Uniform Convergence and the Uniform Metric

To summarize, the proof shows that if \bar{\rho}(f_n,f) \rightarrow 0, then for all \epsilon > 0, there exists N such that for all n \geq N, \bar{\rho}(f_n,f) < \epsilon, and we can also say that \rho(f_n,f) \leq \bar{\rho}(f_n,f) for all n. Therefore, \rho(f_n,f) \rightarrow 0. In summary, we can show that the sequence (fn) converges uniformly to the function f:X--> R if and only if the sequence (fn) converges to f as elements of the metric space (RX, ρ).
  • #1
jmjlt88
96
0
Let X be a set, and let fn : X---> R be a sequence of functions. Let ρ be the uniform metric on the space RX. Show that the sequence (fn) converges uniformly to the function f:X--> R if and only if the sequence (fn) converges to f as elements of the metric space (RX, ρ). [Note: the ρ's should have a bar over them.]

I have a question concerning one direction of our implication.

[My attempt at the solution.]

Since I do not know how to make a d with a bar over it, I'll use ∂ to denote the standard bounded metric relative to d.

Proof:

Assume the sequence (fn) converges to f as elements of the metric space (RX, ρ).

Then, for any ε>0, there is a positive integer N such that ρ(fn,f)<ε for all n≥N.

That is, for n ≥ N, the sup{∂(fn(x),f(x))| x in X} < ε.

If ε≤1, our metrics are the same.

Hence for ε≤1, there exisits an integer N such that for n ≥ N, the sup{∂(fn(x),f(x))| x in X} < ε.

But since ∂(fn(x),f(x)) = d(fn(x),f(x)) for n ≥ N, d(fn(x),f(x)) ≤ sup{∂(fn(x),f(x))| x in X} < ε for all n≥N and x in X, which satisfies our condition for uniform convergence.



My question is about ε. Is this the right approach?
 
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  • #2
To display a bar, you can use the TeX command \bar. For example, to typeset [itex]\bar{\rho}[/itex], use \bar{\rho}.

Just to make sure I understand the question correctly, let me paraphrase it with proper typesetting: by standard bounded metric relative to [itex]d[/itex], you mean
[tex]\bar{d}(x,y) = \min\{d(x,y), 1\}[/tex]
and that the following definitions are in use
[tex]\rho(f,g) = \sup\{d(f(x),g(x)) : x \in X\}[/tex]
[tex]\bar{\rho}(f,g) = \sup\{\bar{d}(f(x),g(x)) : x \in X\}[/tex]
and that the goal is to show that
[tex]\bar{\rho}(f_n,f) \rightarrow 0 \implies \rho(f_n,f) \rightarrow 0[/tex]
Assuming I interpreted the question correctly, what you have done is fine for [itex]\epsilon \leq 1[/itex]. For [itex]\epsilon > 1[/itex], you can simply use the [itex]N[/itex] that works for [itex]\epsilon = 1[/itex].
 

Related to Uniform Convergence and the Uniform Metric

1. What is uniform convergence?

Uniform convergence is a type of convergence of a sequence of functions, where the rate of convergence is independent of the chosen point. It means that the sequence of functions approaches the same limit function as the input variable increases, regardless of the specific value of the input variable.

2. How is uniform convergence different from pointwise convergence?

Pointwise convergence is a type of convergence where the limit function is approached at each individual point of the input variable. In contrast, uniform convergence is a stronger condition, where the limit function is approached uniformly across the entire range of the input variable.

3. What is the uniform metric?

The uniform metric is a measure of the distance between two functions in a space of functions. It is defined as the supremum (or maximum) of the absolute difference between the two functions over the entire range of the input variable.

4. How is the uniform metric used to determine uniform convergence?

If the distance between a sequence of functions and a limit function, as measured by the uniform metric, approaches zero as the input variable increases, then the sequence of functions is said to converge uniformly to the limit function.

5. What are some practical applications of uniform convergence and the uniform metric?

Uniform convergence and the uniform metric are commonly used in the analysis of sequences of functions in various fields such as mathematics, physics, and engineering. They are particularly useful in the study of differential equations and in numerical methods for solving them. They are also used in the analysis of approximation methods in statistics and signal processing.

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