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In mathematics, in the field of [[functional analysis]], a '''Minkowski functional''' is a function that recovers a notion of distance on a linear space.
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Let ''K'' be a symmetric convex body in a linear space ''V''. We define a function ''p'' on ''V'' as
 
:<math>p(x) = \inf \{ \lambda \in \mathbb{R}_{> 0} : x \in \lambda K \} </math>
 
if that [[infimum]] is well-defined.<ref>Thompson (1996) p.17</ref>
 
== Motivation ==
===Example 1===
 
Consider a [[normed vector space]] ''X'', with the norm ||·||. Let ''K'' be the unit sphere in ''X''. Define a function ''p : X →'' '''R''' by
:<math>p(x) = \inf \left\{r > 0: x \in r K \right\}. </math>
 
One can see that <math>p(x) = \|x\|</math>, i.e. ''p'' is just the norm on ''X''. The function ''p'' is a special case of a Minkowski functional.
 
=== Example 2===
 
Let ''X'' be a vector space without topology with underlying scalar field '''K'''. Take ''φ ∈  X' '', the algebraic dual of ''X'', i.e. ''φ : X →'' '''K''' is a linear functional on ''X''. Fix ''a > 0''. Let the set ''K'' be given by
 
:<math>K = \{ x \in X : | \phi(x) | \leq a \}. </math>
 
Again we define
 
:<math>p(x) = \inf \left\{r > 0: x \in r K \right\}. </math>
 
Then
 
:<math>p(x) = \frac{1}{a} | \phi(x) |.</math>
 
The function ''p''(''x'') is another instance of a Minkowski functional. It has the following properties:
 
#It is ''subadditive'': ''p''(''x'' + ''y'') ≤ ''p''(''x'') + ''p''(''y''),
#It is ''homogeneous'': for all ''α'' ∈ '''K''', ''p''(''α x'') = |''α''| ''p''(''x''),
#It is nonnegative.
 
Therefore ''p'' is a [[seminorm]] on ''X'', with an induced topology. This is characteristic of Minkowski functionals defined via "nice" sets. There is a one-to-one correspondence between seminorms and the Minkowski functional given by such sets. What is meant precisely by "nice" is discussed in the section below.
 
Notice that, in contrast to a stronger requirement for a norm, ''p(x) = 0'' need not imply ''x = 0''. In the above example, one can take a nonzero ''x'' from the kernel of ''φ''. Consequently, the resulting topology need not be [[Hausdorff space|Hausdorff]].
 
== Definition ==
 
The above examples suggest that, given a (complex or real) vector space ''X'' and a subset ''K'', one can define a corresponding Minkowski functional
 
:<math>p_K:X \rightarrow [0, \infty)</math>
 
by
 
:<math>p_K (x) = \inf \left\{r > 0: x \in r K \right\},</math>
 
which is often called the gauge of <math>K</math>.
 
It is implicitly assumed in this definition that 0 ∈ ''K'' and the set {''r'' > 0: ''x'' ∈ ''r K''} is nonempty. In order for ''p<sub>K</sub>'' to have the properties of a seminorm, additional restrictions must be imposed on ''K''. These conditions are listed below.
 
#The set ''K'' being [[convex set|convex]] implies the subadditivity of ''p<sub>K</sub>''.
#[[Homogeneous function|Homogeneity]], i.e. ''p<sub>K</sub>''(''α x'') = |''α''| ''p<sub>K</sub>''(''x'') for all ''α'', is ensured if ''K'' is ''balanced'', meaning ''α K'' ⊂ ''K'' for all |''α''| ≤ 1.
 
A set ''K'' with these properties is said to be [[absolutely convex set|absolutely convex]].
 
=== Convexity of ''K'' ===
 
A simple geometric argument that shows convexity of ''K'' implies subadditivity is as follows. Suppose for the moment that ''p<sub>K</sub>''(''x'') = ''p<sub>K</sub>''(''y'') = ''r''. Then for all ''ε'' > 0, we have ''x'', ''y'' ∈ (''r + ε'') ''K'' = '' K' ''. The assumption that ''K'' is convex means '' K' '' is also. Therefore ½ ''x'' + ½ ''y'' is in '' K' ''. By definition of the Minkowski functional ''p<sub>K</sub>'', one has
 
:<math>p_K\left( \frac{1}{2} x + \frac{1}{2} y\right) \le r + \epsilon = \frac{1}{2} p_K(x) + \frac{1}{2} p_K(y) + \epsilon .</math>
 
But the left hand side is ½ ''p<sub>K</sub>''(''x'' + ''y''), i.e. the above becomes
 
:<math>p_K(x + y) \le  p_K(x) + p_K(y) + \epsilon, \quad \mbox{for all} \quad \epsilon > 0.</math>
 
This is the desired inequality. The general case ''p<sub>K</sub>''(''x'') > ''p<sub>K</sub>''(''y'') is obtained after the obvious modification.
 
'''Note''' Convexity of ''K'', together with the initial assumption that the set {''r'' > 0: ''x'' ∈ ''r K''} is nonempty, implies that ''K'' is [[absorbing set|''absorbent'']].
 
=== Balancedness of ''K'' ===
 
Notice that ''K'' being balanced implies that
 
:<math>\lambda x \in r K \quad \mbox{if and only if} \quad x \in \frac{r}{|\lambda|} K.</math>
 
Therefore
 
:<math>p_K (\lambda x) = \inf \left\{r > 0:  \lambda x \in r K \right\}
=  \inf \left\{r > 0:  x \in \frac{r}{|\lambda|} K \right\}
= \inf \left\{ | \lambda | \frac{r}{ | \lambda | } > 0:  x \in \frac{r}{|\lambda|} K \right\}
= |\lambda| p_K(x).
</math>
 
== See also ==
 
* [[Hadwiger's theorem]]
* [[Hugo Hadwiger]]
* [[Morphological image processing]]
 
==Notes==
{{reflist}}
 
==References==
* {{cite book | title=Minkowski Geometry | series=Encyclopedia of Mathematics and Its Applications | first=Anthony C. | last=Thompson | publisher=[[Cambridge University Press]] | year=1996 | isbn=0-521-40472-X }}
 
[[Category:Functional analysis]]
[[Category:Convex analysis]]

Revision as of 20:20, 9 February 2014

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