Properties of Metric $\sigma(x,y)$

In summary, the conversation discusses the proof of $\sigma(x,y) = \min\{1,\rho(x,y)\}$ being a metric if $\rho(x,y)$ is a metric on $X$. The participants discuss the triangle inequality and the need to consider different cases to prove it. It is suggested that the cases should be based on $\rho(x,y)+\rho(y,z)<1$ and $\rho(x,y)+\rho(y,z)>1$, and actual numbers may need to be used to further subdivide these cases.
  • #1
evinda
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Hello! (Wave)
I want to show that if $\rho(x,y)$ is a metric on $X$, then $\sigma (x,y)= \min \{ 1, \rho(x,y) \}$ is a metric.

I have thought the following:

$\rho(x,y)$ is a metric on $X$, so:

  • $\rho(x,y) \geq 0, \forall x,y \in X$
  • $\rho(x,y)=0$ iff $x=y$
  • $\rho(x,y)=\rho(y,x) \forall x,y \in X$
  • $\rho(x,z) \leq \rho(x,y)+\rho(y,z), \forall x,y,z \in X$

  • $\sigma(x,y)= \min \{ 1, \rho(x,y) \} \geq 0 \forall x,y \in X$ since $1 \geq 0$ and $\rho(x,y) \geq 0, \forall x,y \in X$
  • $\sigma(x,y)=0 \Leftrightarrow \min \{ 1, \rho(x,y) \} =0 \Leftrightarrow \rho(x,y)=0 \Leftrightarrow x=y$
  • $\sigma(x,y)= \min \{ 1, \rho(x,y) \}= \min \{ 1, \rho(y,x) \}=\sigma(y,x) \forall x,y \in X$
  • Suppose that $1< \rho(x,y), \forall x,y \in X$. Then $\sigma(x,z)=1 \leq 1+1= \sigma(x,y)+ \sigma(y,z)$.
    Suppose that $1> \rho(x,y), \forall x,y \in X$. Then $\sigma(x,z)= \rho(x,z) \leq \rho(x,y)+ \rho(x,z)= \sigma(x,y)+ \sigma(x,z)$

Could I improve something? Could we also show the last inequalities without distinguishing cases? (Thinking)
 
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  • #2
I would say you're solid on everything except the triangle inequality. I don't see any particular reason to make such a strong assumption as $1>\rho(x,y)$ for all $x,y$. How do you know there aren't two pairs of numbers, one of which has a $\rho$ distance greater than $1$, and the other pair less than $1$? Your breakdown of cases doesn't handle this case. Let me see:
$$\sigma(x,z)=\min\{1,\rho(x,z)\}\le\min\{1,\rho(x,y)+\rho(y,z)\}.$$
If you could say that
$$\min\{1,\rho(x,y)+\rho(y,z)\}\le\min\{1,\rho(x,y)\}+\min\{1,\rho(y,z)\},$$
then you'd be done. Is that the case? Here's where you would need a breakdown by cases - I don't see any way around it. But the cases are the following:
  1. Both $\rho(x,y)$ and $\rho(y,z)$ are less than $1$.
  2. One of the two $\rho$ distances is less than $1$, and one is greater.
  3. Both the $\rho$ distances are greater than $1$.
By symmetry, we can ignore, in the second case, which $\rho$ distance is greater than $1$. I would do a "without loss of generality", and pick one to be bigger, and one smaller.

Does that help?
 
  • #3
Ackbach said:
I would say you're solid on everything except the triangle inequality. I don't see any particular reason to make such a strong assumption as $1>\rho(x,y)$ for all $x,y$. How do you know there aren't two pairs of numbers, one of which has a $\rho$ distance greater than $1$, and the other pair less than $1$? Your breakdown of cases doesn't handle this case. Let me see:
$$\sigma(x,z)=\min\{1,\rho(x,z)\}\le\min\{1,\rho(x,y)+\rho(y,z)\}.$$
If you could say that
$$\min\{1,\rho(x,y)+\rho(y,z)\}\le\min\{1,\rho(x,y)\}+\min\{1,\rho(y,z)\},$$
then you'd be done. Is that the case? Here's where you would need a breakdown by cases - I don't see any way around it. But the cases are the following:
  1. Both $\rho(x,y)$ and $\rho(y,z)$ are less than $1$.
  2. One of the two $\rho$ distances is less than $1$, and one is greater.
  3. Both the $\rho$ distances are greater than $1$.
By symmetry, we can ignore, in the second case, which $\rho$ distance is greater than $1$. I would do a "without loss of generality", and pick one to be bigger, and one smaller.

Does that help?

At the first case, doesn't it always hold that $\min\{1,\rho(x,y)+\rho(y,z)\}=\min\{1,\rho(x,y)\}+\min\{1,\rho(y,z)\}=\rho(x,y)+\rho(y,z)$ ? (Thinking)

Also do we show the last case for example as follows? (Thinking)
$\min\{1,\rho(x,y)+\rho(y,z)\}=1 \leq 1+1=\min\{1,\rho(x,y)\}+\min\{1,\rho(y,z)\}$
 
  • #4
evinda said:
At the first case, doesn't it always hold that $\min\{1,\rho(x,y)+\rho(y,z)\}=\min\{1,\rho(x,y)\}+\min\{1,\rho(y,z)\}=\rho(x,y)+\rho(y,z)$ ? (Thinking)

What about $1=\min(1,0.75+0.75)\overset{?}{=}\min(1,0.75)+\min(1,0.75)=1.5$?

Also do we show the last case for example as follows? (Thinking)

$\min\{1,\rho(x,y)+\rho(y,z)\}=1 \leq 1+1=\min\{1,\rho(x,y)\}+\min\{1,\rho(y,z)\}$

Sure, that works!
 
  • #5
Ackbach said:
What about $1=\min(1,0.75+0.75)\overset{?}{=}\min(1,0.75)+\min(1,0.75)=1.5$?

Ah, I see... But how can we explain the first case formally? (Thinking)

Ackbach said:
Sure, that works!

Great... (Smirk)
 
  • #6
evinda said:
Ah, I see... But how can we explain the first case formally? (Thinking)

Hmm. Well, maybe we broke the cases down incorrectly. What if the cases should be, instead,
  1. $\rho(x,y)+\rho(y,z)<1$
  2. $\rho(x,y)+\rho(y,z)>1$
I would try a few actual numbers in there to see if you need to further subdivide these cases.
 

FAQ: Properties of Metric $\sigma(x,y)$

What is the metric $\sigma(x,y)$?

The metric $\sigma(x,y)$ is a mathematical function used to measure the distance between two points in a two-dimensional space. It is also known as the Euclidean metric or the standard Euclidean distance.

What are the properties of the metric $\sigma(x,y)$?

The properties of the metric $\sigma(x,y)$ include symmetry, non-negativity, identity, and the triangle inequality. These properties ensure that the metric is a valid distance function and can be used in various mathematical calculations.

How is the metric $\sigma(x,y)$ different from other distance functions?

The metric $\sigma(x,y)$ is different from other distance functions because it follows the Pythagorean theorem, which states that the square of the hypotenuse of a right triangle is equal to the sum of the squares of the other two sides. This property makes the metric useful in many geometric and statistical applications.

What are some real-world applications of the metric $\sigma(x,y)$?

The metric $\sigma(x,y)$ has various applications in fields such as physics, engineering, and statistics. It is used to measure distances between objects, calculate velocities and accelerations, and determine the similarity between data points in pattern recognition and machine learning algorithms.

How can the metric $\sigma(x,y)$ be extended to higher dimensions?

The metric $\sigma(x,y)$ can be extended to higher dimensions by using the Pythagorean theorem in n-dimensional space. This results in the Euclidean distance formula, which can be used to measure the distance between two points in any number of dimensions.

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