Continuity of Metric Spaces: Does the Distance Between Points Remain Consistent?

In summary, we are given a metric space (X,d) and a converging sequence {x_n} in X. We need to show that d(b,x_n) converges to d(b,a) for any b in X. To do this, we use the triangle inequality and the definition of convergence to prove that for any epsilon > 0, there exists an N such that for all n >= N, |d(b,x_n)-d(b,a)|<\varepsilon.
  • #1
MaxManus
277
1

Homework Statement



Let (X,d) be a metric space and let [tex]{x_n}[/tex] be a sequence in X converging to a. Show that d(b,[tex] x_n [/tex]) ->d(b,a)

Homework Equations





The Attempt at a Solution



For every eps > 0 there is an N such that d(x_n,a) < eps for all n>= N

But where do I go from here? triangel inequality?

[tex] d(b,a) <= d(b,x_N) + d(x_N, a) <= d(b,x_N) + \epsilon [/tex]
 
Last edited:
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  • #2


Yes, the triangle inequality should be a good next step. But keep in mind what you need to show: you need to prove that

[tex]|d(b,x_n)-d(b,a)|\leq \varepsilon[/tex]

for sufficiently large n.
 
  • #3


Thanks, is this correct?

[tex] |d(b,x_n),a) - d(b,a)| <= |d(b,a) + d(a,x_n) -d(b,a) | = |d(a,x_n)| <= \epsilon [/tex]

for n >= N
 
  • #4


MaxManus said:
Thanks, is this correct?

[tex] |d(b,x_n),a) - d(b,a)| <= |d(b,a) + d(a,x_n) -d(b,a) | = |d(a,x_n)| <= \epsilon [/tex]

for n >= N

No, you can't work with inequalities in absolute values. Here's a counterexample:
Let d(b,xn)=0, d(b,a)=100, d(a,xn)=2, then

[tex]|d(b,x_n)-d(b,a)|=|-100|=100[/tex]

and

[tex]|d(a,x_n)|=2[/tex]

and it is certainly not true that 100<2...
 
  • #5


Thanks again
What about:
[tex] d(b,x_n),a) - d(b,a) <= d(b,a) + d(a,x_n) -d(b,a) = d(a,x_n) <= \epsilon [/tex]

so:[tex]|d(b,x_n)-d(b,a)|\leq \varepsilon[/tex]
 
  • #6


That's already good, but it's not enough. In fact, proving that

[tex]|d(b,x_n)-d(b,a)|<\varepsilon[/tex]

is equivalent to showing that

[tex]-\varepsilon<d(b,x_n)-d(b,a)<\varepsilon[/tex]

You've already shown the last inequality. But you still need to show that

[tex]-\varepsilon<d(b,x_n)-d(b,a)[/tex]
 
  • #7


Thanks
Think I got it
Have to show that
[tex] \epsilon > d(b,a) - d(b,x_n) [/tex]
[tex] d(b,a)- d(b,x_n) < d(a,x_n) + d(x_n,b) - d(b,x_n) = \epsilon [/tex]
 
  • #8


Yes, that should be correct! Congratulations!
 
  • #9


Great
Thanks again for all the help
 
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1. What is a continuous metric?

A continuous metric is a measurement scale where the values can take on any numerical value within a range, and there are no gaps or interruptions in the scale. This means that there are an infinite number of possible values between any two points on the scale.

2. How is continuity determined in a metric?

In order for a metric to be considered continuous, it must have the property of continuity, which means that the values on the scale must change in a smooth and continuous manner. This can be determined by looking for gaps or jumps in the data, or by analyzing the behavior of the metric over time.

3. Why is it important to have a continuous metric?

A continuous metric allows for more precise and accurate measurements, as there are no gaps or interruptions in the scale. This is especially important in scientific research, where precise measurements are necessary for making accurate conclusions and predictions.

4. Is it possible for a metric to be both continuous and discrete?

Yes, it is possible for a metric to have both continuous and discrete components. For example, a measurement scale for time can be considered continuous (as time can be divided into infinite increments) but also discrete (as we typically measure time in specific units such as seconds or minutes).

5. Can a metric be considered continuous if it has outliers?

Yes, a metric can still be considered continuous even if it has outliers. Outliers are extreme values that may not fit within the expected range of values for a metric, but they do not affect the overall continuity of the scale. However, outliers should be carefully examined and considered when interpreting data from a continuous metric.

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