Lecture 15

More examples of tempered distributions and its Fourier transform

The Heviside function

Consider the function

H(x)={1,x>00,x<012,x=0H(x) = \begin{cases}1, x>0\\0,x<0\\\frac{1}{2},x=0\end{cases}

called the Heviside function. It is has a jump discontinunity at 0.

This function is not Schwartz since it is a constant on positive real line, but it is a tempered distribution. It’s derivative in sense of distribution is

Proof. Let φD\varphi\in \mathcal D be a test function. Then (ddx[H],φ)=(H,φ)=+H(x)φ(x)dx=0+φ(x)dx=(limx+φ(x)φ(0))=φ(0)=(δ,φ)(\frac{d}{dx}[H],\varphi) = -(H,\varphi') = -\int_{-\infty}^{+\infty}H(x)\varphi'(x)dx = -\int_{0}^{+\infty}\varphi'(x)dx =-( \lim_{x\to +\infty}\varphi(x) - \varphi(0) )= \varphi(0) = (\delta,\varphi).

The Heviside unit ramp

Let u(x)={0,x0x,x>0u(x) = \begin{cases}0,x\leq 0 \\ x,x>0 \end{cases}. Then u(x)u(x) is a continuous function. One can verify by integration by parts that d2dx2[u(x)]=δ\frac{d^2}{dx^2}[u(x)] = \delta.

The sign function

Let syn(x)={1,x>01,x<00,x=0syn(x) = \begin{cases}1, x > 0 \\ -1, x<0 \\ 0,x = 0\end{cases}. Then similar to the Heviside function one has ddx[syn]=2δ\frac{d}{dx}[syn] = 2\delta. This can also be obtained from the relationship syn(x)=2H(x)1syn(x) = 2H(x) - 1.

The Cauchy principal value

Let fSf \in \mathcal S. Then the improper integral +f(x)xdx\int_{-\infty}^{+\infty}\frac{f(x)}{x}dx may be not converge near 0. Its principal value is given by limϵ0x>ϵf(x)xdx\lim_{\epsilon\to 0}\int_{|x|>\epsilon}\frac{f(x)}{x}dx, denoted by p.v.+f(x)xdxp.v.\int_{-\infty}^{+\infty}\frac{f(x)}{x}dx.

To see that the limit exists, rewrite the integral as two parts ϵ<x<1f(x)xdx+1+f(x)f(x)xdx\int_{\epsilon<|x|<1}\frac{f(x)}{x}dx + \int_{1}^{+\infty}\frac{f(x)-f(-x)}{x}dx. For the first part, observe that ϵ<x<1f(x)xdx=ϵ1f(x)f(x)xdxϵ1f(x)f(x)xdx2supxRf(x)(1ϵ)|\int_{\epsilon<|x|<1}\frac{f(x)}{x}dx |= |\int_{\epsilon}^1 \frac{f(x)-f(-x)}{x}dx|\leq \int_{\epsilon}^1 |\frac{f(x) - f(-x)}{x}|dx \leq 2\sup_{x\in \mathbb{R}}|f'(x)|\cdot (1-\epsilon). Where the last inequality follows from the mean value theorem. For the second part, just use the 1+x21+x^2-trick, 1+f(x)f(x)xdx2supxRxf(x)1+1x2dxCf1,0\int_1^{+\infty}|\frac{f(x) - f(-x)}{x}|dx \leq 2\sup_{x\in \mathbb{R}}|xf(x)|\int_{1}^{+\infty}\frac{1}{x^2}dx \leq C \|f\|_{1,0}.

Then p.v.+f(x)xdxconst(f1,0+f0,1)|p.v. \int_{-\infty}^{+\infty}\frac{f(x)}{x}dx|\leq const\cdot (\|f\|_{1,0}+\|f\|_{0,1}) , which shows p.v.1xp.v.\frac{1}{x} is a tempered distribution.

Fourier transform of the Heviside function

Let’s consider the Fourier tansform of sgnsgn. We have, by derivative theorem, Fddx[sgn]=2πixFsgn\mathcal F\frac{d}{dx}[sgn] = 2\pi i x \mathcal F sgn . On the other hand, we know that ddx[sgn]=2δ\frac{d}{dx}[sgn] = 2\delta. So 2πixFsgn=F2δ=22\pi i x\cdot \mathcal Fsgn = \mathcal F 2\delta = 2. Then it looks like Fsgn=1πix\mathcal Fsgn = \frac{1}{\pi i x} (to be precise, this is actually p.v. 1πix\frac{1}{\pi i x}). The result is correct but the argument is not, because we are dealing with distributions not functions, so the πix\pi i x should be understood as an operator which maps a distribution to a new distribution. When we “devide” an operator it should be invertible, but the πix\pi i x does has kernel.

Lemma.

Let TT be a distribution. if TT is annihilated by xx, i.e. xT=0xT = 0, then TT must be a constant multiple of δ.

Proof. If g(0)=0g(0) = 0, then we can write g(x)=xψ(x)g(x) = x\psi(x) for smooth ψ (see problem set 6). In this case, (T,g)=(T,xψ)=(xT,ψ)=(0,ψ)=0(T,g) = (T,x\psi) = (xT,\psi) = (0,\psi) = 0. For general gg, we can consider gg(0)1g-g(0)\cdot \mathbf{1}, where 1\mathbf{1} is the constant function 1(x)1\mathbf{1}(x)\equiv 1. So 0=T(gg(0)1)=(T,g)g(0)(T,1)0=T(g - g(0)\mathbf{1}) = (T,g) - g(0)(T,\mathbf{1}). So (T,g)=(T,1)g(0)(T,g) = (T,\mathbf{1})g(0), it follows that T=(T,1)δT = (T,\mathbf{1})\delta.

From the above lemma we see that the kernel of πix\pi i x is given by cδc\delta. Then Fsgn=1πix+cδ\mathcal Fsgn = \frac{1}{\pi i x}+c\delta for some constant cc. But since the sgn function is odd, F(sgn)=Fsgn=1πix+cδ\mathcal F(sgn^-) = \mathcal Fsgn^- = -\frac{1}{\pi i x} + c\delta, and on the other hand, Fsgn=F(sgn)=1πixcδ\mathcal F sgn^- = \mathcal F(-sgn) = -\frac{1}{\pi i x}-c\delta. This implies c=0c = 0. So we get

Also since sgn=2H1sgn = 2H-1, we get

Appendix: Partition of unity

Let U1,,UnU_1,\dots,U_n be open sets on Rm\mathbb{R}^m, let U=j=1nUjU = \bigcup_{j = 1}^n U_j. Then for every φCc(U)\varphi \in \mathscr C_c^{\infty}(U), there exists φjCc(Uj)\varphi_j \in \mathscr C_c^{\infty}(U_j) such that φ=j=1nφj\varphi = \sum_{j = 1}^n \varphi_j. If φ0\varphi \geq 0 then one can take all φj0\varphi_j \geq 0.

Proof. Assume φ is supported on compact set KK. Choose open cover V1,,VnV_1,\dots,V_n of KK such that ViViUV_i\subset \overline{V_i}\subset U.

Let ψj\psi_j be smooth function such that

  • ψj1\psi_j\equiv 1 on KjK_j.
  • supp  ψjUjsupp\; \psi_j\subset U_j.

Then let φ1:=φψ1,φ2:=φψ2(1ψ1)\varphi_1:=\varphi \psi_1, \varphi_2 := \varphi\psi_2(1 - \psi_1), φ3:=φψ3(1ψ2)(1ψ1)\varphi_3:=\varphi \psi_3(1 - \psi_2)(1 - \psi_1), ,\dots, φn=φψn(1ψn1)(1ψ1)\varphi_n = \varphi \psi_n(1 - \psi_{n - 1})\cdots(1 - \psi_1). The (φi)i=1n(\varphi_i)_{i=1}^n satisfies the desired properties.