My Favorite Proofs from Analysis1

I've encountered a lot of proofs from MATH254, an introductory real analysis course. From my experience, most of the knowledge vanished the moment I walked out of the final exam room. But some proofs are just so elegant that forgetting them pains me.

This post is a collection of the neatest ones from my perspective. Besides reconstructing proofs, I also try to explain them in my own words to help you and my future self follow the line of thought.

Triangle inequality (In R2\mathbb{R}^2)

Probably the most important inequality in this course.

∣x+y∣≤∣x∣+∣y∣|x+y|\leq|x|+|y|

Proof

First, we prove the claim: ∣x∣≤a  ⟺  −a≤x≤a |x|\leq a \iff -a\leq x\leq a

Here I only prove ‘‘⇒"direction.The other direction can be easily obtained by "opening" the absolute value.Case1. x≥0x=∣x∣≤a,x≥0⇒a≥0⇒−a≤0≤x⇒−a≤x≤aCase2. x<0−x=∣x∣≤a⇒x≥−a⇒x<0≤a⇒−a≤x≤a\text{Here I only prove } ``\Rightarrow" \text{direction.}\\ \text{The other direction can be easily obtained by "opening" the absolute value.}\\ \begin{aligned} &\text{Case1. } x\geq 0 \\ &x = |x|\leq a, x\geq0 \Rightarrow a\geq0\Rightarrow -a \leq 0\leq x\\ \Rightarrow &-a \leq x \leq a\\ &\text{Case2. } x\lt 0 \\ & -x =|x|\leq a \Rightarrow x\geq-a\Rightarrow x<0\leq a\\ \Rightarrow &-a \leq x \leq a\\ \end{aligned}\\

For the triangle inequality:

∣x∣≤∣x∣⇒−∣x∣≤x≤∣x∣∣y∣≤∣y∣⇒−∣y∣≤y≤∣y∣−∣x∣−∣y∣≤x+y≤∣x∣+∣y∣⇒−(∣x∣+∣y∣)≤x+y≤∣x∣+∣y∣⇒∣x+y∣≤∣x∣+∣y∣ by the claim above □\begin{aligned} |x|&\leq |x| \Rightarrow -|x| \leq x\leq|x|\\ |y|&\leq |y| \Rightarrow -|y| \leq y\leq|y|\\ -|x|-|y|&\leq x+y \leq|x|+|y|\\ \Rightarrow -(|x|+|y|)&\leq x+y \leq|x|+|y|\\ \Rightarrow |x+y| &\leq |x|+|y| \text{ by the claim above} \ \square \end{aligned}

Notes

The very obvious ∣x∣≤∣x∣|x|\leq|x| may be hard to come up with. When you have an equality, think about inequalities it may give you. The generalization for Rn\mathbb{R^n} can be proved by induction.

Q\mathbb{Q} is dense in R\mathbb{R}

That is, for any x,y∈R,x<y,∃q∈Qx, y \in \mathbb{R}, x<y, \exists q \in \mathbb{Q} such that x<q<yx < q < y . Equivalently, there are infinite rational numbers between any two real numbers.

Proof

Let n∈N,0<1n<y−x  ⟺  n>1y−xn\in\mathbb{N},0<\frac{1}{n} < y-x \iff n>\frac{1}{y-x} by the Archimedean property.

Consider the set S={k∈N:kn<x} S = \{ k \in \mathbb{N}: \frac{k}{n}\lt x \}. This set is finite, so its complement is infinite, non-empty especially. By the well-ordering principle, the set R∖S={k∈N:kn>x}\mathbb{R}\setminus S = \{k\in\mathbb{N}:\frac{k}{n}>x\} has a least element.

Let mm be the smallest natural number with mn>x\frac{m}{n} > x, especially m−1n<x\frac{m-1}{n}< x. m,n∈N,mn∈Qm, n \in \mathbb{N}, \frac{m}{n} \in \mathbb{Q}.

Claim: x<mn≤y x<\frac{m}{n}\leq y is the number we want.

mn=m−1n+1n<x+y−x=ymn>x by our choice of m⇒ x<mn<y □\begin{aligned} &\frac{m}{n} =\frac{m-1}{n} + \frac{1}{n} < x+y-x =y\\ &\frac{m}{n} >x \text{ by our choice of m}\\ \Rightarrow &\ x<\frac{m}{n}<y \ \square \end{aligned}

Notes

The choice of 1n<y−x\frac{1}{n}<y-x in the first step seems mysterious, but everything follows that is easy to understand. The reason why we made this choice is: 1n\frac{1}{n} is the step size, if it's greater than y−xy-x, assume z<x,z+1nz<x, z+\frac{1}{n} may be greater than yy. That is, we may go past yy directly, which is bad for finding a number between x,yx, y.

Of course, the set S={k∈N:kn<x}S =\{k\in\mathbb{N}:\frac{k}{n} \lt x\} may be empty ( 1n>x\frac{1}{n}\gt x ), but that doesn't matter. R∖S\mathbb{R}\setminus S is "more" infinite. The well-ordering principle holds as long as R∖S\mathbb{R}\setminus S is non-empty. Using infinity to show non-emptiness may seem like an overkill, but interestingly, this kind of "overkill" happens all the time. It's easier to get information from a stronger statement.

We require f to be continuous on the whole domain, but actually we only used continuity at a single point in this proof. -- My analysis professor

Every continuous function on a compact domain is uniformly continuous

Proof

Let f:A→Rf: A→ \mathbb{R} be continuous. Let ϵ>0, \epsilon >0, then ∀ x∈ A, ∃δx>0:∣x−u∣<δx⟹ ∣f( x) −f( u)∣ < 12ϵ\forall \ x\in \ A,\ \exists \delta _{x} >0:|x-u|< \delta _{x} \Longrightarrow \ |f( \ x) \ -f( \ u) |\ < \ \frac{1}{2} \epsilon (1)

Consider the neighborhoods V∗12δ∗x(x), ∀x∈A V*{\frac{1}{2} \delta *{x} }( x) ,\ \forall x\in A. Then, C :={V12δx(x):x∈A} C\ :=\{V_{\frac{1}{2} \delta _{x} }( x) :x\in A\} is an open cover of A since an arbitrary union of open sets are still open. A is compact, so C C has a finite subcover, U:={V∗12δ∗x∗k(x∗1), ⋯ ,V∗12δ∗x∗k(x∗k)} U:=\{V*{\frac{1}{2} \delta *{x*{k} }}( x*{1}) ,\ \cdots ,V*{\frac{1}{2} \delta *{x*{k} }}( x*{k})\} where x∗1,⋯ ,x∗k∈A x*{1} ,\cdots ,x*{k} \in A

Let x, u ∈A x,\ u\ \in A be arbitrary. We want to show ∣x−u∣<δ⟹∣f(x)−f(u)∣<ϵ|x-u|< \delta \Longrightarrow |f( x) -f( u) |< \epsilon. For now, we only know ff behaves nicely locally(within each neighborhood around x_i, 1⩽i⩽k x\_{i} ,\ 1\leqslant i\leqslant k ), but uniformly continuity is a global property.

Intuition: If x,u x, u are in the same neighborhood around x∗i x*{i}, we can utilize the nice, local behavior. The problem is that δ∗x∗i \delta *{x*{i} } depends on x∗i x*{i}, so the required distance varies in different locations on the domain. Can we find a δ\delta that makes x x and u u "close enough" to be considered locally, no matter where they are?

Yes, we can. Why? Because U U is finite.

Let δ:=min⁡{12δ∗x∗1,⋯ ,12δ∗x∗k}\delta :=\min\left\{\frac{1}{2} \delta*{{x}*{1}},\cdots ,\frac{1}{2} \delta*{{x}*{k}}\right\}. UU covers A, so x∈V∗12δ∗xi(x∗i)x \in V*{\frac{1}{2} \delta*{{x}_{i}}}( x*{i})for some x∗i∈A x*{i} \in A.

∣x−xi∣<12δxi<δxi⟹∣f(x)−f(xi)∣<12ϵ by (1)∣u−xi∣=∣u−x+x−xi∣triangle ineq≤∣u−x∣+∣x−xi∣<δ+12δxi≤12δxi+12δxi=δxi∣u−xi∣<δxi⟹∣f(u)−f(xi)∣<12ϵ∣f(x)−f(u)∣=∣f(x)−f(xi)+f(xi)−f(u)∣≤∣f(x)−f(xi)∣+∣f(xi)−f(u)∣<12ϵ+12ϵ=ϵ □\begin{gathered} \begin{aligned} |x-x_{i} | & < \frac{1}{2} \delta _{ {x}_{i} } < \delta _{ {x}_{i} } \Longrightarrow |f( x) -f( x_{i}) |< \frac{1}{2} \epsilon \ \text{by (1)}\\ |u-x_{i} | & =|u-x+x-x_{i} |\\ \text{triangle ineq} & \leq |u-x|+|x-x_{i} |< \delta +\frac{1}{2} \delta _{ {x}_{i} } \leq \frac{1}{2} \delta _{ {x}_{i} } +\frac{1}{2} \delta _{ {x}_{i} } =\delta _{ {x}_{i} }\\ |u-x_{i} |< \delta _{ {x}_{i} }& \Longrightarrow |f( u) -f( x_{i}) |< \frac{1}{2} \epsilon \\ |f( x) -f( u) | & =|f( x) -f( x_{i}) +f( x_{i}) -f( u) |\\ & \leq |f( x) -f( x_{i}) |+|f( x_{i}) -f( u) |\\ & < \frac{1}{2} \epsilon +\frac{1}{2} \epsilon =\epsilon \ \square \\ \end{aligned}\\ \end{gathered}

Notes

If U U is infinite, we are unable to take the minimum of all neighborhoods. To recap, our goal is to find a δ\delta that is "close enough" for arbitrary x, u ∈A x,\ u\ \in A such that ∣f(x)−f(u)∣<ϵ|f( x) -f( u) |< \epsilon. The idea is to use xi x_{i} as a bridge between x x and u u since we only know how f f behaves near each x∗i x*{i}. Choosing 12δ∗xi \frac{1}{2} \delta *{ {x}_{i} }neighborhoods ensures x, u x,\ u are in the same neighborhood of xi x_{i}. This enables us to rely on f f's local behavior.

Bolzano-Weierstrass Theorem

Every bounded sequence in R\mathbb{R} has a convergent subsequence.

The proof is only for R\mathbb{R}, but the theorem also holds in Rn\mathbb{R^n} and Cn\mathbb{C}^n.

Definition: Let (xn)(x_n) be a sequence of real numbers. xNx_N is called a peak if ∀n≥N,xN≥xn\forall n\geq N, x_N\geq x_n i.e., xNx_N is the largest term in the sequence starting from it.

Lemma: Every sequence of real numbers has a monotone subsequence

Proof

Let (xn)(x_n) be a sequence of real numbers. There are two cases regrading the number of peaks:

Case1: (xn)(x_n) has infinitely many peaks. We can find a subsequence (xnk)(x_{n_{k} }) consisting of peaks. (xn1)(x_{n_{1} }) is a peak, so xn1≥xn,∀n≥n1x_{n_{1} } \geq x_n, \forall n\geq n_1. Thus, xn1≥xn2x_{n_{1} }\geq x_{n_{2} }. Similarly, xn2x_{n_{2} } is a peak, so xn2≥xn3≥xn4≥⋯x_{n_{2} } \geq x_{n_{3} } \geq x_{n_{4} } \geq \cdots, etc.

The sequence is monotone increasing.

Case2: (xn)(x_n) has finitely many peaks. Then, we choose our subsequence after the last peak. Let xNx_N be the last peak. For xn,n≥N,∃xm,m>n,such that  xm>xnx_n, n\geq N, \exists x_m, m\gt n, \text{such that } \ x_m \gt x_n since xnx_n is not a peak. Similarly, xmx_m is not a peak, so ∃xk,k>m,such that  xk>xm\exists x_k, k\gt m, \text{such that } \ x_k \gt x_m. Following this procedure, we end up with a monotone increasing subsequence.

In both cases, there exists a monotone subsequence in (xn)(x_n), so the lemma is proved.

Proof of the theorem

(xn)(x_n) is bounded. By lemma, (xn)(x_n) has a monotone subsequence (xnk)(x_{n_{k} }). By the monotone convergence theorem, the sequence converges □\square.

Notes

The proof is elegant and more importantly, easy to follow. The definition of peak is the key observation which makes the proof intuitive.

The power of a perfect definition. -- The Teaching assistant

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