Eigenvectors and eigenvalues - how to find the column vector

In summary, we have discussed the concept of eigenvalues and eigenvectors, as well as examining whether a given column vector satisfies a given equation involving a matrix. We have determined that there does not exist a non-zero column vector that satisfies the given equation, but the zero vector is a solution.
  • #1
Tala.S
43
0
Hi We have a matrix A (picture), the eigenvalues are λ1 = 4 and λ2 = 1 and the eigenvectors are

λ1 : t(1,0,1)
λ2 : t1(1,0,2) + t2(0,1,0)

I have to examine if there's a column vector v that satifies :

A*v = 2 v I would say no there doesn't exist such a column vector v because 2 isn't an eigenvalue:

Av = λv

so

Av = 2v

but we know that λ is 4 or 1 and not 2


Am I wrong ?

I would be nice if someone could give me their opinion :)
 

Attachments

  • Billede 1.png
    Billede 1.png
    1.4 KB · Views: 602
Last edited:
Physics news on Phys.org
  • #2
Tala.S said:
I have to examine if there's a column vector v that satifies :

A*v = 2 v


I would say no there doesn't exist such a column vector v because 2 isn't an eigenvalue:

You seem to understand what eigenvalnes and vectors are, which is good.

But you aren't quite right. There IS a vector that satisfies A*v = 2 v, but you might think it's not a very interesting vector.

Try solving (A - 2I)v = 0, and see what you get.
 
  • #3
When I solve it I get

v =

(1 0)
(0 0)
(0 0)

?
 

Attachments

  • Billede 3.png
    Billede 3.png
    8.1 KB · Views: 742
  • #4
You cannot use Maple in this way. You should use linsolve(A1,b) or LinearSolve(A1,b). Or better, do it by hand.
 
  • #5
I don't understand what you mean by that. Your vectors before were single columns of three numbers. How could v be two columns?

You were close to right when you said before "doesn't exist such a column vector v because 2 isn't an eigenvalue". What is true is that [itex]\lambda[/itex] is an eigenvalue for matrix A if and only if there exist a non-trivial (i.e. non-zero) vector v such that [itex]Av= \lambda v[/itex].

Since two is not an eigenvalue, there cannot exist a non-trivial vector but, as
Aleph-zero said, "There IS a vector that satisfies A*v = 2 v, but you might think it's not a very interesting vector.".
 
  • #6
So the vector is (0,0,0) ?

Or have I completely misunderstood this ?
 
  • #7
Tala.S said:
So the vector is (0,0,0) ?
Right. But it is not counted as an eigenvector.
 
  • #8
Tala.S said:
So the vector is (0,0,0) ?

Or have I completely misunderstood this ?

Correct. The question asked you to find a column vector, not a non-zero column vector!

You only get non-zero solutions of Av = λv when λ is an eigenvalue, but v = 0 is a solution for any value of λ.
 

FAQ: Eigenvectors and eigenvalues - how to find the column vector

What is an eigenvector?

An eigenvector is a vector that does not change direction when it is multiplied by a particular matrix. In other words, the direction of an eigenvector remains the same, but its magnitude may change.

What is an eigenvalue?

An eigenvalue is a scalar value that represents how much an eigenvector is scaled when it is multiplied by a matrix. It is often denoted by the Greek letter lambda (λ).

How do I find the eigenvectors and eigenvalues of a matrix?

To find the eigenvectors and eigenvalues of a matrix, you need to solve the characteristic equation. This involves finding the determinant of the matrix, setting it equal to 0, and solving for the eigenvalues. Once you have the eigenvalues, you can plug them back into the original matrix to find the corresponding eigenvectors.

Why are eigenvectors and eigenvalues important?

Eigenvectors and eigenvalues are important in many areas of science and engineering, including physics, chemistry, and computer science. They are used to solve systems of differential equations, analyze the stability of dynamic systems, and perform data analysis and dimensionality reduction.

Can any matrix have eigenvectors and eigenvalues?

Not all matrices have eigenvectors and eigenvalues. Only square matrices (with the same number of rows and columns) can have eigenvalues and eigenvectors. Additionally, not all square matrices have a complete set of eigenvectors and eigenvalues - this depends on the properties of the matrix.

Back
Top