In this brief post, we investigate the topological foundations of analysis and give some of its first applications. We limit the scope to the topology of a metric space. In particular, we provide the knowledge that is needed for the application to probability and measure theory.

The objective of the next section is to prepare the very basic concept for the definition of a topological space. We start by defining an open and a closed ball, a neighborhood, interior point, an open and a closed set. All these definitions are interlinked to each other as we will see later on.

Parts of this post are based on **[1]** and **[2]**.

**Basic Definitions & Properties**

### Balls, Open & Closed Sets & Neighborhoods

Let denote a metric space.

**Definition 1**

An (open) **ball ** of radius and center is the set of all points of distance less than from , i.e. .

A **closed ball** of radius is the set of all points of distance less than or equal to from , i.e. .

A subset of is called a **neighborhood **of if there is some such that .

An element is called an **interior point** of if there is a neighborhood of such that .

A **set** of is called **open **if every point of is an interior point. A **set ** of is called **closed **in if its complement is open in .

The introduced concepts “interior point” and “open set” depend on the surrounding metric space . It is sometimes useful to make this explicit by saying “ is an interior point of with respect to ” or “ is open in “.

By definition, every superset of a ball is considered to be a neighborhood. Neighborhoods can be defined without mentioning explicitly the corresponding metric space.

**Example 1**

An open ball in is an open interval . However, if we consider embedded in , then is apparently not an open ball in .

For the sake of illustration we sketch a 2-dimensional open ball (i.e. an open interval) as a rectangle even though it would actually be a line without end-points. These end-points are illustrated by the lines crossing the x-axis.

The heuristic reasoning for that is that a one-dimensional interval cannot contain an open two-dimensional ball not matter how small the radius of this ball might be.

Apparently, is an interior point of if and only if there is some real such that . In addition, is open if and only if is a neighborhood of each of its points.

**Example 2**

An open ball in is an open set. Let be an arbitrary point. Set and consider with .

For all we have

(1)

and so is contained in . This shows that the arbitrary point is an interior point of .

That is, a set is open if and only if for every an open ball exists such that .

**Example 3**

The interval is closed in .

Let denote a real number that is not an element of . Then, and the ball with has empty intersection with .

For the sake of illustration we sketch a 2-dimensional open ball as a rectangle even though it would actually be a line without end-points.

Let us now consider the set . For all and we have . Hence, the set is open in .

In the following section we are going to outline basic properties of open & closed sets, which will provide the heuristic for the definition of a topological space.

### Properties of Open & Closed Sets

The following two propositions state basic properties of open and closed sets. It turns out that these properties characterize a topological space.

**Proposition 1** (Open Sets)

Let be a family of open sets.

(i) ;

(ii) if for an arbitrary index set ;

(iii) if for a finite index set .

Proof:

(i) The empty set possesses every property since it does not have an element. Hence, the empty set is closed and open. Correspondingly, the set is open.

(ii) If then there must be an index such that with . All the more holds true.

(iii) Let with be open sets. Since there exists a for each with . If we set then and the assertion follows.

Please note that the proof above would also be valid if we use neighborhoods instead of balls. This would mean that the proof is not dependent on the used metric space and suggests that we could derive a new mathematical structure from it, where –in general– no algebraic operations, distance functions or orders need to be defined. However, given that we restrict ourself to topology for metric spaces, we will not further outline this idea.

The proof of the corresponding proposition for closed sets is apparent given the definition of a closed set.

**Proposition 2** (Closed Sets)

Let be a family of closed sets.

(i) ;

(ii) if for an arbitrary index set ;

(iii) if for a finite index set .

Proof:

Apply the definition and Proposition 1 (Open Sets).

**Example 4**

Let us continue Example 3. The intersection of a finite number of intervals is closed in . Without loss of generality, let . All the sets are closed and so is in .

Let us now consider the family of open sets . For all elements of all open sets , we can find an open ball . Hence, the union of these sets also contain open balls for all elements and is thus an open set itself.

**Example 5**

An infinite intersection of open sets need not to be open. Consider in . Apparently, there cannot be a such that .

Correspondingly, infinite unions of closed sets need not to be closed. To this end, consider in .

## Topological Spaces

Topological spaces can be defined in many ways, however, given our focus on metric spaces and open sets the following definition is the most natural one. Please note that we could also use the properties of closed sets to define a topological space.

**Definition 2** (Topological Space)

A **topological space** is a pair , where is a set and is a family of subsets that satisfies

(i) ;

(ii) if for an arbitrary index set ;

(iii) if for a finite index set .

The following video provides a rather unorthodox way of thinking about a topology. However, it might help to get a heuristic for topological spaces. It also mentions the connection between metrics and a topology.

The next definitions will pave the way to the first application of a topological space — limits.

**Applications**

General Topology has to do with, among other things, notions of convergence. Convergence ca be defined in many different ways. In the following section we study the most popular way to define convergence by a metric. In order to define other types of convergence (e.g. point-wise convergence of functions) one needs to extend the following approach based on open sets. This, however, is not in scope of this post.

**Limits of a Function**

In this section we apply our knowledge about metrics, open and closed sets to limits. We thereby restrict ourselves to the basics of limits. Refer to **[1]** for further details.

Let denote a metric space. If the metric is not specified, we assume that the standard Euclidean metric is assigned.

* Definition 3 (Sequence)*:

A sequence in is a function from to by assigning a value to each natural number . The set is called

**sequence**of . The elements of are called

**terms**of the sequence.

Sequences in are called **real** **number sequences**. Sequences in , are called **real tuple sequences**. Note that latter definition is simply a generalization since number sequences are, of course, -tuple sequences with .

Let be a -tuple sequence in equipped with property . Property holds **for almost all terms** of if there is some such that is true for infinitely many of the terms with .

Sequences are, basically, countably many (– or higher-dimensional) vectors arranged in an ordered set that *may or may not exhibit certain patterns*.

**Example 6**:*(a)* The sequence can be written as and is nothing but a function defined by .

*(b)* Let us now consider the sequence that can be denoted by . The range of the function only comprises two real figures .

*(c)* Now, let us consider the sequence . Here, each natural is mapped on itself.

*(d)* Consider , that can also be written as .

*(e)* Consider the 2-tuple sequence in .

We might think that some of the above examples *contain patterns* of vectors (e.g. number) that are “getting close” to some other vector (e.g. number). Other sequences may not give us that impression. We are interested in what the long-term behavior of the sequence is:

- What happens for large values of ?
- Does the sequence approach a (real) vector/number?

**Convergence of a Sequence**

Now, let us try to formalize our heuristic thoughts about a sequence approaching a number arbitrarily close by employing mathematical terms.

By writing

(2)

we mean that for every real number there is an integer , such that

(3)

whenever . A sequence that fulfills this requirement is called **convergent**. We can illustrate that on the real line using balls (i.e. open intervals) as follows.

Note that represents an open ball centered at the **convergence point** or **limit** x. For instance, for we have the following situation, that all points (i.e. an infinite number) smaller than lie within the open ball . Those points are sketched smaller than the ones outside of the open ball .

Accordingly, a real number sequence is convergent if the absolute amount is getting arbitrarily close to some (potentially unknown) number , i.e. if there is an integer such that whenever .

This limit process conveys the intuitive idea that can be made *arbitrarily close* to provided that is *sufficiently large*. “Arbitrarily close to the limit ” can also be reflected by corresponding open balls , where the radius needs to be adjusted accordingly.

The sequence can also be considered as a function defined by with

(4)

whenever .

If there is no such , the sequence is said to **diverge**. Please note that it also important in what space the process is considered. It might be that a sequence is heading to a number that is not in the range of the sequence (i.e. not part of the considered space). For instance, the sequence Example 6 (a) converges in to 0, however, fails to converge in the set of all positive real numbers (excluding zero).

The definition of convergence implies that if and only if . The convergence of the sequence to 0 takes place in the **standard Euclidean metric space** .

While a sequence in a metric space does not need to converge, if its **limit is unique**. Notice, that a ‘detour’ via another convergence point (triangle property) would turn out to be the direct path with respect to the metric as .

A **convergent sequence** is also **bounded**. We can prove this intuitive statement by setting . Hence, it exists a , such that for all . This implies

(5)

for all . Let for then the assertion follows immediately.

Let us re-consider **Example 6**, where the sequence **(a)** apparently converges towards . Sequence **(b)** instead is alternating between and and, hence, does not converge. Note that example (b) is a bounded sequence that is not convergent. Sequence **(c)** does not have a limit in as it is growing towards and is therefore not bounded. However, sequence **(d)** converges towards 1. Finally, 2-tuple sequence **(e)** converges to the vector .

If we consider one of the converging examples carefully, we will notice that we can chose an arbitrarily small and we will find a correspondingly large , such that with .

For instance, let us define to be in Example 6 (a). Then, we can set and provided that .

A sequence is called **increasing **if for all . If an increasing sequence is bounded above, then converges to the supremum of its range.

**Cauchy Sequences**

If a sequence converges to a limit , its terms must ultimately become close to its limit and hence *close to each other*. That is, two arbitrary terms and of a convergent sequence become closer and closer to each other provided that the index of both are sufficiently large.

**Theorem 1** (Convergent and Cauchy Sequences):

Assume that converges in a metric space to a limit . Then for every real there is an integer such that

Proof: is the limit of , i.e. . Assume is given. Due to the fact that , we can choose an integer , such that for all . If is valid, we can conclude

by employing the triangle inequality of the metric.

The last proposition proved that two terms of a convergent sequence becomes arbitrarily close to each other. This property was used by Cauchy to construct the real number system by adding new points to a metric space until it is ‘completed‘. Sequences that fulfill this property are called Cauchy sequence.

* Definition 4 (Cauchy Sequence)*:

A sequence is called

**Cauchy sequence**, if the following condition holds true:

(6)

Note that **all **pairs of terms with index greater than need to get close together. It is not sufficient to require that two consecutive terms get close together.

**Example 7** (Non-Complete Space):

If we consider embedded in such that symbols such as and can be interpreted and used. The letter is assumed to represent a *rational number* in this example.

Considering the sequence in shows that the actual limit is not contained in .

Hence, a key question is:

- What condition on a sequence of numbers is
*necessary*and*sufficient*for the sequence to converge to a limit but does not explicitly involve the limit?

If we already knew the limit in advance, the answer would be trivial. In general, however, the limit is not known and thus the question not easy to answer. It turns out that the Cauchy-property of a sequence is not only necessary but also sufficient. That is, every convergent Cauchy sequence is convergent (*sufficient*) and every convergent sequence is a Cauchy sequence (*necessary*).

Note that every *Cauchy sequence is bounded*. To see this set , then there is a : and thus for all . This means that all points with lies within a ball of radius 1 with as its center.

**Theorem 2 **(Cauchy Sequences & Convergence):

In an Euclidean space every Cauchy sequence is convergent.

Proof: Let be a Cauchy sequence in and let be the range of the sequence. If is finite, then all except a finite number of the terms are equal and hence converges to this common value.

Now suppose is infinite. We use the Balzano-Weierstrass Theorem to show that has an accumulation point , and then we show that converges to . First, recall that each Cauchy sequence is bounded. Hence, since is infinite there must be an accumulation point according to the Bolzano-Weierstrass Theorem.

Now let , then there exist a such that whenever . Hence, if we have

,

so .

**Topological Spaces, Accumulation & Limit Points**

**Definition 5** (Accumulation & Limit Point)

Let and . We call an **accumulation point** of if every neighborhood of in has non-empty intersection with .

The element is called **limit point** of if every neighborhood of in contains a point of other than . Finally we set

Every point within an open set but also every limit point of this set is an accumulation point as illustrated in the following example. Here, is the open interval in the standard metric space .

Every point in is an interior, an accumulation and a limit point. The points and are limit points and also accumulation points.

Let us now consider a set with a single isolated point in the metric space .

Is an accumulation and/or a limit point of ?

Apparently, every neighborhood of in has non-empty intersection with , i.e. . However, is *not *a limit point since the intersection does not contain a point other than .

Elements of are therefore always accumulation points. This fact is also reflected in the next proposition.

**Proposition 3**: Let be a subset of a metric space .

(i) ;

(ii) if and only if is closed.

Proof: (i) See argumentation above.

(ii) ‘ Let . Since is not an accumulation point of , there is some of all neighborhoods of such that . Thus, , that is, is an interior point of . Consequently is open and is closed in .

‘ Let be closed in . Then is open in according to its definition. For any , there is some such that . This means that and are disjoint, and so is not an accumulation point of , i.e., . Hence, we have proved the inclusion , which is equivalent to . With (i), this implies .

The limit points of a set are the limits of certain sequences in .

**Proposition 4**: An element of is a limit point of if and only if there is a sequence in which converges to .

Proof: Let be a limit point of . For each , choose some element in . Then is a sequence in such that .

Conversely, let be a sequence in such that . Then, for each neighborhood of , there is some such that . This means that . Hence, each neighborhood of contains an element of other than .

**Corollary 1**: An element is an accumulation point of if and only if there is a sequence in such that .

Proof: If is a limit point, then the claim follows from Proposition 4. Otherwise, if is an accumulation point, but not a limit point of , then there is a neighborhood of such that . Thus, , and the constant sequence with for all has the desired property.

We can not characterize closed sets using convergent sequences.

**Proposition 5**: For , the following are equivalent:

(i) is closed;

(ii) contains all its limit points;

(iii) Every sequence in which converges in , has its limit in .

Proof: ‘(i) (ii)’ Any limit point of is also an accumulation point and so is contained in . Since is closed and Proposition 3, , and so all limit points are in .

‘(ii) (iii)’ Let be a sequence in such that in . Then, by Corollary 1, is an accumulation point of . This means that, either is in , or is a limit point of , so, by assumption, .

‘(iii) (i)’ This implication follows from Proposition 3 and Corollary 1.

**Literature:****[1]**

*Principles of mathematical analysis*. 3d ed. New York: McGraw-Hill (International series in pure and applied mathematics).

**[2]**

*Analysis. 1: ...*3. Auflage, 2. Nachdruck. Basel: Birkhäuser (Grundstudium Mathematik).