Joseph Grotowski: [email protected]. 06-206 (Tues 10-12, Thurs 11-12, or by appointment).
Introduction
This course is compulsory for many students. Its prerequisites are MATH2000 and MATH2400 (or equivalents). This is the capstone course for math majors and math undergraduate degrees.
The techniques and problem solving strategies in this course will be beneficial in many ways.
Lecture recordings will be on the Blackboard. Tutorials start in week 1.
Assessment
The best 5 of 6 assignments together count for 20%. The midsemester counts for 20% (one page of handwritten notes is allowed, single-sided). The final exam counts for 60% (one page of handwritten notes, double-sided).
Complex analysis
Cool stuff
A ‘nice’ result which shows all different parts of maths coming together: eiπ=−1.
There are a number of fascinating things about this equation. Despite i not appearing in this expression, it still delves into complex analysis. ∫0∞xsinxdx=2π A reasonable question is does this integral even converge? If we replace sinx with 1, the integral diverges by the p-test. Arguing the integral exists is a bad time without complex analysis, but is really really nice with complex. This will be done towards the end of the semester, making use of contour integrals around a path in the complex plane.
In the more applied realm, we can also do things with fluid flow. A very expensive method would be constructing a physical model then running experiments. With complex analysis, we can perform analysis on a straight pipe, then map to the pipe above without having to build the channel. We can just tweak the parameters in the map to test different scenarios. This is called a conformal transoformation.
Similarly, Joukowski transformations can be used to model air flow around a wing.
We can also get nice results about series like 121+221+321+⋯121−221+321−421+⋯k=1∑∞1+4k2π21=6π2=12π2=21(e−11−21)
Riemann Zeta
ζ(s)=n=1∑∞ns1=pprime∏(1−p−s)−1 (The product of primes result is from Euler. This is called the Riemann zeta function)
Riemann hypothesis: ζ has infinitely many non-trivial zeros and they all lie on the line Re(s)=1/2.
Note that the expression for ζ only makes sense for Res)>1, so we need to extend it to C via analytic continuation. In doing this, the trivial zeros are −2,−4,−6,…
Lecture 2 — Complex Numbers
Lecture 2 — Complex Numbers
Complex numbers have been around for a while.
In 1545, Cardano looked at real roots of x3+ax+b. He found 5+−15 and called it “mental torture”.
In 1575, the algebraic rules for i were first described.
In 1629, a+−b was called “solutions impossibles” by Girard.
Note that Q is actually equivalence classes of “quotients” of integers because certain expressions are equivalent (see MATH2401). R can be defined in several technical ways, such as Dedekind cuts or limits of sequences.
C can be represented in various (equivalent) ways:
z=(x,y)∈C, for x,y∈R. This is the “complex plane”. Alternatively, z=x(1,0)+y(0,1).
z=x+ij, where x=Re(z) and y=Im(z).
i is the complex number represented by (0,1). We say R⊂C by identifying the complex number x+0i with the real number x.
Denoted by × or ⋅ or juxtaposition (that is, putting things next to each other). (x1,y1)⋅(x2,y2)(x1+iy1)⋅(x2+iy2)=(x1x2−y1y2,y1x2+x1y2)=(x1x2−y1y2)+i(y1x2+x1y2) The definition of multiplication formally applies if we use the usual rules for algebra in R and set i2=−1.
Note: Multiplication of two complex numbers sums their angles (where positive is CCW) and multiples their radius.
C is a field
With this addition and multiplication, C is a field. Check: C must be closed under the binary operations + and ⋅.
F2: + has identity 0+0i and inverse (−x)+i(−y). F5: ⋅ has identity 1+0i and inverse z−1=1/(x+iy)⋅(x−iy)/(x−iy)=x2+y2x−ix2+y2y
Since C is a field, it holds: z1,z2=0⟹z1=0 or z2=0. This is the null-factor law and holds because on all fields. Also, we have (z1z2)−1=z1−1z2−1.
Note: i2=−1 and (−i)2=−1. These are the only two solutions of z2=−1 in the complex numbers (we cannot check this yet). This is due to the Fundamental Theorem of Algebra.
Remark: C is not ordered and, in fact, cannot be ordered. Thus, i is no more special then −i.
B.C. 4, 5
Given z=x+iy∈C, there are a few useful functions to have: - modulus: ∣⋅∣:C→[0,∞), where ∣z∣=x2+y2, - real part: Re(z)=x, imaginary part: Im(z)=y (both C→R),
Lecture 3 — Functions of Complex Numbers
Lecture 3 — Functions of Complex Numbers
Complex conjugate
The complex conjugate is defined as a function ⋅ˉ:C→C, where (x+iy)↦(x−iy). Geometrically, this reflects a complex number about the real axis.
A very useful property (from MATH1051) is the triangle inequality: ∣z+w∣≤∣z∣+∣w∣.Proof: More specifically using the cosine rule, ∣z+w∣2=∣z∣2+∣w∣2−2∣z∣∣w∣cosA. This is a true and exact statement. However, in analysis, we often want to make these statements less precise but more useful. Because −1≤cos≤1, ∣z+w∣2⟹∣z+w∣≤∣z∣2+∣w∣2+2∣z∣∣w∣=(∣z∣+∣w∣)2≤∣z∣+∣w∣
Polar coordinates
B.C. 6-9
Given a complex number z=x+iy, we can find r and θ such that x=rcosθ, and y=rsinθ. Then, we can also write it using Euler’s formula (as a formal convention for the moment): z=reiθ=r(cosθ+isinθ).Remark: this formula follows formally from the Taylor series of eiθ.
Here, θ is an (as opposed to the) argument of the complex number z. We write θ=argz. Here, arg is not a (single-valued) function. Given a θ, we can always take θ+2π which will satisfy the x and y equations. Also, for z=0, any θ will work.
To make arg a function, we need to restrict its range. There are two options: 0 to 2π and −π to π. In complex analysis, we normally use the second. Specifically, Argz is defined to be the unique values of θ such that −π<argz≤π.
Examples: - Arg(1+i)=π/4 but arg(1+i)=…,−7π/4,π/4,9π/4,…. - Arg(−1)=π. - Arg(0) is undefined, but arg(0)=R.
In summary, Arg is a function C∖{0}→(−π,π]. Alternative notation for C∖{0} is C∗ or C∗.
Note: ∣eiθ∣(eiθ)−1=e−iθ(reiθ)(ρeiϕ)⟹∣zw∣=1 (easy to check)=eiθ=(rρ)ei(θ+ϕ)=∣z∣∣w∣,arg(zw)+argzargw However, the last equality does not necessarily hold for Arg. For example, with z=w=(−1+i)/2
De Moivre’s formula
z=reiθ⟹zn=rneinθ,n∈Z. In particular, einθ=(cosθ+isinθ)n=cos(nθ)+isin(nθ).
Lecture 4 — Functions as Mappings
Lecture 4 — Functions as Mappings
Roots of a complex number
What is the number z such that zn gives us the original number? By the fundamental theorem of algebra, we know there ar exactly nn-th roots in C.
Consider the n-th roots of z=reiθ, for z∈C∗. That is, we want all w∈C such that wn=z. Notation: exp(ξ)=eξ.
Then, we can use de Moivre’s theorem “in reverse” to see that z has n distinct roots: {r1/nexp(niθ),r1/nexp(niθ+ni2π),…,r1/nexp(niθ+ni2π(n−1))}
B.C. 13 (8th ed 12-13)
Functions & mappings
Suppose we have Ω⊆C and a function f:Ω→C can be viewed as a mapping on Ω, the domain of f. If Ω is not specified, then we take Ω to be as large as possible.
Example: For f(z)=1/z we can take Ω=C∖{0}, so f:C∗→C. As notation, we can also write f:z↦1/x, or w=1/z, or just 1/z if the meaning is clear.
The usual notation is w:(x,y)↦(u,v), i.e. w(x+iy)=u(x+iy)+iv(x+iy) or w(x,y)=u(x,y)+iv(x,y).
This notation is not completely rigorous; u is both a function from C and from R2. We could introduce a map φ:(x,y)↦(x+iy) but this is excessively verbose. There is no real problem with this, but be aware.
Definitions
The domain of f is Ω, also denoted domf.
The range of f is f(Ω)=Rangef={w:w=f(z) for some z∈Ω}.
The inverse of f is f−1(ξ)={z∈Ω:f(z)=ξ}. Note that this returns sets. If there is only one element, we can just call that element the “inverse”.
Examples: - Consider f(z)=1/z. domf=C⋆. f−1(ξ)=1/z is a function C∗→C∗. - For g(z)=1/(1−∣z∣2). domg=C∖{z:∣z∣=1}. The function is g:{z:z=1}→R. The inverse is not a function. - For h(z)=zn where h:C→C, the inverse is also not a function.
Geometric intuition
Let’s aim to get a geometric picture of what a given f does.
Examples: - w=1+z moves each point one unit to the right (in the positive real direction). - reiθ↦rei(θ+π/2) rotates points through an angle of π/2 in the counter-clockwise direction about the origin.
For new and unfamiliar mappings, break them down into compositions of known or easy maps.
Examples: - w=Az+b where A,b∈C and A=0. We can think of A as a dilation and rotation, then +b as a translation. - For z↦Az, write A=aeiα for α,a∈R. This gives us reiθ↦arei(θ+α). Specifically, it dilates the modulus by a factor of a=∣A∣ and rotates through α=argA. - For z↦z+b where b=b1+b2i, b1,b2∈R. This translates b1 to the right and b2 up. If negative, goes in the opposite direction.
Note: The maps above have domain and image C.
Lecture 5 — Mappings 2
Lecture 5 — Mappings 2
Another very important map to look at is z↦1/z on C∗. We can write this as the composition of two slightly more complicated functions.
Define ξ(z)=z/∣z∣2 on C∗ and η(z)=zˉ. For z∈C∗, we can compose these two as η∘ξ(z)=η(ξ(z))=(∣z∣2z)=∣z∣2zˉ=zzˉzˉ=z1.
ξ is called inversion, with respect to the unit circle. η is just reflection about the real axis.
For w=1/z=zˉ/∣z∣2 we can write it as x+iy↦u+iv, where w=x2+y2x−iy⟹u=x2+y2x,v=x2+y2−y. We can use this to show the following statement: 1/z maps circles and lines in the z=plane to circles and lines in the w-plane. Note that this does not require circles to map to circles, or lines to map to lines.
The key point is both circles and lines in the z-plane can be represented as A(x2+y2)+Bx+Cy+D=0, where B2+C2>4AD for A,B,C,D∈R. If A=0, then the equation is a circle. The inequality constraint tells us that (x+2AB)2+(y+2AC)2=(2AB2+C2−4AD)2. Note, for w=1/z, the u and v expressions earlier tell us that w has the form D(u2+v2)+Bu−Cv+A=0 which is a circle or line.
Terminology
f:Ω→C is 1-to-1 or injective if f(z)=f(ξ) implies z=ξ.
f:Ω→Λ, where Λ⊆C, is onto or surjective if for all η∈Λ, there exists (one or more) z∈Ω such that f(z)=η.
Examples: Affine transformations are bijections C→C , and 1/z is a binection C∗→C∗.
Möbius transformations
B.C. 99 (8th ed 93)
Let a,b,c,d∈C where ad−bc=0. Then, w=T(z)=cz+daz+b is called a Möbius (or linear fractional) transformation. The natural domain of definition is - if c=0, then domw=C (because c=0⟹d=0), or - if c=0, then domw=C∖{−d/c}.
Let’s try to understand T geometrically.
Claim:T is injective and surjective from C→C. Proof. For c=0, then to prove injectiveness suppose T(z)=T(ξ). We want to show z=ξ. Substituting into the formula for T, daz+db=daξ+db⟹z=ξ. To prove it is surjective, given w∈C, we need z∈C such that T(z)=w. The value z=d/a(w−b/d) satisfies this.
For c=0, consider w=cz+daz+b=c(z+d/c)a(z+d/c)−ad/c+b=ca+(cbc−ad)cz−d1 This is a composition of a linear transformation, 1/z and another linear transformation.
Thus, T is the composition of linear and 1/z maps. That is, Z1=cz+d,W=1/Z1,w=za+cbc−adW. In both cases, Möbius transformations are compositions of maps previously studied. This means they are bijective.
Recall that T(z)=w=cz+daz+b (ad−bc=0) is a Möbius transformation.
It can be rewritten as Azw+Bz+Cw+D=0 where A=c, B=−a, C=d, D=b. This is called the implicit form.
Recall that case 1 was c=0, which reduces T to a linear transformation which is a bijection C→C. Case 2 was also a bijection from C∖{−d/c}→C∖{a/c}, with inverse T−1(w)=cw−a−dw+b.
A question might be can we extend T to a function C→C in case 2? In particular, such that the extension is injective and surjective. The answer is yes, by “plugging the hole”. We simply define T(−d/c)=a/c. However, this is unsatisfying because the function becomes discontinuous.
An important concept: We are going to extend C to the extended complex plane, written Cˉ. This is done by adding a point at infinity, which is called ∞. We can think of the complex plane as a sphere with the origin at one pole and this ∞ at the other, with distances expanding as you go further from 0.
We then defineT(−d/c)=∞ and T(∞)=a/c. This extends T to a map Cˉ→Cˉ which is injective and surjective.
Remark:Cˉ is a topological space and the above extension is continuous. A topology on a set is a space with so-called “open sets”. Intuitively, points can be ‘nearby’ to other points.
Cˉ can be visualised as the Riemann sphere. The origin 0+0i is at the south pole. A point on the complex plane is mapped uniquely to a point on the sphere. This is done by picking the point on the sphere’s surface on the line between the point and the north pole. “Infinity” can be thought of as the north pole.
A few final remarks on Möbius transformations. Given 3 distinct points in z1,z2,z3∈Cˉ and 3 different distinct points w1,w2,w3∈Cˉ, there exists a unique Möbius transformation T such that T(z1)=w1,T(z2)=w2, and T(z3)=w3. In fact, T is given by (w−w3)(w2−w1)(w−w1)(w2−w3)=(z−z3)(z2−z1)(z−z1)(z2−z3). In practice, it may be easier to directly solve for a,b,c,d than using the above expression.
Note: How does this work with infinity? T(∞)=a/cT(−d/c)=∞⟺∣z∣→∞limT(z)=a/c⟺z→−d/clim1/T(z)=0
Lecture 7 — Exponential Maps
Lecture 7 — Exponential Maps
A note on coronavirus about the recent mail from Joanne Wright, the DVC(A).
Recall the Möbius transformation, and note that is is unique up to scaling for λ>0. w=cz+daz+b=λcz+λdλaz+λb
Remark: Any map from the inside of a (upper half) half-plane to the inside of a circle has the form w=e−iαz−z0z−z0 for some α∈R,z0∈C,Imz0>0.
Exponential map
B.C. 103 (8Ed 104)
z↦ez=expx=w,domw=C. Given a z=x+iy for x,y∈R, w=ez=ex+iy=exeiy=ex(cosy+isiny)=u+iv where uv=excosy=exsiny. This is easier to see by writing w=ρeiϕ where ρ=ex, ϕ=y+2kπ for k∈Z. This function is periodic in C.
Images under exp
Properties
Many of the properties of the real exp extend to C. Such as - e0=1. - e−z=1/ez. - ez1+z2=ez1ez2. - ez1−z2=ez1/ez2. - (ez1)z2=ez1z2.
However, some things do not extend: - ex>0∀x∈R but, for example, eiϕ=−1. - x↦ex is monotone increasing for x∈R but z↦ez is periodic with period 2πi.
Note: As in R, ez=0 has no solution in C. If there was some z=x+iy such that ez=0, then exeiy=0⟹ex=0 because ∣eiy∣=1, contradiction.
Inverses
B.C. 31-33 (8Ed 30-32)
We have a function f:Ω→C. Then, g:Rangef→Ω is an inverse of f if g∘f:Ω→Ω is the identity. That is, (g∘f)(z)=z for all z∈Ω.
Example:z↦z+1 and z↦z−1 are inverses for C→C. z↦1/z is its own inverse C∗→C∗.
Lecture 8 — Logarithm
Lecture 8 — Logarithm
Inverse of the exponential
The inverse of the exponential! It’s probably too much to hope for log=loge to be the inverse, because exp is periodic (with period 2πi) in C.
Begin with ew=z. Write z=reiΘ,r>0, where Θ=Argz∈(−π,π].
We can make our calculations clearer by using polar coordinates in the domain and rectangular coordinates in the range. That is, w=u+iv⟹z=ew=eu+iv=eueiv⟹eu=r,v=Θ+2kπ,k∈Z. So u=lnr, which (notation in this course) means logarithm with base e of the positive real number r. Thus, w=u+iv=lnr+i(Θ+2kπ)k∈Z=ln∣z∣+iargz This defines the multi-valued function log:C∗→C∗. exp(logz)log(expz)=z=z+2kπi We can check the the properties of log translate into C. For example, (note that this is a statement of multi-valued functions) - log(zξ)=logz+logξ. - log(z/ξ)=logz−logξ.
As with Arg and arg, we can define the principal logarithm, denoted Log:C∗→C∗, as Logz=ln∣z∣+iArgz This function is single-valued but has the disadvantage of being discontinuous on the negative real axis and 0, since Arg is discontinuous there. Indeed, Log and Arg are not even defined at 0.
As with Arg, it may be the case that Log(z1z2)=Logz1+Logz2.
Complex exponents
Remark: In reals, we could define dsomething like 22 as limn→∞2an where {an}→2. This doesn’t quite work in complex.
Set zc=exp(clogz). Because log is multi-valued, this may result in a sequence of outputs. For c∈N and 1/c∈Z, we recover the formulas from the fourth lecture.
Remark: B.C. defines z1/n as a multi-valued function and defines the principal value as PV(z1/n)=∣z∣1/nexp(iArgz/n). Similarly for z↦zc, PV(zc)=exp(cLogz)=exp(cln∣z∣+icArga).
Example: As a concrete example, doable but easy to make mistakes, PV[(1−i)4i]=exp(4i(ln∣1−i∣+iArg(1−i)))=exp(4iln2−4(−π/4))=eπexp(4iln2)=eπ(cos(2ln2)+isin(2ln2))
Sometimes, we need to use a different single-valued Log or Arg. For example, if we need to integrate around a contour excluding the −i axis. In this case, we would define Argz such that −π/2<argz≤3π/2. This leads to an alternative single-valued Log and derived functions.
Next: square roots, branch cuts.
Lecture 9 — Branch Cuts & Trigonometric Functions
Lecture 9 — Branch Cuts & Trigonometric Functions
B-C 108.
A branch is a half-open interval of the form α≤θ<α+2π or α<θ~≤α+2π of R.
This is good because we can define a single-valued Arg with values in this interval, a single-valued Log, as well as a single-valued branch of, for example, z1/2.
A branch cut is a subset of C, of the form {z:argz=α}∪{0}. This is where a particular branch is discontinuous.
For example, PV(z1/2) which maps zreiθ↦∣z∣1/2exp(2iArgz)↦rexp(iθ/2) The branch is −π<θ≤π and the branch cut is the negative real axis union with zero.
Consider the behaviour of z↦z1/2 under two different branches, −π<θ≤π and 0≤θ<2π. Exercise: Repeat for (z−z0)1/2.
Trigonometric functions
B-C 37-39 (8Ed 34-35)
For x∈R, eixe−ix⟹cosx⟹sinx=cosx+isinx=cosx−isinx=2eix+e−ix=2ieix−e−ix We can use these expressions to define cos and sin on C. Specifically, cosz=2eiz+e−izandsinz=2ieiz−e−iz. This gives us the following properties: - cosz=cos(−z) - sinz=−sin(−z) - cos(z+ξ)=coszcosξ−sinzsinξ - sin(z+ξ)=sinzcosξ+coszsinξ - sin2z+cos2z=1 (this does not imply that they are bounded in C) - sin(z+π/2)=cosz - sin(z−π/2)=−cosz (these two proven using properties of exp)
Hyperbolic functions
On R, the hyperbolic functions were sinhx=2ex−e−xcoshx=2ex+e−x Recall that sinh is somewhat like a exaggerated cubic and cosh is not unlike a steeper periodic parabola. Also, cosh can be used to model a hanging cable with weight.
Similarly to the first trig functions, we can define the hyperbolic functions on C as coshz=2ez+e−zandsinhz=2ez−e−z. Interestingly, sin(iy)=isinhyandcos(iy)=coshy. Tke z=x and ξ=iy in the sum formulas and we get sin(x+iy)cos(x+iy)=sinxcos(iy)+cosx+sin(iy)=sinxcoshy+icosxsinhy=cosxcoshy−isinxsinhy Together, the two above equalities imply sin(z+2π)=sinz and cos(z+2π)=cosz. Additionally, we have cosh2z=1+sinh2z and ∣sinz∣2∣cosz∣2=sin2xcosh2y+cos2xsinh2y=sin2x(1+sinh2y)+(1−sin2x)sinh2y=sin2x+sinh2y=cos2x+sinh2y
Recall that a function f:Ω→Z is called bounded if there exists M such that ∣f(z)∣≤M for all z∈Ω. Note that there can exist unbounded functions with finite area.
Finally, sin and cos are unbounded on C, because with a sufficiently large imaginary component they can become arbitrarily large.
Lecture 10 — Bounded Functions & Topology
Recall that we can have unbounded functions with bounded area.
Examples:
f(z)=ξ is bounded.
f(z)=1/z is unbounded on C∗ and on {z:0<∣z∣≤1} but bounded on {z:∣z∣≥1} and {z:∣z∣=1}.
f(z)=z is unbounded in C but bounded on any bounded subset of C.
f(z)=1+∣z∣1 is bounded on C.
Definition. A zero of a function is a value of z such that f(z)=0.
For example, the zeros of sin are nπ+0i for n∈Z. This can be derived from the sin(x+iy)=sinxcoshy+icosxsinhy equation. Similarly, the zeros of cos are (n+1/2)π. The zeros of sinh and cosh are nπi and (n+1/2)πi, respectively.
Inverse Trig Functions
If w=arcsinz, then z=sinw and z⟹2iezeiw⟹(eiw)2−2iz(eiw)−1=sinw=2ieiw−e−iweiweiw=2ieiwe2iw−1=e2iw−1=0 We can solve this quadratic using the complex quadratic formula, which doesn’t use ± but instead uses (⋅)1/2 as a multi-valued square root. So, ⟹eiw⟹iww=arcsinz=22iz+(−4z2+4)1/2=iz+(1−z2)1/2=log(iz+(1−z2)1/2)=−ilog(iz+(1−z2)1/2) Note that we have a multi-valued logarithm and for each of those, a double-valued square root. This makes it a lot more fun than real numbers.
Example:arcsin(−i)=−ilog(1+z1/2)=−ilog(1±2). So we need to consider two logarithms. log(1+2)=ln(1+2)+2nπi is relatively fine. Then, log(1−2)=ln∣1−2∣+arg(1−2)=ln(2−1)+(2n+1)πi Putting these together, we get arcsin(−i) is −i(ln(1+2)+2nπi) and −i(ln(2−1)+(2m+1)πi) for n,m∈Z.
Topology
Topology is the study of topos, space. Our basic building block is some ball around an arbitrary point in C.
Definition. Given z0∈C and ϵ>0, Bϵ(z0) denotes the (open) ball of radius ϵ about z0, a.k.a. an ϵ-neighbourhood of z0. In set notation, Bϵ(z0)={z:∣z−z0∣<ϵ}. Similarly, Bϵ(z0) is the closed ball of radius ϵ about z0 (a closedϵ-neighbourhood of z0) given by {z:∣z−z0∣≤ϵ}. A deleted ϵ-neighbourhood of z0 is {z:0<∣z−z0∣<ϵ}.
Note that the only feature of C used by this definition is ∣⋅∣, the modulus. That is, ∣z−z0∣=(x−x0)2+(y−y0)2=∥(x,y)−(x0,y0)∥R2=d((x,y),(x0,y0))R=d(z,z0)C This has obvious analogues to R with d(x,y)R=∣x−y∣ being the absolute value distance. Balls in R are just intervals.
Definition. Given Ω⊆C, z∈C is an interior point of Ω if there exists ϵ>0 such that Bϵ(z)⊂Ω. Note that this implies Bϵ′(z)⊂Ω for all 0<ϵ′<ϵ.
Definition.z∈C is an exterior point of Ω if there exists ϵ>0 such that Bϵ(z)∩Ω=∅.
Definition.z∈C is a boundary point of Ω if for all ϵ>0, Bϵ(z)∩Ω=∅ and Bϵ(z)∩Ωc=∅. That is, any ϵ-neighbourhood around z contains points inside and outside Ω. Here, $^c $ denotes the complement, that is C∖Ω.
Lecture 11 — Topology Definitions
Definition. The boundary of Ω, denoted ∂Ω, is defined as {z∈C:z is a boundary point}.
Recall that interior points are in Ω and exterior points are in Ωc. What about the boundary points?
Let’s look at a circle Ω={z:∣z∣=1}. In this case, we have ∂Ω=Ω. Let’s consider a blob:
Here, z1 is an interior point, z2 is an exterior point, z3 is a boundary point in Ω, and z4 is a boundary point not in Ω.
Definition.IntΩ is the interior of Ω, the set of all interior points. ExtΩ is the exterior of Ω, the set of all exterior points.
Definition.Ω is open if Ω=IntΩ, and Ω is closed if ∂Ω⊆Ω.
Examples:
Consider the open unit ball, Ω1=B1(0)={z:∣z∣<1}. For this set, IntΩ1=Ω1, ExtΩ1={z:∣z∣>1}, ∂Ω1={z:∣z∣=1}. Note that this means Ω1 is open.
Consider the closed unit ball, Ω2=B1(0). Here, IntΩ2=Ω1, ExtΩ2=ExtΩ1, and ∂Ω2=∂Ω1. This means Ω2 is closed.
Consider Ω3={z:0<∣z∣≤1}. IntΩ3={z:0<∣z∣<1}, ExtΩ3=ExtΩ1, ∂Ω3=S1∪{0}. This is neither open nor closed.
Note that Ω1 is open and Ω1c is closed. Ω2 is closed and Ω2c is open. Both Ω3 and Ω3c are neither open nor closed.
Definition. A set which is both closed and open is called clopen.
Definition. A set Ω⊆C is called connected if there do not exist non-empty, open, disjoint sets Ω′ and Ω′′ such that Ω⊆Ω′∪Ω′′ and Ω′∩Ω=∅ and Ω′′∩Ω=∅.
That is, we can’t find two ‘separated’ sets which together contain all of Ω and each contain parts of Ω.
Above, Ω1 is disconnected because we can find such Ω′ and Ω′′. However, Ω2 is connected.
Lecture 12 — Path Connected, Domains and Limits
Definition. A set Ω⊆C is piecewise affinely path connected if any two points in Ω can be connected by a finite number of line segments in Ω, joined end to end.
For open sets in C, this is equivalent to the original definition of connected. (This will not be proved in MATH3401.)
However, it is not so in general. For example, there is a comb space which is connected but not path connected. This is connected because we cannot find open sets
Claim. If Ω1 and Ω2 are open subsets of C, then so is Ω1∩Ω2.
Proof. If Ω1∩Ω2=∅, then we are done because the empty set is open. Otherwise, for any z∈Ω1∩Ω2, there exist ϵ1,ϵ2>0 such that Bϵ1(z)⊆Ω1 and Bϵ2(z)⊆Ω2. Take ϵ=min{ϵ1,ϵ2}. Then, Bϵ(z)⊆Ω1 and Bϵ(z)⊆Ω2 which implies Bϵ(z)⊆Ω1∩Ω2. Since z was arbitrary and this is the definition of interior point, we see that Int(Ω1∩Ω2)=Ω1∩Ω2. Therefore, Ω1∩Ω2 is open. □
Definition. A domain is an open, connected subset of C. A region is a set whose interior is a domain.
Definition. A point z∈C is called an accumulation point of Ω⊆C if any deleted neighbourhood of z intersects Ω. Note that z need not be in Ω.
Examples:
If Ω={i/2n}n∈N, then the only accumulation point is 0.
If Ω=B1, then the set of accumulation points is B1.
Limits
B-C 15-16.
Definition. Let f be a complex-valued function defined on a deleted neighbourhood of z0∈C. Then, we say limz→z0f(z)=w0 if for all ϵ>0, there exists δ>0 such that 0<∣z−z0∣<δ⟹∣f(z)−w0∣<ϵ. Note that f does not need to be defined at z0.
Examples:
If f(z)={01337z=0z=0, then limz→0f(z)=0.
limz→0sinz/z=1.
Remark. If a limit exists, then it is unique.
Limit Theorems
B-C 17 (8 Ed 16)
Suppose z=x+iy and f(z)=u(x,y)+iv(x,y). Let z0=x0+iy0 and w0=u0+iv0.
Theorem 1.z→z0limf(z)=w0⟺{lim(x,y)→(x0,y0)u(x,y)=u0,lim(x,y)→(x0,y0)v(x,y)=v0.andTheorem 2. (Non-exciting facts about operations of limits.) Suppose limz→z0f(z)=w0, limz→z0g(z)=ξ0 and λ∈C. Then,
\begin{align}
\lim_{z \to z_0}(f \pm g)(z) &= w_0 \pm \xi_0 \tag{1}\\
\lim_{z \to z_0}(\lambda f)(z) &= \lambda w_0 \tag{2}\\
\lim_{z \to z_0}(fg)(z) &= w_0\xi_0 \tag{3}\\
\lim_{z \to z_0}\frac{f(z)}{g(z)} &=\frac{ w_0}{\xi_0}\qquad\text{ if } \xi_0 \ne 0\tag{4}
\end{align}
Not that limz→z0g(z)=ξ0 and ξ0=0 implies g(z)=0 within a neighbourhood of z0.
Lecture 13 — Limits, Continuity and Differentiability
Recall the comb space, space with vertical lines of length 1 at x=1/2i and a horizontal line of length 1, with the origin removed. This is not path affinely finite path connected because we cannot move through the origin.
However, it is connected because any open set containing the x=0 line must extend some distance towards the other lines, hence containing the rest of the comb lines. So there do not exist two disjoint open sets which contain this comb, and it’s connected.
Limits at infinity
Recall in R that limx→x0f(x)=∞ means: given M>0, ∃δ>0 such that 0<∣x−x0∣<δ implies f(x)>M.
In C, a neighbourhood of z0∈C is a ball and a neighbourhood of ∞ has the form {z:∣z∣>M}. Note that in the Riemann sphere model, this would be some region around the “north pole”.
image-20200401153303634
So, “close to ∞” ⟺∣z∣ is large ⟺1/∣z∣ is small. Keeping that in mind, this means z→z0limf(z)=∞z→∞limf(z)=w0z→∞limf(z)=∞⟺z→z0limf(z)1=0⟺z→0limf(1/z)=w0⟺z→0limf(1/z)1=0Examples:
Show limz→−1z+1iz+3=∞. We need to show limz→−1iz+3z+1=0 which is true because the function is continuous at −1.
Compute limz→∞z2−12z3−1. This might be ∞ so we need to show limz→02z−3−1z−2−1=limz→02−z3z−z3=0. The limit exists and is 20=0.
Continuity & Differentiability
B.C. 19 (8 Ed 18)
Let f be defined in some neighbourhood of z0.
Definition. We say f is continuous at z0 if limz→z0f(z)=f(z0). That is, given ϵ>0 there exists δ>0 such that ∣z−z0∣<δ⟹∣f(z)−f(z0)∣<ϵ.
Basic results
If f:Ω→U and G:U→W are both continuous, then so is g∘f:Ω→W, where (g∘f)(z)=g(f(z)).
If f is continuous and non-zero at z0, then there exists Bϵ(z0) such that f(z)=0 on this ball.
f:x+iy↦u(x,y)+iv(x,y) is continuous if and only if u and v are continuous. That is, it must be continuous in the component functions.
Differentiability
Recall that f:Ω⊆R→R is differentiable if limh→0hf(x+h)−f(x) exists, and the limit defines f′(x) in R.
Definition. For f:Ω⊆C→C is differentiable if limξ→0ξf(z0+ξ)−f(z0) exists and the limit defines f′(z0).
This definition implies f′(z0)=limΔz→0Δzf(z0+Δz)−f(z0). Writing w=f(z) and Δw=f(z0+Δx)−f(z0), we can write f′(z0)=limΔz→0ΔzΔw=dzdw(z0). These are equivalent ways to write the derivative.
Lecture 14 — Derivatives and Complex Differentiation
Example: Take the derivative of f(z)=4z2 from first principles. Put w=f(z) and take z0∈C. Δz→0limΔzΔw⟹f′(z)=Δz→0limΔzf(z0+Δz)−f(z0)=Δz→0limΔz4(z0+Δz)2−4z02=Δz→0limΔz4z02+8z0Δz+4(Δz)2−4z02=8z0=8z
Example: For f(z)=∣z∣2, f′ doesn’t exist except at z=0. This is a very different situation from the case in R, where the function is differentiable everywhere.
B.C. 23 Ex 2 (8 Ed 22 Ex 2)
Note. Differentiability implies continuity, but the converse does not hold. An example of the converse failing is ∣z∣2 or ∣z∣.
The usual rules apply. For f,g differentiable, (f±g)′(fg)′(f/g)′=f′±g′=fg′+f′g=f2gf′−fg′g=0 We also have the chain rule: if f is differentiable at z0 and g is differentiable at f(z0), then the composition g∘f is differentiable at z0 and the derivative is (g∘f)′(z0)=g′(f(z0))f′(z0) and this can be written as dzdg=dwdgdzdwwhere w=f(z).
Cauchy-Riemann
Let z=x+iy and suppose f:z↦w=u(x,y)+iv(x,y) is differentiable at z0=x0+iy0. Set Δz=Δx+iΔy, then f′(z0)=limΔz→0ΔzΔw.
Key point: If the derivative exists, its value is independent of howΔz→0.
Note that Δw=f(z0+Δz)−f(z0)=u(x0+Δx,y0+Δy)+iv(x0+Δx,y0+Δy)−u(x0,y0)−iv(x0,y0) We can decompose the limit into real and imaginary, f′(z0)=(Δx,Δy)→(0,0)limRe(ΔzΔw)+i(Δx,Δy)→(0,0)limIm(ΔzΔw). These limits must still be independent of the path (Δx,Δy)→(0,0). To start, let (Δx,Δy)→(0,0) along the x-axis, i.e. along (Δx,0) for Δx=0. So, ΔzΔw=Δxu(x0+Δx,y0)−u(x0,y0)+iΔxv(x0+Δx0,y0)−v(x0,y0) which implies (below, ux is the partial derivative of u w.r.t. x) (Δx,Δy)→(0,0)limRe(ΔzΔw)(Δx,Δy)→(0,0)limIm(ΔzΔw)=ux(x0,y0)=∂x∂u(x0,y0)=vx(x0,y0)=∂x∂v(x0,y0) We can derive similar expressions for Δz→0 along the y-axis. For this, we get ΔzΔw=iΔyu(x0,y0+Δy)−u(x0,y0)+iiΔyv(x0,y0+Δy)−v(x0,y0) Being careful with the i, we get (Δx,Δy)→(0,0)limRe(ΔzΔw)(Δx,Δy)→(0,0)limRe(ΔzΔw)=vy(x0,y0)=−uy(x0,y0) Together, because the Δz→0 must be path independent and we’ve found the value along two paths, these must coincide. This gives is the Cauchy-Riemann equations.
Theorem. (Cauchy-Riemann equations) If f=u+iv is differentiable at z0=x0+iy0, then ux=vy and −vx=uy at (x0,y0).
Note. We have shown that C/R are necessary for complex differentiability, but they are not sufficient. There are sufficient conditions.
Sufficient conditions
If we know
f is defined in a neighbourhood of z0,
ux,uy,vx,vy are defined and continuous in a neighbourhood of (x0,y0), and
C/R hold at (x0,y0),
then f′(z0) exists.
Note that there are no is in this board; it is a statement on functions of R2.
Remark. There are no necessary and sufficient conditions for complex differentiability. Otherwise, we would have reduced complex analysis to R2 analysis (how boring!).
Lecture 15 — Wirtinger Operators and Analytic Functions
What does Cauchy-Riemann mean in polar coordinates? Take z=x+iy=reiθ so x=rcosθ and y=rsinθ. By the chain rule, we get uruθvrvθ=uxcosθ+uysinθ=−uxrsinθ+uyrcosθ=vxcosθ+vysinθ=vxrsinθ+vyrcosθ We can derive C/R in polar coordinates as rur=vθ and uθ=−rvr.
Therefore, if f′ exists, then f′=ux+ivx. By using the polar coordinates expression, we also get f′(z)=e−iθ(ur+ivr).
Wirtinger operators
Formally, we are going to change variables from (x,y) to (z,zˉ), where z=x+iy and zˉ=x−iy. This means that x=(z+zˉ)/2 and y=(z−zˉ)/(2i).
This derivation makes use of the multivariate chain rule. Specifically, if x(t) and y(t) are differentiable functions of t and z=f(x,y) is a differentiable function of x and ythen z=f(x(t),y(t)) is differentiable and dtdz=∂x∂z∂t∂x+∂y∂z∂t∂y.
Then, ∂x∂f−i∂y∂f∂x∂f+i∂y∂f=2∂z∂f⟹∂z∂=21(∂x∂−i∂y∂)=2∂zˉ∂f⟹∂zˉ∂=21(∂x∂+i∂y∂)∂z∂ and ∂zˉ∂ are called the Wirtinger operators.
Example: Consider f(z)=zn=(x+iy)n. Then, ∂z∂f∂zˉ∂f=21(∂x∂−i∂y∂)(x+iy)n=21(n(x+iy)n−1−i2n(x+iy)n−1)=21(n(x+iy)n−1+n(x+iy)n−1)=n(x+iy)n−1=nzn−1=f′(z)=0(follows from above)
For f=u+iv complex differentiable, 21∂x∂f=21(ux+ivx)=CR21(vy−iuy)=−2i(uy+ivy)=−2i∂y∂f So C/R holds if and only if ∂zˉ∂f=0. This is version II of the Cauchy-Riemann equations.
But why is this partial derivative equal to the full derivative? From f′=ux+ivx, dzdf=ux+ivx=∂x∂f=−i∂y∂f(by CR)=21(∂x∂f−i∂y∂f)=∂z∂fExample 1: Find f′(z) for f(z)=ez. First, we check the sufficient conditions for f′ to exist. Writing f(z)=u+iv=ex+iy=ex(cosy+isiny), it is defined on C. Moreover, the components are u=excosy and v=exsiny which have partials defined and continuous on C. Then, we need to check C/R by testing ux=vy and uy=−vx or just checking ∂zˉ∂f=0.
Example 2: When is g(z)=∣z∣2 differentiable? Note that g(z)=zzˉ=x2+y2. Checking C/R II, ∂zˉ∂g=0⟹z=0, so gcannot be differentiable for z=0 because C/R is necessary. At z=0, we check the sufficient conditions. It is easy to show that u,v,ux,vx,uy,vy are defined and continuous on a neighbourhood of 0. Therefore, g′(0)=0.
Exercise: Go through the same exercise for z↦1/z on C∗.
Definition. A function f:Ω→C is analytic at z0 if f is differentiable on a neighbourhood of z0.
Definition. A function is singular at z0 if it is not analytic at z0 but is analytic at some point in any neighbourhood of z0. For example, f(z)=1/z is analytic on C∗ and singular at 0.
That is, given Bϵ(0), f is analytic on Bϵ′(z0) for some z0∈Bϵ(0) and ϵ′<∣z0∣.
Definition. A function is entire if it is analytic on all of C. For example, polynomials, sine, cosine, exponential, etc.
Note: If a function is differentiable at precisely one point, it is not analytic there or anywhere (e.g. ∣z∣2).
Also, note that we are calling once-differentiable functions analytic. In real analysis, analytic functions were smooth and equal to their power series (infinitely differentiable). What’s going on?
Lecture 16 — Examples of Derivatives and Taylor Series
Mid-semester exam: Wednesday 22/04/2020 9am.
Remember from real analysis that we have functions differentiable once but not twice.
Continuing with derivatives, consider dzdlogz where ∣z∣>0. Recall that in C, logz=ln∣z∣+iargz=lnr+iθ. Looking at the second expression in its components, u=lnr and v=θ so ur=1/r, uθ=0, vr=0 and vθ=1. Checking C/R in polar coordinates, we need rur=vθanduθ=−rvr which we do have. We need to make log a function so it can be continuous; we need to choose a branch. Pick a subset of C∗ such that α<θ<α+2π then log is differentiable. From Lecture 15, dzdlogz=e−iθ(ur+ivr)=e−iθ/r=1/z. For example, dzdLogz=1/z for −π<Argz<π and ∣z∣>0.
For f(z)=zc where c∈C∗ is fixed, we have f(z)=exp(clogz) and f′(z)=cexp(clogz)/z by the chain rule and using the derivative of log. We can also write this as zcc/z=czc−1 which is valid on any domain of the form {z:∣z∣>0,α<argz<α+2π}, due to the branch cut of log.
Remark: Try this for g(z)=cz.
Notation from real analysis
Given Ω⊆Rn,
C(Ω)=C0(Ω) is the set of continuous functions Ω→R.
Ck(Ω) is the set of functions whose derivatives/partial derivatives of order 0,1,…,k exist and are continuous. (0-th order derivative is f itself.)
C∞(Ω) is the set of functions whose derivatives/partial derivatives exist and are continuous. That is, smooth functions.
Cω(Ω) is the set of real analytic functions. That is, for all x0∈Ω,
f has a power series expansion about x0 (namely, its Taylor series), and
f is given by its power series expansion (i.e. the power series converges to f on some neighbourhood of x0.)
Note that (i) implies f is smooth, and in Rn, (i) does not imply (ii).
Example: Consider an example to illustrate this past point. f(x)={e−1/x20x>0x≤0 Then, f(n)(x) exists for all x=0 trivially and f(n)(0)=0 for all n. Also, f(n) is continuous on R. However, the Taylor series of f about 0 is n=0∑∞n!f(n)(0)xn≡0 so f is not equal to its Taylor series in a neighbourhood of 0. Therefore, f∈C∞(R) but f∈/Cω(R).
In real analysis, we have Cω⊊C∞⊊⋯⊊C1000⊊⋯⊊C1⊊C0. Next, we will be moving onto integration but there are some problems. There was the intuition of ‘area’ but how does this translate to C? We could look at something like a two-dimensional volume under a hypersurface but that doesn’t really work. Instead, we can revert to a complex valued function of real parameters. Next lecture, we will see why this makes sense and how it leads to the familiar integration.
Lecture 17 — Integration, Rules, FToC, Contours
We want integration to give us some notion of (signed) as well as reversing differentiation, with the goal of building up the fundamental theorem of calculus.
Integration
B-C §41-43 (8 Ed §37-39)
Consider a C-valued function of one real variable. That is, w(t)=u(t)+iv(t) for t∈R. Define w′(t)=u′(t)+iv′(t).
The usual rules for real-valued differentiation apply:
(cw)′=cw′
(w1±w2)′=w1′±w2′
dtdect=cect
product rule
quotient rule
etc.
We can also define definite and indefinite integrals for such functions. For a,b∈R, ∫abw(t)dtRe(∫abw(t)dt)Im(∫abw(t)dt)=∫abu(t)dt+i∫abv(t)dt=∫abRe(w(t))dt=∫abIm(w(t))dt∫0∞w(t)dt and similar can be defined analogously. The above expressions certainly make sense if w is continuous, that is w∈C0([a,b]).
Somewhat more generally, it also holds for piecewise continuous functions on [a,b]. That is, w such that there exist c1<c2<⋯<cn∈(a,b) such that
w is continuous on each of (a,c1),(c1,c2),…,(cn,b),
limt→cj−w(t) and limt→cj+w(t) both exist (but they need not coincide), and
limt→a+w(t) and limt→b−w(t) both exist.
Of course, the limits existing for w imply the limits exist for u and v.
image-20200409114835570
Suppose there exists W(t)=U(t)+iV(t) such that W′=w on [a,b]. Then, the fundamental theorem of calculus holds, in the form of ∫abw(t)dt=W(b)−W(b). The next estimate is crucial.
Lemma. Suppose w=u+iv is piecewise continuous on [a,b]. Then, ∣∣∣∣∣∣∫abw(t)dt∣∣∣∣∣∣≤∫ab∣w(t)∣dt.Proof. If ∫abw(t)dt=0, then the left is 0 and right is ≥0 so we are done. Otherwise, there exists r>0 and θ0∈R such that ∫abw(t)dt=reiθ0 which implies ∣∣∣∣∫abw(t)dt∣∣∣∣=r. Then, ∫abw(t)dt∫abe−iθ0w(t)dt⟹r=∫abe−iθ0w(t)dt=reiθ0=r=Re(∫abe−iθ0w(t)dt)=∫abRe(e−iθ0w(t))dt However, Re(e−iθ0w(t))≤∣∣∣e−iθ0w(t)∣∣∣=∣w(t)∣ because ∣∣∣e−iθ0∣∣∣=1. Combining this with the expression for ∣∣∣∣∫abw(t)dt∣∣∣∣=r from earlier, ∣∣∣∣∣∣∫abw(t)dt∣∣∣∣∣∣=r≤∫abRe(e−iθ0w(t))dt≤∫ab∣w(t)∣dt.□
Contours and arcs
A contour is a parametrised curve in C. Given x(t),y(t) continuous on [a,b]→R, z(t)=x(t)+iy(t),a≤t≤b defines an arc in C.
This is both a set of points z([z,b]), called the trace of the arc, and also a recipe for drawing the arc (the parametrisation).
Lecture 18 - Jordan Curves, Simple Closed Contours
Recall that z(t)=x(t)+iy(t) for t∈[a,b]. The parameter t can be thought of as time.
Definition. A Jordan arc (or simple arc) does not intersect itself. That is, z(t1)=z(t2) for t1=t2.
Definition. A Jordan curve (or simple closed curve) is a Jordan arc that has the property z(a)=z(b).
Example 1:z={t+itt+i0≤t≤11<t≤2 is a simple arc, whose trace is the graph of the points. The arc would be traced out with a ‘speed’ of 2 between 0 and 1 because it covers a distance of 2 in 1 time unit.
Example 2:z=z0+Reiθ for 0≤θ≤2π is an arc whose trace is a circle, centred at z0 of radius R.
Example 3:z=z0+Re−iθ for 0≤θ≤2π traces the same circle, but in the opposite direction. We use a negative in the exponent to allow the parameter to be increasing (fitting the time analogy).
Example 4:z=z0+Re2iθ for 0≤θ≤2π again has the same trace, but it “covers” the circle twice.
In these examples, 2 and 3 are Jordan curves and 4 is not.
Definition. An arc/curve is called differentiable if z′(t) exists (at all t∈(a,b) for an arc, and at t∈[a,b] for a curve).
Definition. If z′ exists and is continuous, then ∫ab∣z′(t)∣dt exists and defines the arc length.
This is crucial because the length of an arc does not depend on the particular parametrisation. More specifically, if z(t) is any parametrisation of the image arc, we can define another one by t=Φ(τ) with Φ(α)=a and Φ(β)=b such that Φ∈C([α,β]) and Φ′∈C((α,β)). Then, z(t)=Z(τ)=z(Φ(t)).
We will prove that the arc length is the same. Assume Φ(τ)>0 for all τ (that is, we always move forwards in time). Then, ∫ab∣z′(t)∣dt=∫αβ∣z′(Φ(τ))∣Φ′(τ)dτ=∫αβ∣Z′(τ)∣dτ which implies arc length is independent of parametrisation.
Definition. A contour is an arc/curve/Jordan curve such that z is continuous and z is piecewise differentiable. Additionally, if initial and final values coincide and there are no other self-intersections, it is a simple closed contour.
Theorem (Jordan curve theorem). Any simple closed contour divides C into three parts:
on the curve,
inside the curve, and
outside the curve.
Although it seems obvious, this is actually more complex. Consider a Möbius strip. This would take about 8 lectures to prove, so we’ll trust Jordan on this one.
Remark: The theorem still holds if we remove the requirement that z is piecewise differentiable. This leads to very freaky things such as space-filling curves.
Contour integrals
Given a contour C, a contour integral is written ∫Cf(z)dz or ∫z1z2f(z)dz. We can write the second expression if we know:
the integral is independent of the path z1 to z2 (e.g. integrating a conservative field), or
path is understood.
Suppose the contour C is specified by z(t) with z1=z(a) and z2=z(b), with a≤t≤b, and suppose f is piecewise continuous on C. Then (reminiscent of line integrals), ∫Cf(z)dz=∫abf(z(t))z′(t)dt.
Glossary
arc — z(t)=x(t)+iy(t) for a≤t≤b with x and y continuous on this interval.
simple — no self intersections.
closed curve — a curve which starts and ends at the same points.
smooth — a differentiable arc with z′ continuous on [a,b] and non-zero on (a,b).
contour — an arc/curve/simple closed curve with z continuous and piecewise differentiable, alternatively a finite number of smooth arcs joined end to end.
Lecture 19 — Contour Integrals
Recall that an arc is made up of the trace, the image of points, and the parametrisation, a way of driving along the curve.
Suppose C is a contour given by z(t) for t∈[a,b] with z1=z(a) and z2=z(b). Suppose f is piecewise continuous on C.
Contour integrals
Basic properties
The contour integral is linear: ∫C(αf)(z)dz∫C(f+g)(z)dz=α∫Cf(z)dz for α∈C=∫Cf(z)dz+∫Cg(z)dz
C1+C2 defines a contour only when the end of C1 coincides with the start of C2.
−C is defined as w(t)=z(−t) for −b≤t≤−a. Using the change of parameter formula, we can show that ∫−Cf(z)dz=−∫Cf(z)dz. Hence, C1−C2=C1+(−C2). This means the end of C1 has to be the start of −C2 (i.e. the end of C2).
Example: Evaluate I=∫Czˉdz where C is given by z(θ)=2eiθ for −π/2≤θ≤π/2. This traces the right half of a circle with radius 2 counter-clockwise. We check that C is continuous on C (indeed, differentiable) and f is continuous on C. Note that z′(θ)=2ieiθ. Then, ⟹I=∫−π/2π/2f(z(θ))z′(θ)dθ=∫−π/2π/2(2eiθ)2ieiθdθ=4i∫−π/2π/2e−iθeiθdθ=4i∫−π/2π/2dθ=4πi On C, zzˉ=4 which implies zˉ=4/z. As a corollary, ∫Czdz=πi. See §45 (8 Ed §41) for more examples.
Antidifferentiation
Let D be a domain in C (that is, an open connected subset of C).
Definition. An antiderivative of f on D is F such that F′(z)=f(z) on D.
Theorem. The following three are equivalent:
f has an antiderivative on D,
for any z1,z2∈D and any contour C from z1 to z2 in D, the integral ∫Cf(z)dz is independent of C, and
for any closed contour C in D, it holds that ∫Cf(z)dz=0.
Proof. (i) to (ii) follows from the fundamental theorem of calculus. For (ii) to (iii), take a closed contour C in D with z(a)=z(b)=z1. Fix γ∈(a,b) such that z(γ)=z1. Split C into two contours: C1 with t≤γ and C2 with t≥γ. Then, C1+C2=C and ∫Cf=∫C1+C2f=∫C1f+∫C2f=∫C1−∫−C2f=0, because −C2 and C1 have the same start and end points so their integrals are equal by (ii). For (iii) to (ii) to (i), see B/C. □
In particular, for C from z1→z2 in D, it holds that ∫Cf(z)dz=F(b)−F(a), for any antiderivative F of f.
Further examples of contour integrals
Keep in mind that we are doing integration, which is more of an art than a science. That is, it can be very difficult to get a (closed) for solution for even simple-looking integrands.
Example 2:I=∫01+iz2dz. Here, f(z)=z2 has an antiderivative, such as F(z)=z3/3. By the FToC, I=F(1+i)−F(0)=32(−1+i).Example 3:I=∫Cdz/z2, with C=2eiθ and 0≤θ≤2π. The integrand 1/z2 has an antiderivative on C∗, namely −1/z. Because C is a closed contour lying completely within C∗, (iii) implies I=0.
More generally, the same argument shows that ∫Czndz=0 for all closed contours C and n∈Z∖{−1}.
Lecture 20 — Cauchy-Goursat
Example 4:I=∫Czdz where C=2eiθ and 0≤θ≤2π. We cannot use the argument from earlier example because the antiderivative does not exist along the whole interval (regardless of branch cuts). We can try to split up C into C1 and C2, the left and right halves of the circle. Then, I=I1+I2 where I1 and I2 are the integrals along C1 and C2 respectively.
On a domain D=C∖{R<0∪{0}}, Log is a primitive for 1/z on C1⊂D. The previous lecture’s theorem tells us that I1=Log(2i)−Log(−2i)=πi (recall, Log(z)=ln∣z∣+iArgz). Note that this agrees with our corollary from lecture 19.
For I2, on D′=C∖{R>0∪{0}}, 1/z has a primitive such as Logz=ln∣z∣+iArgz where 0≤Argz≤2π. Note that C2⊂D′. By the theorem, I2=Log(−2i)−Log(2i)=πi (being careful to use our modified argument function).
Therefore, I=I1+I2=2πi. We can conclude that ∫Czndz={02πin∈Z∖{−1},n=0. for any circle C centred at the origin and positively oriented (counter-clockwise).
Cauchy-Goursat
§50 (8 Ed §46).
Theorem. Let C be a simple closed curve in C. If f is analytic on C and its interior, then ∫Cf(z)dz=0.Remark: The converse does not hold. Consider ∫Czndz with n=−2,−3,… which is not analytic at 0 for any circles around 0.
Proof. Prove for a rectangle, then approximate the contour C these squares. The interior cancels and the outer edges approach the integral.
M-ℓ estimate: (This forms a key step of the proof.) Suppose f is continuous on a contour C, given by z=z(t) and a≤t≤b. Then, there exists M such that ∣f(z)∣≤M for all z∈C (by extreme value theorem in R). So, ∣∣∣∣∣∫Cf(z)dz∣∣∣∣∣=∣∣∣∣∣∣∫abf(z(t))z′(t)dt∣∣∣∣∣∣≤∫ab∣f(z(t))∣∣z′(t)∣dt≤M∫ab∣z′(t)∣dt=Mℓ where ℓ=ℓ(C) is the arc length of C.
Cauchy-Goursat extension
Recall (?) that a domain D is simply connected if for every simple closed contour C in D, it holds that IntC⊆D. Roughly speaking, this means that D has “no holes”. That is, all simply closed contours are null homotopic.
If D is not simply connected, it is multiply connected.
Theorem. If f is analytic on a contour C, as well as on C1,…,Cn⊂IntC and on the interior of the domain bordered by C1,C2,…,Cn, and C,C1,…,Cn are all positively oriented, then ∫Cf(z)dz+j=1∑n∫Cjf(z)dz=0. Note that positively oriented means the that while traversing the contour, the region is on your left. This is particularly important for the orientation of C1,…,Cn.
Visually,
image-20200430115113018
Lecture 21 — Cauchy Integral Formula
Theorem (Cauchy integral formula). Let f be analytic on and inside a simple closed curve C that is positively oriented (interior is to the left of the curve’s direction). Then, if z0∈IntC we have f(z0)=2πi1∫Cz−z0f(z)dz,or2πif(z0)=∫Cz−z0f(z)dz. This is quite an amazing result. Roughly, f is differentiable and we can know the value of f at a point by the integral of any curve around that point.
image-20200501112406995
Proof. Note that the integrand is not analytic on IntC because it is not defined at z0. We will “cut out” this discontinuity so we can apply the Cauchy-Goursat theorem. Set Cρ={z(θ)=z0+ρeiθ,0≤θ≤2π} as a curve around our point z0, for ρ sufficiently small such that IntCρ⊂IntC.
We have f(z)/(z−z0) is analytic on IntC∖IntCρ as well as C and Cρ. We apply Cauchy-Goursat’s extension to multiply connected domains and that gives us ∫Cz−z0f(z)dz⟹∫Cz−z0f(z)dz−f(z0)∫Cρz−z0dz=∫Cρz−z0f(z)dz=∫Cρz−z0f(z)−f(z0)dz From lecture 20, we know that ∫Cρz−z0dz=2πi because Cρ is a circle centered at z0 and this holds for any ρ>0. Since f is analytic at z0, it is continuous at z0 so given ϵ>0 there exists δ>0 such that ∣f(z)−f(z0)∣<ϵ for all ∣z−z0∣<δ. Choose ρ<δ and we will have ∣f(z0+ρeiθ)−f(z0)∣<ϵ.
Returning to the equations from above, ∣∣∣∣∣∫Cz−z0f(z)dz−2πif(z0)∣∣∣∣∣≤∫Cρ∣z−z0∣∣f(z)−f(z0)∣dz Note that all points on Cρ are exactly ρ away from z0. Thus, 1/∣z−z0∣=1/ρ. Moreover, the integral ∫Cρ∣f(z)−f(z0)∣dz is bounded by ϵ⋅2πρ by the M-ℓ estimate (here, M is ϵ and ℓ is the circumference of a circle with radius ρ). This gives us, ∫Cρ∣z−z0∣∣f(z)−f(z0)∣dz=ρ1∫Cρ∣f(z)−f(z0)∣dz<ρ1ϵ⋅2πρ=2πϵ By sending ϵ→0, we can make this arbitrarily small which tells us ∣∣∣∣∣∫Cz−z0f(z)dz−2πif(z0)∣∣∣∣∣=0⟺f(z0)=2πi1∫Cz−z0f(z)dz, as required. □
Lecture 22 — Morera, Liouville Theorem
Recall the Cauchy integral formula: If f is analytic on and inside the simple closed curve C, traversed positively, and z0∈IntC, then f(z0)=2πi1∫Cz−z0f(z)dz.Theorem. Under the same conditions, f(n)(z0)=2πin!∫C(z−z0)n+1f(z)dz.Proof. See exercise 9 of §57 (8 Ed §52). □
As a result, this tell us that for f=u+iv and f analytic at z0=x0+iy0, we know that partials of all orders of u and v exist and are continuous at (x0,y0). This is very different from the situation in R, where it is very easy to have functions with continuous derivatives but not differentiable. For example, with f(x)=∣x∣3, f, f′ and f′′ are continuous but f′′′(0) does not exist.
Note: If f is analytic at z0, then its derivatives of all orders exist and are analytic at z0.
Theorem (Morera). Let f be continuous on a domain Ω. If ∫Cf(z)dz=0 for all closed contours C in Ω, then f is analytic on Ω.
Proof. By the theorem from lecture 19, f has a primitive F because ∫Cf(Z)dz=0. But then, F′=f exists and is continuous on Ω by assumption of the theorem. This tell us that F is analytic. Hence, by the note above, f=F′ is also analytic. □
A number of nice results follow from the theorem with f(n)(z0) above.
Result (I). Let f be analytic in and on CR(z0) (curve of a circle of radius R around z0) and set MR=maxz∈CR∣f(z)∣. Then, ∣∣∣∣f(n)(z0)∣∣∣∣≤Rnn!MR. This tells us that if we know what the function does on the circle, we can estimate the size of its derivatives at a point. In fact, the closer we get, the worse this estimate becomes because of the division by Rn.
Proof.MR is well defined by the extreme value theorem. Then, applying the aforementioned theorem, ∣∣∣∣f(n)(zn)∣∣∣∣=∣∣∣∣∣2πin!∫CR(z−z0)n+1f(z)dz∣∣∣∣∣≤2πn!∫CR∣z−z0∣n+1∣f(z)∣dz≤2πRn+1n!MR∫CRdz=Rnn!MR Above, note that ∣z−z0∣=R on this contour, and ∫CRdz is just the arc length of CR (equal to 2πR). □
As a brief discussion, we have all these powerful results about analytic functions in C. However, this hints that being complex differentiable is actually a very restrictive condition.
Result (II – Liouville). If f:C→C is bounded and entire (everywhere differentiable), then f is constant.
Proof. Suppose ∣f∣≤M on all of C and it is entire. Apply result I for n=1 on CR(z0), an arbitrary circle around z0. The result implies that ∣f′(z0)∣≤R1!M=RM. Letting R→∞, we see that f′(z0)=0. Since z0 was arbitrary, we have the result. □
This is clearly not the case in R.
Result (III – Fundamental theorem of algebra). An n-th degree polynomial has exactly n zeros.
Lecture 23 — Conformal Maps, Harmonic Functions
Conformal maps
§112 (8 Ed §101)
Definition. A conformal mapf is a map f:z↦w where f is analytic and f′(z0)=0. Then locally (near z0), f preserves angles, orientation, and shape.
In the image below, Γ1 and Γ2 are the images of C1 and C2 under f. The angles between them are α and β. They intersect at z0 and f(z0), respectively. That is, Γ1=f(C1) and Γ2=f(C2). Conformality tells us that α=β.
If orientation (i.e. sense, direction) is not necessarily preserved but the angle’s magnitude is, the map is called isogonal.
image-20200505115755673
If instead we had an analytic function with f′(z0)=0, then z0 is a critical point of f. This means the angle is not preserved around z0. However, the angle will be multiplied by m where m is the smallest integer such that f(m)(z0)=0.
§113 (8 Ed §103)
Conformality means the map is locally 1-to-1 and onto. That is, f has a local inverse. This follows from MATH2400/1’s inverse function theorem. Specifically, it is locally invertible if detJf=0. In this case, detJf=∣∣∣∣∣uxvxuyvy∣∣∣∣∣=uxvy−uyvx=ux2+vx2=∣ux+ivx∣2=∣f′∣2=0 due to f′(z0)=0 and analyticity of f.
Harmonic functions
We look for a function U:Ω→R such that ΔU=0(or alternatively, ∇2U=0).
Here, Δ or ∇2 is the Laplacian/Laplace operator defined as ΔU=Uxx+Uyy or more generally in Rn, Δ=∑j=1nUjj. This is used to model many physical situations in “steady state”.
Motivation
Take a region Ω⊂R2 or R3. Let Λ be a “sufficiently smooth subdomain of Ω”. Some intuition is that an arbitrary point x on the ∂Λ has an external normal, denoted ν(x) with unit normal ν′(x).
U is the density of something “in equilibrium”, and F is the flux density of U in Ω “in equilibrium”.
This means that along the boundary of Λ, ∫∂ΛF⋅ν′dS=0, where dS is the surface measure on ∂Λ (i.e. one dimension lower). This means the net in-flow and out-flow are equal. In terms of fluids, this means there are no sources and sinks.
We apply Gauss divergence theorem with the above integral which tells us that ∫∂ΛF⋅ν′dS=∫ΛdivFdx=0 where dx=dxdy in 2D, etc. Since Λ is essentially arbitrary, there holds divF=0 in Ω. That is, ∑j=1n∂jFj=0 in Ω.
In many physical situations, F=c∇U with c usually negative (corresponding to repelling forces). This means that divF=cdiv∇U=0⟹div∇U=ΔU=0.
Lecture 24 — Harmonic Conjugates
Recall from last lecture, conformal maps and Laplacian of harmonic functions.
If U is the concentration of something “in equilibrium”, that implies (somewhat) that ΔU=0. There are many solutions to this in general (constants, linear, etc) however we are often interested in boundary conditions.
Can we also study ∂t∂U=αΔU? As the left hand side approaches 0, the Laplacian approaches 0 and the system approaches steady state. This has many physical applications.
If U is concentration of a chemical, this is Fick’s law of chemical diffusion.
If U is temperature, this is Fourier’s law of heat conduction.
If U is the electric potential, this is Ohm’s law of electrical conduction.
Examples:
On R2∖{(0,0)}, f(x,y)=ln(∥(x,y)∥) is harmonic.
On Rn∖{0}, f(x)=1/∥x∥n−2 is harmonic.
Note that these are radial functions around 0. But how badly do they behave?
Theorem. If f(z)=u(x,y)+iv(x,y) is analytic in Ω⊆C, then u and v are harmonic in Ω.
Proof. Recall that if f is analytic then u and v have continuous partials of all orders and C/R holds. That is, ux=vy and uy=−vx. We can differentiate these and apply C/R again to get uxxuyx=vyx=vyyuxyuyy=−vxx=−vyx Since partials of all orders are continuous, by Clairaut’s theorem, uxy=uyx and vxy=vyx. Therefore, uxx=vyx=−uyy and similarly for v, so Δu=0 and Δv=0. □
Definition. If u and v are harmonic and satisfy C/R, then v is called a (not the) harmonic conjugate of u. Note that this is not symmetric.
Theorem.f=u+iv is analytic in Ω if and only if v is a harmonic conjugate of u.
Proof. (→) is done above. (←)v is a harmonic conjugate so u and v are both harmonic and u,ux,uy,uxx,uyy all exist, are continuous and satisfy C/R throughout Ω, f is analytic. □
Example: Suppose v and w are harmonic conjugates of u. This means that u+iv and u+iw are both analytic. Applying C/R, ux=vy=wy,anduy=−vx=−wx. Integrating the derivatives of v and w wrt their partial variable, we get v=w+ϕ(x) and v=w+ψ(y). Therefore, ϕ(x)=ψ(y) which must be a constant. This means v=w+c. ∘
A similar procedure can be used to find a harmonic conjugate of a given harmonic function u.
Example: Find a harmonic conjugate of u(x,y)=y3−3x2y.
ui s a polynomial function of x and y so has continuous partials of all orders. Moreover, uxx+uyy=0. Suppose v is a harmonic conjugate of u. C/R tells us ux=vy so vy=−6xy. Integrating this wrt y gives us v=−3xy2+ϕ(x). Using this in the second part of C/R, uy3y2−3x2ϕ′(x)ϕ(x)=−vx=3y2−ϕ′(x)=3x2=x3+c So, we can choose c=0 and v(x,y)=−3xy2+x3 is a harmonic conjugate of u. Note that in this example, u=Ref and v=Imf where f(z)=iz3.
Lecture 25 — Transformations of Harmonic Functions
Recall harmonic conjugates. That is, v is a harmonic conjugate of u if u and v satisfy C/R.
Remark:v is a harmonic conjugate of u does not imply u is a harmonic conjugate of v.
Example:u=x2−y2 so v=2xy. Then, u+iv=z2 is an entire function (analytic everywhere). Therefore, v is a harmonic conjugate of u. However, if u were actually a harmonic conjugate of v, then v+iu would be analytic. We can check with C/R that this function is analytic nowhere.
Remark: Suppose u is harmonic on a simply connected domain Ω. Then, u has a harmonic conjugate on Ω. (§115, 8 Ed §104)
Physical problems
§116 (8 Ed §115)
“Physical” configurations are often modelled by solutions of partial differential equations. Generally, we are interested in solving a PDE subject to associated initial/boundary conditions.
For example, (D){Δu=0u∣∂Ω=φin Ω, which means that Δu=0 within Ω and u=ϕ on the boundary. Here, Ω and φ are known and u is unknown. In particular, φ:∂Ω→R. This (D) is called the Dirichlet problem for Laplace’s equation, a.k.a. the boundary problem of the first kind.
A practical application is a heat equation with an insulated boundary. (D) can be solved by finding a u that minimises∫Ω∣∇u∣2dxsuch thatu∣∂Ω=φ. This can be solved by calculus of variations and functional derivatives.
There are also boundary conditions of the second kind, called Neumann boundary conditions. This is (N){Δu=0∂ν∂u=ψin Ω,on ∂Ω where ν is the unit normal function on the boundary. Note that ∂ν∂u=∇u(x)⋅ν(x). In practice, we often have homogeneous Neumann boundary conditions, i.e. ψ=0. This is also referred to as no-slip conditions.
Transformations of harmonic functions
image-20200511143259943
Theorem. If f is conformal and h is harmonic in Λ, then H is harmonic in Ω where H(x,y)=h(u(x,y),v(x,y)).
Proof. Messy in general but straightforward when Λ is simply connected. See §115 (8 Ed §104). □
Example: Take h(u,v)=e−vsinu which is harmonic on the upper half-plane. Define w=z2 on Ω, the first quadrant. Thus, w=u+iv where u=x2−y2 and v=2xy.
image-20200511143934011
Applying this theorem, we know that H(x,y)=e−2xysin(x2−y2) is harmonic on Ω. Note that Dirichlet and Neumann boundary conditions are preserved under conformal transformations (more next lecture). ∘
Lecture 26 — Bubbles, Boundary Transformations
We looked at soap film (last year). The key connection is the Neumann boundary conditions. Recall that harmonic functions can be used to minimise some sort of energy function.
In this case, the soap minimises internal potential energy which is done by minimising the surface area of the bubble. This leads to some interesting behaviour for tetrahedral and cubic wire frames with the edges meeting in the middle (as opposed to spanning the face planes).
Transformations
Suppose f is conformal and C is a smooth (infinitely differentiable) arc in Ω (or on the boundary of Ω with some care). Let Γ=f(C) and H(x,y)=h(u(x,y),v(x,y)).
If we have Dirichlet boundary conditions on Γ (i.e. h(u,v)=φ on Γ), then H(x,y)=φ on C.
If we have homogeneous Neumann boundary conditions on Γ (i.e. ∂n∂h=0 for any normal n to Γ), then ∂N∂H=0 for any normal N to C.
Example: In C (called the w-plane), the function h(u,v)=v=Imw is harmonic. In particular, it is harmonic on the horizontal strip Λ where −π/2<Imw<π/2. We claim that f:z↦Logz maps Ω, the right half-plane, onto Λ conformally.
image-20200513122155515
Then, z=x+iy↦Logz⟹H(x,y)=ln∣z∣+iArgz=ulnx2+y2+iviarctan(y/x)=h(u,v)=arctan(y/x) The boundary of Ω is of the form A={0+δi:δ∈R}. Therefore, f(A)=LogA=ln∣A∣+iArgA=ln∣δ∣±iπ/2 which is exactly the boundary of Λ.
Lecture 27 — Heat
Recall that harmonic functions can be mapped to harmonic functions.
Steady-state temperature in a half-plane
§119 (8 Ed §107).
Let Ω be the upper half-plane. We apply heat to the boundary such that the temperature is 1 between −1 and 1 and 0 everywhere else. We want to find the steady state temperature distribution on Ω.
Fourier’s law of heat conductions tells us that ∂t∂T=∇⋅(−k2∇T)=−k2ΔT. Moreover, steady state tells us that this derivative is 0 so ΔT=0.
So, we want to solve (D)⎩⎪⎪⎨⎪⎪⎧ΔT=0T(x,0)={10∣x∣<1∣x∣≥1in Ω,for x∈R. Because the temperature being added is 1, the temperature on the plane is bounded between 0 and 1. However, allowing exponentially growing functions (in y) will lead to non-physical solutions.
Note that in C (call it the w-plane), h(u,v)=v=Imw is harmonic. Back to (D), we are looking for a bounded solution with limy→∞T(x,y)=0 for all x.
Define Ω~={z:Imz≥0,z=±1}, i.e. Ω and its boundary excluding the discontinuities. Define θ1,θ2,r1,r2 on Ω~ such that z−1z+1=r1exp(iθ1)=r2exp(iθ2) Here, these are defining radial coordinates centred at +1 and −1. r1,r2>0 and 0≤θ1,θ2≤π.
We introduce the transformation w=Logz+1z−1, where Log has a branch cut on the negative imaginary axis, so −π/2<Log≤3π/2. Then, w=Logr2exp(iθ2)r1exp(iθ1)=lnr2r1+i(θ1−θ2) We claim that w maps the interior of Ω onto Λ, the horizontal strip 0<v<π. We can look at points along the boundary of Ω and see where they map to on the boundary of Λ.
image-20200514123307468image-20200514123330279
We have transformed our boundary conditions to a problem which can be solved much easier. We just need to find a function satisfying T∣v=πi=1 and T∣v=0=0. Indeed, v/π is a bounded harmonic function satisfying these constraints. So, w⟹v=ln∣∣∣∣∣z+1z−1∣∣∣∣∣+iArgz+1z−1=Arg(z+1z−1z+1z+1)=Arg((x+1)2+y2x2+y2−1+2iy)=arctan(x2+y2−12y) where 0≤arctan≤π with special care when x2+y2=1. The solution is then π1arctanx2+y2−12y. We can check that this is bounded between 0 and 1. This can be visualised using colour or isotherms of the form T(x,y)=c which are circular arcs like x2+(y−cot(πc))2=csc2(πc).
Lecture 28 — Scale Factor, Poisson’s Integral Formula
Recall that a conformal map preserves angles, orientations and is 1-to-1. However, it can scale points.
Suppose f:z↦w is a conformal map (i.e. analytic and f′(z0)=0). For z near z0 with z=z0, ∣z−z0∣∣f(z)−f(z0)∣≈∣f′(z0)∣⟹∣f(Z)−f(z0)∣≈∣f′(z0)∣∣z−z0∣. Here, ∣f′(z0)∣ is the scaling factor or dilation factor, i.e. the magnitude of the stretching or shrinking effect.
Example:f(z)=z2 at z0=1+i (here, z=x+iy and w=u+iv). Then, u=x2−y2 and v=2xy.
image-20200515123353527
Observe that the tangent lines and angles are preserved under f. The scaling factor is ∣f′(z0)∣=2∣z0∣=22. For a small length near z0, its length will be scaled by a factor of 22. Thus, ℓ′≈22ℓ and also, Area(B)≈(22)2Area(A). This relationship holds regardless of the curves C1 and C2, and for all points z0.
Poisson’s integral formula
§135 (8 Ed 7§124)
image-20200515124433001
Recall that from Cauchy, if f is analytic in and on C0, then for z∈IntC0, f(z)=2πi1∫C0ξ−zf(ξ)dξ. Recall that for z=reiθ, r>0, the inverse point to z relative to the circle C0 is r∗eiθ with r∗ such that r∗r=r02. Also, note that z∗=r∗eiθ=rr02eiθ=re−iθr02=zˉr02=zˉξξˉ for ξ∈C0. Now fix z∈IntC0 and z=0. Note that ∫C0ξ−z∗f(ξ)dξ=0becauseξ↦ξ−z∗f(ξ) is analytic in and on C0 since z∗∈ExtC0 by the Cauchy integral formula. Using this in the above expression, f(z)I=2πi1∫C0I(ξ−z1−ξ−z∗1)f(ξ)dξ=(ξ−zξ−ξ−z∗ξ)ξ1=(ξ−zξ−1−ξˉ/zˉ1)ξ1=(ξ−zξ−zˉ−ξˉzˉ)ξ1=(∣ξ−z∣2ξξˉ−zzˉ)ξ1 Recall that z=reiθ. Put ξ=r0eiϕ for 0≤ϕ≤2π. Then, dξ=r0ieiϕdϕ. Substituting this into the integrand, I=∣ξ−z∣2r02−r2⋅r0eiϕ1. This is mostly in nice radial coordinates except for the ∣ξ−z∣ part. Can we rewrite this? Consider the diagram below.
image-20200515183257053
We can appeal to the cosine rule which tells us that ∣ξ−z∣2=r02+r2−2r0rcos(ϕ−θ). Plugging this back into f(z), we have f(z)=f(reiθ)=2πi1∫02π∣ξ−z∣2r02−r2⋅r0eiϕf(r0eiϕ)r0ieiϕdϕ=2πr02−r2∫02πr02−2r0rcos(ϕ−θ)+r2f(r0eiϕ)dϕ
Recall that the real part of a complex analytic function is harmonic. Taking the real part of the above expression and given “nice enough” Φ(r0,ϕ) defined on the boundary C0 of Br0 a (in fact, the) solution of the Dirichlet problem {Δu=0u∣∂Br0=Φ(r0,ϕ)in Br0,on ∂Br0, is given by u(r,θ)=2π1∫02πP(r0,r,ϕ,θ)r02−2r0rcos(ϕ−θ)+r2r02−r2Φ(r,ϕ)dϕ. The middle fraction of the integrand is called the Poisson kernel, denoted P(r0,r,ϕ,θ) and is due to Poisson (1885). This is valid for r=0 too!
Lecture 29 — Sequences and Series
The first part of this lecture was finishing the Poisson integral formula and is written in the previous document.
Complex sequences & series
§60 (8 Ed §55)
Compare this to the situation in R. Formally, a sequence is a function f:N→C (or N0→C), n↦zn, written as {zn}.
Definition (Limit). We say limn→∞zn=z or “{zn} converges to z” if and only if given ϵ>0, there exists N∈N such that n>N implies ∣zn−z∣<ϵ. As in R, this definition does not help us find a limit.
Definition (Series). Formally, ∑n=0∞zn for zn∈C converges as a series if and only if the associated sequence of partial sums {sn} converges as a sequence, where sn=∑k=0nzk.
A typical question we might ask is does ∑zn converge?
An easy test that a series does not converge is the n-th term test, if ∑zn converges, then zn→0 (converse does not hold). Once we know it converges, we know that ∑zn is just a complex number.
Remark: A sequence {zn} is bounded if there exists M such that ∣zn∣<M for all n.
Convergent implies that a sequence is bounded (converse does not hold, see {(−1)n}).
Definition (Absolute convergence). We say that ∑zn converges absolutely if and only if ∑∣zn∣ converges. Absolute convergence implies convergence (converse does not hold, see ∑(−1)n/n).
Definition (Remainder). Given ∑n=0∞zn, set sn=∑k=0nzk as the partial sums. Then, let ρn=∑k=n+1∞zk as the tail or remainder.
Theorem.sn→s if and only if ρn→0.
Example: We claim that ∑n=0∞zn=1/(1−z)=S for ∣z∣<1.
Proof.snzsN⟹(1−z)snsn⟹ρn=s−sn=1+z+⋯+zn=z+z2+⋯+zn+1=1−zn−1=1−z1−zn+1=1−zzn+1 Since ∣z∣<1, ∣ρn∣→0 as n→∞ which implies that sn→s. □
Remark: As in R, we can do simple operations on convergent series:
If ∑an and ∑bn converge, then ∑(an±bn) converges to ∑an±∑bn.
If ∑an converges, then ∑(λan) converges to λ∑an, for λ∈C.
Definition (Power series). A power series centred at z0 is a series of the form n=0∑∞an(z−z0)n. This series has a radius of convergence. R. That is, it converges absolutely within this radius, diverges outside, and may converge or diverge on the boundary. (These can be checked with the ratio test.)
If R=0, the series converges only at z0. If R=∞, it converges on all of C.
Lecture 30 — Taylor Series and Taylor’s Theorem
Theorem (Taylor’s Theorem). Let f be analytic on BR(z0). Then, f has a power series representation on BR(z0) for ∣z−z0∣<R as f(z)=n=0∑∞an(z−z0)nwherean=n!f(n)(z0). Note that this is an incredibly powerful statement; unlike R, the function is given by the power series. However, the analytic condition is very restrictive.
For the case of z0=0, this is called the Maclaurin series.
Example: What is the Maclaurin series of f(z)=ez? f is entire which means that R=∞. Also, f(n)(0)=e0=1 for all n. Thus, ez=n=0∑∞n!zn.Proof. Assume z0=0, otherwise translate. Choose z∈BR, let ∣z∣=r and fix r0∈(r,R). Set C=Cr0, positively oriented.
image-20200530163746259
Cauchy integral formula tells us that the value of this analytic function at z is just f(z)=2πi1∫Cξ−zf(ξ)dξ. Looking at the integrand, ξ−z1=ξ1(1−z/ξ1)=ξ1(n=0∑N−1(z/ξ)n+1−z/ξ(z/ξ)N)=n=0∑N−1ξn+1zn+(ξ−z)ξNzN The integral becomes f(z)=2πi1∫Cξ−zf(ξ)dξ=n=0∑N−12πi1∫Cξn+1f(ξ)zndξ+ρN−1(z)2πizN∫C(ξ−z)ξNf(ξ)dξ We call the rightmost part ρN−1(z). We can use Cauchy’s integral formula along with the extended Cauchy integral formula (which gives us derivatives), we get f(z)=n=0∑N−1n!f(n)(0)zn+ρN−1(z).
At this point, we’d like to show that limN→∞ρN−1(z)=0. Note that ξ∈C⟹∣ξ∣=r0. Suppose there exists MN such that we can bound the integrand with ∣∣∣∣∣(ξ−z)ξNf(ξ)∣∣∣∣∣≤MNon C. Then, we would be able to say ∣ρN−1(z)∣≤2πrNMNℓ(C)=r0rNMN. To find such an MN, f is analytic implies ∣f∣ is continuous. C is closed and bounded, so extreme value theorem (even just of a single parameter along the curve) implies there exists μ such that ∣f∣≤μ on C. Furthermore, ∣ξ∣N=r0N. Using reverse triangle inequality, ∣ξ−z∣≥∣∣ξ∣−∣z∣∣=r0−r (note direction of inequality because this is in the denominator).
Putting this all together, MN=roN(r0−r)μ suffices for what we want. Then, ∣ρN−1(z)∣≤r0rNMN=r0N(r0−r)r0rNμ=r0−rμr0(r0r)N Therefore, ∣ρN−1(z)∣→0 as N→∞ because r/r0<1. □
Remark: In R, a Taylor series might converge but fail to converge to the function (see Lecture 16).
To calculate radius of convergence of a power series ∑an(z−z0)n, we can use the ratio test. First, compute Λ=n→∞lim∣∣∣∣∣anan+1∣∣∣∣∣⟹R=Λ1. Conventionally, Λ=0⟺R=∞ and Λ=∞⟺R=0. This is easier because the fraction is the regular ratio test ordering.
Example:f(z)=ez. For this function, R=∞ because ez=n=0∑∞n!zn⟹Λ=n→∞lim1/n!1/(n+1)!=n→∞limn+11=0.Example:f(z)=z2e3z and find the Maclaurin series. Note that f is entire, so e3z⟹z2e3z=n=0∑∞n!3nzn=n=0∑∞n!3nzn+2=n=2∑∞(n−2)!3n−2zn The last step is because we need a power series to have powers of exactly zn.
Lecture 31 — Laurent Series, Residues at Poles
Example: Consider the Maclaurin series of f(z)=1/(1−z). Note that f is analytic for ∣z∣<1 and indeed, on C∖{1}. Moreover, f(n)(z)=(1−z)n+1n!,z=1. In particular, f(n)(0)=n!. This tells us that the Taylor series of f at 0 is given by Tf,0(z)=n=1∑∞zn. This has Λ=limn→∞1/1=1 which implies R=1. Taylor’s theorem implies that Tf,0 converges tof for ∣z∣<1.
Some things to note here. This is exactly the geometric series formula, which says 1−z1=∑n=0∞zn for ∣z∣<1. The series converges “out to the first singularity”, here 1.
Example: Find the Maclaurin series of 1/(2+4z) on C∖{−1/2}. Note that we can manipulate it into the familiar form above. 2+4z1=1+2z1/2=1−(−2z)1/2=21n=0∑∞(−2z)n=n=0∑∞(−1)n2n−1zn,for ∣−2z∣<1.Example:f(z)=(1+2z2)/(z3+z5). Some clever algebra tricks lead to f(z)=z31(1+z22+2z2−1+z21)=z31(2−1+z21) which is analytic on C∖{0,±i}. Note that this wont converge around 0. For ∣z∣<1, 1/(1+z2)=∑n=0∞(−1)nz2n. f(z)=z31(2−(1−z2+z4−⋯))=z31+z1+−z+z3+⋯. Although this is not defined at 0, it is still useful. It is almost a power series but has some terms involving negative exponents of z.
Laurent series
By Weierstrass (1841) / Laurent (1843).
image-20200530181747176
Theorem. Let f be analytic on the open annulus (donut-like shape) A={z:r1<∣z−z0∣<r2}, centred at z0. Let C be a positively oriented, simple, closed curve in A, and let z∈IntC. Then, f has a series representation, the Laurent series, f(z)=n=0∑∞an(z−z0)n+n=1∑∞(z−z0)nbn on A, where an=2πi1∫C(ξ−z0)n+1f(ξ)dξ,bn=2πi1∫C(ξ−z0)−n+1f(ξ)dξ. The bi’s are essentially coefficients of terms of the series with negative exponents (notice the negative power in the integrand the division by (z−z0)n).
Alternatively, this can be written as f(z)=n=−∞∑∞cn(z−z0)n,where cn=2πi1∫C(ξ−z0)n+1f(ξ)dξ. In particular with a Laurent series, b1=2πi1∫C(ξ−z0)−1+1f(ξ)dξ=2πi1∫Cf(ξ)dξ. This means that if we know the Laurent series, we know the value of this contour integral. This is so important that is has a name, the residue of f at z0, denoted resz=z0f(z). The residue of an analytic function is zero. For example, 1/z has residue 1 at the origin but 0 elsewhere. Here, we are also concerned about the very particular behaviour of the coefficient of z−1.
Notes:
If f is analytic on and inC, then all the bi’s are zero.
The case where r1=0 and r2=∞ are both allowed.
The Taylor and Laurent series are unique. If we have both on an annulus, they coincide. (§72, 8 Ed §66-67).
Example: Find the Laurent series of e1/z. We can just use change of variables with the exponential Taylor series to get e1/z=n=0∑∞n!zn1=1+z1+2!z21 for all ∣z∣>0. Here, there is only one term in the Taylor series part, which is 1. Looking at the coefficient of z−1, we know that 2πi1∫Ce1/ξdξ=b1=1 for all circles about the origin.
Example 7: Compute I=∫Cz(z−1)5z−2dz where C=2eiθ for θ∈[0,2π]. Note that the integrand f(z)=z(z−1)5z−2 is analytic everywhere except for 0 and 1.
Residues and poles
Definition. We say f:Ω→C has a singularity at z0 if
f is not analytic at z0, and
given any ϵ>0, there exists z1∈Bϵ(z0) such that f is analytic at z1 (i.e. at some point arbitrarily close to z0).
In particular, we say f has an isolated singularity at z0 if f is analytic on Bϵ(z0)∖{z0} for some ϵ>0.
Examples:
The function f(z) from Example 7 above has isolated singularities at 0 and 1.
Logz has non-isolated singularities on the negative real axis including the origin.
sinz/z has an isolated singularity at 0.
1/sin(π/z) has isolated singularities at z=1/k for k∈Z and a non-isolated singularity at 0.
Lecture 32 — Cauchy Residue and Product, Singularities
Remark:f(z)=1/(z−i)2 is already a Laurent series about i with b2=1 and other coefficients zero.
Cauchy product of series
§73 (8 Ed §67)
Theorem. Suppose f(z)=∑n=0∞anzn and g(z)=∑n=0∞bnzn converge. Then, (fg)(z)=∑n=0∞cnzn where cn=∑k=0nakbn−k.
Example: Consider for ∣z∣<1, 1+zez=ez1−(−z)1=(1+z+2!z2+⋯)(1−z+z2−⋯)=1+(−1+1)z+(1+1/2−1)z2+⋯=1+2z2+⋯Remark: We can take term-by-term derivatives and integrals of series (see §71 or 8 Ed §65).
Cauchy residue theorem
Theorem. Suppose C is a positively oriented simple closed curve and that f is analytic in and on C except at finitely many isolated points {z1,z2,…,zk}. Then, ∫Cf(z)dz=2πij=1∑kz=zjresf(z).Proof. Take disjoint positively oriented circles C1,…,Ck around each z1,…,zk with disjoint interiors, all lying in the interior of C. Then, C,C1,…,Ck form the boundary of a multiply-connected domain Ω. Then, f is analytic on Ω and its boundary, so the Cauchy-Goursat extension implies that ∫Cf(z)dz=j=1∑k∫Cjf(z)dz=2πij=1∑kresz=zjf(z).□Example: Apply this to example 7 from last lecture. Note that f is analytic on C∖{0,1} and C is the circle of radius 2 around the origin. I=∫Cz(z−1)5z−2dz. There are two methods we can try. First, look close to zero with 0<∣z∣<1 to see that f(z)=−1−z1⋅z5z−2⟹z=0resf(z)=(−1)(1+z+z2+⋯)(5−2/z)=2 Now, we also need a Laurent series around 1 (which is even less fun). We can write f(z)=z−15(z−1)+3⋅1−(−(z−1))1=(5+z−13)(1+(−(z−1))+(−(z−1))2+⋯)⟹z=1resf(z)=3 Because the only coefficient of (z−1)−1 comes from the 3/(z−1).
Alternatively, we can use partial fractions. Note that f can be decomposed into parts which are analytic on C∖{1} and C∖{0}, respectively. f(z)=z−13+z2 Therefore, this is its own Laurent series around the points 0 and 1. The other fraction is analytic near the other singularity, so does not affect the Laurent series. Therefore, resz=0f(z)=2resz=1=3. Note that the expression 3/(z−1) describes, in some sense, the prototypical singularity of a whole family of functions.
Classifying isolated singularities
If z0 is an isolated singularity of f (i.e. f analytic on a deleted neighbourhood), then there exists R such that f has a Laurent series expansion on BR(z0)∖{z0} given by f(z)=n=0∑∞an(z−z0)n+n=1∑∞bn(z−z0)−n. There are three cases to consider.
Case I: If bn=0 for all n, then the singularity is removable. Setting f(z0)=a0 makes f analytic on all of BR(z0).
Case II: At least one but finitely many of the bn’s are nonzero. Such a point called a pole. Define m=max{n:bn=0}. m is the order of the pole and m=1 is a simple pole.
Case III: Infinitely many of the bn’s are nonzero. This is an essential singularity.
Lecture 33 — Picard’s Theorem, More Singularities
Recall that isolated singularities fall into three cases. These are points in the complex plane where a function runs into trouble, but is fine in a punctured disk around that point. The three cases are removable, poles, and essential singularities.
These can be defined by, in a deleted ball Br(z0)∖{z0}, (Case I)(Case II)(Case III)n≥0∑an(z−z0)nn≥−N∑an(z−z0)n,N>1,aN=0n=−∞∑∞an(z−z0)nExample:f(z)=sin(z)/z is a function f:C∗→C with a singularity at z=0. We can expand sin into its Taylor series to show that sinz=n=0∑(2n+1)!(−1)nz2n+1⟹f(z)=z1(z−3!z3+⋯). The powers of z are all non-negative which means the function is analytic on the deleted ball, so the singularity is removable. If we wanted to define an entire function, we could say f^=f on C∗ and f^(0)=a0=1. Also, the residue is 0.
Example:f(z)=z2−1z2−1=1 which is f:C∖{±1}→C. There are (clearly) two removable singularities at ±1. We can’t equate this function to 1, but we could define f^:C→C with f^(z)=1. This ‘fixes’ the singularity in such a way that you could never tell it was there.
Example:f(z)=1/z4 is again defined on C∗. There is an isolated singularity at z=0 and this is a pole of order 4.
Example:f(z)=1/z4+1/z2 is the same story. The pole is still of order 4; we don’t care about the higher order terms.
Example:f(z)=sinh(z2)/z7, with f:C∗→C. Remember that sinhz=∑n=0∞(2n+1)!z2n+1. Then, f(z)=z71n≥0∑(2n+1)!z4n+2=n≥0∑(2n+1)!z4n−5=z51+z13!1+⋯ We have a pole of order 5 and the residue is 1/3! in this case.
Example:f(z)=e1/z. In this case, f(z)=n=0∑∞n!(1/z)n=1+z1+z212!1+⋯ so we have an essential singularity at z=0 and the residue is 1.
Picard’s theorem
(This is the big version.)
Theorem. Suppose f has an essential singularity at z0. Let R>0 be arbitrarily small. Then, on BR(z0)∖{z0}, the function f attains every value in C infinitely often, with the possible exception of one point.
Theorem. If f has a pole of order N at z0, then in Br(z0)∖{z0} we can write f(z)=(z−z0)Nϕ(z) with ϕ analytic in Br and ϕ(z0)=0. Moreover, resz=z0f(z)=(N−1)!ϕ(N−1)(z0).
Lecture 34 — Zeros, Poles and Cauchy Principal Value
What is an example of an essential singularity? Consider f(z)=1/(1−z−1) for z=0. What is the singularity at 0? Observe, f(z)=1−z11=z−1z=1−z−z=−z(1+z+z2+⋯)for0<∣z∣<1. Thus, the singularity is removable because the Laurent series has only non-negative powers. Taking f(0)=0 extends f to an analytic function on C∖{1}. Moreover, this means the Laurent series is only value out to the singularity at z=1.
Recall that if f has a pole of order n at z0 and we can write f(z)=ϕ(z)/(z−z0)n for ϕ analytic in Br(z0) and ϕ(z0)=0, then Resz=z0f=(n−1)!ϕ(n−1)(z0). In particular, if z0 is a simple pole, then Resz=z0f=ϕ(z0).
Example: Consider f(z)=(z+i)/(z2+9). f is analytic on C∖{±3i}, and ±3i are isolated singularities. Near z=3i, we can write f(z)=z−3iϕ(z)whereϕ(z)=z+3iz+i noting that ϕ is analytic and non-zero near 3i. The theorem tells us that Resz=3if=ϕ(3i)=4i/6i=2/3. We can do the same thing at −3i as well.
Example:f(z)=(z3+2z)/(z−i)3. Observe that this is analytic except at i. Near i, f(z)=(z−i)3ϕ(z)andϕ(z)=z3+2z with ϕ analytic and ϕ(i)=0. Using the same theorem, Resz=if=ϕ′′(i)/2!=3i.
Zeros of functions
§82 (8 Ed §75)
We can generalise the theorem to talk about zeros of functions, because poles are essentially zeros of the denominator.
Lemma. If f is analytic at z0, then f has a zero of order m at z0 if {f(j)(z0)=0f(m)(z0)=0.for j=0,…,m−1, andExample:f(z)=(z−i)4(z−4) has a zero of order 4 at i and a simple zero (i.e. zero of order 1) at 4.
Theorem.f is analytic at z0 and has a zero of order m at z0 if and only if f(z)=(z−z0)mg(z) where g is analytic and g(z0)=0.
Zeros & poles
§83 (8 Ed §76)
Theorem. Suppose p and q are analytic at z0, p(z0)=0, and q has a zero of order m at z0. Then, p/q has a pole of order m at z0.
Example:p(z)=1 and q(z)=z(ez−1). We know that p/q has an isolated singularity at 0. p is analytic and non-zero everywhere (obviously). We can check that q(0)=0,q′(0)=0,q′′(0)=1=0. Thus, p/q has a pole of order 2 at 0.
Theorem. Let p,q be analytic at z0. If p(z0)=0 and q(z0)=0, then p/q has a simple pole at z0 and Resz=z0qp=q′(z0)p(z0). Note that there exist higher-order analogues, but they become messy.
Cauchy principal value
The main application of this is contour integrals. In R, recall that ∫−∞∞f(x)dx=m1→∞lim∫−m10f(x)dx+m2→∞lim∫0m2f(x)dx. Note we can replace the split 0 with any fixed c∈R. If the limits on the right exist, then we say the integral exists with the given value.
We cannot in general replace the right-hand side with limm→∞∫−mmf(x)dx. If we do this anyway, it defines the Cauchy principal value (PV) integral.
Example: Let’s look at this in practice. The below improper integral is undefined, because ∫−∞∞xdx=m1→∞lim∫−m10xdx+m2→∞lim∫0m2xdx=m1→−∞lim−m12/2+m2→∞limm22/2. However, the principal value is PV∫−∞∞xdx=m→∞lim∫−mmxdx=m→∞lim[2m2−2m2]=0.Question: When does PV∫−∞∞f=∫−∞∞f? One case is for even functions or non-negative functions. Specifically, if f is even (so f(x)=f(−x) for all x∈R), then ∫0∞f(x)dx=21∫−∞∞f(x)dx=21PV∫−∞∞f(x)dx and these integrals converge or diverge together.
Lecture 35 — Cauchy Principal Value Examples
What’s the connection between these integrals and complex analysis? We signed up for the square root of −1 and that’s what we have fun with.
Suppose f is even and “nice” on R and we want to evaluate ∫−∞∞f(x)dx.
Suppose f is analytic in and on C=Γ1+Γ2, possibly except for isolated singularities in IntC. We know that ∫Cf=∫Γ1f+∫Γ2f. Hopefully, we can evaluate the left-hand side with the residue theorem (sum of residues at singularities). Then, if we let R→∞, the integral over Γ2 is what we want: PV∫−∞∞f=∫−∞∞f because we assumed f is even.
It remains to deal with limR→∞∫Γ1f. Hopefully, we can estimate this to show this goes to zero, for example via M-ℓ.
Example: Evaluate I=∫0∞x2/(x6+1)dx. Note that f(z)=x2/(x6+1) is even and continuous. As x→±∞, f∼1/x4 so I converges by the p-test since p>1. Moreover, the complex function f(z)=z2/(z6+1) is analytic on C except for 6 zeros of z6+1, i.e. (−1)1/6.
f is analytic in and on C=Γ1+Γ2 except for the 3 zeros in the upper half-plane. These singularities are z1=eπi/6, z2=i, and z3=e5πi/6. The residue theorem implies that ∫Cf(z)dz=2πij=1∑3Resz=zjf. We can see that f has the form p/q and at each zj, p(zj)=0, q(zj)=0, and q′(zj)=0. Thus, each singularity is a simple pole. From the theorem last lecture of Resz0p/q=p(z0)/q′(z0), we have ∫Cf(z)dz=2πij=1∑3(z6+1)′z2∣∣∣∣∣z=zj=2πij=1∑36zj5zj2=2πi(6i1−6i1+6i1)=3π. Remember that we’re looking for ∫Cf=∫Γ1f+∫Γ2f. We’ve got the left-hand side now. As the radius R→∞, ∫Γ2f→2I because we’re looking for the integral from 0 to ∞. Now, we want to show that ∫Γ1f→0 as R→∞. We claim that ∣∫Γ1f∣≤MRℓR where ℓR is the length of Γ1, which is πR. Also, MR=z∈Γ1max∣f(z)∣≤∣z∣=Rmax∣∣∣∣∣z6+1z2∣∣∣∣∣≤∣z∣=Rmax∣z∣6−∣1∣∣z∣2≤R6−1R2 where the denominator comes from the inverse triangle inequality. Therefore, R→∞limMRℓR=R→∞limR6−1πR⋅R2=0. Finally, ∫Cf=∫Γ1f+∫Γ2f⟹3π=2I+0⟹I=6π.Example:I=∫0∞sinx/xdx. Firstly, notice that this is an improper integral because the integrand is undefined at 0 and the bound is infinity. Near 0, the integrand approaches 1. Approaching ∞ is an absolute pain to estimate (via real analysis).
The good news is sinx/x is even. If I exists, then I=21∫−∞∞xsinxdx=21PV∫−∞∞xsinxdx. We need to be careful because our theorems don’t work across singularities, despite 0 being a removable singularity. This gives is a 4-part contour.
A standard trick when working with trig functions is take an exponential and use the real or imaginary part as needed. Take f(z)=eiz/z which is analytic on C∗. Cauchy tells us that the integral across the whole contour is 0. Thus, 0=∫γ1+Cρ+γ2+CRf=∫γ1f+∫Cρf+∫γ2f+∫CRf=I1+I2+I3+I4. Let the integrals of the right-hand side be I1,…,I4 respectively. Looking at I1 and I3, I1=∫−R−ρxeixdx=−∫ρR−1e−iw(−w)dwandI3=∫ρRxeixdx. Combining these, I1+I3=∫ρRxeix−e−ixdx=2i∫ρRxsinxdx⟶2i∫0∞xsinxdx after taking the limits ρ→0 and R→∞. Therefore, ∫0∞xsinxdx=−2i1(ρ↓0limI2+R→∞limI4) assuming these limits exist. Looking at I2, we substitute z=ρeiθ and θ:π→0 (direction) with appropriate change of variables formula. I2=∫Cρzeizdz=∫π0ρeiθeiρeiθiρeiθdθ=−i∫0πeiρeiθdθ. Since ∣ρeiθ∣=ρ, the expression eiρeiθ→1 as ρ→0uniformly for θ∈[0,π]. From real analysis, this means that the convergence radius δ is independent of θ, depending on ϵ. This means we can write ρ↓0limI2=−i∫0πρ↓0lim(eiρeiθ)dθ=−i∫0πdθ=−iπ.
Finally, I4→0 as R→∞ by Jordan’s lemma (next lecture) because we cannot use M-ℓ in the usual way. Therefore, ∫0∞xsinxdx=−2i1(ρ↓0limI2+R→∞limI4)=−2i1[−iπ+0]=2π.
Lecture 36 — Jordan’s Lemma and Rouché’s Theorem
We continued the example of ∫0∞sinx/xdx from the previous lecture.
Lemma (Jordan). Suppose f is analytic on the closed upper half plane excluding some disc, {z:Imz≥0}∩{z:∣z∣≥R0}, which satisfies ∣f(z)∣≤M/Rβ for some M,β>0 on the outside arc of ΓR={Reiθ:R>R0,0≤θ≤π}. Then, for all α>0, R→∞lim∫ΓReiαzf(z)dz=0.Proof. Fairly straightforward apart from one estimate. □
Argument principle & Rouché’s theorem
§93–94 (8 Ed §86–87) – Rouche’s Theorem
The argument principle says if f is analytic in and on C, a simple closed curve, except possibly for poles inside C, then the rate of change of the argument along the curve is ΔCargf=2π(z⋅p), where z is the number of zeros inside C counting multiplicity, and p is the number of poles counting sum of orders.
Theorem (Rouché’s theorem). Let f and g be analytic in and on a simple closed curve C (orientation irrelevant). Suppose ∣g(z)∣<∣f(z)∣ for all z on this curve. Then, f and f+g have the same number of zeros (counting multiplicity) inside C.
For example, (z−i)2(z+i)3 has 5 zeros counting multiplicity. Make sure to check conditions before applying theorems.
Example: How many zeros of h(z)=z7−4z3−1 lie inside the unit circle? Let f(z)=−4z3 and g(z)=z7+z−1, noting that both are polynomials and entire. In general for picking f and g with an annulus, take the larger power outside the annulus. This case is a bit more special. First, we have ∣f∣=4 on C and by the triangle inequality, ∣g(z)∣≤∣z7∣+∣z∣+∣−1∣=3<4=∣f(z)∣. Because ∣g∣<∣f∣ on C, Rouché’s theorem implies that f and f+g=h have the same number of zeros inside C, namely 3 because f has a 3-fold zero at 0.
Lecture 37 — Additional
Proof of Jordan’s lemma
Lemma (Jordan). Suppose f is analytic on the closed upper half plane excluding some disc, {z:Imz≥0}∩{z:∣z∣≥R0}, which satisfies ∣f(z)∣≤M/Rβ for some M,β>0 on the outside region ΓR={Reiθ:R>R0,0≤θ≤π}. Then, for all α>0, R→∞lim∫ΓReiαzf(z)dz=0.Proof. On ΓR, we have z=Reiθ and dz=iReiθ. Substituting in, ∣∣∣∣∣∫ΓReiαzf(z)dz∣∣∣∣∣=R∣∣∣∣∣∫0πeiαReiθf(Reiθ)dθ∣∣∣∣∣≤R∫0π∣∣∣∣eiαReiθf(Reiθ)∣∣∣∣dθ. Expanding eiθ in the inner exponent and taking the modulus gives us =R∫0π∣∣∣∣eiα(Rcosθ+iRsinθ)f(Reiθ)∣∣∣∣dθ=R∫0πe−αRsinθ∣∣∣f(Reiθ)∣∣∣dθ. Now we use the bound assumption on f and symmetry of the integrand, ≤Rβ−1M∫0πe−αRsinθdθ=Rβ−12M∫0π/2e−αRsinθdθ. Note that sinθ≥2θ/π for 0≤θ≤π/2 (can be proven via simple calculus). Using this, we have Rβ−12M∫0π/2e−αRsinθdθ≤Rβ−12M∫0π/2e−2αRθ/πdθ=Rβ−12M2αRπ(1−e−αR)=αRβMπ(1−eαR) which we can see goes to 0 as R→∞. □
Trig substitutions in integrals
To solve integrals of the form ∫02πf(sint,cost)dt we can try the substitutions z=cost+isint,z−1=cost−isint with the motivation being z=eit for 0≤t≤2π. Then we just need to integrate around the unit circle. This gives us cost=21(z+z−1)andsint=2i1(z−z−1) which implies that dz=(−sint+icost)dt=izdt.Example: Find I=∫02π1/(2+cost)dt. Write z=eit and using the substitution above, dt=izdtandcost=21(z+z−1). For C={eit,0≤t≤2π}, we have I=∫C2+1/2(z+z−1)1/(iz)dz=−i∫C2z+1/2(z2+1)dz=−2i∫Cz2+4z+1dz. To evaluate the last integral, note that the integrand is analytic except for zeros of the denominator, −2±3. Only −2+3 lies inside C, so by residue theorem I=(−2i)2πiz=−2+3Resz2+4z+11=z=−2+3Resz−(−2+3)1/(z−(−2−3)). The numerator is analytic and non-zero near −2+3 so the integrand has a simple pole at −2+3. The residue is calculated by z=−2+3Resz2+4z+11=ϕ(−2+3)=231 and hence, I=(−2i)2πi/(23)=2π/3.
Laurent series of 1/sinhz
We will try to calculate the Laurent series of 1/sinhz at 0. This has singularities where sinhz=0 which is exactly for z=nπi, n∈Z. Thus, 1/sinhz has a Laurent series on 0<∣z∣<π. Then, writing out the series, sinhz1=z+z3/3!+z5/5!+⋯1=z11+z2/3!+z4/5!+⋯1. For ∣z2/3!+z4/5!+⋯∣<1, we can use the geometric series formula as long as ∣z∣ is sufficiently small. sinhz1=z1[1−(3!z2+5!z4+⋯)+(3!z2+5!z4+⋯)2−⋯] In particular, there is a pole of order 1 at 0 with z=0Ressinhz1=1.
Laurent series of cotz/z2
We start with cotz=cosz/sinz and the usual Taylor series of sin and cos. g(z)=z2cotz=z21sinzcosz=z21(∑n=0∞(−1)nz2n+1/(2n+1)!∑n=0∞(−1)nz2n/(2n)!)=z21(z−z3/3!+z5/5!+⋯)(1−z2/2!+z4/4!−⋯)=z31(1−z2/3!+z4/5!+⋯)(1−z2/2!+z4/4!−⋯) Above, we expanded the power series of sin and cos, then factored a z out of the denominator with the goal of using the geometric series formula. Continuing, z31(1−z2/3!/+z4/5!+⋯)(1−z2/2!+z4/4!−⋯)=z311−(z2/3!+z4/5!+⋯)(1−z2/2!+z4/4!−⋯). The tail of the denominator series converges (why?) and has modulus less than 1 (really?) and so we can write this as a geometric series. Expanding a few terms is sufficient to determine the residue. g(z)=z31(1−z2/2!+z4/4!−⋯)n=0∑∞(z2/3!+z4/5!+⋯)n=z31(1−z2/2!+z4/4!−⋯)(1+(z2/3!+⋯)+(z2/3!+⋯)2+⋯)=z31(1+(3!1−2!1)z2+⋯) Looking at the fractions, 1/6−1/2=−1/3 so the residue at 0 is −1/3. Anything more than this is going to be very hard.