How Do You Compute and Normalize Complex Eigenvectors?

In summary, the conversation discusses finding eigenvalues and eigenvectors of a matrix and normalizing the eigenvectors. The student is having trouble with the next steps and asks for guidance. They also mention that their lecturer's notes are difficult to understand.
  • #1
kel
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Homework Statement


Hi, I'm going over an old exam paper as part of my revision for upcoming exams (joy ! ok, maybe not )

Anyway, I've gotten myself a bit lost here and would appreciate some guidance.
Here we go:

Compute the eigenvalues & eigenvectors of the following matrix. Normalise the 2 eigenvectors.

Matrix = (3 1+i)
(1-i 2)

Homework Equations





The Attempt at a Solution



So far I have the 2 eigenvalues being +1 and +4, but I'm having trouble with the next bit, I think that the eigenvectors will come out as complex numbers,

so far I have reduced to two equations:

2x + (1+i)y = 0
(1-i)x + y = 0

ie 2x + (y+iy) = 0 (so, 2x = -y-iy)
and x-ix + y = 0 (and y = -x+ix)

Is this correct?? if not why? and where do I go from here?

Also, I don't get the idea of normalisation (next part of question), I understand that it has something to do with setting the vector(i think) to 1, but I'm not sure. My lecturers notes aren't the easiest to follow outside of his lectures, so if you could walk me through it a bit I'd be very grateful.

Thanks
Kel
 
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  • #2
kel said:

Homework Statement


Hi, I'm going over an old exam paper as part of my revision for upcoming exams (joy ! ok, maybe not )

Anyway, I've gotten myself a bit lost here and would appreciate some guidance.
Here we go:

Compute the eigenvalues & eigenvectors of the following matrix. Normalise the 2 eigenvectors.

Matrix = (3 1+i)
(1-i 2)

Homework Equations





The Attempt at a Solution



So far I have the 2 eigenvalues being +1 and +4, but I'm having trouble with the next bit, I think that the eigenvectors will come out as complex numbers,

Is this a problem? You are working in a complex vector space, and so the eigen-vectors are entitled to have complex entries!
so far I have reduced to two equations:

2x + (1+i)y = 0
(1-i)x + y = 0

ie 2x + (y+iy) = 0 (1) (so, 2x = -y-iy)
and x-ix + y = 0 (2) (and y = -x+ix)

Is this correct?? if not why? and where do I go from here?

I've not checked your work, but you look to be doing the correct method. Why not try solving eqns (1) and (2) for x and y?
Also, I don't get the idea of normalisation (next part of question), I understand that it has something to do with setting the vector(i think) to 1, but I'm not sure. My lecturers notes aren't the easiest to follow outside of his lectures, so if you could walk me through it a bit I'd be very grateful.

Normalised means that the vectors have unit length. Once you have the eigenvector, v, say, then the normalised vector is v/|v|
 

FAQ: How Do You Compute and Normalize Complex Eigenvectors?

What is an eigenvector?

An eigenvector is a vector that does not change direction when a linear transformation is applied to it. In other words, it is a vector that is only scaled by the transformation.

How do you find eigenvectors?

To find eigenvectors, you first need to find the eigenvalues of the matrix. Then, for each eigenvalue, you can solve for the eigenvector by setting up and solving a system of equations.

What is the importance of normalizing eigenvectors?

Normalizing eigenvectors is important because it allows us to compare their magnitudes and directions. If we do not normalize the eigenvectors, the magnitudes can vary greatly and make it difficult to interpret their significance.

How do you normalize an eigenvector?

To normalize an eigenvector, we divide each element by the magnitude of the eigenvector. This will result in a unit vector with a magnitude of 1.

Can a matrix have more than one eigenvector?

Yes, a matrix can have multiple eigenvectors. In fact, most matrices have multiple eigenvectors associated with different eigenvalues. These eigenvectors can span a vector space, making them useful for finding solutions to systems of linear equations.

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