Lecture 24 (Apr 4)

Today we discussed closed sets and the Cantor set. Click here for more details.

Lecture 23 (Apr 2)

Today we continued discussing point-set topology, in particular, the notion of limit point. Click here for more details.

Lecture 22 (Mar 28)

Today we discussed point-set topology, in particular, the notion of open set. Click here for more details.

Lecture 21 (Mar 26)

Today we considered more examples of metric spaces. Click here for more details.

Lecture 20 (Mar 21)

Today we proved Holder's inequality and gave more examples of metric spaces. Click here for more details.

Lecture 19 (Mar 19)

Today we gave preliminary examples of a metric space. Click here for more details.

Lecture 18 (Mar 14)

Today we proved the Monotone Subsequence Theorem, as well as the alternating series test. Click here for more details.

Lectures 16 and 17 (Mar 7 and 12)

On these days we proved theorems about absolute convergence, the Cauchy Criterion, and Bolzano-Weierstrass. Click here for more details.

Lecture 15 (Mar 5)

Today we proved the Root and Ratio tests. Click here for more details.

Lecture 14 (Feb 28)

Today we proved the Monotone Convergence Theorem and the Integral Test. Click here for more details.

Lecture 13 (Feb 26)

Today we introduce sequences and series. Click here for more details.

Lecture 12 (Feb 14)

Today we proved that any two infinite cardinals are comparable. Click here for more details.

Lecture 11 (Feb 12)

I started by discussing some more of the history of the Continuum Hypothesis. We then proved that ℵ0 is the smallest infinite cardinal; this is a fancy way of saying that any infinite set which injects into ℕ is countable. This result is similar to results we've proved before, and (following suggestions of Dan) we were able to give the following quick proof. Suppose A is an infinite set which injects into ℕ; in our terminology, A ≤ ℕ. On the other hand, we know that A must contain a countable subset, whence ℕ ≤ A. Cantor-Bernstein implies that A ~ ℕ, or in words, A is countable.

This is an elegant proof, but is rather inexplicit. Is it possible to write down the bijection between A and ℕ? The first realization was that this is a bit difficult from a purely notational standpoint, since A is some arbitrary (infinite) set; we have no idea whether its elements are numbers, or animals, or jalopies, or whatever other crazy thing. However, we know that A injects into ℕ; this means that the image of A is (set-theoretically) indistinguishable from A itself. This means that anything we want to prove about A, we can prove about its image instead. The advantage is that the image is some subset of ℕ.

To be more specific, suppose the function f maps A injectively into ℕ. Let A'=f(A). Then A'⊆ℕ, and A' ~ A. By Cantor-Bernstein, it suffices to find an injection g from ℕ into A'. We can do this fairly explicitly using the well-ordering principle. First, let g(1) = least[A']. Next, we let g(2) be the second smallest element of A'; formally, this can be written g(2) = least[A'-{g(1)}]. (It's good to write things in terms of the least element rather than in terms of the second-smallest element, because this way we can apply the well-ordering principle.) Generally, set g(n) = least[A'-g(ℕ< n)]. Note that this is well-defined, since A'-g(ℕ< n) is nonempty for all n. Finally, why is g an injection? Well, suppose m and n are distinct natural numbers. WLOG, m < n, i.e. m ∈ ℕ< n. It follows that g(m) ∉ A'-g(ℕ< n). By contrast, g(n) ∈ A'-g(ℕ< n) by construction. Thus, g(m) ≠ g(n). We conclude that g is injective, as claimed.

In the next lecture, we'll see that this proof generalizes very nicely.

Lecture 10 (Feb 7)

Last time we found an injection from {0,1} into [0,1). (Recall that the former is the set of functions f: ℕ → {0,1}; the latter is the half-open unit interval.) Now we wish to find an injection in the other direction. In other words, to each number x ∈ [0,1) we wish to associate some function fx ∈ {0,1}, in such a way that distinct numbers always correspond to distinct functions. Vaguely, the idea is simple: given x, our function will encode `walking directions' to that number. Here's a more precise description of the idea.

Fix x ∈ [0,1). To define a function fx ∈ {0,1}, I need to tell you the values fx(1), fx(2), etc. We will define the function recursively. fx(1) will indicate which half of the interval x belongs to. If x ∈ [0,1/2), then set fx(1) = 0, and let a1 = 0 and b1 = 1/2. Otherwise, if x ∈ [1/2,1), set fx(1) = 1, and let a1 = 1/2 and b1 = 1. In either case, we have now specified fx(1), and have that x ∈ [a1, b1), which is an interval of length 1/2. We now proceed similarly, at each stage chopping the interval in half and using fx to indicate which half of the interval x belongs to. More precisely, at the n-th stage we are given that x belong to an interval [an-1, bn-1) of length 1/2n-1. If x belongs to the left half of the interval, set fx(n) = 0; otherwise, set fx(n) = 1. Either way, we now know that x lives in some interval [an, bn) which is half the length of [an-1, bn-1). This allows the recursion to continue.

Having defined a function fx we must now show that the map sending x to fx is injective. Suppose fx = fy. This means that fx(k) = fy(k) for every natural number k. This in turn implies that for every natural number n, there exists an interval [an, bn) of length 1/2n such that both x and y live in that interval. (This step is not entirely obvious; make sure you can justify it rigorously!) But this implies that |x-y| ≤ 1/2n for every n, which shows that x = y.

We have thus proved that the map sending x to fx is an injection. We observed in class that it is NOT a surjection. For example, the function g defined g(1) = 0 and g(n) = 1 for all n≥2 will never appear as an output of our map. (Why?)

Applying Cantor-Bernstein, we conclude that {0,1} ~ [0,1). Since P(ℕ) ~ {0,1}, we conclude that P(ℕ) ~ [0,1). In particular, [0,1) must be uncountable.

We concluded lecture with a discussion of infinite cardinals. The cardinality of ℕ is called ℵ0 (ℵ, read aleph, is the first letter of the Hebrew alphabet). The cardinality of ℝ is called c, the continuum. We've proved that ℵ0 < c, a strict inequality. Is there anything in between? The famous Continuum Hypothesis asserts that the answer is no: there is no set whose cardinality is strictly in between ℵ0 and c. In 1940, Kurt Gödel proved that one cannot disprove the Continuum Hypothesis by using the axioms of set theory alone (called ZFC, which we will discuss next week). Twenty years later, Paul Cohen proved that one cannot prove the CH to be true using the axioms of set theory, either. Together, these statements imply that the ZFC axioms for set theory are not robust enough to say anything about CH. The same is true about the Generalized Continuum Hypothesis (GCH), which asserts that there does not exist any cardinal strictly between that of A and P(A), for any infinite set A.

Lecture 9 (Feb 5)

Last time, we proved that A injects into B iff B surjects onto A. Using this, we can restate Cantor-Bernstein: if there exists an injection of A into B and a surjection of A onto B, then there is a bijection from A to B.

Next, we started proving that P(ℕ) ~ [0,1], where P(A) denotes the power set of A. First, you will prove in your assignment that P(ℕ) ~ {0,1}, where the latter denotes the set of all function from ℕ to the set {0,1} of two elements. Thus, it suffices to prove that {0,1} ~ [0,1]. We do this by using Cantor-Bernstein; thus, we must find injections in both directions. Inspired by an idea of Kelvin, we mapped a function f to the number ∑ f(n) / 3n, and proved that this gives an injection. Next lecture we will discuss an injection in the other direction (or, equivalently, a surjection in the same direction).

Lecture 8 (Jan 31)

We began by cleaning up the last step of the proof of the Cantor-Bernstein theorem. We next illustrated the utility of the theorem by giving a nicer proof that ℚ is countable. To do this, we first proved that ℚ>0 ~ ℕ. Since ℕ trivially inject into ℚ>0, it suffices (by Cantor- Bernstein) to find an injection in the other direction. There are many such; the one we used mapped the reduced fraction a/b to 2a3b. Thus, we deduce that ℚ>0 ~ ℕ, so there exists a bijection f : ℚ>0 → ℕ. We easily extended this to a bijection from ℚ to ℤ. Finally, since ℤ ~ ℕ, we conclude that ℚ must be countable.

Next, we turned to uncountable sets. Given a set A, how does one make a bigger set? One way is to look at the power set of A, denoted P(A); this is the set of all subsets of A. We proved that any set A is strictly smaller than its power set; in other words, we proved that there exists an injection from A into P(A), but there does not exist an injection from P(A) into A. There are a couple of nice consequences of this theorem. First off, it gives a nicer proof of the existence of uncountable sets: P(ℕ) must be uncountable, since it is strictly larger than ℕ! (In fact, we will prove that P(ℕ) ~ ℝ.) Moreover, it shows that uncountable sets come in many different sizes: for, if A is any uncountable set, then P(A) is a strictly larger one!

To prove the theorem, we first proved a lemma: A injects into B iff B surjects onto A. Thus, to prove the theorem, it suffices to prove that there does not exist a surjection from A onto P(A). (We also have to prove that A injects into P(A), but this is trivial.) Let g : A → P(A) be any map, and consider the set B = {a ∈ A : a ∉ g(a)}. Clearly, B ∈ P(A). If g were a surjection, there would have to exist an element b ∈ A such that g(b) = B. But this easily leads to a contradiction (does b ∈ B?). This proves that g cannot be a surjection. In other words, there exists no surjection from A onto P(A).

Lecture 7 (Jan 29)

We concluded the proof of the Cantor-Bernstein theorem.

Lecture 6 (Jan 24)

We proved most of the Cantor-Bernstein theorem (click the link for a summary of the proof).

Lecture 5 (Jan 22)

After recalling the definitions, we reinterpreted the notion of countability: an infinite set is countable iff there exists an algorithm which enumerates the elements in such a way that any given element is enumerated by the algorithm after a finite number of steps. We then proved that the open interval (0,1) is uncountable. The idea was to show that, given any algorithm for enumerating the numbers in (0,1), we can construct some number which the algorithm will never reach in finite time. The argument we gave is sometimes called the `diagonal argument'; it is due to Cantor.

Last time, we proved that any infinite set contains a countable subset. We gave an explicit such subset for (0,1). This led to a discussion of comparing sizes of infinite sets. We already have a notion for this: if there is a bijection from A to B, we said that A and B have the same size (which we denoted A~B). But what about sets which don't have the same size, such as ℕ and (0,1)? Given two sets A and B, we will say A ≤ B iff there is an injection from A into B. There are three scenarios:

(1) A ≤ B and B ≤ A;

(2) A ≤ B but B is not ≤ A; and

(3) A is not ≤ B and B is not ≤ A.

On Thursday, we'll prove that if (1) holds, then A~B (this is the Cantor-Bernstein theorem). If (2) holds, we write A < B; we shall see that ℕ < (0,1). Finally, next week we'll prove that (3) never happens; this will be a consequence of the notorious well-ordering theorem.

Lecture 4 (Jan 17)

We recalled the notions (and notations) of injections, surjections, and bijections. I pointed out a different way one could think about injections and surjections: f: A → B is an injection iff [a = b whenever f(a) = f(b)], and a surjection iff [for all b in B there exists a in A such that f(a) = b].

The notion of bijection gives a nice way to compare two infinite sets: we'll say that sets A and B have the same size (denoted A~B) iff there exists a bijection from A to B. Note that, even if A~B, it's possible to construct maps from A to B which are not bijections! We checked that ~ is an equivalence relation (i.e. it's reflexive, symmetric, and transitive). We considered various examples (ℕ~2ℕ, ℕ~ℤ and ℕ~ℚ); we called a set countable iff it has the same size as ℕ. (Note that any countable set must be infinite.) We left as an exercise to find an explicit bijection between ℚ and ℕ.

We concluded lecture by proving two quick theorems. The first was that every infinite set contains a countable subset. The second asserts that every infinite set has the same size as one of its proper subsets. We'll discuss these, and some further developments, next week.

Lecture 3 (Jan 15)

We first reviewed the notion of equivalence relation (and gave as an example the relation ~ on positive integers defined by a~b iff the sum of the digits of a equals the sum of the digits of b; e.g. 701~44). We next recalled the definition of partition, and the fact that any partition on a set induces an equivalence relation on that set. Next, we proved the converse: an equivalence relation on a set induces a partition of that set. More precisely, if ~ is an equivalence relation on S, then S can be partitioned into sets Sα := {x in S : x~α}.

Next we returned to functions. Recall that if f : A → B is a function, then the image of any element of A consists of a single element of B (i.e. |f(a)| = 1 for every a in A). If the preimage of any element of B also consists of a single element of A (i.e. |f-1(b)| = 1 for every b in B), then f is called a bijection. In practice, one often proves that f is a bijection in two stages:
(1) prove that |f-1(b)| ≤ 1 for all b in B, and
(2) prove that |f-1(b)| ≥ 1 for all b in B.
Any function satisfying condition (1) is called an injection; any function satisfying condition (2) is called a surjection. Thus a bijection is a function which is both injective and surjective. I also introduced the fancy arrow notations for such functions (curvy arrow for injections, double-headed arrow for surjections).

We concluded lecture with an analogy for these concepts. Imagine a theatre with a bunch of seats, and a bunch of people with tickets to the theatre; the set of people is A, the set of seats is B, and the function f: A → B plays the role of the ticket. (It's a function means that each ticket assigns a single seat, i.e. no ticket has two different seat numbers on it.) Then f is an injection iff no two people are assigned the same seat, and f is a surjection iff every seat in the house is filled (possibly with more than one person!). Finally, f is a bijection iff each seat has precisely one person sitting in it.

Lecture 2 (Jan 10)

After clarifying the notation for unions of multiple sets, we talked about functions. We talked about the image and preimage of a function. Among other things, even if a function f: A → B is not invertible, we denote the preimage of B'⊆B under f by f-1(B'). We proved several propositions: the preimage of a union (or intersection) is the union (or intersection) of the preimages, and the image of a union is the union of the images. Perhaps surprisingly, the image of an intersection is not necessarily the intersection of the images; Kaidi came up with the example f(x) = x2 with A=[-1,0] and B=[0,1] to demonstrate this.

Next, we discussed partitions and equivalence relations. A partition is a way of breaking a set up into non-overlapping (`pairwise disjoint') pieces; formally, a partition of S is a way of writing S as the union of sets Sα where any two sets Sα and Sβ are either secretly the same set, or are disjoint. This may seem abstract, but we do this all the time in life (and in math). For example, implicit in many jokes is a partition of the set of all people by hair colour (blond, brunette, redhead, etc.). Or we partition ℤ into even and odd numbers. In these examples, we treat elements in each piece of the partition as equivalent (even if they're not equal). For example, the integers 2 and 4 are different, but they're both even; so when we partition ℤ into evens and odds, we are effectively overlooking the differences between 2 and 4 and focusing our attention on their shared characteristic of evenness. Thus, the set of even numbers is an `equivalence class', and the set of odds is another. Stereotypes operate on the same principle -- all members of a single group (race, nationality, gender, religion, age, affluence, hair colour, etc.) are considered equivalent, despite obvious variation between the individuals in most respects. Another example of this we discussed was from Euclidean geometry. A big part of geometry is studying conditions under which two triangles are congruent. Of course, just because two triangle are congruent doesn't mean they're the same -- if they're located in different places in the plane, they're definitely not the same! -- but we overlook this and treat them as equivalent.

We tried to capture what these different equivalences have in common by introducing the concept of an equivalence relation. We say that a binary relation ~ on a set S is an equivalence relation if it satisfies three properties: reflexivity, symmetry, and transitivity. We then observed that given any set S, a partition on S induces a natural equivalence relation on S (a~b iff a and b live in the same slice of the partition). It turns out that the converse to this also holds: any equivalence relation on a set S induces a natural partition of S. We will prove this converse theorem in the next lecture.

Lecture 1 (Jan 8)

In MATA31 and MATA37, you studied the structure of the set ℝ and how functions act on elements of that set. In Linear Algebra, you studied the structure of ℝn and how matrices act on its elements. There were a lot of similarities between the two, and in this course we're going to take this a step further and unify the theory by talking about more general sets and the `operators' which act on their elements.

As an example, consider the set S of functions whose domain and range are both ℝ. There is a natural way of combining together elements of this set -- composition -- which satisfies most of the properties we know about addition in ℝ (associativity, existence of identity, existence of inverses). In other words, in this space, functions play the same role which number do in ℝ. What plays the role of functions, then? There were a couple of ideas: the differential d/dx, and the integral. Certainly, both of these operations can be used to transform one function into another (although not all functions can be differentiated or integrated). There are other important transforms, like the Fourier transform, which you will learn about later. The point is, S behaves in many ways like ℝ. This observation immediately leads to questions about how deep the similarity goes. For example, recall that the rationals are dense in the reals; i.e. even though wecannot write down most real numbers in any way, we can approximate any real number with arbitrary precision by fractions, which are quite simple to write down. Is there a similar result for S? In other words, is there some family of particularly simple functions which are dense in S? As soon as you pose the question, you're forced to ask another: what does it mean for one function to be `close' to another? How does one measure distances between functions? These are the sorts of questions we will address in this course. Among other things, we will develop a notion of how to measure distances between two elements of rather general spaces -- `metric spaces' -- and develop a theory of them and the operators which act upon their elements.

After this, we discussed naive set theory. (Naive, because we never define the terms `set' or `element'. As we discussed, this is actually a pretty serious issue, since it leads to paradoxes. For our purposes, we'll get by naively.) We recalled some basic notations, including unions and intersections of multiple sets. New material begins next lecture!