Lebesgue topological dimension

In summary: I think Munkres is trying to get to the idea of a "basis" for a topology.In summary, the conversation discusses the concept of dimension in topology, specifically in relation to the open sets U_n=\{(x,y)\mid -\infty < x <\infty, n-1<y<n+1\}. The speaker questions how ℝ2 can have dimension 2 and whether the definition should involve balls or squares. They also mention the concept of "order" in Munkres' definition and the idea of a basis for a topology.
  • #1
hofhile
2
0
Hi,

I was reading the definition of dimension from the book: "Topology", Munkres, 2nd ed.
Surely I don't understand, but I wonder how ℝ2 can have dimension 2.

Take the open sets [tex] U_n=\{(x,y)\mid -\infty < x <\infty, n-1<y<n+1\} [/tex] for every integer n. It covers the plane but its order is 2, so the dimension should be less than 2.

Shouldn't be the difinition with balls or "squares"?

Thank you.
 
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  • #2
hofhile said:
Hi,

I was reading the definition of dimension from the book: "Topology", Munkres, 2nd ed.
Surely I don't understand, but I wonder how ℝ2 can have dimension 2.

Take the open sets [tex] U_n=\{(x,y)\mid -\infty < x <\infty, n-1<y<n+1\} [/tex] for every integer n. It covers the plane but its order is 2, so the dimension should be less than 2.

Shouldn't be the difinition with balls or "squares"?

Thank you.

What makes you think the dimension is not 2? What is your understanding of dimension?
 
  • #3
hofhile said:
Hi,

I was reading the definition of dimension from the book: "Topology", Munkres, 2nd ed.
Surely I don't understand, but I wonder how ℝ2 can have dimension 2.

Take the open sets [tex] U_n=\{(x,y)\mid -\infty < x <\infty, n-1<y<n+1\} [/tex] for every integer n. It covers the plane but its order is 2
I presume that Munkres defines the "order" of such a collection of sets. What is that definition and how does it follow that this particular collection has "order 2"?

, so the dimension should be less than 2.
Is there a theorem in Munkres that the dimension of a set is strictly less than the order of a covering collection of such sets?

Shouldn't be the difinition with balls or "squares"?

Thank you.
Definitions like this can be done in many different ways.
 

What is Lebesgue topological dimension?

Lebesgue topological dimension is a mathematical concept that measures the complexity or "size" of a topological space. It is based on the idea that a space can be divided into smaller pieces of varying dimensions, and the dimension of the space is determined by the largest dimension of these pieces.

How is Lebesgue topological dimension different from other types of dimension?

Lebesgue topological dimension differs from other types of dimension, such as Euclidean or Hausdorff dimension, in that it takes into account the topological structure of a space rather than just its metric properties. This means that two spaces with the same topological dimension can have different Euclidean or Hausdorff dimensions, and vice versa.

How is Lebesgue topological dimension calculated?

Lebesgue topological dimension is calculated using a mathematical concept called "covering dimension" which measures the number of sets needed to cover a space. In order to find the Lebesgue topological dimension, one must first determine the covering dimension and then take the supremum (or largest value) of this dimension over all possible open covers of the space.

What types of spaces can be assigned a Lebesgue topological dimension?

Lebesgue topological dimension can be defined for any topological space, including metric spaces, fractals, and abstract topological spaces. However, it is most commonly applied to metric spaces, which have a well-defined distance function between points.

Why is Lebesgue topological dimension important?

Lebesgue topological dimension is important because it provides a way to measure the complexity of a space and understand its structural properties. It has applications in various fields of mathematics, including topology, analysis, and geometry. Additionally, it can be used to classify and compare different types of spaces, allowing for a deeper understanding of their relationships and properties.

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