Proving two metrics are not equivalent

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In summary, the conversation discusses the equivalence of two metrics, d_{∞} and d_{2}, in the vector space of continuous functions on [0,1]. The equations for these metrics are given, and it is stated that they are not equivalent. The conversation then explores the idea of finding a function to show this non-equivalence, with the suggestion of a curve that starts at (0,1) and curves gracefully down to (1,0) while reducing the area. Ultimately, the conversation ends with a suggestion of using e-x to demonstrate the non-equivalence between the two metrics.
  • #1
Lily@pie
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Homework Statement


The vector space of continuous functions on [0,1] is given the following metrics

[itex] d_{∞} (f,g) = sup_{x\in [0,1]} |f(x)-g(x)| [/itex]

[itex] d_{2} (f,g) = (∫^{1}_{0} |f(x)-g(x)|^{2} dx)^{1/2}[/itex]

Are these two metrics equivalent?

Homework Equations


If [itex] d_{∞} [/itex] and [itex] d_{2} [/itex] are equivalent, then for every [itex]f \in C[0,1][/itex] and every [itex]\epsilon>0[/itex], there exist a [itex]\delta>0[/itex] such that [itex]B^{d_{∞}}_{\delta}(f)\subset B^{d_{2}}_{\epsilon}(f)[/itex] and [itex]B^{d_{2}}_{\delta}(f)\subset B^{d_{∞}}_{\epsilon}(f)[/itex]

The Attempt at a Solution



I know that these two metrics are not equivalent, but i can't find a function at which i can find an [itex]\epsilon>0[/itex] for every [itex]\delta>0[/itex] where they are not the subset of each other.

I know that [itex] d_{∞} [/itex] is like the highest point of the g(x) if I let f(x)=0 and g(x) be some other function. [itex] d_{2} [/itex] is like the area under the graph between [0,1].
 
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  • #2
Hi Lily@pie! :smile:
Lily@pie said:
I know that [itex] d_{∞} [/itex] is like the highest point of the g(x) if I let f(x)=0 and g(x) be some other function. [itex] d_{2} [/itex] is like the area under the graph between [0,1].

Yes, you're practically there …

you need a sequence gn(x) with constant maximum but decreasing area :wink:
 
  • #3
I'm clueless :cry:

any hints? :bugeye:
 
  • #4
how about a curve which starts at (0,1) and curves gracefully down to (1,0) ?

then reduce the area :wink:
 
  • #5
Would this be a function g(x)=(x-1)2 where 0≤x≤1??

And f(x) = 0 for all x

For all δ>0 such that [itex]d_{2} (f,g)[/itex] will be undefined! oh my god... what did i do...
 
  • #6
Lily@pie said:
Would this be a function g(x)=(x-1)2 where 0≤x≤1??

i was thinking of somthing that gets flatter a lot quicker,

like e-x (but adjusted to fit into [0,1]) :smile:
 

FAQ: Proving two metrics are not equivalent

1. How do you determine if two metrics are equivalent?

To determine if two metrics are equivalent, you can compare their values and see if they are always equal. This can be done by plotting the data and looking for patterns or by conducting statistical tests such as t-tests or ANOVA.

2. Can two metrics be similar but not equivalent?

Yes, two metrics can be similar in terms of their values or trends, but still not be equivalent. This is because equivalence requires a strict equality between the two metrics, while similarity allows for some variation or deviation.

3. What are some common mistakes when trying to prove two metrics are not equivalent?

One common mistake is assuming that if two metrics differ in one aspect, they must be not be equivalent. It is important to thoroughly examine all aspects of the metrics and conduct appropriate tests to determine their equivalence.

4. Can two metrics be equivalent in one context but not in another?

Yes, two metrics can be equivalent in one context, but not in another. This is because different contexts may require different levels of precision or may have different factors that influence the metrics.

5. How can proving two metrics are not equivalent impact research or decision making?

Proving that two metrics are not equivalent can have significant impacts on research and decision making. It may lead to the rejection of a hypothesis or the adoption of a different approach. It is important to carefully consider the implications of such findings and to ensure that the correct metrics are used for accurate analysis and decision making.

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