Normalization of an Eigenvector in a Matrix

In summary, the number 1/sqrt(2) is added into the answer because it is the value of 45 degrees for both Sin and Cos. This number is seen quite a lot in QM, but there is more to it than that.
  • #1
Dwye
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TL;DR Summary
I am working through some linear algebra questions in the Griffith's Book "Introduction to Quantum Mechanics", and I am unsure why a constant of 1/sqrt(2) is added into the answer.
I understand that the question is detailing a rotation about axis x & y, and that 1/sqrt(2) is the value of 45 degrees for both Sin and Cos, is this the reason for the addition; a generalization?
In fact I have seen this number quite a lot in Quantum Mechanics, is there something more to this number?
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AnswerA.18.JPG
 
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  • #2
Do you know, how to get the norm of a vector in ##\mathbb{C}^2## or, more generally, how to define a scalar product on a complex vector space? It's very important to get these concepts right, before starting to study quantum theory, for which you need the "infinite-dimensional version" of these ideas, the socalled (separable) Hilbert space (more precisely what physicists do with this is rather the extension to a "rigged Hilbert space").
 
  • #3
Dwye said:
Summary:: I am working through some linear algebra questions in the Griffith's Book "Introduction to Quantum Mechanics", and I am unsure why a constant of 1/sqrt(2) is added into the answer.
I understand that the question is detailing a rotation about axis x & y, and that 1/sqrt(2) is the value of 45 degrees for both Sin and Cos, is this the reason for the addition; a generalization?
In fact I have seen this number quite a lot in Quantum Mechanics, is there something more to this number?

The vector ##(1, i)## has magnitude ##\sqrt 2##, so Griffiths decided to normalise it. The question doesn't actually ask for a normalised eigenvector, so ##(1, i)## would be just as valid an answer.

In QM it's generally a good idea to normalise vectors. The factor here has nothing directly to do with being the sine of ##45°##. If the eigenvector were ##(1, 2i)##, then the normalisation factor would be ##\frac 1 {\sqrt{5}}##.
 
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Likes vanhees71 and Dwye
  • #4
PeroK said:
The vector ##(1, i)## has magnitude ##\sqrt 2##, so Griffiths decided to normalise it. The question doesn't actually ask for a normalised eigenvector, so ##(1, i)## would be just as valid an answer.

In QM it's generally a good idea to normalise vectors. The factor here has nothing directly to do with being the sine of ##45°##. If the eigenvector were ##(1, 2i)##, then the normalisation factor would be ##\frac 1 {\sqrt{5}}##.
PeroK said:
The vector ##(1, i)## has magnitude ##\sqrt 2##, so Griffiths decided to normalise it. The question doesn't actually ask for a normalised eigenvector, so ##(1, i)## would be just as valid an answer.

In QM it's generally a good idea to normalise vectors. The factor here has nothing directly to do with being the sine of ##45°##. If the eigenvector were ##(1, 2i)##, then the normalisation factor would be ##\frac 1 {\sqrt{5}}##.
Thank you very much!
 

FAQ: Normalization of an Eigenvector in a Matrix

What is an eigenvector?

An eigenvector is a vector that, when multiplied by a square matrix, results in a scalar multiple of itself. In other words, the direction of the eigenvector remains the same, but its magnitude is scaled by a constant factor.

What is the purpose of normalizing an eigenvector in a matrix?

Normalizing an eigenvector in a matrix is important because it allows us to compare the relative importance of different eigenvectors. By normalizing, we can ensure that all eigenvectors have a unit length, making it easier to interpret and compare their magnitudes.

How is an eigenvector normalized in a matrix?

To normalize an eigenvector in a matrix, we divide each element of the eigenvector by its magnitude (or length). This ensures that the resulting vector has a unit length of 1.

Can an eigenvector be normalized to have a length other than 1?

Yes, an eigenvector can be normalized to have a length other than 1. However, normalizing to a length of 1 is the most common approach as it allows for easier comparison and interpretation of the eigenvectors.

What is the significance of the normalized eigenvector in a matrix?

The normalized eigenvector in a matrix represents the direction of the vector that is most affected by the matrix transformation. It is often used to identify patterns and relationships within the data represented by the matrix.

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