--- title: "Schwartz distribution" firstLetter: "S" publishDate: 2021-02-25 categories: - Mathematics date: 2021-02-25T13:47:16+01:00 draft: false markup: pandoc --- # Schwartz distribution A **Schwartz distribution**, also known as a **generalized function**, is a generalization of a function, allowing us to work with otherwise pathological definitions. Notable examples of distributions are the [Dirac delta function](/know/concept/dirac-delta-function/) and the [Heaviside step function](/know/concept/heaviside-step-function/), whose unusual properties are justified by this generalization. We define the **Schwartz space** $\mathcal{S}$ of functions, whose members are often called **test functions**. Every such $\phi(x) \in \mathcal{S}$ must satisfy the following constraint for any $p, q \in \mathbb{N}$: $$\begin{aligned} \mathrm{max} \big| x^p \phi^{(q)}(x) \big| < \infty \end{aligned}$$ In other words, a test function and its derivatives decay faster than any polynomial. Furthermore, all test functions must be infinitely differentiable. These are quite strict requirements. The **space of distributions** $\mathcal{S}'$ (note the prime) is then said to consist of *functionals* $f[\phi]$ which map a test function $\phi$ from $\mathcal{S}$, to a number from $\mathbb{C}$, which is often written as $\braket{f}{\phi}$. This notation looks like the inner product of a [Hilbert space](/know/concept/hilbert-space/), for good reason: any well-behaved function $f(x)$ can be embedded into $\mathcal{S}'$ by defining the corresponding functional $f[\phi]$ as follows: $$\begin{aligned} f[\phi] = \braket{f}{\phi} = \int_{-\infty}^\infty f(x) \: \phi(x) \dd{x} \end{aligned}$$ Not all functionals qualify for $\mathcal{S}'$: they also need to be linear in $\phi$, and **continuous**, which in this context means: if a series $\phi_n$ converges to $\phi$, then $\braket{f}{\phi_n}$ converges to $\braket{f}{\phi}$ for all $f$. The power of this generalization is that $f(x)$ does not need to be well-behaved: for example, the Dirac delta function can also be used, whose definition is nonsensical *outside* of an integral, but perfectly reasonable *inside* one. By treating it as a distribution, we gain the ability to sanely define e.g. its derivatives. Using the example of embedding a well-behaved function $f(x)$ into $\mathcal{S}$, we can work out what the derivative of a distribution is: $$\begin{aligned} \braket{f'}{\phi} = \int_{-\infty}^\infty f'(x) \: \phi(x) \dd{x} = \Big[ f(x) \: \phi(x) \Big]_{-\infty}^\infty - \int_{-\infty}^\infty f(x) \: \phi'(x) \dd{x} \end{aligned}$$ The test function removes the boundary term, yielding the result $- \braket{f}{\phi'}$. Although this was an example for a specific $f(x)$, we use it to define the derivative of any distribution: $$\begin{aligned} \boxed{ \braket{f'}{\phi} = - \braket{f}{\phi'} } \end{aligned}$$ Using the same trick, we can find the [Fourier transform](/know/concept/fourier-transform/) (FT) of a generalized function. We define the FT as follows, but be prepared for some switching of the names $k$ and $x$: $$\begin{aligned} \tilde{\phi}(x) = \int_{-\infty}^\infty \phi(k) \exp(- i k x) \dd{k} \end{aligned}$$ The FT of a Schwartz distribution $f$ then turns out to be as follows: $$\begin{aligned} \braket*{\tilde{f}}{\phi} &= \int_{-\infty}^\infty \tilde{f}(k) \: \phi(k) \dd{k} = \iint_{-\infty}^\infty f(x) \exp(- i k x) \: \phi(k) \dd{x} \dd{k} \\ &= \int_{-\infty}^\infty f(x) \: \tilde{\phi}(x) \dd{x} = \braket*{f}{\tilde{\phi}} \end{aligned}$$ Note that the ordinary FT $\tilde{f}(k) = \hat{\mathcal{F}}\{f(x)\}$ is already a 1:1 mapping of test functions $\phi \leftrightarrow \tilde{\phi}$. As it turns out, in this generalization it is also a 1:1 mapping of distributions in $\mathcal{S}'$, defined as: $$\begin{aligned} \boxed{ \braket*{\tilde{f}}{\phi} = \braket*{f}{\tilde{\phi}} } \end{aligned}$$ ## References 1. K.W. Jacobsen, *Note on generalized functions (distributions)*, 2020, unpublished.