Eigenanalysis: Finding Eigenvectors

In summary, the homework problem involved solving for the eigenvalues and eigenvectors of a linear system. Eigenanalysis was used as a method to do this. However, the order of the lambda values did not seem to matter. When doing the full eigenanalysis, the order of the lambda values seemed to matter.
  • #1
erok81
464
0

Homework Statement



This is a simple example from the book, but it gets the point across nicely.

In this problem eigenanalysis is used as a method to solve linear systems.

The matrix...

[4 2]
[3 -1]

Eigenvalues are -2, 5.

Homework Equations



[tex](A-\lambda I)v=0[/tex]

x'=[above matrix]x

The Attempt at a Solution



So I solve for the eigenvector using the first case, [tex]\lambda=-2[/tex]

New matrix with above eigenvalue:

[6 2]
[3 1]

The book gives the eigenvector as [1,-3]^T

I get [-1/3,1]^T

From my rref of:
[3 1]
[0 0]

My vector and the books vector are off by a value of -3. Which I think is because I tried the rref and they just used two equations with the two unknowns and "guessed' values.

I ran into this problem a few times in the homework as well, which would eventually lead to an incorrect answer compared to the textbook.

My question is, why do you solve a normal matrix using the rref but it seems you can't do the same with eigenanalysis? Do I just break them into equations and guess solutions?

Everyone I did in the homework I ended up with the same type of answer - off by some value that I can see used to be in the matrix like the above example.
 
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  • #2
Eigenvectors are only defined up to a multiplicative constant. If Mv=lambda*v, then c*v is also an eigenvector with eigenvalue lambda. For any nonzero c. Always. Your eigenvector is (-1/3) times the books eigenvector. You are both right.
 
Last edited:
  • #3
Perfect. Thank you for the quick response and the explanation.

That's good to know. I was worried since none of my answers matched the text exactly. I couldn't see why mine didn't work though - now I do. Both answers are correct.

Thanks again.
 
  • #4
So it turns out I have an additional question regarding these problems.

This time the original matrix is
[1 2]
[2 1]

I solved for [tex]\lambda_1=-1[/tex], and [tex]\lambda_2=3[/tex]

Does it matter which value you choose for _1 and _2? It seems when we found the diagonal matrices in a previous section I had to always choose the lower value in order to get the answer correct. If I didn't, my answer wasn't close due to the different matrices I could get depending on the order of lambda's I chose.

Anyway, back to this problem.

I used the above lambda values to obtain to the eigenvectors of v_1=[-1,1]^T and v_2=[1,1]^T

Since I chose the order I did, I ended up with my x_1 and x_2 for the general solution switched. This, unlike my previous, seems to matter more since they are different parts of a system. In this case, brine tanks.

Any idea where I am going wrong?
 
  • #5
erok81 said:
So it turns out I have an additional question regarding these problems.

This time the original matrix is
[1 2]
[2 1]

I solved for [tex]\lambda_1=-1[/tex], and [tex]\lambda_2=3[/tex]

Does it matter which value you choose for _1 and _2? It seems when we found the diagonal matrices in a previous section I had to always choose the lower value in order to get the answer correct. If I didn't, my answer wasn't close due to the different matrices I could get depending on the order of lambda's I chose.

Anyway, back to this problem.

I used the above lambda values to obtain to the eigenvectors of v_1=[-1,1]^T and v_2=[1,1]^T

Since I chose the order I did, I ended up with my x_1 and x_2 for the general solution switched. This, unlike my previous, seems to matter more since they are different parts of a system. In this case, brine tanks.

Any idea where I am going wrong?

I guess I don't understand the issue. [-1,1] is the eigenvector corresponding to the eigenvalue -1 and [1,1] corresponds to 3. I don't know how the '_1' and '_2' designations have to do with the problem. You might want to quote the whole problem if it's still unclear.
 
  • #6
Dick said:
I guess I don't understand the issue. [-1,1] is the eigenvector corresponding to the eigenvalue -1 and [1,1] corresponds to 3. I don't know how the '_1' and '_2' designations have to do with the problem. You might want to quote the whole problem if it's still unclear.

Sorry about that. I thought that would be an issue and forgot to go back and edit.[strike] Take a look at that post again. I've edited for clarity.

Well...I am going to after I post this. So maybe five minutes from now. :) [/strike]

The _1 and and _2 were the lambda values. But after looking at it, it doesn't matter the order of those. EXCEPT when I was doing the full eigenanalysis. The order seemed to matter there.

EDIT: You are right. I've been doing homework too long today. It doesn't matter which order I do these in, that just changes the order of my c_n*v_n answers.

My x_1(t) and x_2(t) are still swapped from what the book shows though. It looks like it might still be my same issue. My v_1 is off by a multiple of -1. i.e. If I swap the signs in my v_1, the answer comes out right.

Does that still fall into the "off by a constant" thing and my answer is still correct?

I attached a scan of my work. That should help since I am not the best at latex and posting. Sorry about the sideways scan.
 

Attachments

  • worked problem.PDF
    949.6 KB · Views: 197
Last edited:
  • #7
To help explain, if I do it the other way I mentioned, I get this attachment. Which matches the book answer.
 

Attachments

  • worked problem 2.PDF
    858.5 KB · Views: 167
  • #8
Yes, it's just the constant factor again. You can turn your answer into the book's answer merely by replacing c1 by -c1.
 
  • #9
That makes sense, thank you.

I am sure once I start doing these that have initial conditions, I won't see this problem. In fact...come to think of it, the ones I did that used initial conditions, I always ended up with an answer that matched the book.
 

1. What is eigenanalysis?

Eigenanalysis is a mathematical process used to find the eigenvectors and eigenvalues of a square matrix. It is commonly used in fields such as physics, engineering, and data analysis.

2. Why is it important to find eigenvectors?

Eigenvectors are important because they represent the directions in which a transformation acts by simply scaling the vector. They also help in understanding the structure and behavior of a system or dataset.

3. How is eigenanalysis performed?

Eigenanalysis involves finding the roots of the characteristic polynomial of a matrix, which gives the eigenvalues. The eigenvectors are then calculated by solving a system of linear equations using the eigenvalues.

4. What are the applications of eigenanalysis?

Eigenanalysis has many applications, including principal component analysis (PCA), which is used to reduce the dimensionality of high-dimensional datasets. It is also used in image and signal processing, quantum mechanics, and many other fields.

5. Can eigenanalysis be performed on non-square matrices?

No, eigenanalysis can only be performed on square matrices since the characteristic polynomial and eigenvalues are only defined for square matrices. However, there are other methods for finding the "eigenstructure" of non-square matrices, such as singular value decomposition (SVD).

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