]___[0__λ__0]___________________________
[0__0__1]___[0__0__λ]___________________________
______________________________________________
[1-λ__1__0]=0__________________________________
[1___1-λ__0]____________________________________
[0___0____1-λ]__________________________________
_______________________________________________
1-λ|1-λ__0|____-1|1___0__| +0 =0__________________
___|0__1-λ|______|0___1-λ_|________________________
_______________________________________________
(1-λ)(1-λ)^2____-(1-λ) =0
Taking_(1-λ) common
(1-λ)[(1-λ)^2__-1]=0
First eigen value λ1 = 1
Now consider:
[(1-λ)^2_____-1]=0
1-2λ+λ^2-1=0
-2λ+λ^2=0
λ(λ-2)=0
λ2=0
& λ3=2
______________________________________________________
______________________________________________________
For λ1 = 1
Ax = λ*x*I
_____________________________________________________
[1__1__0]_______[x]___= 1 * [x]___________________________
[1__1__0]_______[y]_______[y]___________________________
[0__0__1]_______[z]_______[z]___________________________
______________________________________________________
x+y=x------eq(1)
x+y=y------eq(2)
z=z---------eq(3)
______________________________________________________
Above x vanishes in eq(1), y vanishes in eq(2) & z vanishes in eq(3).
What is the value of μ1?
______________________________________________________
For λ2=0_______________________________________________
[1__1__0]_______[x]___= 0 * [x]____________________________
[1__1__0]_______[y]_______[y]____________________________
[0__0__1]_______[z]_______[z]____________________________
______________________________________________________
x+y=0----------eq(4)_______________________________________
x+y=0----------eq(5)_______________________________________
z=0-------------eq(6)_______________________________________
______________________________________________________
In my view it should be μ=[-1]_______________________________
_____________________[-1]______________________________
_____________________[0]_______________________________
but teacher has got different answer. In addition to this he has done normalization, please guide me what is the need for normalization_______________________________
______________________________________________________
For λ=2________________________________________________
_____________________________________________________
[1__1__0]_______[x]___=_2_*_ [x]___________________________
[1__1__0]_______[y]_______[y]___________________________
[0__0__1]_______[z]_______[z]___________________________
x+y=2x------eq(7)-----------------------------------------------------------------
x=-y----------------------------------------------------------------------------------
x+y=2y-------eq(8)----------------------------------------------------------------
y=x----------------------------------------------------------------------------------
z=2z----------eq(9)---------------------------------------------------------------
Therefor, μ=[-1]_________________________________________
__________[1]_________________________________________
__________[0]___________________________________________
Why we have to normalize the vector?
Somebody please guide me.
Zulfi.
 
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  • #2
zak100 said:
Above x vanishes in eq(1), y vanishes in eq(2) & z vanishes in eq(3).
No. It is correct that you get ##x=y=0##, but ##z=z## means that it is always true. So any value will do, i.e. ##(0,0,1)## is your eigenvector. It spans the eigenspace to the eigenvalue ##\lambda_1=1##, which means all vectors in ##\mathbb{R}\cdot (0,0,1)## are eigenvectors, which corresponds to the free value of ##z##. You don't have to choose ##z=1##, it is simply a matter of convenience.
zak100 said:
x+y=0----------eq(4)_______________________________________
x+y=0----------eq(5)_______________________________________
z=0-------------eq(6)_______________________________________
______________________________________________________
In my view it should be μ=[-1]_______________________________
_____________________[-1]______________________________
_____________________[0]_______________________________
##x+y=0## means ##x=-y##. You have chosen ##x=y## which is wrong, and so is your eigenvector.
zak100 said:
For λ=2________________________________________________
_____________________________________________________
[1__1__0]_______[x]___=_2_*_ [x]___________________________
[1__1__0]_______[y]_______[y]___________________________
[0__0__1]_______[z]_______[z]___________________________
x+y=2x------eq(7)-----------------------------------------------------------------
x=-y----------------------------------------------------------------------------------
x+y=2y-------eq(8)----------------------------------------------------------------
y=x----------------------------------------------------------------------------------
z=2z----------eq(9)---------------------------------------------------------------
Same problem. We have ##x=y## in both equations, so your ##x=-y## is wrong, and so is your eigenvector.
zak100 said:
Why we have to normalize the vector?
See my explanation above. "Have to" is the wrong verb. Maybe you should try to prove, that if ##A\cdot \vec{x}=\lambda \cdot \vec{x}## then all ##\mu \cdot \vec{x}## are also eigenvectors to the eigenvalue ##\lambda##. To choose ##1's## as components makes computations easier.

Btw, this isn't a normalization, at least not according to the usual Euclidean norm. A normalized vector ##(1,1,0)## would be ##\left(\frac{1}{\sqrt{2}},\frac{1}{\sqrt{2}},0 \right)\,.##
 
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  • #3
Hi,
Thanks for your reply.
λ1=1, eigen vector is:___________________________________________________
[0]__________________________________________________________________
[0]__________________________________________________________________
[1]___________________________________________________________________
because equations are:
x+y=x------eq(1) (No x-component, so x is 0
x+y=y------eq(2) (No y-component, so y is 0)
z=z---------eq(3) (Okay z=1)
_____________________________________________
I got it.
For λ2=0, I got:
For λ2=0_______________________________________________
[1__1__0]_______[x]___= 0 * [x]____________________________
[1__1__0]_______[y]_______[y]____________________________
[0__0__1]_______[z]_______[z]____________________________
______________________________________________________
x+y=0---------eq(4)_______________________________________
x+y=0----------eq(5)_______________________________________
z=0-------------eq(6)________
I can't understand what is the Eigen Vector in this case. Please guide me.

Zulfi.

Reference https://www.physicsforums.com/threads/normalization-value-of-eigenvectors.955610/

Reference https://www.physicsforums.com/threads/normalization-value-of-eigenvectors.955610/
 
  • #4
We have
$$
\begin{bmatrix}1&1&0\\1&1&0\\0&0&1\end{bmatrix}\cdot \begin{bmatrix}x\\y\\z\end{bmatrix}=0\cdot \begin{bmatrix}x\\y\\z\end{bmatrix}=\begin{bmatrix}0\\0\\0\end{bmatrix}
$$
and so ##x+y=0## and ##z=0##. So ##\begin{bmatrix}x\\y\\z\end{bmatrix}=\begin{bmatrix}x\\-x\\0\end{bmatrix}=x\cdot \begin{bmatrix}1\\-1\\0\end{bmatrix}##. If you did my exercise, you know that all multiples of an eigenvector is again an eigenvector to the same eigenvalue. Therefore any, say ##x-##multiple of ##(1,-1,0)^\tau## are all eigenvectors, especially the ##1-##fold of it (##x=1##).
 
  • #5
Hi,
Thanks for your response.
We have:
x+y=0 eq(4). So x is +ve in the eq so we would have +1.
But in eq(5)
x + y = 0
& we have to find y:
y = -x., so y =-1

& in last case, we have to find z, & z=0, so we have 0 in the 3rd entry of vector.
If in the 3rd eq, we were having:
x+z=0
So will z be equal to -1 in this case?
& if y+z = 0,
then again will z be zero?

Please guide me.
Zulfi.
 
  • #6
I'm not sure I understand you. Equations (4) and (5) are redundant, as they both yield ##x=-y##, and equation (6) is ##z=0##.
Since we wanted to calculate the vector ##\vec{x}=(x,y,z)## for which ##A\vec{x}=0\cdot \vec{x}=\vec{0}##, we get ##(x,y,z)=(x,-x,0)## which are all possible eigenvectors for the eigenvalue zero.
 
  • #7
Hi,
Thanks for your response. I can't understand how you got (x, -x, 0) from the equations?

Zulfi.
 
  • #8
zak100 said:
Hi,
Thanks for your response. I can't understand how you got (x, -x, 0) from the equations?

Zulfi.
We have ##x+y=0##. This is the same as ##x+y-x=0-x## which is ##y=-x##. The last equation (6) directly says ##z=0##. So ##x## cannot be determined, or ##y## if you like it better. There is one free variable, I chose ##x##. And so ##(x,y,z)=(x,-x,0)\,.##
 
  • #9
Hi,
Thanks for your reply. So from eq:
x+y=0
we can have either x =-1 or y=-1. Is it possible to have (-1, 1, 0)

And once we get x =-1 we would put it into 2nd eq giving:
y=1.
And no problem with z.

Thanks.

Zulfi.
 
  • #10
zak100 said:
Hi,
Thanks for your reply. So from eq:
x+y=0
we can have either x =-1 or y=-1. Is it possible to have (-1, 1, 0)

And once we get x =-1 we would put it into 2nd eq giving:
y=1.
And no problem with z.

Thanks.

Zulfi.
No, we do not get a certain value for ##x## or ##y## from the equations.

Let's change the notation, since you seem to confuse variables and values. We started with ##A\cdot \begin{bmatrix}u\\v\\w\end{bmatrix}=\begin{bmatrix}0\\0\\0\end{bmatrix}## which is an equation full of numbers. Only thing is, we don't know the numbers ##u,v,w## yet. So we try to calculate them. This led us to the equations ##u+v=0## and ##w=0##. More cannot be said. So ##:=\begin{bmatrix}u\\v\\w\end{bmatrix}=\begin{bmatrix}u\\-u\\0\end{bmatrix}=u\cdot \begin{bmatrix}1\\-1\\0\end{bmatrix}##. But ##u## remains arbitrary, because we don't have another equation to fix it. All possible vectors ##u\cdot \begin{bmatrix}1\\-1\\0\end{bmatrix}## are eigenvectors, which means you can choose for ##u## whatever you like: with ##u=\pi## we get the eigenvector ##\begin{bmatrix}\pi \\ -\pi \\ 0 \end{bmatrix}##, for ##u=1## we get ##\begin{bmatrix}1\\-1\\0\end{bmatrix}##, for ##u=-1## we have ##\begin{bmatrix}-1\\1\\0\end{bmatrix}## and for ##u=0## we get ##\begin{bmatrix}0\\0\\0\end{bmatrix}## which is always an eigenvector to any eigenvalue. That's why it is usually not meant by the word eigenvector, nevertheless, it is one.

And I do not want to see the same sign on ##x## and ##y## again! How could their sum ever be zero, if they were both negative?
 
  • #11
Hi,
Thanks for your time. I highly appreciate yourself removing my confusion.

<And I do not want to see the same sign on x and y again!>
I have used the word either. Form the eq:
x+y =0
its clear that either x=-1 & y=1
or x= & y=-1.
which in short I said that either x =-1 or y=-1.

Also
<
we do not get a certain value for x or y from the equations. >

So why its necessary to have x= 1 & y=-1. It could also be possible that x=-1 & y=1.

Zulfi.
Reference https://www.physicsforums.com/threads/normalization-value-of-eigenvectors.955610/
Reference https://www.physicsforums.com/threads/normalization-value-of-eigenvectors.955610/
Reference https://www.physicsforums.com/threads/normalization-value-of-eigenvectors.955610/

Zulfi.
 
  • #12
zak100 said:
So why its necessary to have x= 1 & y=-1. It could also be possible that x=-1 & y=1.
Yes, and many more (cp. my examples). ##x=0## is the only value which cannot be taken, as the zero vector is an eigenvector for all eigenvalues, and even for values, which are not eigenvalues, because ##A\cdot 0 = \mu \cdot 0## is always true, for any ##\mu##. So we need a value different form zero. ##x=1## is as good as any other. However, ##x=1## is far more convenient than ##x=15,613,687,516,498,984,816,098,780,098,732,198,075,519,158## would be. If the eigenvalue was ##\frac{1}{2}## it might be convenient to choose ##x=2##. It depends on what you want to do with the eigenvector. ##x=1## is simply the easiest one.
 

What is normalization and why is it important in scientific research?

Normalization is a statistical process used to standardize data by adjusting it to a common scale. It is important in scientific research because it allows for fair comparison between different datasets, removes bias caused by different measurement units, and improves the accuracy and reliability of statistical analyses.

What is the value of eigenvectors in data analysis?

Eigenvectors are used to determine the direction of the maximum variance in a dataset. This information is useful in data analysis as it can help identify important features or patterns in the data and reduce the dimensionality of the dataset for easier interpretation and visualization.

How are eigenvectors calculated?

Eigenvectors are calculated through a process called eigendecomposition, which involves solving a mathematical equation using linear algebra techniques. This process results in a set of eigenvectors and corresponding eigenvalues, which represent the direction and magnitude of the maximum variance in a dataset, respectively.

Can eigenvectors have negative values?

Yes, eigenvectors can have negative values. The sign of an eigenvector does not affect its direction or importance in data analysis. It is simply a mathematical property that can be positive or negative.

How are eigenvectors used in machine learning?

In machine learning, eigenvectors are often used in dimensionality reduction techniques, such as principal component analysis. They can also be used to initialize the weights of neural networks and as a way to identify important features in a dataset for predictive modeling.

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