"Robustness" of Nielsen-Thurston Classification

In summary, the conversation discusses the conditions for a homeomorphism on a universal cover to give rise to a homeomorphism of the universal cover to itself. The question of whether this is always the case is answered with a yes, and the title refers to a related question about the preservation of classes in Thurston-Nielsen.
  • #1
WWGD
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Hi all,

Let X be a nice-enough topological space so that it admits a universal cover ## \tilde X ##

When does a homeomorphism ## h: X \rightarrow X ## give rise to a homeomorphism

of the universal cover to itself, i.e., we have ## p: \tilde X \rightarrow X ## , then, by

lifting properties this gives rise to (after choosing a specific sheet in the cover) to an

automorphism ## \tilde h : \tilde X \rightarrow \tilde X ## satisfying ## p \tilde h =hp ## ( I wish

I knew how to draw the diagram in here). Question: is ## \tilde h ## always a homeomorphism ?

Thanks.
 
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  • #2
Sorry, I got it, the answer is yes. And the title is for another question I wanted to make but never did. I was trying to ask initially (when I wrote the title) under what condition claases in Thurston-Nielsen were preserved: if we have h with ## h^n \simeq Id ## and g is isotopic with h, does it follow that ## g^n \simeq Id ## (meaning g^n is isotopic to the identity )
 

FAQ: "Robustness" of Nielsen-Thurston Classification

What is the definition of "Robustness" in the context of Nielsen-Thurston Classification?

Robustness refers to the ability of a mathematical classification system, such as the Nielsen-Thurston Classification, to withstand perturbations or changes while still maintaining its validity.

Why is "Robustness" an important factor in Nielsen-Thurston Classification?

Robustness is important because it ensures that the classification system remains accurate and applicable even when the underlying data or parameters are altered. This allows for a more reliable and consistent classification of mathematical objects.

How is "Robustness" measured in Nielsen-Thurston Classification?

Robustness is measured by evaluating the sensitivity of the classification system to changes in the underlying data or parameters. This can be done through various mathematical methods, such as analyzing the stability of the classification under small perturbations.

What are the potential consequences of a lack of "Robustness" in Nielsen-Thurston Classification?

If the classification system is not robust, it may lead to misclassification or inaccuracies in the classification of mathematical objects. This can have significant consequences, such as incorrect conclusions or flawed theories based on the classification results.

What are some strategies for improving "Robustness" in Nielsen-Thurston Classification?

One strategy is to use multiple classification systems or methods to cross-validate the results and ensure consistency. Additionally, incorporating more data or parameters in the classification process can increase robustness by providing a more comprehensive analysis. Another approach is to continuously test and refine the classification system to identify and address potential vulnerabilities.

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