Question 12.7: Let ρ be an infinitely differentiable function on R with sim......

Let ρ be an infinitely differentiable function on R with simple zeros a_1, a_2, \ldots, a_m. Prove that the equation with the unknown distribution T

\rho(x) \cdot T=0        (12.18)

has the same solutions as the equation

\left(\overset{m}{\underset{j=1}{\prod}}\left(x-a_j\right)\right) \cdot T=0 .        (12.19)

Moreover, the solutions of (12.19) (hence also of (12.18)) are of the form

T=\overset{m}{\underset{j=1}{\sum}} C_j \delta_{a_j}

see (12.3), where C_j, j=1,2, \ldots, m, are real constants.

Step-by-Step
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Clearly, it is enough to analyze the case when ρ has only one simple zero at some point a. In that case, we have to prove that equation (12.18) is equivalent with equation

(x-a) \cdot T=0,          (12.20)

and its solution is the distribution T=A \delta_a, for some constant A.
Let us introduce the function \rho_1 by

\rho(x)=(x-a) \rho_1(x) \text {. }        (12.21 )

Then it holds \rho_1(a) \neq 0 \text { and the mapping } \varphi \mapsto \psi=\rho_1 \varphi \text { is a bijection from } \mathcal {D} (\mathbf{R} ) onto itself. Putting (12.21) into (12.18) we obtain

\begin{aligned}\langle\rho(x) \cdot T, \varphi(x)\rangle & =\left\langle T,(x-a) \rho_1(x) \varphi(x)\right\rangle=\langle T,(x-a) \psi(x)\rangle \\& =\langle(x-a) \cdot T, \psi(x)\rangle .\end{aligned}

which implies the equivalence of the equations (12.18) and (12.20).
Let us find now the solution of (12.20). To that end, note that the mapping \psi \mapsto(x-a) \psi from the set

\mathcal {A} =\{\psi \in \mathcal {D} ( \mathbf{R} ) \mid a \notin \operatorname{supp} \psi\}

into itself is, in fact, a bijection. Thus for every test function \varphi with the property a \notin \operatorname{supp} \varphi it holds

\langle T, \varphi\rangle=0

which is equivalent with the statement supp T = {a}. Any distribution T whose support is a single point a is necessarily of the form

T=A \delta_a+\overset{p}{\underset{k=1}{\sum}} A_k \delta_a^{(k)}          (12.22)

for some constants A and A_k, k=1,2, \ldots, p \text {. Choose now } k \in\{1,2, \ldots, p\}. Then for \varphi \in \mathcal {D} (\mathbf{R} ) it holds:

\begin{aligned}\left\langle(x-a) \cdot \delta_a^{(k)}, \varphi\right\rangle & =\left\langle\delta_a^{(k)},(x-a) \varphi(x)\right\rangle \\& =(-1)^k\left\langle\delta_a,((x-a) \varphi(x))^{(k)}\right\rangle \\& =(-1)^k\left\langle\delta_a,(x-a) \varphi^{(k)}(x)+k \varphi^{(k-1)}(x)\right\rangle \\& =\left.(-1)^k\left((x-a) \varphi^{(k)}(x)+k \varphi^{(k-1)}(x)\right)\right|_{x=a} \\& =(-1)^k k \varphi^{(k-1)}(a) .\end{aligned}

Since there exists a \varphi \in \mathcal {D} (\mathbf{R} ) such that \varphi^{(k-1)}(a) \neq 0, it follows that. the distribution \delta_a^{(k)} is not a solution of (12.20), hence also not of (12.18). Thus T from (12.22) is a solution of (12.18) \text { (for } m=1 \text { and } a_1=a \text { ) if and only if } A_1=A_2=\ldots=A_p=0 \text {, } which finally gives us the solution

T=A \delta_a \text { for some constant } A \text {. }

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