Week 6: Topology & Real Analysis

Notes

To this point, we have covered Calculus I, Calculus II, Calculus III, Differential Equations,Linear Algebra, Complex Analysis and Abstract Algebra. These topics probably comprisemore than 90% of the GRE math subject exam. The remainder of the exam is comprisedof a seemingly random selection of problems from a variety of different fields (topology, realanalysis, probability, combinatorics, discrete math, graph theory, algorithms, etc.). We can’thope to cover all of this, but we will state some relevant definitions and theorems in Topologyand Real Analysis.

Topology

The field of topology is concerned with the shape of spaces and their behavior undercontinuous transformations. Properties regarding shape and continuity are phrased usingthe concept of open sets.

Definition 1 (Topology / Open Sets). Let X be a set and τ be a collection of subsetsof X. We say that τ is a topology on X if the following three properties hold:

(i) ∅, X ∈ τ

(ii) If T1, . . . Tn is a finite collection of members of τ , thenn⋂i=1

Ti ∈ τ

(iii) If {Ti}i∈I is any collection of members of τ , then⋃i∈I

Ti ∈ τ

In this case, we call the pair (X, τ) a topological space and we call the sets T ∈ τ open sets.

Note, there are two topologies which we can always place on any set X: the trivial topol-ogy τ = {∅, X} and the discrete topology τ = P(X). Having defined open sets, we are ableto define closed sets.

Definition 2 (Closed Sets). Let (X, τ) be a topological space. A set S ⊂ X is calledclosed iff Sc ∈ τ . That is, S is defined to be closed if Sc is open.

The words open and closed can be a bit confusing here. Often times students mistak-enly assume that a set is either open or closed; that these terms are mutually exclusive anddescribe all sets. This is not the case. Indeed, sets can be open, closed, neither open norclosed, or both open and closed. In any topological space (X, τ), the sets ∅ and X are bothopen and closed. By De Morgan’s laws, since finite intersections and arbitrary unions of opensets are open, we see that finite unions and arbitrary intersections of closed sets remain closed.

Example 3. The set of real numbers R becomes a topological space with open sets definedas follows. Define ∅ to be open and define ∅ 6= T ⊂ R to be open iff for all x ∈ T , there

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exists ε > 0 such that (x − ε, x + ε) ⊂ T . Prototypical open sets in this topology are theopen intervals (a, b) = {x ∈ R : a < x < b}. Indeed, this interval is open because forx ∈ (a, b), we can take ε = min{|x− a| , |x− b|} and we will find that (x− ε, x+ ε) ⊂ (a, b).We can combine open sets via unions or (finite) intersections to make more open sets; forexample (0, 1) ∪ (3, 5) is also an open set. Likewise, prototypical closed sets are closed in-tervals [a, b] = {x ∈ R : a ≤ x ≤ b}, and any intersection or (finite) union of such sets willremain closed. As was observed above ∅ and R are both open and closed; in fact, in thisspace, these are the only sets which are both open and closed, though it is easy to constructsets which are neither open nor closed. Consider the set [0, 1) = {x ∈ R : 0 ≤ x < 1}. Thisset is not open because the point 0 is in the set, but it cannot be surrounded by an intervalwhich remains in the set. The complement of this set is (−∞, 0) ∪ [1,∞). This set is notopen since 1 is in the set but cannot be surrounded by an interval which remains in the set.Since the complement is not open, the set [0, 1) is not closed. Note, this topology is calledthe standard topology on R.

Example 4. While the above example defines the standard topology on R, it is easy tocome up with non-standard topologies as well. Indeed, let us now define T ⊂ R to be openif T can be written as a union of sets of the form [a, b) = {x ∈ R : a ≤ b < x}. These opensets comprise a topology on R. In this topology a prototypical open set is of the form [a, b).What other sets are open in this topology? Notice that

(a, b) =∞⋃n=1

[a+ 1/n, b)

which shows that sets of the form (a, b) remain open in this topology. Also notice that since[a, b) is open, we define

[a, b)c = (−∞, a) ∪ [b,∞)

to be closed. However, both (−∞, a) and [b,∞) are easily seen to be open, so the set(−∞, a)∪ [b,∞) is also open as a union of open sets. Since this set is open, it’s complement[a, b) is closed. Hence in this topology, all sets of the form [a, b) are both open and closed.The intervals [a, b] are closed and not open in this topology. Note, this topology is calledthe lower limit topology on R.

Notice in these example, the lower limit topology contains as open sets all of the setswhich are open in the standard topology. In this way, the lower limit topology has “more”open sets and we can think of the lower limit topology “containing” the standard topology.We define these notions here.

Definition 5 (Finer & Coarser Topologies). Suppose that X is a set and τ, σ are twotopologies on X. If τ ⊂ σ, we say that τ is coarser than σ and that σ is finer than τ .

On any space X, the finest topology is the discrete topology P(X) and the coarsest isthe trivial topology {∅, X}. A finer topology is one that can more specifically distinguishbetween elements.

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Definition 6 (Interior & Closure). Let (X, τ) be a topological space and let T ⊂ X.The interior of T is defined to be the largest open set contained in T . The closure of T isdefined to be the smallest closed set containing T . We denote these by int(T ) and cl(T )respectively. In symbols, we have

int(T ) =⋃

S∈τ,S⊂T

S and cl(T ) =⋂

Sc∈τ,T⊂S

S.

Other common notations are T̊ for the interior of T , and T for the closure of T .

Example 7. Considering R with the standard topology and a, b ∈ R, a < b, we haveint([a, b)) = (a, b) and cl([a, b)) = [a, b].

In both of the examples above, there was some notion of a “prototypical” open set, fromwhich other open sets can be built. We give this notion a precise meaning here.

Definition 8 (Basis (Base) for a Topology). Let X be a set and let β be a collectionof subsets of X such that

(1) X =⋃B∈β

B,

(2) if B1, B2 ∈ β, then for each x ∈ B1 ∩ B2, there is B3 ∈ β such that x ∈ B3 andB3 ⊂ B1 ∩B2.

Then the collection of sets

τ ={T : T = ∪i∈IBi for some collection of sets {Bi}i∈I ⊂ β

}forms a topology on X. We call this τ the topology generated by β, and we call β a basisfor the topology τ .

This is half definition and half theorem: we are defining what it means to be a basis, andasserting that the topology generated by a basis is indeed a topology. If we can identify abasis for a topology, then the basis sets are the “prototypical” open sets, and all other opensets can be built as unions of the basis sets. Morally, basis sets are representatives for theopen sets; if you can prove a given property for basis sets, the property will likely hold forall open sets. Often times it is easiest to define a topology by identifying a basis.

Example 9. Above we defined the standard topology on R by saying that a set T is openif for all x ∈ T , there is ε > 0 such that (x − ε, x + ε) ⊂ T . It is important to see thisdefinition of the topology; however, this is a much more analytic than topological definition.The topological way to define the standard topology on R would be to define it as the topol-ogy generated by the sets (a, b) where a, b ∈ R, a < b. Indeed, these two definitions of thestandard topology are equivalent as the following proposition shows.

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Proposition 10. Suppose that (X, τ) is a topological space and that β is a basis for thetopology τ . Then T ∈ τ iff for all x ∈ T , there is B ∈ β such that x ∈ B and B ⊂ T .

It is important to identify when two bases in a topological space generate the same topol-ogy and this next proposition deals with that question.

Proposition 11. Suppose that X is a set and β1, β2 are two bases for topologies τ1 and τ2.Then τ1 ⊂ τ2 iff for every B1 ∈ β1, and for every x ∈ B1, there is B2 ∈ β2 such that x ∈ B2

and B2 ⊂ B1. (Be very careful not to mix up the inclusions in this statement. What thisis essentially saying is that β2 generates a larger (finer) topology iff β2 has more (smaller)sets.) Informally, the basis β2 generates a finer topology if we can squeeze basis sets from β2inside basis sets from β1 (and not only that, but we can construct basis sets from β1 out ofbasis sets from β2).

Just as all groups have subgroups and all vector spaces have subspaces, there is a naturalway to define subspaces of a topological space.

Definition 12 (Subspace Topology). Let (X, τ) be a topological space and let Y ⊂ X.Then the collection of sets

σ = {Y ∩ T : T ∈ τ}

forms a topology on Y . This topology σ is called the subspace topology on Y inherited from(X, τ).

Again, this is part definition and part theorem; we are asserting that such σ does indeeddefine a topology on Y .

Example 13. Consider [0, 3] ⊂ R with the standard topology on R. Note that the subspacetopology on [0, 3] includes standard open sets like (1, 2) since this set is open in R and

(1, 2) = [0, 3] ∩ (1, 2).

Now consider the set (1, 3]. This set is not open in R; however, it is open in the subspacetopology on [0, 3], because (1, 4) is open in R and

(1, 3] = [0, 3] ∩ (1, 4).

Likewise there is a natural way to combine topological spaces in a Cartesian product.

Definition 14 (Product Topology). Let (X, τ), (Y, σ). Recall the Cartesian product isgiven by coupling elements of X and Y : X×Y ..= {(x, y) : x ∈ X, y ∈ Y }. It is tempting todefine a topology on X × Y comprised of sets of the form T × S for T ∈ τ, S ∈ σ. However,these do not form a topology on X × Y since a union of sets of this form will not be of thisform anymore. So rather, we let β = {T × S : T ∈ τ, S ∈ σ} form the basis for a topologyon X × Y . The topology generated by β is denoted τ × σ and the space (X × Y, τ × σ) is

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the called the product space of X and Y .1

Example 15. Consider R with the standary topology, which we call τ1. The product space(R×R, τ1× τ1) can be visualized by drawing the plane with standard open sets as rectangles(a, b)× (c, d) = {(x, y) ∈ R× R : a < x < b and c < y < d}. Alternatively, we can considerthe set R2 of 2-dimensional vectors. On this space, we consider the topology τ2 generatedby circles: Br(v) = {z ∈ R2 : ‖v − z‖ < r} for v ∈ R2 and r > 0 (indeed, this is called thestandard topology on R2). We can identify each vector v = ( xy ) ∈ R2 with the coordinates(x, y) ∈ R × R. Since this is a bijective map, the sets R × R and R2 are really the same.We’d like to know if the topologies τ1× τ2 and τ2 are the same. To prove they are the same,consider the bases

β1 = {(a, b)× (c, d) : a, b, c, d ∈ R, a < b, c < d} and

β2 ={Br(v) : v = ( xy ) ∈ R2, r > 0

}.

For any B1 = (a, b)×(c, d) ∈ β1, take any (x, y) ∈ B1 and let r = min{x−a, b−x, y−x, d−y}.Then for v = ( xy ), we will have x ∈ Br(v) ⊂ B1; this shows that for any (x, y) ∈ B1, we canfind a set B2 ∈ β2 such that (x, y) ∈ B2 and B2 ⊂ B1. Hence by Proposition 11, τ1×τ1 ⊂ τ2.Conversely, let v = ( xy ) and r > 0 and consider the set B2 = Br(v) ∈ β2. For any u = ( zw ) ∈Br(v), define r′ = (r−‖u− v‖2)/

√2. Then the square B1 = (z− r′, z+ r′)× (w− r′, w+ r′)

satisfies u = ( zw ) ∈ B1 and B1 ⊂ B2. Thus by Proposition 11, we have τ2 ⊂ τ1 × τ1,and we can conclude that τ2 = τ1 × τ1. (Note, this inclusions of basis sets is pictured inFigure 1.) That is, the standard topology on R2 is the product of two copies of the standardtopology on R. More generally, for n ∈ N, we can define the standard topology on Rn to bethe topology generated by open balls and we will find that this is the same as the productof n copies of the standard topology on R.

Topology gives us the minimum structure required to discuss limits and continuity. In-deed, in calculus we were only able to discuss these things because R is naturally a topologicalspace with the standard topology. We give the topological definitions of limits and continuityhere and discuss some of their properties.

Definition 16 (Limit of a Sequence). Let (X, τ) be a topological space and let {xn}∞n=1

be a sequence of values in X. We say that {xn}∞n=1 converges to a limit x ∈ X if for anyopen set T ∈ τ such that x ∈ T , there is N ∈ N, such that xn ∈ T for all n ≥ N . We writethis as xn → x or lim

n→∞xn = x.

1Note, this is only a good definition for the product topology when we are taking the product of a finitenumber of spaces. Indeed, if {(Xi, τi)}i∈I is an arbitrary collection of topological spaces, it is most naturalto define the product topology on X =

∏i∈I Xi to be the coarsest topology so that the projection maps

πi : X → Xi are continuous. The topology defined generated by sets of the form∏

i∈I Ui where Ui ∈ τi isthen called the box topology. One can show that for a finite Cartesian product, the product topology and boxtopology agree with each other; this is not necessarily true for infinite products. (Another way to “correctly”define the product topology for an infinite product X =

∏i∈I Xi is to let it be generated by sets of the form∏

i∈I Ui where Ui ∈ τi and Ui = Xi for all but finitely many i ∈ I.)

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Figure 1: A basis set from either topology τ1× τ1 or τ2 can be fit around any point in a basisset from the other topology.

Note that this is a generalization of the definition we gave for a limit in calculus; in cal-culus we are always using the standard topology on R. One feature of the limit in calculusis that limits are unique: if xn → x and xn → y, then x = y. This is not true in a generaltopological space.

Example 17. Consider a space X with the trivial topology τ = {∅, X}. Take any sequence{xn}∞n=1 in X and apply the definition of the limit. For any x ∈ X, and any N ∈ N, we seethat if T ∈ τ and x ∈ T , then T = X and xn ∈ T for all n ≥ N . Thus in this space, everysequence converges to every point.

There are non-trivial topologies where limits are still non-unique, but our intution tellsus that limits should be unique and we can add a simple property to ensure that they are.

Definition 18 (Hausdorff Space). A topological space (X, τ) is called a Hausdorff space(or is said to have the Hausdorff property), if for all x, y ∈ X with x 6= y, there are opensets Tx, Ty ∈ τ such that x ∈ Tx, y ∈ Ty and Tx ∩ Ty = ∅.

Proposition 19. Limits in Hausdorff spaces are unique. That is, if (X, τ) is a Hausdorffspace and {xn}∞n=1 is a sequence in X, then {xn}∞n=1 can have at most one limit x ∈ X.

Now we would like to discuss maps between spaces. As with linear transforms in linearalgebra and homomorphisms in abstract algebra, we restrict our discussion to maps whichpreserve some of the underlying structure of the space. In topology, these are the continuousfunctions.

Definition 20 (Continuous Function). Let (X, τ) and (Y, σ) be two topological spaces.A function f : X → Y is said to be continuous iff

f−1(V ) = {x ∈ X : f(x) ∈ V } ∈ τ whenever V ∈ σ.

That is, f is continuous if the preimage of every open set in Y is open in X.

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This gives us a way to define continuity without ever considering individual points. Con-trast this with the calculus definition of continuity where we first define what it means for afunction to be continuous at a point, and then define continuous functions on a domain tobe those which are continuous at each point. Of course, topology still has a notion of whatit means to be continuous at a point.

Definition 21 (Pointwise Continuity). Let (X, τ) and (Y, σ) be two topological spacesand let f : X → Y . We say that f is continuous at the point x ∈ X, if for all V ∈ σ suchthat x ∈ V , we have f−1(V ) ∈ τ .

It is a good exercise to prove that when both X, Y are R with the standard topology, thisdefinition of continuity is equivalent to the ε-δ definition of continuity presented in calculus.

Continuity is an important property because it preserves certain features of topologicalspaces. Indeed, if f : X → Y , we define the image of X under f by f(X) = {f(x) : x ∈ X}.We define σf to be the collection of subsets V ⊂ f(X) such that f−1(V ) is open in X. If fis continuous, this collection will form a topology, and this topology σf on f(X) will be thesame as the subspace topology that f(X) inherits from (Y, σ). This shows that continuousfunctions respect the structure of the underlying spaces; in other words, continuous functionsare the morphisms in the category of topological spaces.

Above we showed that the spaces (R2, τ2) and (R × R, τ1 × τ1) are essentially the samespace; it is important to be able to be able to identify equivalent spaces or distinguish be-tween distinct topological spaces and continuity helps us do that. We define a few moreproperties of topological spaces here.

Definition 22 (Connectedness). Let (X, τ) be a topological space and let C ⊂ X. Wesay that the set C is disconnected iff there exist non-empty A,B ∈ τ such that C ⊂ A ∪ Band A ∩B = ∅. We say the space is connected iff it is not disconnected.

Figure 2: Topologist’s Sine Curve

Example 23. Intuitively, a set is connected if it isin one whole piece; disconnected sets have separatepieces broken off from each other. Thus for exam-ple, in R with the standard topology, the set [5, 9) isconnected while the set {1} ∪ [5, 9) ∪ (10,∞) is dis-connected. However note, this is only a heuristic! Itworks very well in R, but even in R2 there are fa-mous examples that challenge this intuition. Indeed,consider the set C ⊂ R2 (pictured right) given by

C = {(0, y) : y ∈ [−1, 1]}∪{(x, sin

(1x

)): x ∈ (0,∞)

}.

This is called the Topolgist’s Sine Curve, and while itis defined as the disjoint union of two sets, it is actu-ally connected. Indeed, any open set containing thevertical strip {(0, y)}y∈[−1,1] will necessarily contain

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some of the curve. Thus this set cannot be covered bytwo disjoint, open sets. (Actually, a stronger notion of connectedness is path-connectedness.Roughly speaking, a set is path-connected if one can draw a path between any two pointsin the set without leaving the set. At first glance, path-connectedness may seem equivalentto connectedness, but the topologist’s sine curve is connected without being path-connected.)

One important result involving connectedness helps us classify sets which are both openand closed.

Proposition 24. Suppose that (X, τ) is a topological space. Then X is connected iff theonly sets which are both open and closed in X are ∅ and X itself.

Definition 25 (Compactness). Let (X, τ) be a topological space and let C ⊂ X. Wesay that C is compact iff from any collection of open sets {Ui}i∈I such that C ⊂ ∪i∈IUi, wecan extract a finite collection of sets Ui1 , . . . , Uin such that C ⊂ ∪nk=1Uik .

Such a collection {Ui}i∈I in the definition of compactness is called an open cover of C.Thus, in words, a set C is compact if every open cover of C admits a finite subcover.

Example 26. Compactness is somehow a generalization of closedness and boundedness.Indeed, we give examples here of a bounded set which is not closed and a closed set whichis not bounded and prove that neither are compact. Consider R with the standard topol-ogy and consider the bounded set (0, 1). This set is not closed, and we show that it is notcompact. Consider Uk = (0, 1− 1/k) for k ∈ N. We see that (0, 1) ⊂ ∪∞k=1Uk so {Uk} formsan open cover of (0, 1). However, if we take any finite subcollection Uk1 , . . . , UkN of {Uk}and let K = max{k1, . . . , kN}. Then (0, 1) 6⊂ ∪Nn=1Ukn = (0, 1 − 1/K). Thus there cannotbe a finite subcover for this particular cover, and so this cover violates the definition ofcompactness and we conclude that (0, 1) is not compact. Similarly, consider the set [0,∞).This set is closed, but is not bounded, and we show that it is not compact. Consider thecollection Uk = (−1, k) for k ∈ N . We see [0,∞) ⊂ ∪∞k=1Uk, but again, any finite collectionUk1 , . . . , UkN will satisfy [0,∞) 6⊂ ∪Nn=1Ukn = (−1, K) where K = max{k1, . . . , kN}, and sothis open cover admits no finite subcover, and hence we conclude that (0,∞) is not compact.By contrast, a closed and bounded subset of R like [0, 1] is compact, though this is not at alltrivial to prove. In general topological spaces, it is easier to show that a set isn’t compact,since this only requires exhibiting one example of an infinite cover that does not admit afinite subcover. In R (and more generally, in metric topologies), there are nice theoremswhich give concrete lists of properties which are equivalent to compactness. We cover someof these theorems in the Real Analysis portion of these notes.

Proposition 27. Suppose that (X, τ) and (Y, σ) are topological spaces and that f : X → Yis continuous. Let U ⊂ X and consider the image of U under f defined by f(U) ..= {f(x) :x ∈ U} ⊂ Y . If U is connected in X, then f(U) is connected in Y . Likewise, if U is compact,then f(U) is compact in Y .

This proposition tells us that these properties of connectedness and compactness are in-

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variant under continuous maps. Statements like this help us characterize topological spaces.Note however that the converse is not true: a continuous map could still map a disconnectedset to a connected set (for example, the continuous function f(x) = x2 on R maps the dis-connected set (−1, 0)∪ (0, 1) to the connected set (0, 1)), or a non-compact set to a compactset (for example, the continuous map f(x) = sin(x) maps the non-compact set (0,∞) to thecompact set [−1, 1]). If we want a continuous map to not change the structure of a space atall, we need to require something more.

Definition 28 (Homeomorphism). Suppose that (X, τ) and (Y, σ) are topologicalspaces. A function f : X → Y is called a homeomorphism iff the following four propertieshold:

1. f is one-to-one,

2. f is onto,

3. f is continuous,

4. f−1 is continuous.

If such a function f exists, the topological spaces (X, τ) and (Y, σ) are called homeomorphicand we write X ∼= Y .

In words, a homeomorphism between two topological spaces is a bicontinuous bijection;it maps each space bijectively and continuously to the other. Homeomorphic spaces shareessentially all important properties in common, so when two spaces are homeomorphic wethink of them as ”morally the same space.” For this reason it can be important to identifywhether two spaces are homemorphic. We make a few final statements about properties thathomeomorphic spaces share, and give one example in conclusion.

Proposition 29. Let (X, τ) and (Y, σ) be topological space, let U ⊂ X and let f : X → Ybe a homeomorphism. Then

• U is open in X iff f(U) is open in Y ,

• U is closed in X iff f(U) is closed Y ,

• U is connected in X iff f(U) is connected in Y ,

• U is compact in X iff f(U) is compact in Y ,

• X is Hausdorff iff Y is Hausdorff.

Example 30. Consider R with the usual topology. Any open interval (a, b) is homeomorphicto the interval (0, 1) under the map f : (a, b)→ (0, 1) defined by

f(x) =x− ab− a

, x ∈ (a, b).

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Likewise, (0, 1) is homeomorphic to R under the map g : (0, 1)→ R defined by

g(x) = tan(π(x− 1

2

)), x ∈ (0, 1).

Homemorphism is an equivalence relation (if two spaces are homemorphic to the same space,they are homemorphic to each other); indeed we can always compose homemorphic mapsand retain a homemorphism. Thus any interval (a, b) is homemorphic to R.

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Real Analysis

Real analysis is concerned with the rigorous underpinnings of calculus. However, whenwe teach calculus, we do everything formally and so everything is assumed to be “nice”(all the commong functions from calculus are smooth, for example). Now we make no suchassumptions: analysis is largely about tearing down out intuition from calculus and buildingit back up again but with rigor. Accordingly, most real analysis courses start with the basicconstruction of R; for brevity, we leave this out and simply start talking about properties ofsequences, functions, sets, etc. Much of this will overlap with the preceding topology notes,but often the same concepts are tackled in very different ways. Some of this will also berepeated from the Calculus I & II notes.

We’ll start by discussing general metric spaces, giving several definitions and theorems,and later specialize the conversation to R.

Definition 31 (Metric Space). Let X be a set and let d : X ×X → [0,∞). We call d ametric on X (and call (X, d) a metric space) if the following three properties hold:

1. d(x, x) = 0 for all x ∈ X and d(x, y) > 0 when x, y ∈ X, x 6= y,

2. d(x, y) = d(y, x) for all x, y ∈ X,

3. d(x, z) ≤ d(x, y) + d(y, z) for all x, y, z ∈ X.

Metrics generalize the notion of distance to non-Euclidean spaces.

Example 32. The prototypical example of a metric space is R with the metric d(x, y) =|x− y|. This can be generalized to Rn. Indeed, in Rn, we define the metric

d(x, y) = ‖x− y‖ ..=

(n∑i=1

(xi − yi)2)1/2

, x, y ∈ Rn.

Another example: for any set X, we can define the discrete metric d(x, y) = 0 if x = y andd(x, y) = 1 if x 6= y.

Definition 33 (Metric Topology). Let (X, d) be a metric space, x ∈ X and r > 0.Define the ball centered at x of radius r by

Br(x) = {y ∈ X : d(x, y) < r}.

Let τ be the topology generated by the set β = {Br(x) : x ∈ X, r > 0}. This is called themetric topology on X. In this topology, a set U ⊂ X is open iff for all x ∈ U , there is r > 0such that Br(x) ⊂ U . We will also refer to τ as the topology generated by the metric d.Conversely, if we have a topological space (X, τ) and there is a metric d on X that generatesτ , then we call τ metrizable.

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For some example, the discrete metric on a set will generate the discrete topology. Thestandard metric d(x, y) = |x− y| on R will generate the standard topology on R.

Metric topologies have very nice structure. Most of the topological properties discussedabove can be given new definitions only using the metric structure that d lends to X. Thus,the definitions in real analysis and topology may look different at a first glance, but they arealways compatible. We discuss some properties of metric topologies now.

Proposition 34. Let (X, d) be a metric space. The metric topology on (X, d) is Hausdorff.

This is very easy to prove: if x, y ∈ X and x 6= y then d(x, y) > 0. Then Bε(x) andBε(y), where ε = d(x, y)/3, are open neighborhoods of x and y respectively which are dis-joint, proving that the space is Hausdorff.

Definition 35 (Limit of a Sequence). Let (X, d) be a metric space and let {xn}∞n=1 bea sequence in X. We say that x ∈ X is the limit of xn iff for all ε > 0, there is N ∈ N suchthat d(x, xn) < ε for all n ≥ N . In this case, we say that xn converges to x and we writexn → x or limn→∞ xn = x.

This definition of the limit is exactly as in calculus but generalized to arbitrary metricspaces. Limits give us a way to characterize closed sets in metric topologies.

Definition 36 (Limit Points). Let (X, d) be a metric space and let U ⊂ X. A pointx ∈ X is called a limit point of U if there is a sequence {xn} in U such that xn 6= x for alln ∈ N and xn → x.

Proposition 37. Let (X, d) be a metric space and let C ⊂ X. Then C is closed in themetric topology iff for all sqeuences {xn} in C converging to a limit x ∈ X, we have thatx ∈ C. In the terminology of the above definition, a subset of a metric space is closed iff itcontains all of its limit points.

Recall, in topology a set is closed iff the complement of the set is open. This theoremgives an alternate definition, and it is often times easier to check that a set contains its limitpoints than to check that its complement is open.

Definition 38 (Closure). Let U ⊂ X and let L = {x ∈ X \U : x is a limit point of U.}.Then the closure of U is defined by U = U ∪ L. That is, the closure of U is the set U plusall of the limit points of U .

Note, we already defined the closure in topology to be smallest closed set containing aset. Again, these notions are compatible: U defined in the definition above is the smallestset which is closed in the metric topology and contains U . In many metric spaces, we canbuild any point in the space by considering a smaller set and taking limits.

Definition 39 (Dense Set). Let (X, d) be a metric space and let D ⊂ X. We say that Dis dense in X iff for all x ∈ X, there is a sequence {xn} in D such that xn → x. Equivalently,

12

D is dense in X iff for all x ∈ X and ε > 0, there is y ∈ D such that d(x, y) < ε. Again,equivalently, D is dense in X if D = X. One last equivalent statement: D is dense in X ifevery open subset of X contains a least one point in D.

Intuitively, a dense set is tightly packed into X; it may not include all elements, but thegaps between elements are infinitesimally small. In this way it seems like a dense set mustcontain “most” of the space, but this is a place where intuition fails. Indeed, a dense setcan actually be quite small in a few different senses. We define one sense here and discuss itmore when we discuss R later.

Definition 40 (Countable Set). A set C is countable if there is a injective mapf : C → N. Equivalently, a set C is countable if the elements of C can be listed in asequence: C = {cn}∞n=1. A set is C countably infinite if there is a bijective map f : C → N.

Definition 41 (Separable Space). Let (X, d) be a metric space (or more generally atopological space). We say that X is separable if there is a countable set D ⊂ X which isdense in X: D = X.

Again, intuitively, it may seem like a separable space needs to be “small” because it hasa “small” dense set, but this intuition is not true in any meaningful sense. There are highlynon-trivial separable spaces.

Definition 42 (Cauchy Sequence). Let (X, d) be a metric space and let {xn} be asequence in X. We call {xn} a Cauchy sequence iff for all ε > 0, there is N ∈ N such thatd(xn, xm) < ε for all n,m ≥ N .

A Cauchy sequence is one that eventually begins to cluster together. Intuitively, we maythink that if the sequence clusters together, it must cluster around some point and thus itwill converge to that point. However, if the space X is “missing” some points, then thesequence may cluster around a missing point and thus fail to converge to any member of X.Thus we use Cauchy sequences to define a notion of a space not having any “missing” points.

Definition 43 (Complete Space). We call a metric space (X, d) complete iff for allCauchy sequences {xn} in X, there is x ∈ X such that xn → x.

Complete spaces are nice because to prove a sequence {xn} has a limit one first needs toidentify a candidate x and then prove that d(x, xn) becomes small. However, the candidatex may be difficult or impossible to identify. However, if the space is complete, to prove thata sequence converges, one no longer needs to identify a candidate; rather can instead provethat {xn} is a Cauchy sequence and conclude that it converges in that manner.

Besides sequences, much of calculus is concerned with functions and their properties likecontinuity, differentiability and integrability. We can discuss continuity in general metricspaces; the other concepts require some of the structure of R, so we leave them for later.

Definition 44 (Continuity). Let (X, dX) and (Y, dY ) be two metric spaces, let f : X → Y

13

and let x ∈ X. We say that f is continuous at x iff for all ε > 0, there is δ = δ(x, ε) > 0such that for z ∈ X, dX(x, z) < δ =⇒ dY (f(x), f(z)) < ε. We say that f is continuouson X (or merely, f is continuous) iff f is continuous at every point x ∈ X. Equivalently, afunction f is continuous on X iff for all x, y ∈ X and all ε > 0, there is δ = δ(x, y, ε) > 0such that dX(x, y) < δ =⇒ dY (f(x), f(y)) < ε.

This is the exact notion of continuity that we presented in calculus, but generalized tometric spaces. Again, it is a useful exercise to prove that this notion of continuity is equiv-alent to the topological notion of continuity. Because metric spaces have nice structure, wecan also characterize continuity in terms of limits of sequences.

Theorem 45 (Sequential Criterion Theorem). Let (X, dX) and (Y, dY ) be metricspaces and let f : X → Y . Then f is continuous iff for all sequences {xn} in X convergingto a point x ∈ X, we have that f(xn)→ f(x) in Y .

A function satisfying the latter condition is said to be sequentially continuous, so thisproposition tells us that a function between metric spaces is continuous iff it is sequentiallycontinuous.

In calculus, this would conclude our discussion of continuity. However, in analysis, wedefine more stringent definitions of continuity to more finely differentiate between classes offunctions.

Definition 46 (Uniform Continuity). Let (X, dX) and (Y, dY ) be two metric spaces andlet f : X → Y . We say that f is uniformly continuous iff for all ε > 0, there is δ = δ(ε) > 0such that for all x, y ∈ X, dX(x, y) < δ =⇒ dY (f(x), f(y)) < ε.

At first glance this definition looks identical to the definition of continuity, but it is not.The subtle difference is in the order of the quantifiers. In the definition of continuity, the δis allowed to depend on the particular x and y you are testing; in the definition of uniformcontinuity, δ cannot depend on x and y: there must be a uniform δ that only depends ε. Inlogical notation this different is expressed as such: f is continuous iff

(∀ε > 0)(∀x, y ∈ X)(∃δ > 0) ; dX(x, y) < δ =⇒ dY (f(x), f(y)) < ε,

whereas f is uniformly continuous iff

(∀ε > 0)(∃δ > 0)(∀x, y ∈ X) ; dX(x, y) < δ =⇒ dY (f(x), f(y)) < ε.

Thus uniform continuity is a stronger condition: if f is uniformly continuous, then f iscontinuous, but not vice versa. And even stronger notion of continuity is as follows.

Definition 47 (Lipschitz Continuity). Let (X, dX) and (Y, dY ) be two metric spacesand let f : X → Y . We say that f is Lipschitz continuous iff there is a constant L > 0 suchthat for all x, y ∈ X, dY (f(x), f(y)) ≤ L · dX(x, y). In this case, the smallest such L iscalled the Lipschitz constant of f .

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Thus Lipschitz continuous function have an explicit bound on the distance between f(x)and f(y) in terms of the distance between x and y. If f is Lipschitz continuous with constantL, then for any ε > 0, we can take δ = ε/L and we will find that f satisfies the definition ofuniform continuity; hence Lipschitz continuity is even stronger.

With this we drop generality and talk specifically about the analytic and topologicalstructure of R. Again, we will not explicitly construct the real numbers, but we’ll presentthe rough idea, which is to start with rational numbers and define real numbers as limitpoints of Cauchy sequences of rationals.

Definition 48 (Rational Numbers). Define the set of rational numbers to be thosewhich can be written as a ratio of integers. That is, Q = {p

q: p, q ∈ Z, q 6= 0}. With this

definition, the rational numbers form a field: they are an abelian group under addition and,neglecting the additive identity, they are an abelian group under multiplication.

The rational numbers become a metric space with the metric d(a, b) = |a− b| for a, b ∈ Q.

Proposition 49. Q is countably infinite. That is, we can enumerate the rationals in asequence Q = {qn}∞n=1.

This requires what is called a diagonalization argument. One can list the rationals ina two-dimensional table where row k corresponds to the rationals whose denominator is k.Then one can traverse the table down each diagonal, assigning a natural number to eachrational number.

Proposition 50. Q is indiscrete in the sense that between any two rational numbers, onecan find another rational number. Indeed, if a, b ∈ Q and a < b, then for large enoughn ∈ N, we have a < a+ 1

n< b and a+ 1

nis still rational.

From this proposition, it seems that the rational numbers do not have any large holes,and this may lead us to believe that the rational numbers are complete, but this is incorrect.Indeed, the rational numbers do not form a complete metric space.

Proposition 51. There is no x ∈ Q such that x2 = 2, but there is a Cauchy sequence {xn}in Q such that x2n → 2.

This proposition ruins completeness. Define f : Q→ Q by f(x) = x2 for x ∈ Q. We seethat f(0) = 0 and f(2) = 4, so intuitively because f cannot jump over points, we should beable to find x ∈ Q such that f(x) = 2, but we’ve just asserted that this is impossible. Thuswe conclude that either f is discontinuous because it jumps over a point (but it is easy toshow that f is indeed continuous), or that Q is missing some points. It is the latter thatis true. In the terminology of metric spaces, this shows that Q is not complete. However,for any metric space, we can define a notion of the completion of the space; that is, we candefine a new metric space by adding some points, which is compatible with the original butis complete. This is how we define R. That is, the real numbers are defined as those numbers

15

which can be realized as limit points of Cauchy sequences of rational numbers. Thus anyrational is real, but the numbers x such that x2 = 2 are real without being rational. Thoughthis is essentially the definition of R, we state this as a proposition.

Proposition 52. Any real number is a limit of rational numbers. That is, Q is dense inR, and thus any open set in R contains rational numbers. Furthermore R is complete underthe metric d(x, y) = |x− y| , x, y ∈ R.

Definition 53 (Irrational Numbers). The irrational numbers to be those which are realbut not rational; that is, the irrational numbers are given by R \Q = {x ∈ R : x 6∈ Q}.

Thus, for example, the solution to x2 = 2 are irrational, and of course, since f : [0,∞)→[0,∞) defined by f(x) = x2 for x ∈ [0,∞) is bijective, we can define an inverse map, and oncewe’ve done, we denote the solutions to x2 = 2 as

√2 and −

√2. Other common irrational

numbers are π and e.We asserted before that Q is a countable set. It is reasonable to ask if R is still countable

since all numbers in R are simply limits of numbers in Q.

Proposition 54. The set R of real numbers is not countable, and thus R\Q is not countableeither.

To prove this, one can use another type of diagonalization argument. If we assume thatthe numbers between 0 and 1 are countable, then we can list their decimal representations,but then it is not difficult to explicitly construct a number between 0 and 1 which is notaccounted for in the list. In this sense Q is much smaller than R, but is still manages to bedense in R. While we know there are holes in Q, we might still wonder about the generalstructure of Q within R.

Proposition 55. Q is disconnected in R.

Indeed, we see that Q ⊂ (−∞,√

2) ∪ (√

2,∞). which shows that Q is contained in twodisjoint open sets. We can make this even stronger.

Proposition 56. The irrational numbers R \Q are dense in R.

Thus in between any two rationals, we can find an irrational, and we can use this to showthat if a set of rational numbers has two points, then it cannot be connected; that is, Q istotally disconnected as a subset of R.

This example that√

2 6∈ Q displays something troubling about the structure of Q. Con-sider the set A = {x ∈ Q : x2 < 2}. It is easy to see that this set is bounded (the elements ofthis set do not get arbitrarily large; for example, for x ∈ A, we will certainly have x < 5), butthere is no tight upper bound in Q. That is, for any rational number q such that x < q for anyx ∈ A, one could find a smaller rational number p < q such that x < p for all x ∈ A. In short:there is no least upper bound for this set in Q. This is a feature which is fixed by moving to R.

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Definition 57 (Supremum & Infimum). Suppose that A ⊂ R. The supremum of Ais defined to be the least upper bound of A. That is, the supremum is the number S ∈ R(if such a number exists) such that x ≤ S for all x ∈ A, and if x ≤ M for all x ∈ A, thenS ≤ M . Likewise, the infimum of the set A is defined to the be greatest lower bounded ofA. That is, the infimum is the number I ∈ R (if such a number exists) such that x ≥ I forall x ∈ A, and if x ≥ m for all x ∈ A, then I ≥ m. When such numbers S, I exist, we writeS = sup(A) and I = inf(A).

Definition 58 (Bounded Set). A set A ⊂ R is called bounded, if there is M > 0 suchthat |x| ≤M for all x ∈ A.

Proposition 59. Every non-empty bounded set in R has a finite supremum and infimum.(Indeed, if we allow the supemum or infimum to take the values ±∞, then every non-emptyset in R has a supremum and infimum.)

This is one more way in which we have “filled in the holes” when moving from Q to R.

We want to further study the analytical and topological properties of R. We have alreadyremarked that R is complete and thus every Cauchy sequence in R has a limit in R. As wesaid before, one advantage of this is that when testing if a sequence has a limit, we do notneed to identify a candidate for the limit to prove convergence. We would like other suchtests to characterize when limits in R exist.

Theorem 60 (Monotone Convergence Theorem). Suppose that {xn} is a sequencein R which is increasing (that is, xn ≤ xn+1 for all n ∈ N) and bounded above (that is, thereexists M > 0 such that xn ≤M for all n ∈ N). Then {xn} converges (and in fact, {xn} willconverge to the least M such that xn ≤M for all n ∈ N). Likewise if {xn} is decreasing andbounded below then it converges.

Loosely speaking, there are two possible ways for a sequence not to converge. It couldeither blow up to ±∞ as with the sequence xn = n2 or it could oscillate between certainvalues as with the sequence xn = (−1)n. In the first case, no matter how we look at it, thesequence will always diverge, but in the latter case, if we look only along the even termsx2n = (−1)2n = 1, then we have a stable sequence which converges. The succeeding defini-tions and theorems deals with this situation.

Definition 61 (Subsequence). Suppose that {xn}∞n=1 is a sequence in R. A subsequenceis a sequence {xnk

}∞k=1 such that nk < nk+1 for all k ∈ N. Thus {xnk} ⊂ {xn}.

Theorem 62 (Convergence Along Subsequences). Suppose that {xn} is a sequencein R such that xn → x ∈ R as n → ∞. Then every subsequence {xnk

} will also satisfyxnk→ x as k →∞.

Theorem 63 (Bolzano-Weierstrass Theorem). Every bounded sequence has a con-vergent subsequence. That is, suppose that {xn} is sequence in R and there is M > 0 such

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that |xn| ≤M for all n ∈ N . Then there exists some susbsequence {xnk} which converges.

Another way to state the above theorem is that if the sequence {xn} resides in thebounded set (a, b), then along a subsequence we can find a limit. If instead we consider theclosed set [a, b], then this set contains its limit points and so the limit must also lie in [a, b].With this is mind, we state a theorem characterizing compact sets in R.

Theorem 64 (Heine-Borel Theorem). A subset of R is compact (in the sense of opencovers) iff it is closed and bounded.

Thus compact sets are precisely those which contain all their limit points and are nottoo large in either direction. With this in mind, the prototypical compact sets in R are theclosed intervals [a, b] where a, b ∈ R, a < b. However, this theorem applies more generallyin the metric topology on Rn. In light of the Bolzano-Weierstrass Theorem, we can addanother equivalent statement.

Proposition 65. Suppose that C ⊂ R. Then the following are equivalent:

• C is compact,

• C is closed and bounded,

• every sequence in C has subsequence converging to a point in C.

Sets satisfying the third property are called sequentially compact and this theorem tellsus that in R, sets are compact iff they are sequentially compact.

Now a valid question is: why is compactness an important property? The definition ofcompactness is somewhat opaque, but compactness allows you to narrow your focus frominfinitey many things to finitely many things. This especially comes in handy when dealingwith functions. Indeed, we will move from here to discussing functions on R.

Definition 66 (Bounded Function). Suppose that f : A → R where A ⊂ R. We saythat f id bounded iff there is M > 0 such that |f(x)| ≤ M for all x ∈ A. That is, f isbounded if the image {f(x) : x ∈ A} is a bounded set in R.

Proposition 67. Continuous functions from compact sets into R are bounded, and achievemaximum and minimum values. Specifically, suppose that C ⊂ R, C is compact andf : C → R is continuous. Then there are xmin, xmax ∈ C such that f(xmin) ≤ f(x) ≤ f(xmax)for all x ∈ C. [Note: this is not only asserting that f remains trapped between two extremevalues, it is also asserting the existence of xmin and xmax where f meets those extreme values.]

How does compactness come into play here? Consider, if f is continuous then the setsUn = {x ∈ C : f(x) < n} for n ∈ N are open since they are the pre-image of the opensets (−∞, n). Also since f(x) ∈ R for all x ∈ C, for each x ∈ C we can find n ∈ N suchthat x ∈ Un. This shows that {Un} is an open cover of C. If C is compact, there is afinite subcover Un1 , · · · , UnK

. However, these sets are nested, so this shows that C ⊂ UN

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where N = max{n1, . . . , nK}. Then for all x ∈ C, we have f(x) < N , which shows that fis bounded from above. What has happened here? We began with infinitely many differentbounds f(x) < n for n ∈ N each of which may have applied at different portions of C.Using compactness we were able to pare this down to a finite number of bounds, and thensimply pick the largest one. Making a similar argument, we can get a lower bound, andthus the range {f(x) : x ∈ C} is bounded. Since the set is bounded, it has an infimumand supremum. The theorem also asserts that f will meet these values. How do we find thepoint that meets the supremum? We can contruct a maximizing sequence {xn} such that

f(xn)→ supx∈C

f(x) = sup{f(x) : x ∈ C}.

By sequential compactness, the sequence has a subsequence {xnk} with a limit xmax ∈ C

and by continuity, we will have f(xnk) → f(xmax) and f(xmax) = supx∈C f(x). Thus while

compactness helps us arrive at the bound on f , sequential compactness helps us actuallyfind the point where f achieves the bound.

Above we defined not only continuity but also uniform continuity and Lipschitz conti-nuity. We would like easy ways to identify which functions satisfy these stronger propertiesand this is somewhere where compactness can help a bit as well.

Proposition 68. Continuous functions on compact sets are uniformly continuous. That is,suppose that C ⊂ R and f : C → R is continuous. If C is compact, then f is uniformlycontinuous.

Again, we should observe how compactness comes into play. Fix ε > 0. Recall, if f iscontinuous at each point x ∈ C, then for each individual point, we can find δx > 0 such thatfor y ∈ C, |x− y| < δx =⇒ |f(x)− f(y)| < ε. Here we have (possibly) infinitely manydifferent δx > 0, but if we want to satisfy the definition of uniform continuity, we need tohave a single δ > 0. If the number δx > 0 works in the definition of continuity at x ∈ C, thenany smaller number 0 < δ′ < δx will also work. Thus one idea is to take the minimum overall such δx > 0, and this minimal δ will work for all x ∈ C. However, the set {δx}x∈C may nothave a minimum and its infimum maybe zero, so this doesn’t quite work. But we note thatthe sets (x− δx, x + δx) form any open cover of C. If C is compact, we can extrace a finitesubcover (x1− δx1 , x1 + δx1), . . . , (xK − δxK , xK + δxK ) which still covers all of C. Now thereare only finitely many δxk to choose from; choosing the minimum δ = min{δx1 , . . . , δxK} willprovide a δ > 0 which works uniformly over all x ∈ C, proving that f is uniformly contin-uous. Again, we started with an infinite collection, and compactness allowed us to pare itdown to a finite collection.

Lipschitz continuity can also be easier to identify via compactness but in a slightly morecomplicated way. First, recall a few definitions and theorems from calculus (for more expo-sition regarding the calculus topic, one can look back to the calculus notes).

Definition 69 (Lipschitz Continuity). Suppose that f : R → R. We call f Lipschitzcontinuous if there is a constant L > 0 such that for all x, y ∈ R, |f(x)− f(y)| ≤ L |x− y| .

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Definition 70 (Differentiability). Suppose that f : R → R and x ∈ R. We say that fis differentiable at x if the limit

limh→0

f(x+ h)− f(x)

h

exists. In this case we call the limit f ′(x). We say that f is differentiable in a set A ⊂ R iff is differentiable at all x ∈ A.

Theorem 71 (Mean Value Theorem). Suppose that f : R → R is differentiable in R.Then for any a, b ∈ R, a < b, there is c ∈ (a, b) such that

f(b)− f(a)

b− a= f ′(c).

This, in turn, implies that |f(b)− f(a)| = |f ′(c)| |b− a| .

Note the similarity between the last statement and the definition of Lipschitz continuity.There’s a very formal similarity that hints at some connection of the form |f ′(c)| ∼ L. In-deed, we can make this precise.

Proposition 72. Suppose that f : R→ R is differentiable on R. If the derivative f ′ : R→ Ris continuous, then f is Lipschitz continuous on any compact subset of R. If the derivativef ′ is bounded, then f is Lipschitz continuous on all of R. If f ′ is unbounded on some subsetof R, then f is not Lipschitz continuous on that subset.

This gives a rough equivalence between Lipschitz continuous functions and continuouslydifferentiable functions. However, based on this continuous differentiability still seems a bitstronger than Lipschitz continuity and indeed, it is. Take for example, the function f(x) = |x|for x ∈ R. This function if Lipschitz continuous with Lipschitz constant 1 because of thereverse triangle inequality:

|f(x)− f(y)| =∣∣ |x| − |y| ∣∣ ≤ |x− y| .

However, this function is not differentiable on all of R. [In fact, a famous theorem statesthata function is Lipschitz continuous functions iff it is differentiable almost everywhere2

and the a.e. derivative is essentially bounded.]

Finally, we discuss sequences of functions and the interplay between sequences of func-tions and the Riemann integral. Indeed, one of the reasons that the Lebesgue integral andthe field of measure theory came about was because the Riemann integral does not play nicewith sequences of functions, as we will see shortly.

Definition 73 (Pointwise Convergence). Let A ⊂ R and let {fn} be a sequence offunctions fn : A → R. We say that the sequence {fn} converges pointwise to a function

2Here almost everywhere has a technical meaning.

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f : A → R iff for all x ∈ A, the sequence {fn(x)} in R converges to f(x). That is, {fn}converges pointwise to f iff for every x ∈ A and ε > 0, there is N = N(x, ε) ∈ N such that|fn(x)− f(x)| < ε for all n ≥ N .

Example 74. Consider the sequences fn, gn : [−1, 1]→ R given by

fn(x) = |x|n , and gn(x) =√x2 + 1

nfor x ∈ [−1, 1].

Note thatfn(0) = 0, fn(±1) = 1, for all n ∈ N.

If x ∈ [−1, 1] \ {−1, 0, 1}, then log |x| < 0 and so

limn→∞

fn(x) = limn→∞

|x|n = limn→∞

en log|x| = 0.

Thus fn converges pointwise to the function f : [−1, 1]→ R defined by

f(x) =

{1, x = −1, 1,0, x ∈ (−1, 1).

Next for gn, note that for all x ∈ [−1, 1],

|x| =√x2 ≤

√x2 + 1

n= gn(x).

Conversely, if a, b ≥ 0 then |a2 + b2| ≤ a+ b (one can easily verify this inequality by squaringboth sides), and so

gn(x) =

√x2 +

1

n≤ |x|+ 1√

n.

Combining the inequalities and subtracting |x|, we see

0 ≤ gn(x)− |x| ≤ 1√n, x ∈ [−1, 1].

Thus for all x ∈ [−1, 1], we havelimn→∞

gn(x) = |x|

and so gn converges pointwise to the absolute value function on [−1, 1].

There are two interesting differences to point out between these examples. In the firstexample, we had a sequence of continuous functions which converged pointwise to a discon-tinuous function, which is somewhat disconcerting (this is similar to before when we hada sequence of rationals converging to an irrational; morally, this indicates that continiuousfunctions are “incomplete with respect to pointwise limits”). The other difference is thatproving the convergence of fn required special consideration for different values of x, whereasproving the convergence for gn did not. To address both of these, we introduce a strongernotion of convergence.

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Definition 75 (Uniform Convergence). Let A ⊂ R and let {fn} be a sequence offunctions fn : A → R. We say that the sequence {fn} converges uniformly to a functionf : A → R iff for every ε > 0, there is N = N(ε) ∈ N such that |fn(x)− f(x)| < ε for allx ∈ A and n ≥ N .

Again, at first glance this looks roughly the same as pointwise convergence, but the word“uniform” indicates that the same N in the definition of convergence works for all x in theset. Because there are two different modes of convergence, when considering functions thestatement fn → f is vague, and one should always specify what type of convergence is be-ing proven/assumed (there are many other types of convergence as well; these are typicallyaddressed in a first course on measure theory). Uniform convergence is important for thefollowing reason.

Proposition 76. A uniform limit of continuous functions is continuous. That is, if A ⊂ Rand {fn} is a sequence of continous functions fn : A→ R such that fn converges to f : A→ Runiformly, then f is also continuous.

Morally, this proposition states that continuous functions are “complete with respect touniform convergence.” We would like to place some topological structure on sets of continu-ous functions to make this more rigorous.

Definition 77 (Continuous Functions as a Vector Space). Suppose that A ⊂ R.Define the set C(A) = {f : A → R | f is continuous}. This set becomes a vector space overR under pointwise addition and pointwise scalar multiplication. That is, if f, g ∈ C(A) andα ∈ R, define (f + αg) : R→ R by

(f + αg)(x) = f(x) + αg(x), x ∈ A.

Then (f + αg) ∈ C(A).

Definition 78 (Norm/Metric on Continuous Functions). Suppose that A ⊂ R iscompact. Define the map ‖ · ‖ : C(A)→ [0,∞) by

‖f‖ = supx∈A|f(x)| , f ∈ C(A).

This map defines a norm on C(A). Thus d : C(A)× C(A)→ [0,∞) defined by

d(f, g) = ‖f − g‖, f, g ∈ C(A)

is a metric on C(A).

Using Proposition 76 one can prove that for A ⊂ R compact, the metric space (C(A), d)with d defined as in Definition 78 is a complete metric space (indeed convergence in thismetric space is precisely uniform convergence). This fact is very important in differentialequations, where one uses Picard iteration to prove existence and uniqueness of certainequations. There are several more theorems regarding the structure of C(A), focusing, for

22

example, on identifying sets which are compact in the metric topology or identifying thecontinuous dual space of C(A). These are beyond the scope of these notes.

Lastly, we want to examine the interplay between the Riemann integral and convergenceof sequences of functions. We will not recall the definition of the Riemann integral here (seethe notes on Calculus I), except to remind the reader that it is essentially “area under thecurve” and can be computed using the Fundamental Theorem of Calculus. With this inmind, it is reasonable to think that if fn converges to f (in some sense) then the limit of theRiemann integrals of fn should converge to the Riemann integral of f . However, this is notalways the case.

Figure 3: Example 79, fn(x)

Example 79. We’ll do two examples herewhere we have functions fn → f (in somesense) but the limit of the integral is not theintegral of the limit. This will show thatin many cases

∫ bafn(x)dx 6→

∫ baf(x)dx even

when fn → f . For the first example, con-sider fn : [0, 1]→ R defined by

fn(x) =

4n2x, 0 ≤ x < 1

2n,

4n2(1n− x), 1

2n≤ x < 1

n,

0, 1n≥ x ≤ 1.

This is graphed in figure 3. Note that forall n ∈ N, fn(0) = 0 and for any x ∈ (0, 1],we have fn(x) = 0 for all n ≥ d1/xe. Thisshows that fn(x) → 0 for all x ∈ [0, 1] andwe conclude that fn converges pointwise tothe zero function f ≡ 0. However, calcu-lating the area under the curve, we see that∫ 1

0f(x)dx = 1

2· 1n· 2n = 1 for all n ∈ N, and

so we have a situation where

1 = limn→∞

∫ 1

0

fn(x)dx 6=∫ 1

0

f(x)dx = 0.

[Note: this (and similar examples) is often referred to as “vertical escape to ∞”; the massunder the curve vanished as n→∞ because the functions got very large on a very small set]

For a second example, consider the functions fn : [0,∞)→ R defined by

fn(x) =

0, 0 ≤ x < n,

1n, n ≤ x ≤ 2n,

0, n < x <∞.

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Here we see that 0 ≤ fn(x) ≤ 1/n for all x ∈ [0,∞) and all n ∈ N, from which it readilyfollows that fn converges uniformly to the zero function f ≡ 0. However, calculating theintegral, we find that

∫∞0fn(x)dx = 1

n· (2n− n) = 1 and so once again, we have

1 = limn→∞

∫ 1

0

fn(x)dx 6=∫ 1

0

f(x)dx = 0.

[Note: this (and similar examples) is referred to as “horizontal escape to∞”; the mass underthe curve vanished as n→∞ because the support of the functions went to ∞.]

Both of these display cases where we may have∫ bafn(x)dx 6→

∫ baf(x)dx. What was the

key feature of each example? In the first example, the convergence was non-uniform; inthe second example, the domain was non-compact. If both these are rectified, then we canguarantee that limits of integrals are integrals of limits.

Proposition 80. Suppose that a, b ∈ R and a sequence fn : [a, b]→ R converges uniformlyto f : [a, b]→ R. Then

limn→∞

∫ b

a

fn(x)dx =

∫ b

a

f(x)dx.

This proposition shows that the map f 7→∫ baf(x)dx is a continuous functional on

C([a, b]). One final note: the conditions of Proposition 80 are sufficient but they arenot necessary. Going back to the example fn(x) = |x|n for x ∈ (−1, 1) and n ∈ N, we notethat fn converges pointwise to the zero function f ≡ 0 and indeed we also have∫ 1

−1|x|n dx =

2

n+ 1→ 0 =

∫ 1

−1f(x)dx,

so in this case uniform convergence and a compact domain were not necessary. This wasone of the motivating factors for developing measure theory and the Lebesgue integral: inorder to find less stringent conditions on the behavior of fn while still guaranteeing that∫Xfn(x)dx→

∫Xf(x)dx when fn → f . There is one theorem that is particularly helpful for

this. Because we do not have the machinery of measure theory, we cannot state the theoremin full generality but we will state a particular case.

Theorem 81 (Lebesgue Dominated Convergence Theorem (Baby Version)). Sup-pose that fn : [a, b] → R is a sequence of continuous functions converging pointwise tof : [a, b] → R. Further suppose that there is a constant M > 0 such that |fn(x)| ≤ M forall n ∈ N and all x ∈ [a, b]. Then

limn→∞

∫ b

a

fn(x)dx =

∫ b

a

f(x)dx.

This version of the theorem essentially says that so long as there is no possibility of“vertical/horizontal escape to ∞” as in Example 79, we will have the desired behavior forthe limit of the integrals.

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