Fourier transform, domains, ranges, L^p-spaces

In summary, the Schwartz space on \mathbb{R}^d is a space of smooth, rapidly decreasing functions defined by a maximum norm. The Fourier transform defined on this space is a continuous mapping onto itself and can be extended to L^p(\mathbb{R}^d) for 1\leq p < \infty. It is a known result that \mathcal{F}(L^2)=L^2 and it is possible that \mathcal{F}(L^p)=L^q with 1/p+1/q=1. However, this has not been proven and there are
  • #1
jostpuur
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The Schwartz space on [itex]\mathbb{R}^d[/itex] is defined to be

[tex]
S(\mathbb{R}^d) := \{f\in C^{\infty}(\mathbb{R}^d,\mathbb{C})\;|\; \|f\|_{S,N}<\infty\;\forall N\in\{0,1,2,3,\ldots\}\}
[/tex]

where

[tex]
\|f\|_{S,N} := \underset{|\alpha|,|\beta|\leq N}{\textrm{max}}\;\underset{x\in\mathbb{R}^d}{\textrm{sup}}\; |x^{\alpha}\partial^{\beta}f(x)|.
[/tex]

Alpha and beta are multi-indexes. It turns out, that when Fourier transform is defined on this space, with the integral formula, one obtains a continuous mapping [itex]\mathcal{F}:S(\mathbb{R}^d)\to S(\mathbb{R}^d)[/itex]. Since the Schwartz space is dense in [itex]L^p(\mathbb{R}^d)[/itex], [itex]1\leq p < \infty[/itex], it is possible to obtain a continuous extension of the Fourier transform onto the [itex]L^p(\mathbb{R}^d)[/itex] too. I have not seen explicit counter examples yet, but I've heard that one cannot define the Fourier transform directly with the integral formula in [itex]L^p[/itex] when [itex]p>1[/itex].

My question deals with the range of the Fourier transform. Am I correct to guess, that we have

[tex]
\mathcal{F}(L^p)=L^q
[/tex]

with [itex]1/p+1/q=1[/itex]? It is a known result, that [itex]\mathcal{F}(L^2)=L^2[/itex]. It is also easy to show for example that [itex]\|\mathcal{F}f\|_{\infty}\leq \|f\|_1[/itex], but I'm not sure how to show equality. [itex]\mathcal{F}(L^p)=L^q[/itex] would seem plausible result, but I couldn't find it with a quick skim over the Rudin's Fourier transform chapter at least.
 
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Related to Fourier transform, domains, ranges, L^p-spaces

1. What is a Fourier transform?

A Fourier transform is a mathematical operation that breaks down a function into its constituent frequencies. It transforms a function from the time or spatial domain to the frequency domain, revealing the individual sinusoidal components that make up the original function.

2. What are the different domains and ranges of a Fourier transform?

The domain of a Fourier transform can be either time or space, depending on the type of function being transformed. The range of the transform is always the frequency domain, with the values representing the different frequencies present in the original function.

3. What are L^p-spaces?

L^p-spaces are mathematical spaces that are used to study functions and their properties. They are named after the function spaces L^p, which are defined as the set of all functions that are p-integrable. The value of p determines the weight given to different parts of the function, with higher values of p emphasizing the higher frequencies in the function.

4. What is the significance of L^p-spaces in Fourier analysis?

In Fourier analysis, L^p-spaces are used to study the convergence and properties of Fourier transforms. They also help in understanding the behavior of functions in different frequency ranges and how they contribute to the overall function. L^p-spaces are also important in applications such as signal processing and image reconstruction, where the Fourier transform is used extensively.

5. How are L^p-spaces related to other function spaces?

L^p-spaces are a subset of the Lebesgue function spaces, which are used to study the properties of measurable functions. They are also related to the Sobolev spaces, which are used in the study of partial differential equations. In general, L^p-spaces provide a framework for studying various types of functions, including those that are not continuous or differentiable.

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