Continuous functions on metric spaces

In summary, continuous functions on metric spaces are those that preserve the notion of closeness between points. A function \( f: X \rightarrow Y \) between two metric spaces is continuous at a point \( x_0 \in X \) if, for every \( \epsilon > 0 \), there exists a \( \delta > 0 \) such that for all points \( x \) in \( X \) within \( \delta \) of \( x_0 \), the images \( f(x) \) are within \( \epsilon \) of \( f(x_0) \). This property can be extended to the entire space, meaning \( f \) is continuous on \( X \) if it is continuous at
  • #1
Lambda96
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Homework Statement
Show that the mapping ##\phi## is continuous
Relevant Equations
none
Hi,

I don't know if I have solved task correctly

Bildschirmfoto 2024-05-17 um 13.50.12.png


I used the epsilon-delta definition for the proof, so it must hold for ##f,g \in (C^0(I), \| \cdot \|_I)## ##\sup_{x \in [a,b]} |F(x)-G(x)|< \delta \longrightarrow \quad |\int_{a}^{b} f(x)dx - \int_{a}^{b} g(x)dx |< \epsilon##

I then proceeded as follows

##|\int_{a}^{b} f(x)dx - \int_{a}^{b} g(x)dx | =|F(b)-F(a)-G(b)+G(a)|=|F(b)-G(b)+G(a)-F(a)|##

Then I used the supremum norm to estimate the above expression ##|F(b)-F(a)-G(b)+G(a)|=|F(b)-G(b)+G(a)-F(a)| \le \sup_{x \in [a,b]} |F(x)-G(x)|+\sup_{x \in [a,b]} |F(x)-G(x)|+\sup_{x \in [a,b]} |G(x)-F(x)|=2\sup_{x \in [a,b]} |F(x)-G(x)|<2\delta## it then follows that ##\delta## must be defined as follows ##\delta= \frac{\epsilon}{2} ##
 
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  • #2
You want to find a ##\delta## such that if ##||f-g|| = \sup_{x \in I}|f(x)-g(x)| < \delta## then ##|\phi(f) - \phi(g)|< \varepsilon##. It seems to me that you have instead used the primitive function of ##f## and ##g## in the supremum norm.
 
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  • #3
Hint: By basic properties of the Riemann integral, [tex]
\left| \int_a^b f(x)\,dx \right| \leq \int_a^b |f(x)|\,dx \leq (b- a) \sup |f|.[/tex]
 
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  • #4
Orodruin said:
You want to find a ##\delta## such that if ##||f-g|| = \sup_{x \in I}|f(x)-g(x)| < \delta## then ##|\phi(f) - \phi(g)|< \varepsilon##. It seems to me that you have instead used the primitive function of ##f## and ##g## in the supremum norm.
How do you know the space ##C^0## has a vector space structure that allows you to subtract functions? Edit: Never mind, it's a normed space, hence a vector space.
Seems you could too, just use the open set definition and verify that the inverse image of an interval is open in ##C^0## with the given norm.
 
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  • #5
Thank you Orodruin, pasmith and WWGD for your help 👍👍👍

I have now used the tip from pasmith and received the following

The following must apply:
##\sup_{x \in [a,b]} |f(x)-g(x)|< \delta \longrightarrow \quad |\int_{a}^{b} f(x)dx - \int_{a}^{b} g(x)dx |< \epsilon##

Then I determined ##\delta## as follows
$$|\int_{a}^{b} f(x)dx - \int_{a}^{b} g(x)dx \le |\int_{a}^{b} |f(x)|dx - \int_{a}^{b} |g(x)|dx \le (b-a) \sup f|x|-(b-a) \sup |g(x)| = (b-a) \bigl( \sup|f(x)| - \sup|g(x)| \bigr)$$

Then ##\delta## must be ##\delta = \frac{\epsilon}{b-a}##
 
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  • #6
Lambda96 said:
Then I determined ##\delta## as follows
$$|\int_{a}^{b} f(x)dx - \int_{a}^{b} g(x)dx \le |\int_{a}^{b} |f(x)|dx - \int_{a}^{b} |g(x)|dx \le (b-a) \sup f|x|-(b-a) \sup |g(x)| = (b-a) \bigl( \sup|f(x)| - \sup|g(x)| \bigr)$$
The first inequality is incorrect.
 
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  • #7
Lambda96 said:
$$|\int_{a}^{b} f(x)dx - \int_{a}^{b} g(x)dx \le |\int_{a}^{b} |f(x)|dx - \int_{a}^{b} |g(x)|dx \le (b-a) \sup f|x|-(b-a) \sup |g(x)| = (b-a) \bigl( \sup|f(x)| - \sup|g(x)| \bigr)$$
That all looks ill-conceived to me. For example, I don't think you've corrently identified the metric that is derived from the given supremum norm.

PS as pointed out in post #2.
 
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  • #8
[tex]|\phi(f) - \phi(g)| = \left| \int_a^b f(x)\,dx - \int_a^b g(x)\,dx \right| = \left| \int_a^b f(x) - g(x)\,dx\right| [/tex] etc.
 
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  • #9
Thank you Orodruin, PeroK and pasmith for your help 👍👍👍

$$ \bigg \vert \int_{a}^{b} f(x) dx - \int_{a}^{b} g(x)dx \bigg \vert= \bigg \vert\int_{a}^{b} f(x)-g(x) dx \bigg \vert \le \int_{a}^{b} |f(x)-g(x)| dx \le (b-a) \sup|f(x)-g(x)|$$


From this follows ##\delta=\frac{\epsilon}{b-a}##
 
  • #10
Lambda96 said:
Thank you Orodruin, PeroK and pasmith for your help 👍👍👍

$$ \bigg \vert \int_{a}^{b} f(x) dx - \int_{a}^{b} g(x)dx \bigg \vert= \bigg \vert\int_{a}^{b} f(x)-g(x) dx \bigg \vert \le \int_{a}^{b} |f(x)-g(x)| dx \le (b-a) \sup|f(x)-g(x)|$$


From this follows ##\delta=\frac{\epsilon}{b-a}##
That's fine as far as it goes, but you really need a sound, logical proof. All ##\epsilon-\delta## proofs, in principle, should start with "Let ##\epsilon > 0 \dots ##".
 
  • #11
WWGD said:
How do you know the space ##C^0## has a vector space structure that allows you to subtract functions? Edit: Never mind, it's a normed space, hence a vector space.
Even without that, it should be pretty clear that a pointwise sum of continuous functions is a continuous function. All of the vector space axioms are clearly satisfied.

WWGD said:
Seems you could too, just use the open set definition and verify that the inverse image of an interval is open in ##C^0## with the given norm.
This is always my preferred way forward. However, the OP is most likely not aware of this definition of continuity.
 
  • #12
WWGD said:
Seems you could too, just use the open set definition and verify that the inverse image of an interval is open in ##C^0## with the given norm.
Orodruin said:
This is always my preferred way forward. However, the OP is most likely not aware of this definition of continuity.
I'd like to see a simple proof in this case using that method. Or any proof, simple or otherwise, that isn't essentially just the ##\epsilon-\delta## we have already.
 
  • #13
Many thanks to all of you for your help 👍👍👍👍👍


For the above problem, I had decided to use the proof for the epsilon delta definition, as my lecturer told me that I would have to know it for the exam.

According to my professor's lecture notes, if the mapping is linear, you can also use boundedness to prove continuity

Bildschirmfoto 2024-05-18 um 16.21.50.png


I have another problem where I have to show continuity, can I post it here or should I open a new thread?
 
  • #14
You should start a new thread. Note that this new question is a generalization of the question you posted in this thread.
 
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  • #15
Lambda96 said:
According to my professor's lecture notes, if the mapping is linear, you can also use boundedness to prove continuity

View attachment 345476
Do you see the close relationship between the proof of that theorem and the proof in this particular case?
 

FAQ: Continuous functions on metric spaces

What is a continuous function on a metric space?

A continuous function on a metric space is a function f: X → Y between two metric spaces (X, d_X) and (Y, d_Y) such that for every point x in X and for every ε > 0, there exists a δ > 0 such that if d_X(x, x') < δ for any x' in X, then d_Y(f(x), f(x')) < ε. This means that small changes in the input result in small changes in the output.

How do you prove that a function is continuous on a metric space?

To prove that a function f: X → Y is continuous at a point x_0 in a metric space (X, d_X) and (Y, d_Y), you must show that for every ε > 0, there exists a δ > 0 such that if d_X(x_0, x) < δ, then d_Y(f(x_0), f(x)) < ε. You can do this by taking an arbitrary ε and finding a suitable δ based on the properties of the function f and the metrics involved.

What are the properties of continuous functions on metric spaces?

Continuous functions on metric spaces have several important properties: they preserve limits (the limit of f(x_n) as x_n approaches x is f(x)), they map compact sets to compact sets, and they are uniformly continuous on compact subsets. Additionally, the composition of continuous functions is continuous, and the image of a continuous function is an open set if the function is also an open mapping.

Can a function be continuous in one metric space and not in another?

Yes, a function can be continuous in one metric space and not in another. This is often due to differences in the topology induced by the metrics. For example, a function that is continuous with respect to the standard Euclidean metric may not be continuous when the space is equipped with a different metric that alters the structure of convergence and open sets.

What is the relationship between continuous functions and open/closed sets in metric spaces?

In metric spaces, the preimage of an open set under a continuous function is open, and the preimage of a closed set is closed. This property is fundamental in topology and helps establish the continuity of functions by examining the behavior of sets rather than individual points. It also allows for the characterization of continuous functions in terms of the topological structure of the spaces involved.

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