Solving Eigenvectors for Root -7 of 2x3 Matrix

In summary, both 1/-3 and -1/3 are correct eigenvectors for the given eigenvalue of -7. The eigenvector is not unique and can have different signs. The condition for a correct eigenvector is that when multiplied by the original matrix, it should equal the eigenvalue times the eigenvector.
  • #1
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Homework Statement



Find eigenvector for the root -7 of:

|2 3|
|3 -6|

Homework Equations



|2 3|
|3 -6|

The Attempt at a Solution



I got
1
-3

But my books says
-1
3

I am only wondering if this is possibly the same answer, because when I check my answer by multiplying the eigenvector by the original matrix and the root by the eigenvector the answer appears correct.

I.e.
|2 3|
|3 -6| times (1/-3) = (-7)(1/-3) = -7/21, while if you do the same for (-1/3), allbeit a different answer, but the condition still holds.

Is this correct? Is this condition truly the indicator of if you got the correct answer? (original vector * eigenvector) = (root * eigenvector)
 
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  • #2
Yes, any "eigenvector", v, corresponding to a given eigenvalue (I would not say "root") [itex]\lambda[/itex], of matrix A, has the property that [itex]Av= \lambda v[/itex].

What you are missing is that if [itex]v[/itex] is such an eigenvector then [itex]av[/itex], for any number a, is also an eigenvector, corresponding to eigenvalue [itex]\lambda[/itex]: [itex]A(av)= a (Av)= a(\lambda v)= \lambda (av)[/itex].

In particular, with a= -1, if v is an eigenvalue then so is -v. The eigenvector is NOT unique.
 
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  • #3
HallsofIvy said:
Yes, any "eigenvector", v, corresponding to a given eigenvalue (I would not say "root") [itex]\lambda[/itex], of matrix A, has the property that [itex]Av= \lambda v[/itex].

What you are missing is that if [itex]v[/itex] is such an eigenvector then [itex]av[/itex], for any number a, is also an eigenvector, corresponding to eigenvalue [itex]\lambda[/itex]: [itex]A(av)= a (Av)= a(\lambda v)= \lambda (av)[/itex].

In particular, with a= -1, if v is an eigenvalue then so is -v. The eigenvector is NOT unique.

Thanks a lot.

Just to confirm, the signs in this case do not matter and both answers are correct? I am such a noobie...
 
Last edited:

1. What are eigenvectors and eigenvalues?

Eigenvectors and eigenvalues are mathematical concepts used in linear algebra. An eigenvector is a vector that, when multiplied by a square matrix, gives a scalar multiple of itself. The corresponding scalar multiple is called the eigenvalue.

2. How do you solve for eigenvectors?

To solve for eigenvectors, you first need to find the eigenvalues of the matrix. This can be done by finding the roots of the characteristic equation det(A-λI) = 0, where A is the matrix and λ is the eigenvalue. Once you have the eigenvalues, you can use them to find the eigenvectors by solving the equation (A-λI)x = 0, where x is the eigenvector.

3. What is the purpose of finding eigenvectors?

Finding eigenvectors is useful in many areas of science and engineering. They can be used to simplify calculations, solve differential equations, and identify important patterns in data. Additionally, eigenvectors are often used in computer graphics and machine learning algorithms.

4. How do you find the eigenvectors for a specific eigenvalue?

To find the eigenvectors for a specific eigenvalue, you can use the same process as solving for eigenvectors in general. Once you have the eigenvalue, plug it into the equation (A-λI)x = 0 and solve for x. This will give you the eigenvector corresponding to that eigenvalue.

5. Can a matrix have more than one eigenvector for a given eigenvalue?

Yes, a matrix can have multiple eigenvectors for a given eigenvalue. This is because eigenvectors are not unique and can be scaled by any non-zero scalar value. Therefore, there can be infinitely many eigenvectors for a single eigenvalue.

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