How do eigenvalues and eigenvectors relate to matrices?

In summary, the person is trying to learn how to solve matrix equations and needs someone to teach them the basics. They are confused about how to find the eigenvalues and vectors, and would appreciate any help they can get.
  • #1
S_Flaherty
75
0
I have to be able to figure out eigenvalues and eigenvectors for 2x2 and 3x3 matrices for my physics course, but I have never taken linear algebra so I obviously have no idea what they even are. I need someone to basically teach me how to solve these problems because I have no knowledge of this material and cannot find any useful source to help me.

First I have to find the eigenvalues and vectors for

[a 0 1]
[0 b 0]
[0 0 c] where a =/= b =/= c.

I looked around online and could only find that the answers for the eigenvalues are a, b, and c but I have no idea how that answer is derived. And I have no idea how to solve for eigenvectors except so any help would be great.

Next I have to do the same thing for

[a a 0]
[2a 0 0]
[0 a a] where a =/= 0.

Again I know that the solution for the values is -a, a, and 2a but no clue on how to actually do the problem.

I know nothing of this material except how to figure out the determinant of a matrix. So detailed steps on how to solve for eigenvalues and eigenvectors would be greatly appreciated.
 
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  • #2
An eigenvalue λ and its eigenvector v are defined by:

Av = λv

That is, you try to find a vector that if you multiply A with it you find the same vector, except for length.

To solve this, you do:
Av - λv = 0
(A - λI)v = 0

This means (A - λI) must be singular, so its determinant must be zero.
det(A - λI)=0 yields a polynomial in λ, that is called the "characteristic polynomial".
Its roots are the eigenvalues of A.

Once you have an eigenvalue, you solve:
(A - λI)v = 0
to find a vector v (or its multiple) that satisfies the equation.
 
  • #3
Applied to your first matrix you have:
det(A - λI)=0
(a-λ)(b-λ)(c-λ)=0
Roots are a, b, and c.

For the first eigenvalue "a", you get:
A - aI =
[0 0 1]
[0 b 0]
[0 0 c]

The vector v=(1,0,0) satisfies (A - aI)v = 0, meaning that is the eigenvector.
 
  • #4
I like Serena said:
Applied to your first matrix you have:
det(A - λI)=0
(a-λ)(b-λ)(c-λ)=0
Roots are a, b, and c.

For the first eigenvalue "a", you get:
A - aI =
[0 0 1]
[0 b 0]
[0 0 c]

The vector v=(1,0,0) satisfies (A - aI)v = 0, meaning that is the eigenvector.

So i understand how you get the eigenvalues but I'm still confused with how you are arriving at the answer for the eigenvector.
 
  • #5
The matrix A - aI can be row reduced to
[tex]\begin{bmatrix}0&0&1\\0&1&0\\0&0&0 \end{bmatrix}[/tex]

This represents the equation (A - aI)x = 0, where x is a column vector with coordinates x1, x2 and x3.

The reduced matrix represents the system
x3 = 0
x2 = 0
with x1 being arbitrary. Since it's arbitrary, it's reasonable to set it to 1, giving the vector <1, 0, 0> as an eigenvector for the eigenvalue a.
 

1. What are Eigenvectors and Eigenvalues?

Eigenvectors and Eigenvalues are two key concepts in linear algebra that are used to understand and analyze systems of linear equations. An eigenvector is a special vector that, when multiplied by a matrix, results in a scalar multiple of itself. This scalar multiple is known as the eigenvalue.

2. Why are Eigenvectors and Eigenvalues important?

Eigenvectors and Eigenvalues are important because they provide insight into the behavior and properties of linear systems. They are used in a variety of applications, such as image processing, machine learning, and quantum mechanics.

3. How do you find Eigenvectors and Eigenvalues?

To find the eigenvectors and eigenvalues of a matrix, we can use a process called diagonalization. This involves solving the characteristic equation of the matrix, which is a polynomial equation that involves the eigenvalues. Once the eigenvalues are found, we can use them to find the corresponding eigenvectors.

4. What is the significance of the eigenvalues in diagonalization?

The eigenvalues play a crucial role in diagonalization because they determine the behavior of the system. If there are distinct eigenvalues, then the system will have a unique set of eigenvectors. If there are repeated eigenvalues, then the system may have multiple sets of eigenvectors.

5. Can a matrix have complex eigenvalues?

Yes, a matrix can have complex eigenvalues. In fact, complex eigenvalues are often encountered in systems that involve rotation or oscillation, such as in quantum mechanics or electrical circuits. In these cases, the eigenvectors may also be complex numbers.

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