Fourier transform of f \in C_c^\infty(R^n)

In summary, the conversation discusses proving that if both a function f and its Fourier transform are smooth and compactly supported on n-dimensional euclidean space, then f must be identically zero. One approach mentioned is using the Fourier inversion theorem to show that f agrees almost everywhere with a continuous function and then using continuity to prove that one (or both) of those functions must be zero. Another proof is mentioned, which involves showing that the Fourier transform of a function in C0∞ cannot have compact support.
  • #1
chub
2
0
I have noticed that this result is hinted at in several books, but am having trouble proving it:

[tex]f, \hat{f} \in C_c^\infty(R^n) \Rightarrow f \equiv 0. [/tex]

in other words, if both f and its Fourier transform
are smooth, compactly supported functions on n-dimensional euclidean space
then f is identically zero.

any advice? i thought of using the Fourier inversion theorem, which tells me that f agrees almost everywhere (Lebesgue) with the continuous function
[tex]f_0 = (\hat{f})\check{} = (\check{f})\hat{}[/tex]
and then showing that one (or both) of those are zero; continuity of f would then take care of the "almost everywhere" part. but I'm not really sure what to do.
 
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  • #2
Ok here is one proof I found (g in C0): since exp(-i k x) is entire, the reimann sums approximating F[g] are entire. The reimann sums must converge uniformly on compact sets since g is in C0, so F[g] is entire. Since entire functions don't have compact support, F[g] can't have compact support.

I don't really like that proof very much.
 

FAQ: Fourier transform of f \in C_c^\infty(R^n)

What is the Fourier transform of a function?

The Fourier transform of a function is a mathematical operation that decomposes a function into its constituent frequencies. It converts a function from its original domain (often time or space) to a representation in the frequency domain.

What is the significance of the Fourier transform in mathematics and science?

The Fourier transform has many applications in mathematics and science, including signal processing, image processing, quantum mechanics, and differential equations. It allows us to analyze and understand complex functions by breaking them down into simpler components.

What types of functions can be transformed using the Fourier transform?

The Fourier transform can be applied to any function that is square integrable, meaning it has finite energy and decays sufficiently fast as the independent variable approaches infinity. In particular, the Fourier transform can be applied to functions in the space Cc(Rn), which consists of smooth functions with compact support.

How is the Fourier transform calculated?

The Fourier transform can be calculated using the integral formula: F(ω) = ∫-∞ f(x)e-iωxdx, where f(x) is the function in the original domain and F(ω) is its transform in the frequency domain. There are also various numerical methods for computing the Fourier transform, such as the fast Fourier transform (FFT) algorithm.

Are there any limitations to the Fourier transform?

The Fourier transform has some limitations, such as the requirement for a function to be square integrable and the inability to handle functions with discontinuities or singularities. Additionally, the Fourier transform is a global operation, meaning it transforms the entire function at once, which may not be suitable for analyzing local features in a function.

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