On limit of convolution of function with a summability kernel

In summary, the paper discusses the behavior of convolution between a function and a summability kernel, focusing on the conditions under which the limit of such convolutions exists. It explores various mathematical properties and theorems related to summability kernels, providing insights into their applications in functional analysis and harmonic analysis. The study highlights how these convolutions can converge to a certain function under specific conditions, contributing to a deeper understanding of integral transforms and their limits.
  • #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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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.
 

FAQ: On limit of convolution of function with a summability kernel

What is a summability kernel in the context of convolution?

A summability kernel is a function used to improve the convergence properties of a sequence or function when convolved with another function. It typically has properties that ensure the resulting convolution behaves well, especially in terms of convergence and continuity. Common examples include the Dirichlet kernel and the Fejér kernel.

How does convolution with a summability kernel affect the limit of a function?

Convolution with a summability kernel can smooth out a function, leading to better convergence properties. It can help in approximating functions and ensuring that the limit of the convolution converges to a desired function, often under certain conditions related to the summability kernel's properties.

What are the key properties of summability kernels that influence convergence?

Key properties include the kernel's boundedness, non-negativity, and normalization (i.e., the integral of the kernel over its domain equals one). Additionally, the decay properties of the kernel, which determine how quickly it approaches zero away from its center, play a crucial role in the convergence of the convolution.

Can you provide an example of a summability kernel and its application?

An example of a summability kernel is the Fejér kernel, which is defined as the average of Dirichlet kernels. It is used in the context of Fourier series to show that the partial sums of the series converge to the function being represented, under certain conditions, thus demonstrating the effectiveness of summability kernels in ensuring convergence.

What are the implications of the limits of convolutions in practical applications?

The limits of convolutions with summability kernels have significant implications in various fields such as signal processing, image analysis, and probability theory. They are used to filter signals, smooth data, and analyze distributions, thereby enhancing the reliability and accuracy of practical applications in these areas.

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