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Differentiation of Distributions 📂Distribution Theory

Differentiation of Distributions

Buildup

The distribution cannot be differentiated in the same manner as functions defined over real numbers since its domain is a function space. However, for regular distributions, there is a corresponding locally integrable function uLloc1u\in L_{\mathrm{loc}}^{1}, expressed as follows.

Tu(ϕ)=u(x)ϕ(x)dx,ϕD T_{u}(\phi) =\int u(x)\phi (x) dx,\quad \phi \in \mathcal{D}

Hence, the action SS on uu could yield Su=uSu=u^{\prime}; if uu^{\prime} remains a locally integrable function, then there exists a corresponding distribution TuT_{u^{\prime}}. Therefore, the idea is to think of the action SS on uu as if it were the action on TuT_{u}. This idea is extended over all distributions to define the differentiation of distributions.

3.PNG

Below, we assume uCu\in C^{\infty}, although it’s not strictly necessary. You could also simply discuss up to the nnth derivative with uCnu \in C^{n}.

Let’s say a smooth function uCu\in C^{\infty} is given. The test function ϕ\phi has a compact support, so it’s harmless to assume that uu is defined over some compact set KK that contains the support of the test function. Since smooth functions on a compact set are locally integrable, we can consider the corresponding regular distribution TuT_{u} for uu.

Meanwhile, uu, being a smooth function, is differentiable, and uu^{\prime}, being locally integrable, has a corresponding regular distribution TuT_{u^{\prime}}. Thus, by applying the integration by parts to the test function ϕD\phi \in \mathcal{D}, it can be represented as follows.

Tu(ϕ)=u(x)ϕ(x)dx=[u(x)ϕ(x)]u(x)ϕ(x)dx \begin{align*} T_{u^{\prime}}(\phi) &= \int u^{\prime}(x)\phi (x)dx \\ &= \left[ u(x) \phi (x) \right]_{-\infty}^{\infty} -\int u(x)\phi ^{\prime} (x) dx \end{align*}

Here, since ϕ\phi has compact support, the first term is 00. Hence, we obtain the following.

Tu(ϕ)=u(x)ϕ(x)dx=Tu(ϕ) T_{u^{\prime}}(\phi)=-\int u(x)\phi ^{\prime} (x) dx=-T_{u}(\phi^{\prime})

Definition1

The derivative of the distribution TT is defined as follows.

(DT)(ϕ):=T(Dϕ) (DT)(\phi):= -T(D\phi)

Here, DD is the differentiation operator. For the multi-index α\alpha, it is as follows.

(DαT)(ϕ):=1αT(Dαϕ) (D^{\alpha}T)(\phi):= \left| -1 \right|^{\left| \alpha \right| } T(D^{\alpha}\phi)


Since the derivative of a test function is also a test function, there is no problem with the domain, and apart from that aspect, it is simply multiplied by the constant term 1α\left| -1 \right|^{\left| \alpha \right|}, thus DαTD^{\alpha}T is also a distribution. Of course, this can be proved using the definition of distributions, but it is not strictly necessary.


  1. Gerald B. Folland, Fourier Analysis and Its Applications (1992), p308-309 ↩︎