Explicit computation of convolutions

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Compute the convolution of the characteristic functions χ[1,2](x)∗χ[3,4](x)\chi_{[1,2]}(x) * \chi_{[3,4]}(x).

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Compute the convolution f(x)∗χ[2,4](x)f(x) * \chi_{[2,4]}(x), where

f(x)={x+1,−1⩽x⩽01−x,0<x⩽1 f(x) = \begin{cases} x+1, & -1 \leqslant x \leqslant 0 \\ 1-x, & 0 < x \leqslant 1 \end{cases}
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Prove that the convolution ωa,b∗χ[c,d]\omega_{a, b} * \chi_{[c, d]}, where ωa,b\omega_{a, b} is the "bump" from problem 15.1, is a function of the "hat" type from problem 15.2.

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Find the Weierstrass transform

F(x)=1π∫−∞+∞e−(x−y)2f(y) dy. F(x)=\dfrac {1}{\sqrt{\pi }}\int _{-\infty }^{+\infty }e^{-(x-y)^{2}}f(y)\, dy .

Let:

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(a)

f(y)=1f(y)=1;

(b)

f(y)=y2f(y)=y^{2};

(c)

f(y)=e2ayf(y)=e^{2ay};

(d)

f(y)=cos⁡ayf(y)=\cos ay.

Compute the integral, which is of significant importance in probability theory,

φ(x)=12πσ1σ2∫−∞+∞e−ξ22σ12e−(x−ξ)22σ22 dξ(σ1>0, σ2>0). \varphi (x)=\dfrac {1}{2\pi \sigma _{1}\sigma _{2}}\int _{-\infty }^{+\infty }e^{-\frac{\xi ^{2}}{2\sigma _{1}^{2}}}e^{-\frac{(x-\xi )^{2}}{2\sigma _{2}^{2}}}\, d\xi \quad (\sigma _{1}>0,\ \sigma _{2}>0) .
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Let f1f_1 and f2f_2 be locally integrable functions on R\mathbb {R} whose supports are bounded to the left. Then the convolution f1∗f2f_1*f_2 can be defined, and it has the same property. Compute explicitly the convolutions:

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(a)

[θ(x)xα]∗[θ(x)xβ][\theta (x)x^\alpha ]*[\theta (x)x^\beta ];

(b)

[eaxθ(x)]∗[ebxθ(x)][e^{ax}\theta (x)]*[e^{bx}\theta (x)].

Compute the Fourier transform of the characteristic function of an interval. For n=1,2,3,…n = 1,2,3, \ldots, let gng_{n} be the characteristic function of [−n,n][-n, n], let hh be the characteristic function of [−1,1][-1,1], and compute gn∗hg_{n} * h explicitly. (The graph is piecewise linear). Show that gn∗hg_{n} * h is the Fourier transform of a function fn∈L1f_{n} \in L^{\mathbf{1}}; except for a multiplicative constant,

fn(x)=sin⁡xsin⁡nxx2 f_{n}(x) = \frac{\sin x \sin n x}{x^{2}}

Show that ∥fn∥1→∞\left\| f_{n}\right\|_{1} \rightarrow \infty and conclude that the mapping f→f^f \rightarrow \hat{f} maps L1L^{1} into a proper subset of C0C_{0}. Show, however, that the range of this mapping is dense in C0C_{0}.

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The following parts give some concrete examples of convolutions courtesy of Jones, pages 278-279.

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(a)

Let 0<b≤a0 < b \leq a. Let f=  1(−a,a)f = \; \mathbb {1}_{(-a, a)} and g=  1(−b,b)g = \; \mathbb {1}_{(-b, b)}. Then for each x∈Rx \in \mathbb {R}

f∗g(x)={2b∣x∣≤a−ba+b−∣x∣a−b≤∣x∣≤a+b0a+b≤∣x∣. f * g(x) = \begin{cases} 2b & \left|x\right| \leq a - b \\ a + b - \left|x\right| & a - b \leq \left|x\right| \leq a + b \\ 0 & a + b \leq \left|x\right| . \end{cases}
(b)

Let aa and bb denote distinct real numbers. Let f(x)=e−ax  1[0,+∞)(x)f(x) = e^{-ax} \; \mathbb {1}_{[0, +\infty )}(x) and g(x)=e−bx  1[0,+∞)(x)g(x) = e^{-bx} \; \mathbb {1}_{[0, +\infty )}(x) for each x∈Rx \in \mathbb {R}. Then

f∗g(x)={e−ax−e−bxb−ax≥00x<0. f * g(x) = \begin{cases} \dfrac {e^{-ax} - e^{-bx}}{b - a} & x \geq 0 \\ 0 & x < 0 . \end{cases}
(c)

If in (b) we have a=ba = b, then f∗g(x)=xe−axf * g(x) = x e^{-ax} for each x∈Rx \in \mathbb {R}.

(d)

If a+b>0a + b > 0, f(x)=e−ax2f(x) = e^{-ax^{2}}, and g(x)=e−bx2g(x) = e^{-bx^{2}} [for all x∈Rx \in \mathbb {R}], then

f∗g(x)=πa+b e−aba+bx2for all x∈R. f * g(x) = \sqrt{\frac{\pi }{a + b}} \, e^{-\frac{ab}{a + b} x^{2}} \quad \text{for all } x \in \mathbb {R} .
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(a)

Let X1∼N(0,σ12)X_1 \sim N(0,\sigma_1^2) and let X2∼N(0,σ22)X_2 \sim N(0,\sigma_2^2) be independent of X1X_1. Convolve the density of X1X_1 with that of X2X_2 to show that X1+X2X_1+X_2 is Gaussian, N(0,σ12+σ22)N(0,\sigma_1^2+\sigma_2^2).

(b)

Let W1,W2W_1, W_2 be IID normalized Gaussian rv s. Show that a1W1+a2W2a_1W_1+a_2W_2 is Gaussian, N(0,a12+a22)N(0,a_1^2+a_2^2).

(c)

Combine (b) with induction to show that all linear combinations of IID normalized Gaussian rv s are Gaussian.

References

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