Difference between revisions of "Metric space"

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A [[Normed space|normed space]] is a special case of a metric space, to see the relationships between metric spaces and others see: [[Subtypes of topological spaces]]
 
A [[Normed space|normed space]] is a special case of a metric space, to see the relationships between metric spaces and others see: [[Subtypes of topological spaces]]
 
==Definition of a metric space==
 
==Definition of a metric space==
A metric space is a set <math>X</math> coupled with a "distance function"<ref name="Topology">Introduction to Topology - Bert Mendelson</ref>:
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A metric space is a set <math>X</math> coupled with a "distance function"<ref name="Topology">Introduction to Topology - Bert Mendelson</ref>{{rITTGG}}:
 
* <math>d:X\times X\rightarrow\mathbb{R}</math> or sometimes
 
* <math>d:X\times X\rightarrow\mathbb{R}</math> or sometimes
* <math>d:X\times X\rightarrow\mathbb{R}_+</math><ref name="Analysis">Analysis - Part 1: Elements - Krzysztof Maurin</ref>
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* <math>d:X\times X\rightarrow\mathbb{R}_+</math><ref name="Analysis">Analysis - Part 1: Elements - Krzysztof Maurin</ref>, Note that here I prefer the notation <math>d:X\times X\rightarrow\mathbb{R}_{\ge 0}</math>
 
With the properties that for <math>x,y,z\in X</math>:
 
With the properties that for <math>x,y,z\in X</math>:
# <math>d(x,y)\ge 0</math>
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# <math>d(x,y)\ge 0</math> (This is implicit with the {{M|1=d:X\times X\rightarrow\mathbb{R}_{\ge 0} }} definition)
 
# <math>d(x,y)=0\iff x=y</math>
 
# <math>d(x,y)=0\iff x=y</math>
 
# <math>d(x,y)=d(y,x)</math> - Symmetry
 
# <math>d(x,y)=d(y,x)</math> - Symmetry
 
# <math>d(x,z)\le d(x,y)+d(y,z)</math> - the [[Triangle inequality]]
 
# <math>d(x,z)\le d(x,y)+d(y,z)</math> - the [[Triangle inequality]]
  
We will denote a metric space as <math>(X,d)</math> (as <math>(X,d:X\times X\rightarrow\mathbb{R})</math> is too long and [[Mathematicians are lazy]]) or simply <math>X</math> if it is obvious which metric we are talking about on <math>X</math>
+
We will denote a metric space as <math>(X,d)</math> (as <math>(X,d:X\times X\rightarrow\mathbb{R}_{\ge 0})</math> is too long and [[Mathematicians are lazy]]) or simply <math>X</math> if it is obvious which metric we are talking about on <math>X</math>
  
 
==Examples of metrics==
 
==Examples of metrics==
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{{End Proof}}{{End Theorem}}
 
{{End Proof}}{{End Theorem}}
  
===Discreet Metric===
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===[[Discrete metric and topology|Discrete Metric]]===
This is a useless metric, but is a metric and induces the Discreet [[Topological space|Topology]] on X, where the topology is the powerset of <math>X</math>, <math>\mathcal{P}(X)</math>.
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{{:Discrete metric and topology/Metric space definition}}
 
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====Notes====
It is given by:
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{{:Discrete metric and topology/Summary}}
* <math>d_{\text{discreet}}(x,y)=\left\{\begin{array}{lr}
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      0 & x=y\\
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      1 & \text{otherwise}
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    \end{array}\right.</math>
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'''Note:''' it is sometimes called the ''trivial'' metric<ref name="FA">Functional Analysis - George Bachman and Lawrence Narici</ref>
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{{Begin Theorem}}
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Proof that this is a metric
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{{Begin Proof}}
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{{Todo|Really easy though}}
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{{End Proof}}{{End Theorem}}
+
  
 
==See also==
 
==See also==
 +
* [[Topology induced by a metric]]
 +
* [[Connected space]]
 
* [[Topological space]]
 
* [[Topological space]]
 
+
==Notes==
 +
<references group="Note"/>
 
==References==
 
==References==
 
<references/>
 
<references/>
  
 
{{Definition|Topology|Metric Space}}
 
{{Definition|Topology|Metric Space}}

Latest revision as of 06:07, 27 November 2015

A normed space is a special case of a metric space, to see the relationships between metric spaces and others see: Subtypes of topological spaces

Definition of a metric space

A metric space is a set X

coupled with a "distance function"[1][2]:

  • d:X×XR
    or sometimes
  • d:X×XR+
    [3], Note that here I prefer the notation d:X×XR0

With the properties that for x,y,zX

:

  1. d(x,y)0
    (This is implicit with the d:X×XR0 definition)
  2. d(x,y)=0x=y
  3. d(x,y)=d(y,x)
    - Symmetry
  4. d(x,z)d(x,y)+d(y,z)
    - the Triangle inequality

We will denote a metric space as (X,d)

(as (X,d:X×XR0)
is too long and Mathematicians are lazy) or simply X
if it is obvious which metric we are talking about on X

Examples of metrics

Euclidian Metric

The Euclidian metric on Rn

is defined as follows: For x=(x1,...,xn)Rn
and y=(y1,...,yn)Rn
we define the Euclidian metric by:

dEuclidian(x,y)=ni=1((xiyi)2)

[Expand]

Proof that this is a metric


Discrete Metric

Let X be a set. The discrete[2] metric, or trivial metric[4] is the metric defined as follows:

  • d:X×XR0
    with d:(x,y){0if x=y1otherwise

However any strictly positive value will do for the xy case. For example we could define d as:

  • d:(x,y){0if x=yvotherwise
    • Where v is some arbitrary member of R>0[Note 1] - traditionally (as mentioned) v=1 is used.

Note: however in proofs we shall always use the case v=1 for simplicity

Notes

Property Comment
induced topology discrete topology - which is the topology (X,P(X)) (where P denotes power set)
Open ball Br(x):={pX| d(p,x)<r}={{x}if r1Xotherwise
Open sets Every subset of X is open.
Proof outline: as for a subset AX we can show xAr[Br(x)A] by choosing say, that is A contains an open ball centred at each point in A.
Connected The topology generated by (X,ddiscrete) is not connected if X has more than one point.
Proof outline:
  • Let A be any non empty subset of X, then define B:=Ac which is also a subset of X, thus B is open. Then AB= and AB=X thus we have found a separation, a partition of non-empty disjoint open sets, that separate the space. Thus it is not connected
  • if X has only one point then we cannot have a partition of non empty disjoint sets. Thus it cannot be not connected, it is connected.

See also

Notes

  1. Jump up Note the strictly greater than 0 requirement for v

References

  1. Jump up Introduction to Topology - Bert Mendelson
  2. Jump up to: 2.0 2.1 Introduction to Topology - Theodore W. Gamelin & Robert Everist Greene
  3. Jump up Analysis - Part 1: Elements - Krzysztof Maurin
  4. Jump up Functional Analysis - George Bachman and Lawrence Narici