Smoothness of Fourier Transforms for Rapidly Decaying Functions

In summary, the conversation discusses the relationship between a function's decay rate and the smoothness of its Fourier transform. The speaker suggests that if the function and all its derivatives decay faster than any polynomial, the Fourier transform will be smooth. They also mention that this is a non-trivial result and provide a reference to a proof from a PDEs course. The conversation also touches on the concept of the Schwartz space and how it relates to functions with compact support. The speaker provides an example of a function with compact support and explains how it decays rapidly towards infinity.
  • #1
mnb96
715
5
Hello,
I read somewhere that if a function f decays rapidly (e.g. [itex]\lim_{x \to \infty}f(x)=0 )[/itex], then its Fourier transform F is smooth.
How can I prove this? (Reference to some sources are welcome too).
Thanks.
 
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  • #2
I think f(x) has to tend to zero for the Fourier transform to even exist.
Just did some musing. If f and all its derivatives decay faster than any polynomial (eg, exponentially fast), F should be smooth. I'm sure there are less strict conditions though.

Sometimes the FT is studied as an isomorphism from the Schwartz space to itself (functions from ___ to ___ whose derivatives of all orders and types decay exponentially fast. Sometimes it's expressed as having a certain finite norm)
 
  • #3
Jerbearrrrrr said:
...If f and all its derivatives decay faster than any polynomial (eg, exponentially fast), F should be smooth...

do you have a reference to a proof for this statement?
It doesn´t look like a trivial result, at least to me.
 
  • #4
My lecture notes lol. You need to use the fact that FT(f ') = k*FT(f), and the inverse/dual (not sure precisely what to call it) of this fact.From my PDEs course last term. It's actually like the first thing that was proven after defining the FT:

Defn (Fourier Transform)
for [tex]f \in L^1(R^n;C) \ \ \ \ \ \ \ \ \hat{f} (\mu) := \int _{R^n} f(x) e^{-ix.\mu} dx[/tex]
[tex] (\mu \in R^n, \hat{f} : R^n \to C)[/tex]

Lemma
Let [tex]f \in S(R^n)[/tex]. Then [tex] \hat{f} \in S(R^n)[/tex].
Proof
For any multi-indices [tex]k_1, k_2[/tex]
[tex] sup_\mu | \mu^{k_1} \nabla ^{k_2} \hat{f} (\mu)| \leq sup |F(\nabla _x ^{k_1} (x^{k_2} f))(\mu)|\leq | | \nabla _x ^{k_1}(x^{k_2} f) | | _{L^1}[/tex]

I've sloppily used F(f) to be the Fourier transform of f that changes x to mu. The ||.|| thing is the L1 norm which is just "integrate me over all space".
It's presented as a one liner but I guess there are quite a few ideas involved.

Oh, and S is
[tex]S:= \{f \in C^{\infty} (R) : lim_{x \to \infty} \frac{|\nabla ^k f(x) |}{|x| ^m} \to 0 \ \ \forall k,m\}[/tex]
So thingies in S are definitely smooth and decaying.
 
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  • #5
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  • #6
Oh. Yes, it is clear.
Compact support means (well it doesn't, but it does) the function is non-zero only locally (in some closed set).

ie it looks like a bump.
 
  • #7
I tried to prove it this way (by induction on the power of [itex]|x|^m[/itex]).
The cases when [itex]m\geq 0[/itex], are trivial so we assume m<0, and f is smooth with [itex]\lim_{x \to \infty}f(x)=0[/itex].
Let k=-m

Base (k=1):

[tex]\lim_{x\to +\infty} xf^{(n)}}(x) = \lim_{x\to +\infty} \frac{x}{[f^{(n)}(x)]^{-1}} = \lim_{x \to \infty}\frac{f^{(n+1)}}{-[f^{(n)}(x)]^{-2}} = 0[/tex]Induction:

[tex]\lim_{x\to +\infty} x^{i+1}f^{(n)}}(x) = \lim_{x\to +\infty} \frac{x}{[x^{i}f^{(n)}(x)]^{-1}}= \lim_{x\to +\infty} \frac{1}{D[(x^{i}f^{(n)}(x))^{-1}]} = 0[/tex]

The last step is due to the fact that the denominator is the product of two smooth functions (which by induction hypothesis tends to 0, and so all its derivatives).

If this is correct, it looks more clear to me. Perhaps I don't clearly understand the definition of function with compact support (at least I don't know how to use that definition).
 
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  • #8
[PLAIN]http://img707.imageshack.us/img707/2655/compsupp.png
This is a smooth function with compact support. (Assume it's smooth anyway)

It's only non-zero locally.
So towards infinity...of course it decays fast, because it IS zero.
 
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1. What is Fourier transform smoothness?

Fourier transform smoothness is a mathematical concept that measures the rate at which a function changes over its domain. It is a measure of how rapidly the function oscillates or fluctuates. A function is considered smooth if it has a low rate of change, meaning it has few abrupt changes or discontinuities.

2. How is Fourier transform smoothness calculated?

The calculation of Fourier transform smoothness involves taking the Fourier transform of a function and then analyzing its frequency components. The Fourier transform is a mathematical tool that breaks down a function into its individual sine and cosine components, and the smoothness is determined by the amplitude and frequency of these components.

3. What are the applications of Fourier transform smoothness?

Fourier transform smoothness has various applications in signal processing, image processing, and data analysis. It is used to remove noise from signals, enhance image quality, and determine the periodicity of data. It is also used in audio and video compression techniques to reduce file size while maintaining quality.

4. How does Fourier transform smoothness relate to the Nyquist-Shannon sampling theorem?

The Nyquist-Shannon sampling theorem states that to accurately reconstruct a signal from its samples, the sampling rate must be at least twice the highest frequency component in the signal. Fourier transform smoothness is related to this theorem because a smoother function will have fewer high-frequency components, allowing for a lower sampling rate without losing important information.

5. Can Fourier transform smoothness be adjusted?

Yes, Fourier transform smoothness can be adjusted by manipulating the function's frequency components. By removing or changing specific frequency components, the function's smoothness can be altered. This is often done in signal processing to remove noise or in image processing to enhance certain features.

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