Understanding metric space definition through concrete examples

In summary, the definition of an open set in a metric space means that for every point in the set, there exists a small open ball contained entirely within the set. This can be visualized as the set having no boundary and can be seen in examples such as the set (0,1) in the real numbers with the usual metric. However, this intuition may not hold in general metric spaces, as seen with the discrete metric.
  • #1
Ricster55
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Right now, I am studying Advanced Calculus (proof based), and it is hard thinking through some of the definitions without first thinking about it concretely (meaning how to visualize it better geometrically, if that makes any sense?). I need help with this definition.

Definition

Let X be a metric space. A set G ⊂ X is open if for every a ∈ G there exists r > 0 such that Br(a) ⊂ G. A subset F ⊂ X is closed if F^C = X - F is open.

How do I try to "visualize" this definition, through say, a diagram or a set example?
 
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  • #2
It just means that for every point in the set, you can find a small open ball that is contained entirely in the set.

If you think about this in general ##\mathbb{R}^n## space (where the intuition comes from), and consider a bounded set, this means that nothing of a boundary is included in the set itself, because if there would be a point on the boundary, any ball with center that point will intersect the complement of the set, so the set isn't open.

Therefore, I like to think about openness as if the set has no boundary.

For example, ##(0,1)## is open in the reals with the usual metric, because it does not contain its 'boundary points' and ##[0,1)## is not open, because any ball with center 0 will contain a point smaller than ##0##.

Notice that this intuition starts to break down in general metric spaces. Take for example any set with the discrete metric.
 
  • #3
I'd first take the real line as example, so the open sets are open intervals. The ##B_r(a)## are required to be open: ##B_r(a)=\{x\in X\,: \,||x-a|| < r\}## which is usually written ##U_r(a)##. The ##B_r(a)## are commonly reserved for closed balls.

After that you could do the same in the plane.
 
  • #4
Math_QED said:
It just means that for every point in the set, you can find a small open ball that is contained entirely in the set.

If you think about this in general ##\mathbb{R}^n## space (where the intuition comes from), and consider a bounded set, this means that nothing of a boundary is included in the set itself, because if there would be a point on the boundary, any ball with center that point will intersect the complement of the set, so the set isn't open.

Therefore, I like to think about openness as if the set has no boundary.

For example, ##(0,1)## is open in the reals with the usual metric, because it does not contain its 'boundary points' and ##[0,1)## is not open, because any ball with center 0 will contain a point smaller than ##0##.

Notice that this intuition starts to break down in general metric spaces. Take for example any set with the discrete metric.
aah, I think I get it now. Thanks for the reply
 
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What is a metric space?

A metric space is a mathematical concept that represents a set of objects where the distance between any two points can be measured. It consists of a set of elements and a metric, which is a function that assigns a non-negative value to the distance between any two elements in the set.

What are some examples of metric spaces?

Some common examples of metric spaces include Euclidean space, which represents distances between points in a three-dimensional coordinate system, and the set of real numbers with the metric of absolute value. Other examples include the set of integers with the metric of absolute difference and the set of strings with the metric of edit distance.

How is a metric space defined?

A metric space is defined by a set of elements and a metric function that satisfies three properties: positivity (the distance between any two points is always non-negative), symmetry (the distance between two points is the same regardless of the order in which they are considered), and triangle inequality (the distance between two points plus the distance between those points and a third point is greater than or equal to the distance between the first and third point).

What is the importance of understanding metric spaces?

Metric spaces are a fundamental concept in mathematics and are used in various fields, including analysis, topology, and geometry. They allow for the precise measurement of distances between objects and provide a framework for understanding and solving problems in these areas.

How can I better understand metric spaces through concrete examples?

One way to better understand metric spaces is to work through concrete examples and see how the metric function is applied to different sets of elements. Another approach is to visualize metric spaces using diagrams or graphs, which can help to develop an intuition for how the distance between points is calculated and how the properties of a metric space are satisfied.

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