Finding Eigenvalues and questions

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In summary, the conversation discusses finding eigenvalues for a given matrix and the method of finding them by taking the determinant of a 3x3 matrix. The conversation also mentions the possibility of using tools such as WolframAlpha, but the speaker states that it is not allowed for the exam. The speaker also mentions that there is not a faster way to get to a triangular matrix and that the given method is the fastest way.
  • #1
flyingpig
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Homework Statement



http://img703.imageshack.us/img703/4489/unledzh.th.png

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The Attempt at a Solution



a)

Ax = λx

Ax = x

Ax - x = 0

(A - I)x = 0


I set up my matrix

[PLAIN]http://www3.wolframalpha.com/Calculate/MSP/MSP154819f61i5gh6d9fed500006aa20fbb6bgf59g1?MSPStoreType=image/gif&s=17&w=207&h=56

RowReduced it and I got

[PLAIN]http://www3.wolframalpha.com/Calculate/MSP/MSP155119f61i5gh6d9fed50000387i4eb69hc6g67e?MSPStoreType=image/gif&s=17&w=80&h=56

Because this matrix is linearly dependent (can I say the "set is linearly dependent?" I have trouble with these names like "set", "systems"), 1 is an eigenvalue.

b) I know I could just do it the long way, but is there a faster way? Because this is not in triangular form
 
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  • #2
I'm not sure what you mean with the long way, but afaik the method is as follows:

Ax=λx ⇒ det(A-λI)=0

Calculating this will give you a 3rd order polynomial to solve.
Luckily you already know one eigenvalue (which is 1), this makes it easier to solve and in passing by also verifies that 1 is an eigenvalue.

Do you know how to do this?
 
  • #3
No... unfortunately
 
  • #4
flyingpig said:
No... unfortunately

Well, here's a wiki page that explains how to take the determinant of a 3x3 matrix: http://en.wikipedia.org/wiki/Determinant#3-by-3_matrices

And your equation is:
[tex]\det \begin{pmatrix}-\lambda & -4 & -6 \\
-1 & -\lambda & -3 \\
1 & 2 & 5-\lambda
\end{pmatrix} = 0[/tex]

Can you combine those to yield a 3rd order polynomial?

Btw, which tools are you allowed to use?
Are you allowed to use e.g. WolframAlpha?
 
  • #5
No I know I could that, but this is a question from an exam paper, so no technologies. I am wondering if there is a faster way to get to a triangular matrix and just "read it off"
 
  • #6
flyingpig said:
No I know I could that, but this is a question from an exam paper, so no technologies. I am wondering if there is a faster way to get to a triangular matrix and just "read it off"

Nope, I'm afraid not. This is the fastest way.
But you don't need to go to a triangular matrix to do this.
So though your answer to a) was correct (and yes, you can say that the set of equations is linearly dependent), doing it this way, would answer a) as well.
 

Related to Finding Eigenvalues and questions

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are concepts in linear algebra that are used to analyze and understand the behavior of linear transformations. Eigenvalues represent the scale factor by which an eigenvector is stretched or compressed when it is transformed by a linear transformation. Eigenvectors are the non-zero vectors that remain unchanged by the linear transformation, except for a possible change in scale.

2. How do you find eigenvalues and eigenvectors?

To find eigenvalues and eigenvectors, you must first find the characteristic polynomial of a given matrix. This polynomial is then solved for its roots, which are the eigenvalues. To find the corresponding eigenvectors, the eigenvalues are substituted back into the original matrix and the system of equations is solved for the eigenvector values.

3. What is the importance of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are important because they provide valuable information about the behavior of linear transformations. They can be used to determine if a matrix is diagonalizable, to simplify matrix computations, and to understand the stability of dynamic systems in fields such as physics, engineering, and economics.

4. Can you have complex eigenvalues and eigenvectors?

Yes, it is possible to have complex eigenvalues and eigenvectors. In fact, complex eigenvalues and eigenvectors are often encountered when dealing with matrices that represent rotations or reflections in higher dimensional spaces. In these cases, the eigenvalues and eigenvectors can be used to understand the behavior of these transformations.

5. Are there any real-world applications of eigenvalues and eigenvectors?

Yes, there are many real-world applications of eigenvalues and eigenvectors. Some examples include image and signal processing, quantum mechanics, and data compression. Eigenvalues and eigenvectors are also used in machine learning and data analysis to reduce the dimensionality of large datasets and identify patterns in the data.

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