What are the Eigenvalues and Eigenvectors of a 2x2 Matrix?

In summary, the conversation discusses finding the eigenvalues and eigenvectors of a given matrix A and its cubed form A^3. The process involves solving the characteristic equation and then using the resulting eigenvalues to find the corresponding eigenvectors. An example is provided for further clarification, and the same steps apply for A^3.
  • #1
eddysd
39
0

Homework Statement



A=[1 0] Calculate
[2 3]
a) Eigenvalues of A
b) Eigenvectors of A
c) Eigenvalues and eigenvectors of A^3

The Attempt at a Solution


I had no idea what I was doing, but I saw someone attempt one somewhere and used the same method

Getting x=3 and 1 for part a)

However, I have no idea if this is correct, or even if it is in the correct format. Any help would be greatly appreciated.
 
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  • #2
The eigenvalues of a matrix can be found as follows:

[tex] A\vec{x} = \lambda\vec{x} [/tex]

[tex] (A - \lambda I) \vec{x} = 0 [/tex]

Now we know that this equation will only have a nontrivial solution if:

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

So to look at your question, we consider:

[tex] \left|\begin{array}{cc}1-\lambda&0\\2&{3-\lambda} \end{array}\right| = 0 [/tex]

[tex] (1 - \lambda)(3 - \lambda) - 0 = 0 [/tex]

[tex] \lambda = 1, 3 [/tex]

So you are right.

To find the eigenvectors, we go back and solve this equation:

[tex] (A - \lambda I) \vec{x} = 0 [/tex]

for each [itex] \lambda [/itex] in turn.
 
  • #3
Yes, that's correct. The eigenvalues of a matrix A are those that satisfy the "characteristic equation"

[tex] |\lambda \textbf{I} - \textbf{A}| = 0. [/tex]

So for your A, we have

[tex] (\lambda - 1)(\lambda - 3) - (0)(-2) = (\lambda - 1)(\lambda - 3) = 0. [/tex]

So the eigenvalues of A are [tex] \lambda_1 = 1 [/tex] and [tex] \lambda_2 = 3. [/tex]

For part (b), the eigenvectors of A are all vectors in the nullspace of [tex]\lambda \textbf{I} - \textbf{A}, [/tex] i.e., they satisfy the equationthe equation

[tex] (\lambda \textbf{I} - \textbf{A})\vec{x} = \vec{0}. [/tex]

EDIT: I didn't see hgfalling's post until after I'd already posted...grrr...haha. Well here's mine for what it's worth anyways.
 
  • #4
Ok, thanks for the help, but I still don't really understand the eigenvectors part of it. It would be useful if someone could write out an example. And for the A^3 bit is it the same as parts a) and b) but for AxAxA?
 

What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are mathematical concepts used to describe the properties of a square matrix. Eigenvalues represent the scalar values that scale the eigenvectors in a linear transformation, while eigenvectors are the non-zero vectors that remain in the same direction after the transformation.

How do you find the eigenvalues of a 2x2 matrix?

To find the eigenvalues of a 2x2 matrix, you first need to find the determinant of the matrix. Then, you can use the quadratic formula to solve for the eigenvalues. The eigenvalues will be the roots of the characteristic polynomial, which is found by setting the determinant equal to 0.

What is the significance of eigenvalues in a 2x2 matrix?

The eigenvalues of a 2x2 matrix can provide important information about the matrix, such as its determinant and trace. They also play a crucial role in solving systems of differential equations and in understanding the behavior of linear transformations.

Can a 2x2 matrix have complex eigenvalues?

Yes, a 2x2 matrix can have complex eigenvalues. This is because complex eigenvalues can arise when the matrix has complex coefficients or when the characteristic polynomial has complex roots. Complex eigenvalues and eigenvectors are important in understanding and analyzing non-real transformations.

How do eigenvalues and eigenvectors relate to each other?

Eigenvalues and eigenvectors are closely related, as the eigenvalues of a matrix determine the eigenvectors and vice versa. The eigenvectors of a matrix form a basis for its vector space, and each eigenvector corresponds to a particular eigenvalue. The relationship between eigenvalues and eigenvectors is used in many applications, such as principal component analysis and image compression.

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