On limit of convolution of function with a summability kernel

  • #1
psie
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TL;DR Summary
I'm stuck at proving a corollary regarding the limit of a convolution with a positive summability kernel and an arbitrary function.
I'm reading the following theorem in Fourier Analysis and its Applications by Vretblad.

Theorem 2.1 Let ##I=(-a,a)## be an interval (finite or infinite). Suppose that ##(K_n)_{n=1}^\infty## is a sequence of real-valued, Riemann-integrable functions defined on ##I##, with the following properties:
1) ##K_n(s)\geq 0##,
2)##\int_{-a}^a K_n(s)ds=1##, and
3) if ##\delta>0##, then ##\lim\limits_{n\to\infty}\int_{\delta<|s|<a} K_n(s)ds=0.##
If ##f:I\to\mathbb{C}## is integrable and bounded on ##I## and continuous for ##s=0##, we then have $$\lim_{n\to\infty}\int_{-a}^a K_n(s)f(s)ds=f(0).$$

Corollary 2.1 If ##(K_n)_{n=1}^\infty## is a positive summability kernel on the interval ##I##, ##s_0## is an interior point of ##I##, and ##f## is continuous at ##s=s_0##, then $$\lim_{n\to\infty}\int_I K_n(s)f(s_0-s)ds=f(s_0).$$

The proof is left as an exercise (do the change of variables ##s_0-s=u##.

It's silly, but I'd like to prove the corollary and I'm getting stuck. I'm a little unsure if ##I## in the corollary is also of the form ##(-a,a)##. Moreover, the change of variables as suggested gives us ##s=s_0-u##, so ##K_n(s)## becomes ##K_n(s_0-u)##. Is this a kernel still centered at ##0##? If I'm understanding things right, the author alludes to using $$\lim_{n\to\infty}\int_{-a}^a K_n(s)f(s)ds=f(0),$$ from theorem 2.1 to prove the corollary. Appreciate any help.
 
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  • #2
[itex]I[/itex] is throghout assumed to be of the form [itex](-a,a)[/itex].

Easier than substitution is to define [itex]g(s) \equiv f(s_0 - s)[/itex] so that [itex]g(0) = f(s_0)[/itex] and apply the theorem to [itex]g[/itex].
 
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  • #3
Thank you @pasmith.

1. Do you know if positive summability kernels (i.e. a sequence of functions satisfying 1), 2) and 3) above) are even functions?
2. It looks to me that if ##K_n(s)## is a positive summability kernel, then so is ##K_n(-s)##. Is this right?

When we make the substitution ##u=-s## in the integral in the corollary, we obtain $$\int_I K_n(-u)f(s_0+u)du,$$ where ##I=(-a,a)## remains unchanged. If ##K_n(-u)=K_n(u)## or if ##K_n(-u)## is also a positive summability kernel over ##I##, and we set ##s_0=0##, then we can apply the theorem.
 

What is the limit of convolution of a function with a summability kernel?

The limit of convolution of a function with a summability kernel is the value that the convolution approaches as the size of the kernel approaches infinity.

How is the limit of convolution of a function with a summability kernel calculated?

The limit of convolution can be calculated using various mathematical techniques, such as Fourier transforms or Laplace transforms, depending on the specific properties of the function and kernel involved.

Why is the limit of convolution of a function with a summability kernel important?

Understanding the limit of convolution is important in many areas of mathematics and signal processing, as it can provide insights into the behavior of functions and help solve complex problems.

What are some applications of the limit of convolution of a function with a summability kernel?

Applications include image processing, data compression, and signal filtering, where convolutions are used to manipulate and analyze data efficiently.

Are there any limitations to the concept of the limit of convolution of a function with a summability kernel?

While the concept is powerful, it may not always be applicable to every function or kernel, and certain assumptions or conditions may need to be met for the limit to be well-defined.

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