Eigenvalues of a linear transformation (Matrix)

In summary, the conversation discusses finding the eigenvectors of a linear transformation T defined on the four dimensional vector space M22. The process involves writing T in terms of a basis and then computing the eigenvectors of T in that basis. T can be represented as a 4x4 matrix in the basis, and the eigenvectors can be found by writing T(ei) as a linear combination of the basis vectors and creating a matrix P from these vectors.
  • #1
Iconate
21
0

Homework Statement


Let T: M22 -> M22 be defined by
T[itex]

\[ \left( \begin{array}{cc}
a & b \\
c & d \\
\end{array} \right)\]

[/itex]=
[itex]

\[ \left( \begin{array}{cc}
2c & a+c \\
b-2c & d \\
\end{array} \right)\]

[/itex]

Find the eigenvectors of T



The Attempt at a Solution



My main question is, Which matrix am I using to compute my eigenvectors?
Do I need to compute a basis first?

Where this problem differs from my other questions is that I am no longer producing a matrix from my basis vectors which I use to create [T]B

Any insight would be great, thanks.
 
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  • #2
M22 is a four dimensional vector space, right? A basis is e1=[[1,0],[0,0]], e2=[[0,1],[0,0]], e3=[[0,0],[1,0]] and e4=[[0,0],[0,1]], right? So T must be a 4x4 matrix in that basis, yes? Can you write out what it is in the {e1,e2,e3,e4} basis?
 
  • #3
Yeah I did that, do I find the eigenvectors with each of their matricies?, normally id put my basis vectors INTO a matrix, but I have matricies, i figure they have to go somewhre, just don't know where
 
  • #4
I'm not sure I understand that. Just pretend [[a,b],[c,d]] is a 4 vector, [a,b,c,d]. T maps it to another 4 vector [2c,a+c,b-2c,d]. The fact they write these vectors as matrices is just a technicality.
 
  • #5
I figured it out. I have to write T[e1] as a linear combination of the basis vectors.

Ex. T(e1) = [[0,1],[0,0]] = 0*e1 + 1*e2 + 0*e3 + 0*e4
= (0,1,0,0)

And Now i have my vector! Computing this for all ei's will create my matrix P.
 
  • #6
Iconate said:
I figured it out. I have to write T[e1] as a linear combination of the basis vectors.

Ex. T(e1) = [[0,1],[0,0]] = 0*e1 + 1*e2 + 0*e3 + 0*e4
= (0,1,0,0)

And Now i have my vector! Computing this for all ei's will create my matrix P.

Exactly.
 

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are concepts in linear algebra that are used to analyze linear transformations. An eigenvalue is a scalar value that represents how a particular vector is scaled by a linear transformation. An eigenvector is a non-zero vector that remains in the same direction after being transformed by the linear transformation.

2. How are eigenvalues and eigenvectors calculated?

The process of calculating eigenvalues and eigenvectors involves solving a system of linear equations. This can be done by finding the roots of the characteristic polynomial of the matrix, or by using specialized algorithms such as the QR algorithm or the power method.

3. What is the significance of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are important because they provide information about the behavior of a linear transformation. They can be used to determine the stability of a system, to identify important directions of a transformation, and to simplify complex systems by representing them in terms of their eigenvectors.

4. Can a matrix have multiple eigenvalues and eigenvectors?

Yes, a matrix can have multiple eigenvalues and corresponding eigenvectors. However, the number of eigenvalues and eigenvectors is limited by the size of the matrix. For an n x n matrix, there can be at most n distinct eigenvalues and n corresponding eigenvectors.

5. How are eigenvalues and eigenvectors used in real-world applications?

Eigenvalues and eigenvectors have a wide range of applications in various fields such as physics, engineering, and computer science. They are used in image processing, data compression, quantum mechanics, and many other areas. For example, in finance, eigenvalues and eigenvectors are used to analyze stock market trends and to optimize investment portfolios.

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