Derivative of p-fold convolution

In summary, the derivative of a p-fold convolution can be calculated using two different approaches. The first approach involves taking the derivative of the integral, while the second approach involves using the Fourier transform. However, both approaches have contradicting results and it is unclear which one is correct. Additionally, the derivative of a function that operates on Y(\omega) can be written in terms of its Fourier transform, but the result will include a factor of 1/Y'(\omega).
  • #1
divB
87
0
Hi,

What is the derivative of a p-fold convolution?

[tex]
\frac{\partial}{\partial Y(\omega) } \underbrace{Y(\omega) * \dots * Y(\omega)}_{p-\text{times}}
[/tex]

EDIT: I have two contradicting approaches - I guess both are wrong ;-)

As a simple case, take the 2-fold convolution. FIRST approach:

[tex]
\frac{\partial}{\partial Y(\omega)} Y(\omega)*Y(\omega) = \frac{\partial}{\partial Y(\omega)} \int_{-\infty}^{\infty} Y(\tau) Y(\omega-\tau) d\tau = \int_{-\infty}^{\infty} Y(\tau) \underbrace{\frac{\partial Y(\omega-\tau)}{\partial Y(\omega)}}_{\delta(\tau)} d\tau = \int_{-\infty}^{\infty} Y(\tau)\delta(\tau) d\tau = 1
[/tex]

Here I think the derivative is wrong but intuitively it makes sense at least: Differentiation is the opposite of smoothing, smoothing is convolution, so taking away the convolution is taking away smoothing.

SECOND approach:

[tex]
\frac{\partial}{\partial Y(\omega)} Y(\omega)*Y(\omega) = \mathcal{F}\left\{ \mathcal{F}^{-1}\left\{ \frac{\partial}{\partial Y(\omega)} Y(\omega)*Y(\omega) \right\} \right\} = \mathcal{F}\left\{ -j y(t) y^2(t) \right\}= \mathcal{F}\left\{ -j y^3(t) \right\} = -jY(\omega)*Y(\omega)*Y(\omega)
[/tex]

Here is the point where I am stuck - I am sure I can't apply the derivative theorem here because it's not a derivative by [tex]\omega[/tex]. But how to do this? Anyways, this result does the opposite from above and is also against my inuition, so I think it's wrong ...
 
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  • #2
Ok I think I got it. Is this correct? (I was not able to do simple differentiation)

[tex]
\frac{\partial}{\partial Y(\omega)} Y(\omega)*Y(\omega) =
\frac{\partial}{\partial Y(\omega)} \int_{-\infty}^{\infty} Y(\tau) Y(\omega-\tau) d\tau = \\
\int_{-\infty}^{\infty} Y(\tau) \frac{\partial Y(\omega-\tau)}{\partial Y(\omega)} d\tau =
\int_{-\infty}^{\infty} Y(\tau) \frac{\partial \omega}{\partial \omega} \frac{\partial Y(\omega-\tau)}{\partial Y(\omega)} d\tau =
\int_{-\infty}^{\infty} Y(\tau) \frac{\partial \omega}{\partial Y(\omega)} \frac{\partial Y(\omega-\tau)}{\partial \omega} d\tau = \\
\int_{-\infty}^{\infty} Y(\tau) \left(\frac{\partial Y(\omega)}{\partial \omega}\right)^{-1} \frac{\partial Y(\omega-\tau)}{\partial (\omega-\tau)} \frac{\partial (\omega-\tau)}{\partial \omega} d\tau =\\
\int_{-\infty}^{\infty} Y(\tau) \frac{1}{Y'(\omega)} Y'(\omega-\tau) d\tau =
\frac{Y'(\omega) * Y(\omega)}{Y'(\omega)}
[/tex]


In any case, now I am struggling with something different where I do not know if there is even a solution to it!

Suppose the same setup as above. However, instead of having just a convolution, I have a function which operates on [itex]Y(\omega)[/itex]:


[tex]
\frac{\partial}{\partial Y(\omega)} F(Y(\omega))
[/tex]

However, I do not have the analytical form of F. However, I know the analytical form of its Fourier transform f.

Can I write the equation above in terms of the Fourier transform [itex]y(t)[/itex] and [itex]f[/itex] ?

I can do it but I still have the factor of [itex]1/Y'(\omega)[/itex]. For the example above:

[tex]
\frac{1}{Y'(\omega)} j \mathcal{F}\left( t \cdot y^2(t) \right)
[/tex]

Oh, wait, I didn't realize that I can also trivially expand [itex]1/Y'(\omega)[/itex]:

[tex]
-\frac{\mathcal{F}\left\{ t \cdot y^2(t) \right\}}{\omega \mathcal{F}\left\{(t) \right\}}
[/tex]

Is this correct?
 

Related to Derivative of p-fold convolution

1. What is the definition of the derivative of p-fold convolution?

The derivative of p-fold convolution is a mathematical operation that represents the rate of change of a p-fold convolution function with respect to one of its variables. It measures how the output of the convolution changes as the input variables change.

2. What is the purpose of using the derivative of p-fold convolution?

The derivative of p-fold convolution is useful for understanding how a system or process changes over time, and for optimizing parameters in mathematical models. It also has applications in signal processing, image processing, and machine learning.

3. How is the derivative of p-fold convolution calculated?

The derivative of p-fold convolution is calculated using the derivative rules for convolution, which involve taking the derivative of each term in the convolution function and then summing them together. It can also be calculated using Fourier transforms.

4. Are there any limitations to using the derivative of p-fold convolution?

Yes, there are some limitations to using the derivative of p-fold convolution. It can only be applied to functions that are continuous and differentiable, and it may not always produce a meaningful result for complicated or non-linear systems.

5. How is the derivative of p-fold convolution used in real-world applications?

The derivative of p-fold convolution has many practical applications, such as in digital signal processing to filter and analyze signals, in image processing for edge detection and feature extraction, and in machine learning for gradient-based optimization algorithms. It is also used in physics, engineering, and economics for modeling and analyzing complex systems.

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