Finding eigenvectors of a matrix

But the second row is zero so the vector (0, 0, 0, 1) is also an eigenvector.In summary, when a matrix has a multiplicity greater than 1 for an eigenvalue, it means there are multiple linearly independent eigenvectors corresponding to that eigenvalue. In the case of the given matrix, the eigenvalue 1 has a multiplicity of 2, meaning there are two eigenvectors: (1, 1, -1, 0) and (0, 0, 0, 1). These eigenvectors correspond to the eigenvalue 1 and span a 2-dimensional subspace. To find these eigenvectors, we can use
  • #1
JaysFan31

Homework Statement


I need to find the eigenvectors for the matrix shown below.


Homework Equations


Nothing relevant.


The Attempt at a Solution


I have the matrix
2 1 -1 0
0 4 -2 0
0 3 -1 0
0 3 -2 1

The eigenvectors are 1,1,2,2.

To get the eigenvectors for 1,1, I use the matrix
-1 1 -1 0
0 -3 -2 0
0 3 2 0
0 3 -2 0

When I bring this to RREF, I have
1 0 0 0
0 1 0 0
0 0 1 0
0 0 0 0

So I know that the vector
0
0
0
1
is an eigenvector.

But the book also says that
1
2
3
0
is an eigenvector for the eigenvalue 1.

If the matrix only has one solution, how do I get this other eigenvector?
 
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  • #2
JaysFan31 said:

Homework Equations


Nothing relevant.

See if this equations help:

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

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

where [tex]A[/tex] is the matrix, [tex]x[/tex] is an eigenvector and [tex]\lambda[/tex] is it's corresponding eigenvalue
 
  • #3
When an eigenvalue has a multiplicity > 1 it means that there multiple linearly independent eigenvectors corresponding to that eigenvalue. In particular, the eigenvectors span an N-dimensional subspace, where N is the multiplicity of the eigenvalue.

As a trivial example, the n-dimensional identity matrix [itex]I_n[/itex] has eigenvalue 1 with multiplicity n. The eigenvalues span the original space. (In other words, any vector in [itex]\mathbb R^n[/itex] is an eigenvector of [itex]I_n[/itex]).
 
  • #4
JaysFan31 said:

Homework Statement


I need to find the eigenvectors for the matrix shown below.


Homework Equations


Nothing relevant.


The Attempt at a Solution


I have the matrix
2 1 -1 0
0 4 -2 0
0 3 -1 0
0 3 -2 1

The eigenvectors are 1,1,2,2.

To get the eigenvectors for 1,1, I use the matrix
-1 1 -1 0
0 -3 -2 0
0 3 2 0
0 3 -2 0
This is, I take it, your original matrix with 1 subtracted from the diagonal values? Or, at least it's supposed to be: check the second row: 4-1= 3, not -3.

When I bring this to RREF, I have
1 0 0 0
0 1 0 0
0 0 1 0
0 0 0 0

So I know that the vector
0
0
0
1
is an eigenvector.

But the book also says that
1
2
3
0
is an eigenvector for the eigenvalue 1.

If the matrix only has one solution, how do I get this other eigenvector?

1 1 -1 0
0 3 -2 0
0 3 -2 0
0 3 -2 0

row reduces to
1 0 -1/3 0
0 1 -2/2 0
0 0 0 0
0 0 0 0

That gives both (1, 2, 3, 0) as eigen value.
 

What is the definition of an eigenvector?

An eigenvector is a vector that, when multiplied by a square matrix, results in a scalar multiple of itself. The scalar multiple is known as the eigenvalue of the eigenvector.

How do you find eigenvectors of a matrix?

To find eigenvectors of a matrix, you must first calculate the eigenvalues of the matrix. Then, for each eigenvalue, you must solve the equation (A - λI)v = 0, where A is the matrix, λ is the eigenvalue, and v is the eigenvector. This will give you the eigenvector(s) corresponding to that eigenvalue.

Why are eigenvectors important in linear algebra?

Eigenvectors are important in linear algebra because they represent the directions along which a linear transformation has a simple effect. They also provide a way to decompose a matrix into simpler forms, making it easier to understand and manipulate.

Can a matrix have multiple eigenvectors with the same eigenvalue?

Yes, a matrix can have multiple eigenvectors with the same eigenvalue. This is because the eigenvectors of a matrix are not unique, and any scalar multiple of an eigenvector is also an eigenvector with the same eigenvalue.

What is the significance of the eigenvectors with the largest eigenvalues?

The eigenvectors with the largest eigenvalues are known as the dominant eigenvectors. They represent the most significant directions of change in the matrix, and are often used in applications such as principal component analysis and Markov chain analysis.

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