Showing Complex Vectors are Orthonormal

In summary, the question asks to verify that the complex unit vectors defined as ε± = 1/√2 (ε1 ± iε2) constitute a set of complex orthonormal unit vectors, meaning that their dot product with each other should equal 0 and their dot product with themselves should equal 1. The approach is to expand the dot product using the dot product formula and show that the resulting terms reduce to the desired values of 0 and 1.
  • #1
Crush1986
207
10

Homework Statement


let [tex] \epsilon_1 [/tex] and [tex] \epsilon_2 [/tex] be unit vectors in R3. Define two complex unit vectors as follows:
[tex] \epsilon_{\pm} = \frac{1}{\sqrt{2}}(\epsilon_1 \pm i \epsilon_2)[/tex]

verify that epsilon plus minus constitutes a set of complex orthonormal unit vectors. That is, show that [tex] (\epsilon_\pm)^* \cdotp \epsilon_\mp = 0 [/tex]

Homework Equations


Dot Product.

The Attempt at a Solution


So... I don't know what I can possibly be missing. I do the dot product say of [tex] (\epsilon_+)^* \cdotp i \epsilon_- [/tex]
and I'm ending up with, [tex] \frac{1}{2} [1-i \epsilon_2 \cdotp \epsilon_1-i \epsilon_2 \cdotp \epsilon_1-1] [/tex]
So the complex parts don't go away? I'd appreciate any help... Thanks.
 
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  • #2
The complex inner product is not linear in both arguments.
 
  • #3
PeroK said:
The complex inner product is not linear in both arguments.

I'm sorry, I don't follow exactly.
 
  • #5
Hrm, ok I see. When you take the dot product one of the vectors must be changed to it's complex conjugate? I still must be doing something wrong as I'm not getting the advertised result.
 
  • #6
Crush1986 said:
Hrm, ok I see. When you take the dot product one of the vectors must be changed to it's complex conjugate? I still must be doing something wrong as I'm not getting the advertised result.

In a complex vector space, you have:

##\langle \alpha u, v \rangle = \alpha^* \langle u, v \rangle##

##\langle u, \alpha v \rangle = \alpha \langle u, v \rangle##

This is the physicist's version. In pure mathematics, it's the other way round (like in the link I gave you). You need to know which approach is being used in your book or course.
 
  • #7
Oh no... I'm sorry I have a typo. I think that is what could be some of my confusion.

I'm trying [tex] \epsilon_+^* \cdotp \epsilon_- [/tex] not [tex] \epsilon_+^* \cdotp i \epsilon_- [/tex]

Sorry if that has wasted your time. I've definitely learned something about complex dot products but I still haven't been able to resolve this unfortunately.

That first dot product is supposed to be zero...
 
  • #8
Crush1986 said:
Oh no... I'm sorry I have a typo. I think that is what could be some of my confusion.

I'm trying [tex] \epsilon_+^* \cdotp \epsilon_- [/tex] not [tex] \epsilon_+^* \cdotp i \epsilon_- [/tex]

Sorry if that has wasted your time. I've definitely learned something about complex dot products but I still haven't been able to resolve this unfortunately.

That first dot product is supposed to be zero...

You may be trying to take too many shortcuts. Write it out in full, starting with:

##\langle \epsilon_+, \epsilon_- \rangle = \langle \frac{1}{\sqrt{2}}(\epsilon_1 + i \epsilon_2), \frac{1}{\sqrt{2}}(\epsilon_1 - i \epsilon_2) \rangle##
 
  • #9
yes I have that, except that the epsilon plus is complex conjugate of itself. so [tex] \langle \epsilon_+^*, \epsilon_- \rangle = \langle \frac{1}{\sqrt{2}}(\epsilon_1 - i \epsilon_2), \frac{1}{\sqrt{2}}(\epsilon_1 - i \epsilon_2) \rangle [/tex]

Is that not correct? Then I get the cross terms don't cancel? and if the i for the epsilon - is positive I get 1? I may just not be getting how to do a complex dot product. Funny we didn't go over this, perhaps we did it in our math methods last year... but I don't remember doing them at all if we did.
 
  • #10
Crush1986 said:
yes I have that, except that the epsilon plus is complex conjugate of itself. so [tex] \langle \epsilon_+^*, \epsilon_- \rangle = \langle \frac{1}{\sqrt{2}}(\epsilon_1 - i \epsilon_2), \frac{1}{\sqrt{2}}(\epsilon_1 - i \epsilon_2) \rangle [/tex]

Is that not correct? Then I get the cross terms don't cancel? and if the i for the epsilon - is positive I get 1? I may just not be getting how to do a complex dot product. Funny we didn't go over this, perhaps we did it in our math methods last year... but I don't remember doing them at all if we did.

You don't take the complex conjugate in that way. The inner product of two vectors is:

##\langle u, v \rangle##

What you are doing is:

##\langle u^*, v \rangle##

Which is the inner product of ##u^*## and ##v##.

You may be thinking of the inner product expressed as a product of coefficients:

##\langle u, v \rangle = u^*_1v_1 + u^*_2v_2 + u^*_3 v_3##
 
  • #11
The problem says to show that the complex conjugate of epsilon plus dotted with epsilon minus is 0 though. I'm just not seeing any possible way the real and complex can disappear... I've tried this so many different times trying different things...
 
  • #12
Crush1986 said:
verify that epsilon plus minus constitutes a set of complex orthonormal unit vectors. That is, show that [tex] (\epsilon_\pm)^* \cdotp \epsilon_\mp = 0 [/tex]

This is wrong. You need to show that ##\epsilon_+ \cdotp \epsilon_- = 0## and ##\epsilon_\pm \cdotp \epsilon_\pm = 1##

Perhaps whoever set the question got confused.

Sorry, I didn't notice that this was part of the question, not your approach.
 
  • #13
PeroK said:
This is wrong. You need to show that ##\epsilon_+ \cdotp \epsilon_- = 0## and ##\epsilon_\pm \cdotp \epsilon_\pm = 1##

Perhaps whoever set the question got confused.

Sorry, I didn't notice that this was part of the question, not your approach.
Ok, I'll try that after some sleep. I was beginning to think there was an issue or typo perhaps! Thanks!
 
  • #14
Crush1986 said:
yes I have that, except that the epsilon plus is complex conjugate of itself. so [tex] \langle \epsilon_+^*, \epsilon_- \rangle = \langle \frac{1}{\sqrt{2}}(\epsilon_1 - i \epsilon_2), \frac{1}{\sqrt{2}}(\epsilon_1 - i \epsilon_2) \rangle [/tex]

Is that not correct? Then I get the cross terms don't cancel? and if the i for the epsilon - is positive I get 1? I may just not be getting how to do a complex dot product. Funny we didn't go over this, perhaps we did it in our math methods last year... but I don't remember doing them at all if we did.

If ##\varepsilon_1## and ##\varepsilon_2## are orthogonal, the cross terms vanish. Although it isn't mentioned, I believe they need to be orthonormal vectors.
 

FAQ: Showing Complex Vectors are Orthonormal

How do you define orthonormal vectors?

Orthonormal vectors are a set of vectors that are both orthogonal (perpendicular) to each other and have a unit length of 1. This means that the dot product of any two vectors in the set is equal to 0 and the magnitude of each vector is 1.

What is the importance of showing that vectors are orthonormal?

Showing that vectors are orthonormal is important because it allows us to simplify calculations and make geometric interpretations. Orthonormal vectors form a basis, which means they can be used to represent any other vector in a given space. This makes them useful in many areas of mathematics, physics, and engineering.

How can you prove that a set of vectors is orthonormal?

To prove that a set of vectors is orthonormal, you need to show that they are orthogonal (perpendicular) to each other and that each vector has a magnitude of 1. This can be done by calculating the dot product of each pair of vectors and showing that it is equal to 0, and then finding the magnitude of each vector using the Pythagorean theorem.

What is the difference between orthonormal and orthogonal vectors?

Orthonormal vectors are a special type of orthogonal vectors. While both types of vectors are perpendicular to each other, orthonormal vectors also have a magnitude of 1, whereas orthogonal vectors can have any length. Additionally, orthonormal vectors are often used as a basis for vector spaces, while orthogonal vectors may not necessarily be used in this way.

How can orthonormal vectors be used in practical applications?

Orthonormal vectors have many practical applications, such as in computer graphics, where they are used to represent rotations and transformations. They are also used in signal processing to represent signals in a simplified and efficient way. In quantum mechanics, orthonormal vectors are used to describe the state of a quantum system. Additionally, orthonormal vectors are used in machine learning algorithms, such as principal component analysis, to reduce the dimensionality of data and extract important features.

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