Metric Space

A metric space is a set for which a distance function can be defined.

Not to be confused with a measure space.


Description

A metric space is the double (X,d) composed of:

The distance function must satisfy these conditions: for any points a, b, and c in X...

Compactness

A set is compact when every cover has a finite subcover. In a metric space like R, it is always possible to design an infinite series that converges to the limit point of a set without including that limit point. Crucially however, no finite subset of these can converge to that limit point. Therefore whenever a set does not include its own limit points, there is an infinite cover with no finite subcover.

Thus, in a metric space...

In Rn specifically, per the Heine-Borel theorem, a set being closed and bounded implies compactness, and vice versa.

Comparison to Topological Spaces

Topological spaces are a similar concept that use 'closeness' rather than a distance function. There are however some advantages to using a metric space.

Within a metric space, a set is open if every point within the set can be perturbed in any direction and remain within the set. Clearly this is only ever not the case if a point is a limit point of a set, so an open set does not include any of its limit points while a closed set includes all of them.

The topological definition of openness relies on a neighborhood topology function, i.e. for a set A the neighborhood of point p is given by NA(p). A similar function can be defined for metric spaces; a ball containing all points less than r distance away from point p is the neighborhood Nr(p). This has a distinct advantage in that neighborhoods are always open. This is proven by demonstrating that for every point in a neighborhood, the neighborhood of that new point is a subset of the original neighborhood.


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Analysis/MetricSpace (last edited 2026-08-09 18:59:41 by DominicRicottone)