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].
Contents
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]
[2]