Orthogonality of 2 complex exponentials

  • #1
Nana113
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For 2 complex functions, to find the orthogonality, one of the function has to be in complex conjugate? Because in the lecture note, the first formula is without complex conjugate, so I’m a bit confused

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  • #2
The inner product of two complex things always involves the complex conjugate of one of the things. In this case:$$\langle u, v \rangle = \int_a^b u(x)^*v(x) \ dx$$There are loads of hits on Goggle if you search for "complex inner product".
 
  • #3
PeroK said:
The inner product of two complex things always involves the complex conjugate of one of the things. In this case:$$\langle u, v \rangle = \int_a^b u(x)^*v(x) \ dx$$There are loads of hits on Goggle if you search for "complex inner product".
so this condition always applies and the integral always equates to 0 when asking for orthogonality?
 
  • #4
Nana113 said:
so this condition always applies and the integral always equates to 0 when asking for orthogonality?
That's the definition of orthogonality. It's a generalization of the concept of orthogonality for 2D or 3D vectors. Vectors (or functions) are orthogonal if $$\langle u, v \rangle = 0$$In this case that means that two functions are orthogonal if$$\int_a^b u(x)^*v(x) \ dx = 0$$
 
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  • #5
Just to add a couple of things.

PeroK said:
The inner product of two complex things always involves the complex conjugate of one of the things. In this case:$$\langle u, v \rangle = \int_a^b u(x)^*v(x) \ dx$$There are loads of hits on Goggle if you search for "complex inner product".
The reason for this being that the inner product is supposed to satisfy ##\langle v,u\rangle = \langle u,v\rangle^*##, resulting in linearity in one argument and anti-linearity in the other.

PeroK said:
In this case that means that two functions are orthogonal if$$\int_a^b u(x)^*v(x) \ dx = 0$$
It should however be noted that an inner product on a function space will often come with an additional weight function. In this case it does not, but it is good to be aware.
 
  • #6
Orodruin said:
Just to add a couple of things.The reason for this being that the inner product is supposed to satisfy ##\langle v,u\rangle = \langle u,v\rangle^*##, resulting in linearity in one argument and anti-linearity in the other.
It also, i'd say the main point, makes it positive definite. ##\langle u, u \rangle > 0## for non zero vectors.
 
  • #7
PeroK said:
The inner product of two complex things always involves the complex conjugate of one of the things. In this case:$$\langle u, v \rangle = \int_a^b u(x)^*v(x) \ dx$$There are loads of hits on Goggle if you search for "complex inner product".

An inner product on a complex vector space is by definition linear in its first argument and Hermitian (see eg. here), so that [tex]\langle \alpha u, v \rangle = \alpha \langle u, v \rangle[/tex] but [tex]\langle u, \alpha v \rangle = (\langle \alpha v, u \rangle)^{*} = \alpha^{*}\langle u, v \rangle.[/tex] Hence we must have [tex]\langle u, v \rangle = \int_a^b u(x)v^{*}(x)\,dx.[/tex]
 
  • #8
pasmith said:
An inner product on a complex vector space is by definition linear in its first argument and Hermitian (see eg. here), so that [tex]\langle \alpha u, v \rangle = \alpha \langle u, v \rangle[/tex] but [tex]\langle u, \alpha v \rangle = (\langle \alpha v, u \rangle)^{*} = \alpha^{*}\langle u, v \rangle.[/tex] Hence we must have [tex]\langle u, v \rangle = \int_a^b u(x)v^{*}(x)\,dx.[/tex]
Yeah, but that messes up Dirac notation!
 
  • #9
pasmith said:
An inner product on a complex vector space is by definition linear in its first argument
Note: This is the typical definition among mathematicians. Among phycisists, the typical convention is that the linearity is in the second argument. One needs to be careful to ensure oneself which convention a particular source uses.
 
  • #10
PeroK said:
Yeah, but that messes up Dirac notation!
Because Dirac notation is typically used by physicists. See above.
 
  • #11
so my working out of the question attached is correct?
 
  • #12
Nana113 said:
so my working out of the question attached is correct?
Looks fine to me.
 

Related to Orthogonality of 2 complex exponentials

What does it mean for two complex exponentials to be orthogonal?

Two complex exponentials are said to be orthogonal if their inner product (integral of their product over a specific interval) is zero. This concept is crucial in signal processing and Fourier analysis, as it ensures that the signals do not interfere with each other.

How do you mathematically determine the orthogonality of two complex exponentials?

To determine the orthogonality of two complex exponentials, you compute the inner product of the two functions over a specified interval. For example, if the exponentials are \( e^{i m \omega t} \) and \( e^{i n \omega t} \), they are orthogonal over the interval \([0, T]\) if:\[ \int_{0}^{T} e^{i m \omega t} \cdot e^{-i n \omega t} \, dt = 0 \]for \( m \neq n \).

Why is orthogonality important in the context of complex exponentials?

Orthogonality is important because it ensures that signals can be separated and individually analyzed without interference. This property is fundamental in Fourier series and transforms, where complex exponentials serve as basis functions. Orthogonal basis functions allow for efficient and accurate signal decomposition and reconstruction.

Can two complex exponentials with the same frequency be orthogonal?

No, two complex exponentials with the same frequency cannot be orthogonal. Orthogonality requires that the inner product be zero, which only occurs if the exponentials have different frequencies (or other distinguishing parameters). When the frequencies are the same, their inner product over any non-zero interval will not be zero.

How is the orthogonality of complex exponentials utilized in practical applications?

Orthogonality of complex exponentials is utilized in various practical applications, including signal processing, communications, and Fourier analysis. For example, in communications, orthogonal frequency-division multiplexing (OFDM) uses orthogonal sub-carriers to transmit data efficiently without interference. In Fourier analysis, orthogonal exponential functions allow for the decomposition of signals into frequency components, facilitating analysis and filtering.

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