Proving Convolution in R^n using Isometric Isomorphism and Lp Spaces

In summary, the conversation discusses how to prove that if \delta\in L_1(\mathbb{R}^n) and f\in L_p(\mathbb{R}^n), then the convolution \delta * f\in L_p(\mathbb{R}^n) with \lVert \delta * f\rVert_p\leq\lVert\delta\rVert_1\lVert f\rVert_p. The conversation also mentions using the natural isometry between L_q and L_p^* and the Minkowski inequality theorem to finish the proof. The individual also requests help in understanding how to apply the natural isometry in this case.
  • #1
hatsoff
20
3

Homework Statement



Prove the following: If [tex]\delta\in L_1(\mathbb{R}^n)[/tex] and [tex]f\in L_p(\mathbb{R}^n)[/tex] then the convolution [tex]\delta * f\in L_p(\mathbb{R}^n)[/tex] with [tex]\lVert \delta * f\rVert_p\leq\lVert\delta\rVert_1\lVert f\rVert_p[/tex].

Homework Equations



We use the natural isometry (or isometric isomorphism, if you like) [tex]h\mapsto\lambda_h[/tex] between [tex]L_q[/tex] and [tex]L_p^*[/tex], where [tex]\frac{1}{p}+\frac{1}{q}=1[/tex], and where we define each [tex]\lambda_h[/tex] by [tex]\lambda_h(f)=\int fh[/tex].

The Attempt at a Solution



Well I can show that [tex]\lVert (\delta * f)h\rVert_1\leq\lVert\delta\rVert_1 \lVert f\rVert_p\lVert h\rVert_q[/tex]. Supposedly I can use this along with the natural isometry between [tex]L_q[/tex] and [tex]L_p^*[/tex] to finish the proof. But I don't see how that natural isometry is applicable.

Any help would be much appreciated. Thanks!
 
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  • #2
I think you need to use here Minkowski inequality theorem, and the fact that dirac-delta measure is invriant under raising its power.

http://www.math.duke.edu/~wka/math204/conv.12.4.pdf
it's in page 2, but there's a misprint, and the LHS, the aboslute value integrand should be raised to power of p.
 
  • #3
Thanks, I appreciate the link, and indeed that is a very nice proof of the theorem in question. However, I'm looking to finish the particular approach I was given.

Basically, I have to show, using the fact that for all [tex]h\in L_q[/tex] we have

[tex]\lVert (\delta * f)h\rVert_1\leq\lVert\delta\rVert_1 \lVert f\rVert_p\lVert h\rVert_q[/tex]

and also using the natural isometric isomorphism between Lp* and Lq, that the [tex]\delta *f\in L_p[/tex].
 

What is a convolution?

A convolution is a mathematical operation that combines two functions to produce a third function. In the context of R^n, it is used to calculate the integral of the product of two functions over a particular region in n-dimensional space.

What is the purpose of a convolution proof in R^n?

The purpose of a convolution proof in R^n is to demonstrate the validity of a particular mathematical statement or formula involving convolution in n-dimensional space. It helps to provide a rigorous and logical explanation for why a certain result is true.

What are the basic steps involved in a convolution proof in R^n?

The basic steps involved in a convolution proof in R^n include defining the functions involved, setting up the integral, applying appropriate mathematical properties or theorems, and simplifying the result to reach the desired conclusion. It also involves careful consideration of the limits of integration and any special cases that may arise.

What are some common applications of convolution in R^n?

Convolution in R^n has a wide range of applications in many fields, including signal processing, image processing, probability and statistics, and differential equations. It is used to model real-world phenomena and solve complex problems by breaking them down into simpler parts.

Are there any limitations to using convolution in R^n?

While convolution is a powerful mathematical tool, it does have some limitations. One limitation is that it may be computationally expensive for very large or complex functions. Additionally, it may not be suitable for all types of problems and may require alternative methods for certain cases.

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