Any important inequalities with convolutions?

In summary, the conversation discusses finding upper bounds for derivatives of convolutions and possible results involving numbers C_{f,g}. One result proposed is |D_x(f*g)(x)| \leq \|f'\|_{\infty} \|g\|_1 and another is |D_x(\varphi *\psi)(x)| \leq \|\varphi'\|_{\infty} \|\psi\|_1, but both have limitations due to the functions involved. The conversation also mentions the possibility of proving that D_x(\varphi *\psi)(x) is almost the same as \psi'(x) near a spike in \psi.
  • #1
jostpuur
2,116
19
I am interested to know who to find upper bounds for derivatives of convolutions. If I know something of [itex]f[/itex] and [itex]g[/itex], are there any major results about what kind of numbers [itex]C_{f,g}[/itex] exist such that

[tex]
|D_x((f *g)(x))| \leq C_{f,g} ?
[/tex]
 
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  • #2
Truth is that I didn't think about that much before posting. I thought that it would probably be better to ask about old results, before trying to come up with own ones.

But now, after very short thinking, I already came up with one very natural result. It seems that the following is true:

[tex]
|D_x(f*g)(x)| \leq \|f'\|_{\infty} \|g\|_1
[/tex]

It could be that this is what I was after. If somebody has something else to add, I'm still all ears.
 
  • #3
Suppose [itex]\psi[/itex] is a function, which is mostly smooth, but has a little spike somewhere so that [itex]\psi'[/itex] jumps badly. Also suppose that the spike is so small that the values of [itex]\psi[/itex] don't jump very much. Only the derivative jumps. And suppose that [itex]\varphi[/itex] is some typical convolution kernel, which is approximately a delta function, but still so wide that it makes the spike in [itex]\psi[/itex] almost vanish.

It should be possible to prove that [itex]D_x(\varphi *\psi)(x)[/itex] is almost the same as [itex]\psi'(x)[/itex] with exception of the [itex]x[/itex] that is close to the little spike. Close to the spike [itex]\psi'(x)[/itex] jumps, but [itex]D_x(\varphi *\psi)(x)[/itex] behaves as if the spike did not exist.

Approximation

[tex]
|D_x(\varphi *\psi)(x)| \leq \|\varphi'\|_{\infty} \|\psi\|_1
[/tex]

is useless because [itex]\|\varphi'\|_{\infty}[/itex] is very large, and

[tex]
|D_x(\varphi *\psi)(x)| \leq \|\psi'\|_{\infty} \|\varphi\|_1
[/tex]

is useless too because [itex]\|\psi'\|_{\infty}[/itex] is very large because of the spike.

So there must be some other upper bound for [itex]|D_x(f *g)(x)|[/itex], better than the one I mentioned in the #2 post.
 
Last edited:

Related to Any important inequalities with convolutions?

1. What are convolutions and why are they important?

Convolutions are mathematical operations that involve combining two functions to produce a third function. In the context of signal processing and image recognition, convolutions are used to extract features and patterns from data. They are important because they allow us to analyze and understand complex data in a more efficient way.

2. How do convolutions relate to inequalities?

Convolution inequalities arise when we apply convolutions to functions that have certain properties, such as being non-negative or decreasing. These inequalities provide bounds on the resulting convolution function and can help us make predictions about the behavior of the data.

3. Can convolutions be used to solve real-world problems?

Yes, convolutions have numerous applications in various fields such as physics, engineering, and computer science. They are often used in image and audio processing, natural language processing, and machine learning to name a few. Their versatility and ability to capture complex patterns make them a powerful tool for solving real-world problems.

4. Are there any limitations or drawbacks to using convolutions?

While convolutions are a valuable tool, they do have some limitations. One limitation is that they assume linear relationships between data points, which may not always hold true. Additionally, the size of the convolution kernel can greatly impact the results, so choosing the right kernel size can be a challenge.

5. How can I learn more about convolutions and their applications?

There are many resources available to learn about convolutions and their applications. Online tutorials, textbooks, and courses are great places to start. Additionally, connecting with other scientists and researchers in the field can provide valuable insights and resources for further learning.

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