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Support and Locality

Support of a Distribution

A distribution TT is said to be supported in a closed set KK if T,ϕ=0\langle T, \phi \rangle = 0 for all ϕCc(Rd)\phi \in \mathcal{C}_c^{\infty}(\mathbb{R}^d) with supp(ϕ)K=\text{supp}(\phi) \cap K = \emptyset.

The support of TT, denoted supp(T)\text{supp}(T), is the smallest set KK such that TT is supported in KK.

Example 1

  • The support of the Dirac delta is supp(δ)={0}\text{supp}(\delta) = \{ 0 \} .

  • If ff is a continuous function, supp(Tf)={x:f(x)0}\text{supp}(T_f) = \overline{\{ x : f(x) \neq 0 \}}.

Local Structure

Two distributions T1T_1 and T2T_2 are equal on an open set Ω\Omega if T1,ϕ=T2,ϕ\langle T_1, \phi \rangle = \langle T_2, \phi \rangle for all ϕ\phi.

If TfT_f is a regular distribution and Tf=0T_f = 0 on an open set Ω\Omega, then f=0f = 0 almost everywhere on Ω\Omega.

Distributions with Point Support

A fundamental result: If TT is a distribution supported at a single point x0x_0, then TT is a finite linear combination of derivatives of the delta function at x0x_0:

T=αmcαDαδx0T = \sum_{|\alpha| \leq m} c_{\alpha} D^\alpha \delta_{x_0}

where mm is the order of TT.

The Structure Theorem for Distributions

The results above characterize distributions with point support. The structure theorem generalizes this to all distributions: every distribution is, locally, a finite-order derivative of a continuous function.

Theorem 1 (Structure theorem for distributions)

Let TD(Ω)T \in \mathcal{D}'(\Omega) and let KΩK \subset \Omega be compact. Then there exist an integer N0N \geq 0 and continuous functions fαC(K)f_\alpha \in C(K), αN|\alpha| \leq N, such that

T=αNDαfαon a neighborhood of K.T = \sum_{|\alpha| \leq N} D^\alpha f_\alpha \qquad \text{on a neighborhood of } K.

If TT has finite order mm globally (i.e., the seminorm bound T,φCKαmsupDαφ|\langle T, \varphi \rangle| \leq C_K \sum_{|\alpha| \leq m} \sup |D^\alpha \varphi| holds with the same mm for all compact KK), then we can take N=mN = m uniformly.

Proof 1

We sketch the argument in one dimension for clarity; the general case is identical in structure.

Step 1: From the order bound to a norm estimate. On a compact set KK, the distribution TT has some finite order mm:

T,φCKk=0mφ(k)for all φDK.|\langle T, \varphi \rangle| \leq C_K \sum_{k=0}^{m} \|\varphi^{(k)}\|_\infty \qquad \text{for all } \varphi \in \mathcal{D}_K.

This means TT is a continuous linear functional on DK\mathcal{D}_K equipped with the CmC^m norm.

Step 2: Extension to a continuous linear functional. The space DK\mathcal{D}_K is a subspace of Cm(K)C^m(K). By the Hahn-Banach theorem, TT extends to a continuous linear functional on Cm(K)C^m(K).

Step 3: Representation via antiderivatives. A continuous linear functional on Cm(K)C^m(K) can be written as a finite sum of integrals against Radon measures μα\mu_\alpha, αm|\alpha| \leq m:

T,φ=αmDαφdμα.\langle T, \varphi \rangle = \sum_{|\alpha| \leq m} \int D^\alpha \varphi \, d\mu_\alpha.

Each Radon measure μα\mu_\alpha on a compact subset of Rd\mathbb{R}^d has a continuous antiderivative: there exists fαC(K)f_\alpha \in C(K) such that ψdμα=(1)αfαDαψdx\int \psi \, d\mu_\alpha = (-1)^{|\alpha|} \int f_\alpha \, D^\alpha \psi \, dx for test functions ψ\psi. (In one dimension, this is just the fact that the antiderivative of a measure is a function of bounded variation, and one more antiderivative gives a continuous function.) Substituting:

T,φ=αN(1)αfαDαφdx=αNDαfα,φ\langle T, \varphi \rangle = \sum_{|\alpha| \leq N} (-1)^{|\alpha|} \int f_\alpha \, D^\alpha \varphi \, dx = \sum_{|\alpha| \leq N} \langle D^\alpha f_\alpha, \varphi \rangle

where N=m+dN = m + d accounts for the additional antiderivatives needed in dd dimensions.

Remark 1 (Distributions are derivatives of continuous functions)

The structure theorem demystifies distributions: no matter how singular a distribution may appear, it is always locally a finite sum of derivatives of continuous functions. The Dirac delta is HH' (derivative of the Heaviside function, which is bounded and measurable — or the second derivative of the continuous function max(x,0)\max(x,0)). The derivative δ\delta' is the third derivative of max(x,0)\max(x,0). And so on.

The price of increased singularity is simply a higher derivative order, not a fundamentally different kind of object.

The structure theorem immediately implies the point support result stated above:

If TD(Rd)T \in \mathcal{D}'(\mathbb{R}^d) with supp(T)={x0}\operatorname{supp}(T) = \{x_0\}, then T=αmcαDαδx0T = \sum_{|\alpha| \leq m} c_\alpha D^\alpha \delta_{x_0}.

Proof 2

By the structure theorem, on a ball BB around x0x_0, we have T=αNDαfαT = \sum_{|\alpha| \leq N} D^\alpha f_\alpha for continuous fαf_\alpha. Since supp(T)={x0}\operatorname{supp}(T) = \{x_0\}, the distribution TT vanishes on B{x0}B \setminus \{x_0\}, which means each fαf_\alpha is a polynomial of degree at most NαN - |\alpha| on B{x0}B \setminus \{x_0\}, and by continuity on all of BB. A polynomial supported at a point must be constant times a suitable derivative of δx0\delta_{x_0}. Collecting terms gives T=αmcαDαδx0T = \sum_{|\alpha| \leq m} c_\alpha D^\alpha \delta_{x_0} where mNm \leq N is the order of TT.