Morphism which preserves convolution?

In summary: This is where the second problem comes in. Zero is not a real number, so one cannot simply take its logarithm.
  • #1
mnb96
715
5
Hello,
I was wondering if there exists a (iso)morphism which preserves the operation of convolution, in respect to the pointwise-addition operation.
For example: it is well known that the Discrete Fourier Transform is a morphism which preserves convolution in respect to pointwise-multiplication:
[tex]F(f\ast g) = F(f)\cdot F(g)[/tex]

Is it possible to find another operator [tex]\mathcal{G}[/tex] (different than the FT) for which the following is valid?
[tex]\mathcal{G}(f\ast g) = \mathcal{G}(f)+\mathcal{G}(g)[/tex]
 
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  • #2
Just looking I would say if you defined G(f) = ln(F(f)), then you would have G(f*g) = ln(F(f)F(g)) = ln(F(f)) + ln(F(g)) = G(f) + G(g).

Right?
 
  • #3
Matticus thanks for your reply!
I am quite amazed for the fact that I didn't figure out that before. In fact, I think your suggestion makes perfectly sense.
Moreover, if we restrict ourselves to the discrete domain, taking the logarithm of the DFT would be equivalent to applying a coordinate transformation of the kind:

[tex]\bar{x_i} = log(x_i) [/tex]

[tex]x_i = e^{\bar{x_i}} [/tex]

and if I am not wrong, the Jacobian of such a transformation is never 0. This would imply that it is a one-to-one mapping, and so an isomorphism. I guess this observation could be applied as well to continuous functions, but I don't know how.
Does it make sense?
 
  • #4
...uhm, actually there are problems is one is looking for an isomorphism. The isomorphism I mentioned with the log transformation is perfectly valid only in the domain of real positive numbers. Now, the Fourier transform outputs complex numbers, including real negative numbers and zero, so two problems arise:

1) the logarithm of a negative number can have multiple values
2) the logarithm of zero is not defined

The first problem can be solved by considering only the principal first branch of the negative logarithm, and that's fine.

But how can one ensure that the Fourier transform has always non-zero components?
 

1. What is a morphism that preserves convolution?

A morphism that preserves convolution is a function between two mathematical structures that preserves the operation of convolution. This means that when the function is applied to the inputs of the convolution, the resulting output is the same as if the convolution were applied to the original inputs and then the function was applied.

2. Why is it important to preserve convolution?

Preserving convolution is important because it allows for the efficient computation of convolutions. If a function can be applied before or after the convolution operation without changing the result, it can help simplify the calculation and save time and resources.

3. What are some examples of structures where convolution is commonly used?

Convolution is commonly used in signal processing, image processing, and in the study of differential equations. It is also used in machine learning, particularly in convolutional neural networks for tasks such as image recognition and natural language processing.

4. How do you determine if a function preserves convolution?

To determine if a function preserves convolution, you can apply the function to the inputs of the convolution and compare the resulting output to the output of the convolution using the original inputs. If they are the same, then the function preserves convolution.

5. What are the benefits of using a morphism that preserves convolution?

Using a morphism that preserves convolution can aid in simplifying calculations and reducing computational resources, as well as maintaining the integrity of the data being convolved. It can also make it easier to apply convolutions to a wide range of mathematical structures, allowing for the transfer of knowledge and techniques across different fields.

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