Metric Spaces .... the Uniform Metric .... Garling, Proposition 11.1.11

In summary, Garling's book "A Course in Mathematical Analysis: Volume II: Metric and Topological Spaces, Functions of a Vector Variable" focuses on Chapter 11: Metric Spaces and Normed Spaces. The reader needs help with an aspect of the proof of Theorem 11.1.11, which states that taking the supremum preserves inequality in certain cases. The proof uses the fact that d_\infty(f,g) + d_\infty(g,h) is an upper bound for a set, and by definition of "least upper bound," it follows that \sup\{d(f(s),g(s)) : s \in S\} \leq d_\infty(f,g) + d_\infty
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This thread involves a proof that the uniform metric is indeed a metric ...
I am reading D. J. H. Garling's book: "A Course in Mathematical Analysis: Volume II: Metric and Topological Spaces, Functions of a Vector Variable" ... ...

I am focused on Chapter 11: Metric Spaces and Normed Spaces ... ...

I need some help with an aspect of the proof of Theorem 11.1.11 ...

Garling's statement and proof of Theorem 11.1.11 reads as follows:
Garling - 1 - Proposition 11.1.11 ... ... PART 1 .png

Garling - 2 - Proposition 11.1.11 ... ... PART 2 .png

Near the end of Garling's proof above we read the following:

" ... ... Suppose that ##f,g,h \in B_X(S)## and that ##s \in S##. Then

##d(f(s), h(s)) \le d(f(s), g(s)) + d(g(s), h(s)) \le d_\infty (f, g) + d_\infty (g, h)## ... ... ... (1)

Taking the supremum, ##d_\infty (f, h) \le d_\infty (f, g) + d_\infty (g, h)##

... ... ... "Now (1) is true for arbitrary s and so it is true for all ##s## including the point for which ##d(f(s), h(s))## is a maximum ... if a maximum exists ...

But in the case where a maximum does not exist ... how do we know that taking the supremum preserves inequality (1) ...Hope someone can help ...

Peter
 
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Line (1) asserts that [itex]d_\infty(f,g) + d_\infty(g,h)[/itex] is an upper bound for [itex]\{d(f(s),g(s)) : s \in S\}[/itex]. By definition of "least upper bound" it follows that [itex]\sup\{d(f(s),g(s)) : s \in S\} \leq d_\infty(f,g) + d_\infty(g,h)[/itex].
 
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Thanks for the help, pasmith ...

Peter
 

Related to Metric Spaces .... the Uniform Metric .... Garling, Proposition 11.1.11

1. What is a metric space?

A metric space is a mathematical structure that consists of a set of objects and a function that measures the distance between any two objects in the set. This function, known as a metric, must satisfy certain properties, such as being non-negative and symmetric.

2. What is the uniform metric?

The uniform metric, also known as the sup metric or the Chebyshev metric, is a type of metric used in metric spaces. It measures the distance between two points as the maximum difference between their corresponding coordinates. In other words, it measures the greatest distance in any single dimension between the two points.

3. What is Garling, Proposition 11.1.11?

Garling, Proposition 11.1.11 is a theorem in mathematics that states that if a sequence of functions converges uniformly on a metric space, then it also converges pointwise on that space. This proposition is important in the study of metric spaces and their properties.

4. What are some examples of metric spaces?

Some common examples of metric spaces include the real numbers with the standard Euclidean metric, the space of continuous functions on a closed interval with the sup metric, and the space of square-integrable functions with the L2 metric. Other examples include graphs, trees, and vector spaces.

5. How are metric spaces used in science?

Metric spaces are used in various scientific fields, such as physics, chemistry, and biology, to model and analyze real-world phenomena. They are particularly useful in studying the behavior of continuous systems and in analyzing the convergence of sequences and series. Metric spaces also have applications in computer science, such as in data mining and machine learning algorithms.

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