Help with hausdorff dimension and countableness.

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In summary, the example does not disprove the theorem you cite. The theorem says that if a set has Hausdorff dimension larger than 0, then it is uncountable. The example is a counterexample to the converse of the stated theorem.
  • #1
icantadd
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My question, because I keep seeing this on the internet, is that if S is a subset of R and Hausdorff dimension greater than 0, it is uncountable... is this true.

It seems not to be. If one were to modify the Cantor third set and remove some length of 1/n from the middle of the sets at each iteration, one would achieve a set with Hausdorff dimension: 2 = n^d => ln2/ln n =d, and as 1/n -> 1 , n -> infinity, and ln2/ln n -> 0. Yet the set is still uncountable.

Perhaps I have missed something, or perhaps most of the time the relationship holds. Does anyone know either way? or if there are only special cases where this happens, what they are?

Thanks,

jon
 
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  • #2
icantadd said:
My question, because I keep seeing this on the internet, is that if S is a subset of R and Hausdorff dimension greater than 0, it is uncountable... is this true.

It seems not to be. If one were to modify the Cantor third set and remove some length of 1/n from the middle of the sets at each iteration, one would achieve a set with Hausdorff dimension: 2 = n^d => ln2/ln n =d, and as 1/n -> 1 , n -> infinity, and ln2/ln n -> 0. Yet the set is still uncountable.

Perhaps I have missed something, or perhaps most of the time the relationship holds. Does anyone know either way? or if there are only special cases where this happens, what they are?

Thanks,

jon
Your example does NOT disprove the theorem you cite. The theorem says that if a set has Hausdorff dimension larger than 0, then it is uncountable. It does NOT say that if the Hausdorff dimension is 0, it MUST be uncountable. Your example is a counterexample to the converse of the stated theorem.
 
  • #3
HallsofIvy,

You are absolutely correct, thank you. If a set is countable then it must have H. dimension >0. But it does not hold that if Hausdorff dimension = 0, that the set must be countable.

Yet, I still do not understand the why of it. A countable set is a qualification of smallness, as is H.dimension = 0, but I cannot come to see how having a Hausdorff dimension greater than zero implies that the set is uncountable. I can't find a proof of it, I can't think of how to go about proving it.
 

1. What is Hausdorff dimension?

Hausdorff dimension is a mathematical concept that measures the degree of "roughness" or "irregularity" of a geometric object or set. It was introduced by mathematician Felix Hausdorff in the early 20th century.

2. How is Hausdorff dimension calculated?

Hausdorff dimension is calculated by taking the logarithm of the number of "balls" or "cubes" of a given size needed to cover the object, and dividing it by the logarithm of the size of the balls or cubes. This process is repeated for smaller and smaller sizes, and the limit as the size approaches zero is the Hausdorff dimension.

3. What is the significance of Hausdorff dimension?

Hausdorff dimension has many applications in mathematics, physics, and other fields. It can be used to classify the complexity of fractals, measure the dimension of chaotic systems, and determine the properties of certain sets in topology and measure theory.

4. How does Hausdorff dimension relate to countability?

Hausdorff dimension and countability are two distinct concepts. While Hausdorff dimension measures the "size" of a geometric object, countability refers to the number of elements in a set. However, there are certain sets with Hausdorff dimension equal to zero that are considered uncountable, such as the Cantor set.

5. Can Hausdorff dimension be used to measure the dimension of non-geometric objects?

Yes, Hausdorff dimension can be extended to measure the dimension of non-geometric objects, such as time series or data sets. This is done by using a modified version of the formula for calculating Hausdorff dimension, which takes into account the "distance" between points in the data set rather than physical distance.

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