Fourier transforms of smooth, compactly supported functions

In summary, a Fourier transform is a mathematical operation that converts a function from its original domain to its representation in the frequency domain. Smooth and compactly supported functions are well-behaved and localized, making them ideal for Fourier transforms. These transforms are useful for analyzing frequency components and predicting the behavior of functions. They can be computed using integral formulas or numerical methods. Applications of Fourier transforms for smooth, compactly supported functions include signal processing, image processing, and solving differential equations in fields such as physics.
  • #1
chub
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Homework Statement



[tex] f, \hat{f} \in C_c^\infty(\mathbb{R}^n)[/tex]


Homework Equations




[tex] \hat{f} = \int_{\mathbb{R}^n} f(x) e^{-2\pi i \xi \cdot x} \,dx [/tex]
[tex] \check{f} = \int_{\mathbb{R}^n} \hat{f}(x) e^{2\pi i \xi \cdot x} \,d\xi [/tex]


The Attempt at a Solution



As [tex] C_c^\infty \subset \mathcal{S} [/tex] (the Schwarz space) we know that the Fourier transformation is invertible, and that
[tex] f = (\hat{f})\check{} = (\check{f})\hat{} [/tex]
in other words
[tex] f =
\int_{\mathbb{R}^n} \hat{f} e^{2 \pi i \xi \cdot x} \,d\xi
= \int_{\mathbb{R}^n} \check{f} e^{-2 \pi i \xi \cdot x} \,dx [/tex]

Somehow these must be zero. I am familiar with the idea that the smoother f is, the faster its transform must decay at infinity; and vice versa. Since [tex]C^\infty, C_c[/tex] are the ultimate of both this must somehow indicate that the situation is "too good to be true" in a nontrivial way. But I do not know how to implement this. Advice?
 
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  • #2


Thank you for your post. It is great to see that you are working with the Fourier transformation and its properties.

Firstly, let me clarify that the Fourier transform is not necessarily zero for smooth functions. It is only guaranteed to decay at infinity for functions in the Schwartz space, as you mentioned. However, the decay rate at infinity can vary depending on the smoothness of the function. For example, a C^\infty function will have a faster decay than a C^2 function.

To understand why this is the case, we need to look at the definition of the Schwartz space. The Schwartz space is defined as the space of all infinitely differentiable functions whose derivatives decay faster than any polynomial at infinity. This means that the functions in this space have very fast decaying derivatives, which in turn leads to a fast decay of the Fourier transform.

On the other hand, functions in C_c^\infty have compact support, meaning that they are zero outside of a bounded set. This implies that their derivatives will also have compact support, and hence, their Fourier transform will not decay as fast at infinity. In fact, the faster the decay of the derivatives at infinity, the slower the decay of the Fourier transform.

In summary, the smoothness of a function affects the decay rate of its Fourier transform, but it is not necessarily zero. I hope this helps clarify your doubts.
 

Related to Fourier transforms of smooth, compactly supported functions

What is a Fourier transform?

A Fourier transform is a mathematical operation that decomposes a function into its individual frequency components. It converts a function from its original domain (such as time or space) to its representation in the frequency domain.

What does it mean for a function to be smooth and compactly supported?

A smooth function is one that has continuous derivatives of all orders. This means that the function is well-behaved and has no sudden changes or discontinuities. A compactly supported function is one that is zero outside of a finite interval. This means that the function is localized and does not extend infinitely in any direction.

Why are Fourier transforms useful for smooth, compactly supported functions?

Fourier transforms are useful for these types of functions because they can accurately represent the function in the frequency domain. This allows us to analyze the function's frequency components and make predictions about its behavior.

How do you compute a Fourier transform of a smooth, compactly supported function?

The Fourier transform can be computed using an integral formula or through numerical methods. The integral formula involves integrating the function over its domain with respect to the frequency variable. Numerical methods use algorithms to approximate the integral and compute the transform.

What are some applications of Fourier transforms for smooth, compactly supported functions?

Fourier transforms have many applications in fields such as signal processing, image processing, and physics. They are commonly used for filtering and noise reduction, spectral analysis, and solving differential equations. They are also essential in the study of waves and vibrations in physics.

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