Shouldn't this definition of a metric include a square root?

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Discussion Overview

The discussion revolves around the definition of a metric in relation to inner product spaces and whether a square root should be included in the definition. Participants explore the implications of different definitions and their adherence to the triangle inequality, focusing on theoretical aspects of metrics and distance functions.

Discussion Character

  • Debate/contested
  • Technical explanation
  • Conceptual clarification

Main Points Raised

  • One participant references Mathworld's definition of a metric derived from inner product spaces and questions its validity regarding the triangle inequality.
  • Another participant suggests that Mathworld's definition may be incorrect.
  • Some participants note that the square of the Euclidean metric could also be considered a metric, but it does not satisfy the triangle inequality.
  • There is a discussion about the distinction between the terms "metric" and "distance," with some arguing that "metric" is a more technical term while "distance" is more interpretative.
  • One participant mentions that in certain contexts, such as in ##L^2## spaces, the concept of distance may lack intuitive understanding, leading to the use of terms like norms and metrics.
  • Another participant points out that "distance" can also refer to specific metrics, such as the Hamming distance.

Areas of Agreement / Disagreement

Participants express differing views on the definitions and implications of metrics and distances, with no consensus reached regarding the correctness of Mathworld's definition or the necessity of including a square root in the metric definition.

Contextual Notes

Some participants highlight the potential for multiple metrics on a vector space and the implications of these definitions on properties like the triangle inequality, but do not resolve these complexities.

nomadreid
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TL;DR
Mathworld.Wolfram says that there is a metric on an inner product space (with inner product <.,.>) defined by <v-w,v-w>. Shouldn't that be the square root of <v-w,v-w>?
In https://mathworld.wolfram.com/InnerProduct.html, it states
"Every inner product space is a metric space. The metric is given by
g(v,w)= <v-w,v-w>."
In https://en.wikipedia.org/wiki/Inner_product_space , on the other hand,
"As for every normed vector space, an inner product space is a metric space, for the distance defined by
d(x,y) = ||y-x||"
after it had defined ||x|| as sqrt(<x,x>)

(I am assuming that a metric is the same as a distance function.)

The Mathworld definition appears to be the square of the Wikipedia definition. Whereas one can have more than one metric on a vector space, the former definition would seem to violate the triangle inequality
[Example: take the inner product on the one-dimensional vector space (the number line) as the dot product (which reduces to multiplication of the coordinates of the representative vectors from the origin), and check the triangle inequality for the a=0, b=2, c=5: 4+9 < 25. ]

Did Wolfram make a mistake, or am I missing some elementary and obvious point?
 
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nomadreid said:
Did Wolfram make a mistake?
It looks like it.
 
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I haven't checked the triangle identity, but isn't the square of the Euclidean metric a metric, too?
 
fresh_42 said:
I haven't checked the triangle identity, but isn't the square of the Euclidean metric a metric, too?
It doesn't obey the triangle inequality.
 
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It looks metric is a general term, while distance is specific.
 
The only difference is, that metric is a technical term, and distance is a kind of interpretation. E.g. if we consider an ##L^2## space of functions, then we have a metric, and therewith a distance. However, there is no intuition of the distance between two functions, so people speak about norms and metrics.
 
Sometimes the term "distance" is also used as a technical term outside of the intuitive Euclidean distance: for example, the Hamming distance (which is a metric).
 
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nomadreid said:
Sometimes the term "distance" is also used as a technical term outside of the intuitive Euclidean distance: for example, the Hamming distance (which is a metric).
I constructed a problem in one of my challenge threads that was the theorem of Thales disguised in the language of ##L^2## spaces.
 

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