In [60]:

using Plots, ComplexPhasePortrait, ApproxFun, SingularIntegralEquations
gr();



# M3M6: Methods of Mathematical Physics

$$\def\dashint{{\int\!\!\!\!\!\!-\,}} \def\infdashint{\dashint_{\!\!\!-\infty}^{\,\infty}} \def\D{\,{\rm d}} \def\dx{\D x} \def\dt{\D t} \def\C{{\mathbb C}} \def\CC{{\cal C}} \def\HH{{\cal H}} \def\I{{\rm i}} \def\qqfor{\qquad\hbox{for}\qquad}$$

Dr. Sheehan Olver
s.olver@imperial.ac.uk

Office Hours: 3-4pm Mondays, Huxley 6M40
Website: https://github.com/dlfivefifty/M3M6LectureNotes

# Lecture 19: Logarithmic singular integrals

1. Evaluating logarithmic singular integrals
2. Logarithmic singular integrals and orthogonal polynomials
3. Solving a logarithmic singular integral equation
4. Application: electrostatic potentials in 2D
• Potential arising from a point charge and a single plate

The motivation behind this lecture is to calculate electrostatic potentials. An example is the Faraday cage: imagine a series of metal plates connected together so that they have the same charge. If configured to surround a region, this configuration will shield the interior from an external charge:

Here, the coloured lines are equipotential lines, and there is a point source at $x = 2$, which corresponds to a forcing of $$\log\| (x,y) - (2,0) \| = \log|z - 2|$$ where $z = x + \I y$.

## Logarithmic singular integrals

From the Green's function of the Laplacian, it is natural to consider logarithmic singular integrals, and we focus yet again on $[-1,1]$:

$$v(z) = {1 \over \pi }\int_{-1}^1 f(t) \log | z - t| \dt$$

Note that off $[-1,1]$, $v(z)$ solves Laplace's equation, and is continuous on $[-1,1]$:



In [5]:

t = Fun()
f = sqrt(1-t^2)*exp(t)
v = z -> logkernel(f, z)  # logkernel(f,z) calculates 1/π * \int f(t)*log|t-z| dt

xx = yy = -2:0.01:2
V = v.(xx' .+ im*yy)

contour(xx, yy, V)
plot!(domain(t); color=:black, label="contour")




Out[5]:

-2

-1

0

1

2

-2

-1

0

1

2

-

0.75

-

0.50

-

0.25

0

0.25

0.50

-

0.75

-

0.50

-

0.25

0

0.25

0.50

contour




In [6]:

surface(xx, yy, V)




Out[6]:

-

0.75

-

0.50

-

0.25

0

0.25

0.50



Continuity follows since $\log|x-t|$ is integrable for $-1 \leq x \leq 1$ provided $f(t)$ has weaker than pole singularities.

For $z \notin (-\infty,1]$ this can also be seen since $v$ is the real part of an analytic function: $$v(z) = \Re {1 \over \pi} \int_{-1}^1 f(t) \log ( z - t) \dt$$ Note that the integrand avoids the branch cut of $\log z$. To extend this to $z\in (-\infty,-1]$ (or more generally, $z \notin [-1,\infty)$), we can use the alternative expression $$v(z) = \Re {1 \over \pi} \int_{-1}^1 f(t) \log (t-z) \dt$$ which follows from $\log|z-t| = \log|t-z|$.



In [7]:

z = 2.0 +3.0im
@show v(z)
@show sum(f*log(abs(z-t)))/π
@show sum(f*log(z-t))/π
@show sum(f*log(t-z))/π;




v(z) = 0.7068168642791222
sum(f * log(abs(z - t))) / π = 0.7068168642791223
sum(f * log(z - t)) / π = 0.7068168642791222 + 0.5923597267719309im
sum(f * log(t - z)) / π = 0.7068168642791222 - 1.1831399624402499im



### Evaluating logarithmic singular integrals

How can we actually evaluate $$Lu(z) = {1 \over \pi} \int_{-1}^1 u(t) \log (z-t) \dt$$ for $z$ in the complex plane? We relate this to the indefinite integral of the Cauchy transform, and then use that to reduce it to a Cauchy transform itself.

#### Integrated Cauchy transform

For $z$ away from the branch cut our integrand is "nice" (assuming $u$ itself is "nice") and we can exchange integration and differentiation to determine $${\D \over \D z} Lu(z) = {1 \over \pi} \int_{-1}^1 u(t) {1\over z -t} \dt = -2 \I \CC u(z)$$ thus if we can calculate $\int^z Cu(\zeta) \D \zeta$, we are in good shape.

Example If $u(x) = {1 \over \sqrt{1-x^2}}$, then recall $$Cu(z) = {-1 \over 2 \I \sqrt{z-1} \sqrt{z+1}}$$ We therefore have $${\D \over \D z} Lu(z) = {1 \over \sqrt{z-1} \sqrt{z+1}}$$ and therefore for some constant of integration $D$ we have $$Lu(z) + D = \pi \int^z {\D z \over \sqrt{z-1} \sqrt{z+1}} = 2 \pi \log\left(\sqrt{z-1} + \sqrt{z+1}\right)$$ We need the right constant of integration. we can find that via the behaviour as $x \rightarrow \infty$: $$Lu(x) ={1 \over \pi} \int_{-1}^1 u(t) \log(x-t) \dt = {\log x \over \pi} \int_{-1}^1 u(t) \dt + {1 \over \pi} \int_{-1}^1 u(t) \log(1-t/x) \dt = {\log x \over \pi}\int_{-1}^1 u(t) \dt + O(x^{-1})$$ Since we have $$\int_{-1}^1 {1 \over \sqrt{1-x^2}} \dx = \pi$$ and $$2 \log(\sqrt{x-1} + \sqrt{x+1}) = 2 \log \sqrt x + 2 \log(\sqrt{1-1/x} + \sqrt{1 + 1/x}) = \pi \log x + 2\pi \log(2) + O(1/x)$$ therefore, $$Lu(z) = 2 \log\left(\sqrt{z-1} + \sqrt{z+1}\right) - 2 \log 2$$

Demonstration We want to calculate the log kernel of $1/\sqrt{1-x^2}$:



In [10]:

x = Fun()
u = 1/sqrt(1-t^2)
z = 1+im
sum(u*log(z-x))/π




Out[10]:

0.3681278813450904 + 0.9045568943023814im



We saw that its derivative is given in terms of the Cauchy transform, here we test it using finite difference approximation:



In [17]:

h = 0.0001;
(sum(u*log(z+h-x))/π - sum(u*log(z-x))/π)/h , sum(u/(z-x))/π, -2im*cauchy(u,z)




Out[17]:

(0.3515911336637867 - 0.5688482444998755im, 0.35157758425414287 - 0.5688644810057831im, 0.3515775842541429 - 0.568864481005783im)



But we already know the Cauchy transform in closed form:



In [22]:

Cu = z -> -1/(2im*sqrt(z-1)sqrt(z+1))

-2im*Cu(z)




Out[22]:

0.3515775842541429 - 0.568864481005783im



This we can integrate in closed form to determine:



In [24]:

Lu = z -> 2*log(sqrt(z-1) + sqrt(z+1)) - 2*log(2)
Lu(z)




Out[24]:

0.36812788134509056 + 0.9045568943023812im



#### Reduction to a Cauchy transform

The representation is not ideal as it involves indefinite integration in the complex plane. However, we will see that we can actually express $L u (z)$ as a Cauchy transform of $\int_x^1 u(t) \dt$.

Lemma (Log kernel as Cauchy transform) Suppose $u$ is "nice" (as in Plemelj). Then $$Lu(z) = {\log(z-a) \over \pi} \int_a^b u(x) \dx +2 \I C U(z)$$ where $$U(x) = \int_x^b u(t) \dt$$

Proof

We use Plemelj to prove this result (of course!) and assume $[a,b] = [-1,1]$. Note that $Lu$ satisfies the following Riemann–Hilbert problem:

1. $Lu(z) \sim {1 \over \pi} \int_{-1}^1 u(t) \dt \log z + o(1)$
2. $L^+ u(x) - L^- u(x) = 2 \I \int_x^1 u(t) \dt$ for $-1 < x < 1$
3. $L^+ u(x) - L^- u(x) = 2\I \int_{-1}^1 u(t) \dt$ for $x < -1$

The first part follows immediately by noting for $z \rightarrow + \infty$ we have $$\log(z - x) = \log(z (1 - x/z)) = \log z + \log(1-x/z) = \log z + o(1)$$

We now see where the jumps come from. Since $Lu'(z) = -2 \I Cu(z)$ and This let's us think of $Lu (z)$ as (2 is arbitrary here) $$L u(z) = Lu(2) + \int_2^z C u (\zeta) \D\zeta$$ where the contour is a straight line (just like in the problem sheet for $\log z$ that I'm sure everyone has done by now), but we can think of it also as an arc that avoids $[-1,1]$.

Consider $x < -1$. Given an ellipse $\gamma$ surrounding $[-1,1]$ we can write the integral representations and exchange orders to get: $$L u^+(x) - L u^-(x) = -2 \I \oint_\gamma C u(\zeta) \D\zeta = -2 \I \int_{-1}^1 u(t) {1\over 2 \pi \I} \oint_\gamma {1 \over t-z} \dt \D \zeta = 2 \I \int_{-1}^1 u(t) \dt$$

For $-1 < x < 1$, note that $L u(1)$ is well-defined as there is only a logarithmic singularity times an algebraic one. Thus deforming the contour onto the interval we have $$L^\pm u(x) = -2 \I \int_1^x C^\pm u(t) \dt + L u(1),$$ hence $$L^+ u(x) -L^- u(x) = 2 \I \int_x^1 (C^+ u(t) - C^- u(t)) \dt =2 \I \int_x^1 u(t) \dt$$ ⬛️

Example For the problem above, we have that $\int_x^1 {1 \over \sqrt{1-t^2}}\dt = {\rm arccos}\, x$, which gives us:



In [37]:

x = Fun()

u = 1/sqrt(1-x^2)
U = acos(x)

Lu = z -> 2*log(sqrt(z-1) + sqrt(z+1)) - 2*log(2)

sum(u) * log(z+1)/π + sum(U/(x-z))/(π), Lu(z)




Out[37]:

(0.36812788134509017 + 0.9045568943023812im, 0.36812788134509056 + 0.9045568943023812im)



# Log transforms of weighted orthogonal polynomials

Now consider ${1 \over \pi} \int_a^b p_k(x) w(x) \log |z-x| \dx$, which we write in terms of the real part of $$L_k(z) = L[p_k w](z) = {1 \over \pi } \int_a^b p_k(x) w(x) \log (z-x) \dx$$

For $k > 0$ we have $\int_a^b p_k(x) w(x) dx = 0$ due to orthogonality, and hence we actually have no branch cut:



In [40]:

T₅ = Fun(Chebyshev(), [zeros(2);1])
w = 1/sqrt(1-x^2)
L₅ = z->cauchyintegral(w*T₅, z)

phaseplot(-3..3, -3..3, L₅)




Out[40]:

-3

-2

-1

0

1

2

3

-3

-2

-1

0

1

2

3



#### Example: Weighted Chebyshev log transform

For classical orthogonal polynomials we can go a step further and relate the indefinite integrals to other orthogonal polynomials.

For example, recall that $${\D\over \dx}[\sqrt{1-x^2} U_n(x)] = -{n+1 \over \sqrt{1-x^2}} T_{n+1}(x)$$ in other words, $$\int_x^1 {T_k(t) \over \sqrt{1-t^2}} \dt = -{\sqrt{1-x^2} U_{k-1}(x) \over k}$$ Thus for $k=1,2,\ldots$, $$L_k(z) = -{1 \over n+1} \CC[\sqrt{1-\diamond^2} U_{k-1}](z)$$ and $${1 \over \pi} \int_{-1}^1 {T_k(x) \over \sqrt{1-x^2}}\log(z-x) \dx = {2 \I \over k} \CC[\sqrt{1-\diamond^2} U_{k-1}](z)$$



In [42]:

T₅ = Fun(Chebyshev(), [zeros(5);1])
U₄ = Fun(Ultraspherical(1), [zeros(4);1])
x = Fun()
L₅ = z->sum(T₅/sqrt(1-x^2) * log(z-x))/π

L₅(z), 2im*cauchy(sqrt(1-x^2)*U₄,z)/5




Out[42]:

(0.00018695792399762436 - 0.0009741971129007227im, 0.0001869579239976236 - 0.000974197112900722im)



As we saw last lecture, Cauchy transforms of OPs satisfy simple recurrences, and this relationship renders log transforms equally calculable.

### Solving logarithmic singular integral equations

Oddly enough, it is easier to solve a singular integral equation involving the logarithmic kernel than to actually calculate the logarithmic singular integral, so we begin here. Consider the problem of calculating $u(x)$ such that $${1 \over \pi} \int_{-1}^1 u(t) \log | x-t| \dt = f(x) \qqfor -1 < x < 1$$

To tackle this problem we first need to understand the additive jump $Re L^\pm(x) = {L^+(x) + L^-(x) \over 2}$. Thinking in terms of integrals of Cauchy transforms we see that $$L^+u(x) + L^-u(x) = -2\I (2\int_2^1 \CC u(x) \dx + \int_1^x [C^+ u(t) + C^-u(t)] \dt) = D -2 \I \int_1^x (-\I H u(t)) \dt = D + 2\int_x^1 H u(t) dt$$ That is, it is expressed as an indefinite integral of the Hilbert transform. We therefore have: $${\D \over \dx} {1 \over \pi} \int_{-1}^1 u(t) \log | x-t| \dt = {\D \over \dx} \int_x^1 H u(t) dt = - Hu(x)$$ where the minus sign comes from the fact that $x$ is the lower limit. In other words, our original SIE becomes equivalent to inverting the Hilbert transform: $$\HH u(x) = -f'(x)$$ recall, we can express the solution as $$u = {1 \over \sqrt{1-x^2}} \HH[\sqrt{1-\diamond^2} f'] + {C\over \sqrt{1-x^2}}$$

Let's do a numerical example:



In [43]:

x = Fun()
f = exp(x)

C = randn()

u₁ = hilbert(sqrt(1-x^2)*f')/sqrt(1-x^2)

u = u₁ + C/sqrt(1-x^2)

@show hilbert(u, 0.1) + f(0.1)
@show logkernel(u,0.1) - f(0.1)  # didn't work 😩
@show logkernel(u,0.2) - f(0.2);  # but we are only off by a constant




hilbert(u, 0.1) + f(0.1) = -4.440892098500626e-16
logkernel(u, 0.1) - f(0.1) = -0.18115023187927326
logkernel(u, 0.2) - f(0.2) = -0.18115023187927304



Remember: for inverting the Hilbert transform we had a degree of freedom: every solution plus $C/\sqrt{1-x^2}$ was also a solution. But here, since we differentiated, we use that degree of freedom to ensure that we have arrived at the right solution.

To choose $C$, we use the fact that $$f(0) = {1 \over \pi} \int_{-1}^1 u(t) \log |t| \dt$$ If we can calculate the right integral, we can use this to choose $C$:



In [44]:

# choose C so that
# logkernel(u₁, 0) + C*logkernel(1/sqrt(1-x^2), 0) == f(0)
C = (f(0) - logkernel(u₁, 0))/logkernel(1/sqrt(1-x^2), 0)
u = u₁ + C/sqrt(1-x^2)

@show hilbert(u, 0.1) + f(0.1)
@show logkernel(u,0.1) - f(0.1)  # Works!
@show logkernel(u,0.2) - f(0.2);  # And at all x!




hilbert(u, 0.1) + f(0.1) = -4.440892098500626e-16
logkernel(u, 0.1) - f(0.1) = 0.0
logkernel(u, 0.2) - f(0.2) = 2.220446049250313e-16



Example We now do an example which can be solved by hand. Find $u(x)$ so that: $$\int_{-1}^1 u(t) \log | x-t| \dt = 1.$$ Differentiating, we know that $$\int_{-1}^1 {u(t) \over x-t} \dt = 0$$ hence $u(x)$ must be of the form ${C \over \sqrt{1-x^2}}$. Which $C$? Make it work for $x = 0$: $$1 = \int_{-1}^1 u(t) \log | 0 - t| \dt = C \int_{-1}^1 {\log | t| \over \sqrt{1-t^2}} \dt$$ To evaluate the integral, we can use trigonmetric variables: $$\int_0^1 {\log t \over \sqrt{1-t^2}} \dt = \int_0^{\pi \over 2} {\cos \theta \log \sin \theta \over \sqrt{1-\sin^2 \theta}} \D\theta = \int_0^{\pi \over 2} \log \sin \theta \D\theta = - {\pi \log 2\over 2}$$ (The last identity takes some work: I'll leave it as an excercise.)

Thus we have $C = -{1 \over \pi \log 2}$



In [49]:

x = Fun()
C = -1/(log(2))
u = C/sqrt(1-x^2)

logkernel(u, 0.2)




Out[49]:

1.0



Physically, this solution gives us the potential field $$\int_{-1}^1 u(t) \log|z-t| \dt$$ corresponding to holding a metal plate at constant potential:



In [52]:

v = z -> π*logkernel(u, z)

xx = yy = -2:0.01:2
V = v.(xx' .+ im*yy)

contour(xx, yy, V)
plot!(domain(x); color=:black)




Out[52]:

-2

-1

0

1

2

-2

-1

0

1

2

-

4

-

3

-

2

-

1

0

1

2

3

-

4

-

3

-

2

-

1

0

1

2

3

y2




In [53]:

surface(xx, yy, V)




Out[53]:

-

4

-

3

-

2

-

1

0

1

2

3



## Application: Potential arising from a point charge and a single plate

Now imagine we put a point source at $x = 2$, and a metal plate on $[-1,1]$. We know the potential on the plate must be constant, but we don't know what constant. This is equivalent to the following problem (see e.g. [Chapman, Hewett & Trefethen 2015]): \begin{align*} v_{xx} + v_{yy} = 0 &\qqfor \hbox{off $[-1,1]$ and $2$} \\ v(z) \sim \log |z - 2| + O(1) &\qqfor z \rightarrow 2 \\ v(z) \sim \log|z| + o(1) &\qqfor z \rightarrow \infty \\ v(x) = \kappa &\qqfor -1 < x < 1 \end{align*} where $\kappa$ is an unknown constant. We write the solution as $$v(z) = {1 \over \pi} \int_{-1}^1 u(t) \log|t-z| \dt + \log|z-2|$$ for a to-be-determined $u$. On $-1 < x < 1$ this satisfies $${1 \over \pi} \int_{-1}^1 u(t) \log|t-x| \dt = \kappa - \log(2-x)$$

We can calculate the solution numerically as follows:



In [133]:

x = Fun()
u₀ = SingularIntegral(0)  \ (-log(2-x)) # solution with κ = 0
u = u₀  - sum(u₀)/(π*sqrt(1-x^2)) # ensure correct asymptotics
v = z -> logkernel(u, z) + log(abs(2-z))
xx = yy = -4:0.011:4
V = v.(xx' .+ im*yy)

contour(xx, yy, V)
plot!(domain(x); color=:black, legend=false)




Out[133]:

-4

-2

0

2

4

-4

-2

0

2

4

-

4

-

3

-

2

-

1

0

1

-

4

-

3

-

2

-

1

0

1



We can solve this equation explicitly. By differentiating we see that $u$ satisfies the following: $$Hu(x) = -f'(x) = {1 \over x-2}$$ We now use the inverse Hilbert formula to determine: $$u(x) = -{1\over\sqrt{1-x^2}} H[{1 \over \diamond-2} \sqrt{1-\diamond^2}](x) + {D \over \sqrt{1-x^2}}$$ Using the usual procedure of taking an obvious ansatz and subtracting off the singularities at poles and $\infty$ we find that: $$C[{\sqrt{1-\diamond^2} \over x -2}](z) = \underbrace{\sqrt{z-1} \sqrt{z+1} \over 2 \I (z-2)}_{\hbox{ansatz}} - \underbrace{\sqrt 3\over 2 \I (z-2)}_{\hbox{remove pole near z=2}} - \underbrace{{1 \over 2 \I}}_{\hbox{remove constant at \infty}}$$ This implies that $$H[{1 \over\diamond-2} \sqrt{1-\diamond^2}](x) = \I(C^+ + C^-)[{1 \over\diamond-2} \sqrt{1-\diamond^2}](x) = -{\sqrt 3 \over x -2} - 1$$ in other words, $$u = {\sqrt{3} \over (x - 2) \sqrt{1-x^2}} + {D \over \sqrt{1-x^2}}$$ We still need to determine $D$. This is chosen so that we tend to $\log |z|$ near $\infty$. In particular, we know that $${1 \over \pi} \int_{-1}^1 \log|z-x| u(x) \dx \sim {\int_{-1}^1 u(x) \dx \over \pi} \log |z|$$ so we need to choose $D$ so that $\int_{-1}^1 u(x) \dx = 0$ and only the $\log|z-2|$ singularity appears near $\infty$. We first note that \begin{align*} C[{1 \over (\diamond - 2) \sqrt{1-\diamond^2}}](z) &= \underbrace{{\I \over 2 \sqrt{z-1} \sqrt{z+1} (z-2)}}_{\hbox{ansatz}} - \underbrace{{\I \over 2\sqrt{3} (z-2)}}_{\hbox{remove pole}} \\ & = -{\I \over 2 \sqrt{3} z} + O(z^{-2}) \end{align*} near $\infty$. Since $$Cw(z) ={ \int_{-1}^1 w(x) \dx \over -2\pi \I z} + O(z^{-2})$$ we can infer the integral from the Cauchy transform and find that $$\int_{-1}^1 {1 \over (x - 2) \sqrt{1-x^2}} \dx = -{ \pi \over \sqrt{3}}$$ On the other hand, $$\int_{-1}^1 {\dx \over \sqrt{1-x^2}} = \pi$$ Thus we require $D = 1$ and have the solution: $$u(x) = {\sqrt{3} \over (x - 2) \sqrt{1-x^2}} - {1 \over \sqrt{1-x^2}}$$

Demonstration We first check that differentiation reduces to the Hilbert transform:



In [149]:

t = Fun()
f = -log(2-t)
hilbert(u, 0.1) ≈ -f'(0.1) ≈ 1/(0.1-2)




Out[149]:

true



In the inverse Hilbert transform we calculated the following Cauchy transform:



In [150]:

z = 1+im
cauchy((-f')*sqrt(1-t^2), z) ≈ sqrt(z-1)sqrt(z+1)/(2im*(z-2)) - sqrt(3)/(2im*(z-2)) - 1/(2im)




Out[150]:

true



Which means that:



In [153]:

hilbert((-f')*sqrt(1-t^2), 0.1), -sqrt(3)/(0.1-2) - 1




Out[153]:

(-0.08839431180585401, -0.08839431180585411)



Thus we have the inverse Hilbert transform:



In [140]:

D = randn()
ũ = sqrt(3)/((x-2)*sqrt(1-x^2))  + D/sqrt(1-x^2)
hilbert(ũ,0.1) ≈ -f'(0.1)




Out[140]:

true



We still need to determine the constant $D$. What goes wrong if we have the wrong choice is that we don't tend to precisely $\log z$ near $\infty$:



In [141]:

z = 200000
logkernel(ũ,z), log(z)




Out[141]:

(-24.76573192820616, 12.206072645530174)



We determined the integral



In [142]:

sum(1/((x-2)*sqrt(1-x^2))) ≈ -2π/(2*sqrt(3))




Out[142]:

true



Therefore:



In [160]:

sum(u)




Out[160]:

1.743934249004316e-16



We thus found $u$:



In [154]:

x = 0.1; u(x), sqrt(3)/((x-2)*sqrt(1-x^2)) + 1/sqrt(1-x^2)




Out[154]:

(0.08883962601869715, 0.08883962601869722)