One parameter family of metrics

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In summary, The conversation is discussing the concept of a one parameter family of metrics and the search for a topology that can accommodate this family. It is mentioned that this topology may not necessarily be metricizable. There is a mention of examples and the possibility of the topology being metrizable, and a reference to Hawking's use of a similar idea in his formulation of stable causality.
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Haelfix
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Sorry about the horrible confusion in this post, I am befuddled and likely spouting gibberish.

Consider the general case of a one parameter family of metrics, where the parameter in question need not be finite (it can limit to infinity for instance). I am looking for a topology that is big enough to accommodate the entire family. (im not even sure how this should work, since I am used to thinking off one topology for one metric)

It seems to me in general, whatever this huge topology is, it need not and probably cannot be metricizable. Is this true, and can you think of any other constraints?
 
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Hmm... lots of large topological spaces are metrizable, so don't give up hope. But without more context (maybe some examples of the kind of one parameter family of metrics you are looking at it?) I at least cannot suggest a candidate for a suitable topology.

It's nice to see an intriguing question, though :smile:
 
  • #3
I don't know enough to be any of help (hopefully Chris can), but I do know that Hawking used a similar idea in his formulation of the concept of stable causality. A spacetime (M,g) is stably causal if there isn't another Lorentz metric g' "close" to g such that (M,g') admits closed timlike curves.

Hawking defined "close" using a suitable topology on a collection of Lorentzian metrics on M. See pages 197/198 of Hawking and Ellis.
 

What is a one parameter family of metrics?

A one parameter family of metrics is a set of mathematical functions that describe the distance or similarity between objects in a space. It is called a "one parameter" family because it is defined by a single variable, or parameter, that can be varied to produce different metrics within the family.

Why are one parameter families of metrics useful in science?

One parameter families of metrics are useful because they allow scientists to explore and compare different ways of measuring or quantifying relationships between objects. This can be helpful in fields such as physics, biology, and computer science, where understanding the relationships between objects is crucial.

What are some examples of one parameter families of metrics?

One example is the family of p-norm metrics, which are used to measure the distance between points in a space. Another example is the family of Mahalanobis metrics, which are used to measure the similarity between objects based on multiple variables.

How do scientists choose which metric from a one parameter family to use?

The choice of which metric to use from a one parameter family depends on the specific research question or problem being investigated. Scientists may consider factors such as the type of data being analyzed, the desired level of sensitivity, and the underlying assumptions of each metric.

Can one parameter families of metrics be used in real-world applications?

Yes, one parameter families of metrics can be applied in a variety of real-world applications, such as data analysis, machine learning, and optimization problems. They can also be used to compare different models or theories in scientific research.

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