Problem : Metrics and Induced Topologies

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In summary, the Euclidean metric is defined as the sum of the squared differences between two points, raised to the power of 1/2. Metrics dp for each p in {1, 2, 3, ...} are defined similarly, but with the differences raised to the power of p. The goal is to prove that each dp induces the same topology as the Euclidean metric. This can be done by showing that for every \epsilon > 0 and for every x \in \mathbb{R}^n, there are \delta _1,\, \delta _2 > 0 such that for every y \in \mathbb{R}^n, the inequalities hold. This can be done through
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AKG
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The Euclidean metric, d, is defined by:

[tex]d(x, y) = \left [\sum _{i = 1} ^n |x_i - y_i|^2\right ]^{1/2}[/tex]

Define metrics dp for each p in {1, 2, 3, ...} as follows:

[tex]d_p(x,y) = \left [\sum _{i = 1} ^n |x_i - y_i|^p\right ]^{1/p}[/tex]

Prove that each dp induces the same topology as the Euclidean metric.

To do this, I want to show that for every [itex]\epsilon > 0[/itex] and for every [itex]x \in \mathbb{R}^n[/itex], there is are [itex]\delta _1,\, \delta _2 > 0[/itex] such that for every [itex]y \in \mathbb{R}^n[/itex]:

[tex]\left [\sum _{i = 1} ^n |x_i - y_i|^2\right ]^{1/2} < \delta _1 \Rightarrow \left [\sum _{i = 1} ^n |x_i - y_i|^p\right ]^{1/p} < \epsilon[/tex]

and

[tex]\left [\sum _{i = 1} ^n |x_i - y_i|^p\right ]^{1/p} < \delta _2 \Rightarrow \left [\sum _{i = 1} ^n |x_i - y_i|^2\right ]^{1/2} < \epsilon[/tex]

Is this the right way to prove it? Where do I go from here? Induction on n, or p? Or maybe both? Or is there a way to do it without induction? Help would be very much appreciated!
 
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  • #2
Actually, the problem I really have to solve is to show that, assuming each dp is a metric, they all induce the usual topology on Rn, and I figured the best way to do this was to show that they induced the same topology as the Euclidean metric since these "metrics" (they might not all be metrics, but the problem says to assume they are) look a lot like the Euclidean metric.
 
  • #3
Well, when you don't understand something, draw a picture. :smile:

A circle (or an n-sphere, in general) is a characteristic of the Euclidean metric, right? What about these other metrics?
 

FAQ: Problem : Metrics and Induced Topologies

What is the importance of metrics and induced topologies in problem solving?

Metrics and induced topologies are essential tools in problem solving as they allow for a systematic and quantitative approach to understanding and analyzing complex problems. By providing a way to measure and compare different variables, metrics and induced topologies help identify the root cause of a problem and guide the development of effective solutions.

What is a metric and how is it used in problem solving?

A metric is a mathematical function that assigns a numerical value to a set of data, allowing for comparisons and analysis. In problem solving, metrics help quantify the characteristics and behaviors of a system, making it easier to identify patterns and relationships that can lead to a deeper understanding of the problem at hand.

How are induced topologies derived and applied to problem solving?

Induced topologies are derived from a given metric and define the structure of a space based on the relationships between elements in the metric. In problem solving, induced topologies can provide a framework for organizing and analyzing data, allowing for the identification of key variables and patterns that may be crucial to finding a solution.

What are the limitations of using metrics and induced topologies in problem solving?

While metrics and induced topologies are powerful tools in problem solving, they do have limitations. For example, the choice of metric and induced topology can greatly influence the results and may not always accurately represent the complexity of a real-world problem. Additionally, metrics and induced topologies may not account for all variables and may not be suitable for all types of problems.

How can metrics and induced topologies be used to evaluate the effectiveness of problem solving strategies?

Metrics and induced topologies can be used to measure the effectiveness of problem solving strategies by providing a way to compare the results of different approaches. By tracking and analyzing data using the chosen metric and induced topology, it is possible to determine which strategies are most successful in addressing the problem and achieving the desired outcome.

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