Definition of a Topological Space

In summary, the definition of a topological space requires that arbitrary unions and finite intersections of open sets are open. However, the condition that the whole space and the empty set are also open sets is redundant, as it is implied by the first two properties. This may cause issues in certain contexts, but can be resolved by considering a universe or specific set.
  • #1
Jamma
432
0
Just a small (and, really, quite useless) little nugget here:

In the definition of a topological space, we require that arbitrary unions and finite intersections of open sets are open. We also need that the whole space and the empty set are also open sets.

However, this last condition is actually redundant - the empty union is empty and, in the context of subsets of some set, the empty intersection is the whole set, so that the whole space and the empty set are open is actually implied by the first two properties!

Of course, I wouldn't suggest not including the last condition in the definition when teaching topology, just thought it was interesting to point out :)
 
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  • #2
Isn't the empty intersection still the empty set? You mean to say [tex]\emptyset\cap\emptyset=S[/tex]? (Where S is the whole set)
 
  • #3
I think the OP means "intersection over no sets".

A point x belongs to an intersection if and only if it belongs to each of the sets over which the intersection is taken. If the intersection is over no sets, there are no conditions to check. The statement "x belongs to the intersection" is then vacuously true.
 
  • #4
Exactly.

An arbitrary union indexed over some set is the set of points x which have the property that they belong to one of the indexed sets. Hence, the empty union is always empty.

The empty intersection is a little more troublesome in the general context. As you say, the condition is vacuously true, so you get issues with Russell's paradox and so on. These are all fixed if in the context of a universe, or if you are talking about subsets of a particular set (e.g. the set of elements in your topological space).

http://en.wikipedia.org/wiki/Intersection_(set_theory)#Nullary_intersection
 
  • #5



I appreciate the attention to detail and the desire for precision in definitions. While the last condition may seem redundant, it is important to include it in the definition in order to ensure clarity and avoid any potential confusion. Additionally, having all three conditions explicitly stated allows for easier comparisons and discussions of different topological spaces. In the grand scheme of things, it may seem like a small and useless nugget, but in mathematics, even the smallest details can have significant impacts.
 

FAQ: Definition of a Topological Space

1. What is a topological space?

A topological space is a mathematical concept that describes the properties and relationships between points in a given space. It is a set of points with a collection of open sets, which determine the topology of the space.

2. What are open sets in a topological space?

Open sets in a topological space are subsets of the space that contain all of their boundary points. They are used to define the topology of the space and determine which points are close to each other.

3. How is a topological space different from a metric space?

A topological space is a more general concept than a metric space. While a metric space uses a distance function to define the relationships between points, a topological space uses open sets and does not rely on a specific notion of distance.

4. What is the importance of topological spaces in mathematics?

Topological spaces are important in mathematics because they provide a general framework for studying the structure and properties of different types of spaces. They are used in various branches of mathematics, including topology, analysis, and geometry.

5. How are topological spaces applied in real-world situations?

Topological spaces have a wide range of applications in real-world situations, such as in physics, computer science, and economics. They can be used to model complex systems, study the behavior of networks, and analyze the properties of data sets.

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