Complex conjugation in scalar product?

In summary, complex conjugation in scalar product is a mathematical operation that involves taking the complex conjugate of one of the vectors. This is important because it simplifies and manipulates complex numbers in equations and is crucial in fields like quantum mechanics and signal processing. In the calculation of scalar product, complex conjugation is used to ensure the product is a real number. It can only be applied to complex vectors, not real ones, and has many real-life applications in areas like electrical engineering, physics, and signal processing.
  • #1
ATY
34
1
Hey guys,
I got the following derivation for some physical stuff (the derivation itself is just math)
http://thesis.library.caltech.edu/5215/12/12appendixD.pdf
I understand everything until D.8.

After D.7 they get the eigenvalue and eigenvectors from ε. The text says that my δx(t) gets aligned in the same direction as the the eigenvektor of the largest eigenvalue (this would be my explanation for the fact that there is no eigenvector in the scalar product, but this might be wrong, cause I do not know much about eigenvectors).
But what I don't get is why in D.8. my δx(t) is suddenly complex conjugated. I can not find the reason for this.
I would be really happy about any explanation.

have a nice day
ATY

PS: sorry for the weird titel. Had no clue how to describe my problem (mea culpa)
 
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  • #2


Dear ATY,

Thank you for your question. I have reviewed the derivation in the link you provided and I believe I can provide an explanation for the complex conjugation of δx(t) in D.8.

Firstly, let's review what we know from the previous steps. In D.7, the authors have obtained the eigenvalue equation εu = Au, where u is an eigenvector and A is a matrix. They then substitute u = δx(t)/|δx(t)|, where δx(t) is a vector representing the displacement of a particle at time t. This substitution makes sense because we are dealing with a physical system and the displacement vector should have a magnitude of 1.

Now, in D.8, the authors take the complex conjugate of the eigenvalue equation from D.7. This is done because the eigenvector u is complex, and in order to obtain a real solution for δx(t), we need to take the complex conjugate of the equation. This is a common step in solving eigenvalue problems in quantum mechanics.

In summary, the complex conjugation in D.8 is necessary in order to obtain a real solution for δx(t) and does not affect the physical interpretation of the problem.

I hope this explanation helps. If you have any further questions, please don't hesitate to ask.
 

FAQ: Complex conjugation in scalar product?

1. What is complex conjugation in scalar product?

Complex conjugation in scalar product is a mathematical operation that involves taking the complex conjugate of one of the vectors in a scalar product. This is done by changing the sign of the imaginary part of the vector.

2. Why is complex conjugation important in scalar product?

Complex conjugation is important in scalar product because it helps to simplify and manipulate complex numbers in mathematical equations. It also plays a crucial role in fields such as quantum mechanics and signal processing.

3. How is complex conjugation used in the calculation of scalar product?

In the calculation of scalar product, complex conjugation is used to ensure that the product of two complex numbers is a real number. This is done by taking the complex conjugate of one of the vectors before multiplying it with the other vector.

4. Can complex conjugation be applied to any type of vector in scalar product?

No, complex conjugation can only be applied to complex vectors in scalar product. Real vectors do not have an imaginary component, so complex conjugation is not necessary in their calculation.

5. Are there any real-life applications of complex conjugation in scalar product?

Yes, complex conjugation is used in a wide range of real-life applications, such as in electrical engineering for analyzing AC circuits, in physics for calculating quantum mechanical properties, and in signal processing for filtering and analyzing signals.

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