Counting independent components

In summary, the conversation is about a set of tensors in D dimensions, specifically the tensors h_{\mu\nu}, H^{\mu\nu}, t_{\mu}, and T^{\mu}, and their relations. The question is how many independent components this set of fields constitutes and whether it can be derived analytically. The answer given by Mathematica is 1/2D(D+1), the same as for a symmetric rank D tensor.
  • #1
haushofer
Science Advisor
Insights Author
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Hi,

I have a question about counting (how difficult should that be ;) )

I have the set of tensors in D dimensions

[tex]
\{h_{\mu\nu}, H^{\mu\nu}, t_{\mu}, T^{\mu}\}
[/tex]

with the relations

[tex]
H^{\mu\nu} h_{\nu\rho} = \delta^{\mu}_{\rho} - T^{\mu}t_{\rho}
[/tex]

[tex]
T^{\mu}t_{\mu} = 1
[/tex]

[tex]
H^{\mu\nu}t_{\nu} = h_{\mu\nu}T^{\nu} = 0
[/tex]

and h and H are symmetric tensors of rank (D-1).

The question now is: how many independent components does this set of fields constitute? Mathematica gives as answer 1\2D(D+1), the same amount as for a symmetric rank D tensor, but how can I derive this analytically?
 
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  • #2
Maybe this thread is better off in another place here? :)
 

1. What is meant by "counting independent components" in scientific research?

Counting independent components refers to the process of determining the number of distinct, non-redundant components or variables in a system or dataset. This is important in many areas of scientific research, such as signal processing, data analysis, and machine learning.

2. Why is it important to accurately count independent components?

Accurately counting independent components allows researchers to better understand the complexity and underlying structure of a system or dataset. It also helps to prevent overfitting and ensures that statistical analyses are valid.

3. How is the number of independent components determined?

The number of independent components can be determined using various mathematical and statistical techniques, such as principal component analysis, independent component analysis, and factor analysis. These methods identify patterns and relationships within the data to determine the number of distinct components.

4. Can the number of independent components change in different analyses?

Yes, the number of independent components can vary depending on the specific analysis being performed and the parameters chosen. It is important for researchers to carefully consider the techniques and parameters used when counting independent components.

5. Are there any limitations to counting independent components?

While counting independent components can be a useful tool in scientific research, it is not always a straightforward process. Some datasets may have overlapping or correlated components, making it difficult to determine an exact number. Additionally, different methods may produce different results, leading to potential discrepancies in the number of independent components.

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