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In [[mathematics]], the '''Hausdorff dimension''' is a way of defining a fractional area for all dimensional figures such that the dimension describes partially the way that an area moves in the space around it.  For example, a [[plane (geometry)|plane]] would have a Hausdorff dimension of 2.  However, it would not make sense to give the [[Sierpinski triangle]] [[fractal]] a dimension of 2, since it does not fully occupy the 2-dimensional realm.  Therefore, its Hausdorff dimension describes it mathematically, creating a relationship between the number of new self-similar sections and their scale.
In [[mathematics]], the '''Hausdorff dimension''' is a way of defining a possibly fractional exponent for all figures in a [[metric space]] such that the dimension partially describes the amount that the set fills the space around it.  For example, a [[plane (geometry)|plane]] would have a Hausdorff dimension of 2, because it fills a 2-parameter subset.  However, it would not make sense to give the [[Sierpiński triangle]] [[fractal]] a dimension of 2, since it does not fully occupy the 2-dimensional realm.  The Hausdorff dimension describes this mathematically by measuring the size of the set.  For self-similar sets there is a relationship to the number of self-similar subsets and their scale.


[[Category: Mathematics Workgroup]]
== Informal definition ==
[[Category: CZ Live]]
Intuitively, the dimension of a set is the number of independent parameters one has to pick in order to fix a point.  This is made rigorously with the notion of ''d''-dimensional (topological) [[manifold]] which are particularly regular sets.  The problem with the classical notion is that you can easily break up the digits of a real number to map it bijectively to two (or ''d'') real numbers.  The example of space filling curves shows that it is even possible to do this in a continuous (but non-bijective) way.
 
The notion of Hausdorff dimension refines this notion of dimension such that the dimension can be any non-negative number.
 
Benoît Mandelbrot discovered<ref>B.B. Mandelbrot: ''The fractal geometry of nature'', Freemann '''(1983)''', ISBN 978-0-716-711-865</ref> that many objects in nature are not strictly classical smooth bodies, but best approximated as fractal sets, i.e. subsets of '''R'''<sup>''N''</sup> whose Hausdorff dimension is strictly greater than its topological dimension.
 
 
== Hausdorff measure and dimension ==
Let ''d'' be a non-negative real number and ''S'' ⊂ ''X'' a subset of a metric space (''X'',''ρ'').  The ''d''-dimensional Hausdorff measure of scale ''δ''>0 is
:<math> H^{d*}_\delta(S) := \inf \{\sum_{i=1}^\infty r_i^d : S\subset\bigcup_{i=1}^\infty B_{r_i}(x_i), r_i\le\delta \}</math>
where B<sub>''r''<sub>''i''</sub>(''x''<sub>''i''</sub>)</sub> is the open ball around ''x''<sub>''i''</sub> ∈ ''X'' of radius ''r''<sub>''i''</sub>.  The ''d''-dimensional Hausdorff measure is now the limit
:<math> H^{d*}(S) := \lim_{\delta\to0+} H^{d*}_\delta(S)</math>.
As in the Carathéodory construction a set  ''S'' ⊂ ''X'' is called ''d''-measurable iff
:<math> H^{d*}(T) = H^{d*}(S\cap T)+ H^{d*}(T\cap X\setminus S)</math> for all  ''T'' ⊂ ''X''.
A set ''S'' ⊂ ''X'' is called Hausdorff measurable if it is H<sup>''d''</sup>-measurable for all ''d''≥0.
 
In general it is quite difficult to determine the Hausdorff measure of a set  ''S'' ⊂ ''X''.  However the following two comparisons to the [[Lebesgue measure]] are helpful:
H<sup>0</sup> is the counting measure, i.e. the number of points in  ''S'' ⊂ ''X'' and in particular all sets are H<sup>0</sup>-measurable.
Let ''X'' = '''R'''<sup>''N''</sup> and ''N'', ''d'' ∈ '''N''' natural numbers, then
:<math> H^d = c_d\lambda^d</math>,
i.e. the ''d''-dimensional Hausdorff measure is the ''d''-dimensional Lebesgue measure up to a scaling factor ''c''<sub>''d''</sub> which is the volume of the unit ball in '''R'''<sup>''d''</sup>.
 
The Hausdorff measures fulfill the following monotonicity properties:
 
For ''S'' ⊂ ''T'', H<sup>''d''</sup>(''S'') ≤ H<sup>''d''</sup>(''T'').
If H<sup>''d''</sup>(''S'') > 0, then H<sup>''e''</sup>(''S'') = ∞ for all ''e''<''d''. 
If H<sup>''d''</sup>(''S'') < ∞, then H<sup>''e''</sup>(''S'') = 0 for all ''e'' > ''d''.
 
The Hausdorff dimension of a Hausdorff measurable set ''S'' ⊂ ''X'' is the non-negative real number ''d'' such that H<sup>''e''</sup>(''S'')= ∞ for ''e'' < ''d'' and H<sup>''e''</sup>(''S'') = 0 for ''e'' > ''d''. 
Also the Hausdorff dimension fulfills the monotonicity property:
For ''S'' ⊂ ''T'' Hausdorff measurable, dim<sub>H</sub> ''S'' ≤ dim<sub>H</sub> ''T''.
 
== Elementary properties ==
1. The Hausdorff dimension of ''d''-dimensional embedded sub[[manifold]]s of '''R'''<sup>''N''</sup> is ''d''.
 
2. The Hausdorff dimension of a countable union is
:<math> \mathrm{dim}_H \bigcup_{i=1}^\infty F_i = \sup_i \mathrm{dim}_H F_i</math>
where ''F''<sub>''i''</sub> ⊂ ''X'' are Hausdorff measurable sets.  In particular the Hausdorff dimension is monotone.  Also the Hausdorff dimension of a countable set is 0.
 
3. The Hausdorff measure behaves under a [[Lipschitz continuous]] map ''f'': ''X''→''X'', i.e. <math>\rho(f(x),f(y))\le c\rho(x,y)</math> as follows
:<math> H^d(F) \le c^d H^d(F)</math>
for every Hausdorff measurable set ''F'' ⊂ ''X''.  Therefore the Hausdorff dimension can be estimated from above as
:<math>\dim_H f(F)\le \dim_H F</math>.
For bi-Lipschitz maps, i.e. there are positive constants ''c''<sub>1/2</sub> > 0 such that <math>c_1\rho(x,y)\le\rho(f(x),f(y))\le c_2\rho(x,y)</math> from a complete metric space (''X'',''ρ''), the dimension is preserved, because the inverse map is also bi-Lipschitz.
 
== Examples ==
=== Iterated function systems ===
Consider the map
:<math> S\colon 2^X\to 2^X: F\mapsto \bigcup_{i=1}^n S_i(F)</math> 
where each ''S''<sub>''i''</sub>: ''X''→''X'' is a contraction, i.e. there is a number 0≤''c''<1 such that :<math>\rho(S_i(x),S_i(y) \le c\rho(x,y) \forall x, y\in X</math>.
It is not hard to prove that there is a unique compact nonempty set ''F'' ⊂ ''X'' such that ''S''(''F'') = ''F'', i.e. a fixed-point.  In order to prove that one uses the Hausdorff metric
:<math> \rho(A,B) :=\inf\{\delta\ge0: A\subset B_\delta \land B\subset A_\delta\}</math>
where ''A''<sub>δ</sub> is the δ-parallel extension of ''A''.
 
Given the simplest case ''X''='''R'''<sup>''N''</sup> and the ''S''<sub>''i''</sub> scalings with factor ''c''<sub>''i''</sub> each, then we see that the whole set ''F'' is the union of ''n'' scaled copies of itself.  Assuming that ''F'' has finite positive Hausdorff measure, this must fulfill
:<math> H^d(F) = \sum_{i=1}^n H^d(S_i(F)) = \sum_{i=1}^n c_i^d H^d(F)</math>
and thus the dimension ''d'' of ''F''
:<math> 1 = \sum_{i=1}^n c_i^d</math>. (d)
This heuristic argument is justified if the subsets ''F''<sub>''i''</sub> := ''S''<sub>''i''</sub>(''F'') are not too strongly overlapping.  This can be ensured if there is a bounded nonempty open set ''O'' ⊂ ''X'' such that
:<math> O \supset \bigcup_{i=1}^n S_i(O)</math> (O)
where the union is a disjoint union.  The heuristic estimate remains an upper bound if we cannot find a separating open set ''O''.
 
As more particular examples consider the following:
 
1. The ''mid-third Cantor set''.  This is constructed from the interval [0,1] where in the first step we remove the inner third (1/3,2/3) and in the ''N''th step we remove the inner part of length 1/3<sup>''N''</sup> from each of the 2<sup>''N''-1</sup> intervals.  The intersection of all the intermediate steps is the mid-third Cantor set.  Obviously this is the fixed-point set under the two maps: ''S''<sub>1</sub>:'''R'''→'''R''':''x''→''x''/3 and ''S''<sub>2</sub>:'''R'''→'''R''':''x''→(''x''+2)/3 which each have contraction factor c=1/3.  The heuristic argument gives the dimension equation: 1=2•(1/3)<sup>''d''</sup> with the unique solution ''d'' = log2/log3.  The heuristic argument is justified because for ''O''=(0,1) the equation (O) is fulfilled.
 
2. ''Von Koch's snowflake'' is the " infinite curve" obtained from the following construction:  Start with the unit interval [0,1] in '''R'''<sup>2</sup>.  In each step partition each line segment into thirds and replace the middle third by the other two sides of the equilateral triangle over it.  Obviously the snowflake is the fixed point of the union of 4 scalings with factor 1/3 each.  The heuristic equation is therefore: 1=4(1/3)<sup>''d''</sup> with the unique solution ''d'' = log4/log3 > 1.  Again the heuristic argument is justified, because the open set that covers the unit line segment without the endpoints is mapped into four disjoint pieces within itself if the ends lie within 60º angles.
 
3. The Sierpiński triangle is the limit of the following process.  Start with a solid equilateral triangle.  In each step divide each equilateral triangle into 4 equilateral pieces (by dividing the edges into halves) and remove the inner head-down piece.  Obviously this is the fixed-point figure of the union of the three scalings with factor 1/2.  The heuristic equation is therefore 1 = 3(1/2)<sup>''d''</sup> with the unique solution ''d'' = log3/log2.  The heuristic argument is justified, because the open equilateral triangle (i.e. without border) fulfills the equation (O).
 
== Comparison to the box dimension ==
The box dimension arises from the following measuring process.  Let ''S'' ⊂ ''X'' be given and determine for δ>0 the minimum number ''N''<sub>δ</sub>(S) of balls of radius δ that cover ''S''.  If ''S'' is a rectifiable curve, then ''N''<sub>δ</sub> grows with decreasing δ as ''N'' ≈ ''c''δ<sup>-1</sup> (for small δ).  If ''S'' is a rectifiable ''d'' dimensional hypersurface, then ''N''  ≈ ''c''δ<sup>-''d''</sup> (for small δ).  We therefore define the box dimension as
:<math> \underline\dim_B S := \varliminf_{\delta\to0+} \frac{\log N_\delta(S)}{-\log\delta} </math>,
:<math> \overline\dim_B S := \varlimsup_{\delta\to0+} \frac{\log N_\delta(S)}{-\log\delta} </math>
and dim<sub>B</sub> ''S'' the common value if the lower and upper box dimension coincide.
 
The box dimension also remains the same with the following modifications:
# instead of balls we take cubes in '''R'''<sup>''N''</sup>,
# we take lattice cubes in '''R'''<sup>''N''</sup>,
# the smallest amount of sets of diameter δ that cover ''S'',
# the largest amount of disjoint balls of diameter δ whose centers are in ''S''.
 
The problem of the box dimension is that it is only stable under finite union, i.e.
:<math> \dim_B F_1\cup F_2 = \max(\dim F_1,\dim F_2)</math>.
In particular the box dimension of a countable but dense set in '''R'''<sup>''N''</sup> is already ''N''.
 
In comparison to the Hausdorff dimension we have
:<math> \dim_H F\le \underline{\dim}_B F\le \overline{\dim}_B F</math>.
 
== Comparison to the packing dimension ==
The shortcoming of the box dimension is that it is not countably stable.  This can be enforced by modifying the definition in the following sense
:<math> \underline\dim_{MB} F:= \inf \{\sup_i \underline\dim_B F_i: F\subset \bigcup_{i=1}^\infty F_i \}</math>.
Unfortunately the computation of the modified box dimension is now as complicated as of the Hausdorff dimension, however the modified box dimension is countably stable.  It turns out that the modified box dimension can be easily computed for ''compact'' dimensionally homogeneous sets, i.e.:
Suppose that ''S'' ⊂ ''X'' is box measurable and for every point ''x'' ∈ ''S'' and every sufficiently small open set ''x'' ∈ ''O'' ⊂ ''X'' we have dim<sub>B</sub> (''O''∩''S'') = dim<sub>B</sub> ''S'', then
:<math> \dim_{MB} S = \dim_B S</math>.
 
The main difference between the Hausdorff and box dimension is the definition of the underlying "measure".  In the box dimension case we try to exhaust the set with balls of equal radius δ, while in the Hausdorff measure we cover with balls of radius at most δ and weigh corresponding to the radius.  The Hausdorff analogue of the box dimension is the following ''packing measure''
:<math> P^{d*}_\delta(S) := \sup\{\sum_{i=1}^\infty r_i^d : \bigcup_{i=1}^\infty B_{r_i}(x_i)\text{ disjoint and }x_i\in S, r_i\le\delta \}</math>,
:<math> P^{d*}_0(S) := \lim_{\delta\to0+} P^{d*}_\delta(S)</math>,
:<math> P^{d*}(S) := \inf\{\sum_{i=1}^\infty P^{d*}_0(F_i): S\subset\bigcup_{i=1}^\infty F_i\}</math>.
The problem is that P<sub>0</sub><sup>''d''*</sup> is not yet an (outer) measure, but P<sup>''d''*</sup> is.  The packing measure has properties analogue to the Hausdorff measure and the packing dimension dim<sub>P</sub> is defined analogue to the Hausdorff dimension as the border where the packing measures change from 0 to ∞.
 
Unfortunately the computation of the packing measure is even more difficult than that of the Hausdorff measure.  However it is easy to relate the packing dimension to the upper modified box dimension as
:<math> \dim_P = \overline\mathrm{dim}_{MB}</math>.
 
In total we obtain the following comparison between the Hausdorff, lower modified box dimension, and packing dimension for Hausdorff measurable sets ''F'' ⊂ ''X''
:<math> \dim_H F\le \underline\mathrm{dim}_{MB} F\le \dim_P F\le \overline\mathrm{dim}_B F</math>.
 
== Means of computing the Hausdorff dimension ==
Given the definition of (lower modified) box dimension it is often not too hard to find an upper bound for the Hausdorff dimension.  It is however usually much harder to find a lower bound.  The following method can be used to achieve that.
 
Let μ be any measure on ''F'' ⊂ ''X'' such that μ(B<sub>''r''</sub>(x)) ≤ ''r''<sup>''d''</sup>, then
:<math> H^d(F)\ge \frac{\mu(F)}{c}</math>.
 
The ''s''-potential of a measure μ on '''R'''<sup>''N''</sup> is
:<math> I_s(\mu) := \int_{\R^N} \Phi_s(x)\mathrm{d}\mu(x) = \int_{\R^N\times\R^N} \frac{\mathrm{d}\mu(x)\,\mathrm{d}\mu(y)}{|x-y|^s}</math>.
 
If there is a positive measure μ on ''F'' ⊂ '''R'''<sup>''N''</sup> with I<sub>''s''</sub>(μ) < ∞, then H<sup>''s''</sup>(F) = ∞, and in particular dim<sub>H</sub> F ≥ ''s''.
 
Conversely, if H<sup>''d''</sup>(''F'') > 0, then there is a measure μ on ''F'' ⊂ '''R'''<sup>''N''</sup> with I<sub>''s''</sub>(μ) < ∞ for all ''s'' < ''d''.
 
It is also possible to capitalize from Fourier transforms in the estimate of Hausdorff measures in '''R'''<sup>''N''</sup>, see e.g. Triebel<ref>H. Triebel: ''Fractals and spectra.''  Related to Fourier Analysis and function theory.  ''Monographs in Mathematics.'' '''vol 91 (1997)''', Birkhäuser Verlag Basel. ISBN 978-3-0348-0033-4.</ref>
 
== Behavior under projection ==
Given '''R'''<sup>''N''</sup>, then an orthogonal projection is a Lipschitz map with ''C''=1.  Therefore the dimension of a projected set cannot be bigger than the dimension of the original set.  Note that the dimension of the image is also restricted by the dimension of the range of the projection.
 
== Product formula ==
Remember that the Lebesgue measures (which are equivalent to the Hausdorff measures of integral dimension) fulfill the relation
:<math> \lambda^m\times\lambda^n = \lambda^{m+n}</math>.
This is related to the dimension theory of embedded submanifolds ''M'' and ''N'' as
:<math>\dim M\times N = \dim M+\dim N</math>.  (n)
 
However for arbitrary Hausdorff measurable sets ''F'' and ''G'', the weaker condition
:<math> \dim_H F\times G\ge \dim_H F +\dim_H G</math>
holds.
 
We obtain an opposite estimate from the box dimension as follows
:<math> \dim_H F\times G\le \dim_H F+\overline\mathrm{dim}_B\, G</math>,
i.e. if we have a set ''G'' for which the upper box dimension and the Hausdorff dimension coincide, then the naive product formula (n) also holds.
 
== Literature ==
<references/>
3. K.J. Falconer: ''Fractal geometry'', Wiley &amp; Sons '''(2003)''', 2nd edition, ISBN 0-470-84861-8.[[Category:Suggestion Bot Tag]]

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In mathematics, the Hausdorff dimension is a way of defining a possibly fractional exponent for all figures in a metric space such that the dimension partially describes the amount that the set fills the space around it. For example, a plane would have a Hausdorff dimension of 2, because it fills a 2-parameter subset. However, it would not make sense to give the Sierpiński triangle fractal a dimension of 2, since it does not fully occupy the 2-dimensional realm. The Hausdorff dimension describes this mathematically by measuring the size of the set. For self-similar sets there is a relationship to the number of self-similar subsets and their scale.

Informal definition

Intuitively, the dimension of a set is the number of independent parameters one has to pick in order to fix a point. This is made rigorously with the notion of d-dimensional (topological) manifold which are particularly regular sets. The problem with the classical notion is that you can easily break up the digits of a real number to map it bijectively to two (or d) real numbers. The example of space filling curves shows that it is even possible to do this in a continuous (but non-bijective) way.

The notion of Hausdorff dimension refines this notion of dimension such that the dimension can be any non-negative number.

Benoît Mandelbrot discovered[1] that many objects in nature are not strictly classical smooth bodies, but best approximated as fractal sets, i.e. subsets of RN whose Hausdorff dimension is strictly greater than its topological dimension.


Hausdorff measure and dimension

Let d be a non-negative real number and SX a subset of a metric space (X,ρ). The d-dimensional Hausdorff measure of scale δ>0 is

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle H^{d*}_\delta(S) := \inf \{\sum_{i=1}^\infty r_i^d : S\subset\bigcup_{i=1}^\infty B_{r_i}(x_i), r_i\le\delta \}}

where Bri(xi) is the open ball around xiX of radius ri. The d-dimensional Hausdorff measure is now the limit

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle H^{d*}(S) := \lim_{\delta\to0+} H^{d*}_\delta(S)} .

As in the Carathéodory construction a set SX is called d-measurable iff

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle H^{d*}(T) = H^{d*}(S\cap T)+ H^{d*}(T\cap X\setminus S)} for all TX.

A set SX is called Hausdorff measurable if it is Hd-measurable for all d≥0.

In general it is quite difficult to determine the Hausdorff measure of a set SX. However the following two comparisons to the Lebesgue measure are helpful: H0 is the counting measure, i.e. the number of points in SX and in particular all sets are H0-measurable. Let X = RN and N, dN natural numbers, then

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle H^d = c_d\lambda^d} ,

i.e. the d-dimensional Hausdorff measure is the d-dimensional Lebesgue measure up to a scaling factor cd which is the volume of the unit ball in Rd.

The Hausdorff measures fulfill the following monotonicity properties:

For ST, Hd(S) ≤ Hd(T). If Hd(S) > 0, then He(S) = ∞ for all e<d. If Hd(S) < ∞, then He(S) = 0 for all e > d.

The Hausdorff dimension of a Hausdorff measurable set SX is the non-negative real number d such that He(S)= ∞ for e < d and He(S) = 0 for e > d. Also the Hausdorff dimension fulfills the monotonicity property: For ST Hausdorff measurable, dimH S ≤ dimH T.

Elementary properties

1. The Hausdorff dimension of d-dimensional embedded submanifolds of RN is d.

2. The Hausdorff dimension of a countable union is

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \mathrm{dim}_H \bigcup_{i=1}^\infty F_i = \sup_i \mathrm{dim}_H F_i}

where FiX are Hausdorff measurable sets. In particular the Hausdorff dimension is monotone. Also the Hausdorff dimension of a countable set is 0.

3. The Hausdorff measure behaves under a Lipschitz continuous map f: XX, i.e. Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \rho(f(x),f(y))\le c\rho(x,y)} as follows

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle H^d(F) \le c^d H^d(F)}

for every Hausdorff measurable set FX. Therefore the Hausdorff dimension can be estimated from above as

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \dim_H f(F)\le \dim_H F} .

For bi-Lipschitz maps, i.e. there are positive constants c1/2 > 0 such that Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle c_1\rho(x,y)\le\rho(f(x),f(y))\le c_2\rho(x,y)} from a complete metric space (X,ρ), the dimension is preserved, because the inverse map is also bi-Lipschitz.

Examples

Iterated function systems

Consider the map

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle S\colon 2^X\to 2^X: F\mapsto \bigcup_{i=1}^n S_i(F)}

where each Si: XX is a contraction, i.e. there is a number 0≤c<1 such that :Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \rho(S_i(x),S_i(y) \le c\rho(x,y) \forall x, y\in X} . It is not hard to prove that there is a unique compact nonempty set FX such that S(F) = F, i.e. a fixed-point. In order to prove that one uses the Hausdorff metric

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \rho(A,B) :=\inf\{\delta\ge0: A\subset B_\delta \land B\subset A_\delta\}}

where Aδ is the δ-parallel extension of A.

Given the simplest case X=RN and the Si scalings with factor ci each, then we see that the whole set F is the union of n scaled copies of itself. Assuming that F has finite positive Hausdorff measure, this must fulfill

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle H^d(F) = \sum_{i=1}^n H^d(S_i(F)) = \sum_{i=1}^n c_i^d H^d(F)}

and thus the dimension d of F

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle 1 = \sum_{i=1}^n c_i^d} . (d)

This heuristic argument is justified if the subsets Fi := Si(F) are not too strongly overlapping. This can be ensured if there is a bounded nonempty open set OX such that

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle O \supset \bigcup_{i=1}^n S_i(O)} (O)

where the union is a disjoint union. The heuristic estimate remains an upper bound if we cannot find a separating open set O.

As more particular examples consider the following:

1. The mid-third Cantor set. This is constructed from the interval [0,1] where in the first step we remove the inner third (1/3,2/3) and in the Nth step we remove the inner part of length 1/3N from each of the 2N-1 intervals. The intersection of all the intermediate steps is the mid-third Cantor set. Obviously this is the fixed-point set under the two maps: S1:RR:xx/3 and S2:RR:x→(x+2)/3 which each have contraction factor c=1/3. The heuristic argument gives the dimension equation: 1=2•(1/3)d with the unique solution d = log2/log3. The heuristic argument is justified because for O=(0,1) the equation (O) is fulfilled.

2. Von Koch's snowflake is the " infinite curve" obtained from the following construction: Start with the unit interval [0,1] in R2. In each step partition each line segment into thirds and replace the middle third by the other two sides of the equilateral triangle over it. Obviously the snowflake is the fixed point of the union of 4 scalings with factor 1/3 each. The heuristic equation is therefore: 1=4(1/3)d with the unique solution d = log4/log3 > 1. Again the heuristic argument is justified, because the open set that covers the unit line segment without the endpoints is mapped into four disjoint pieces within itself if the ends lie within 60º angles.

3. The Sierpiński triangle is the limit of the following process. Start with a solid equilateral triangle. In each step divide each equilateral triangle into 4 equilateral pieces (by dividing the edges into halves) and remove the inner head-down piece. Obviously this is the fixed-point figure of the union of the three scalings with factor 1/2. The heuristic equation is therefore 1 = 3(1/2)d with the unique solution d = log3/log2. The heuristic argument is justified, because the open equilateral triangle (i.e. without border) fulfills the equation (O).

Comparison to the box dimension

The box dimension arises from the following measuring process. Let SX be given and determine for δ>0 the minimum number Nδ(S) of balls of radius δ that cover S. If S is a rectifiable curve, then Nδ grows with decreasing δ as Ncδ-1 (for small δ). If S is a rectifiable d dimensional hypersurface, then Ncδ-d (for small δ). We therefore define the box dimension as

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \underline\dim_B S := \varliminf_{\delta\to0+} \frac{\log N_\delta(S)}{-\log\delta} } ,
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \overline\dim_B S := \varlimsup_{\delta\to0+} \frac{\log N_\delta(S)}{-\log\delta} }

and dimB S the common value if the lower and upper box dimension coincide.

The box dimension also remains the same with the following modifications:

  1. instead of balls we take cubes in RN,
  2. we take lattice cubes in RN,
  3. the smallest amount of sets of diameter δ that cover S,
  4. the largest amount of disjoint balls of diameter δ whose centers are in S.

The problem of the box dimension is that it is only stable under finite union, i.e.

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \dim_B F_1\cup F_2 = \max(\dim F_1,\dim F_2)} .

In particular the box dimension of a countable but dense set in RN is already N.

In comparison to the Hausdorff dimension we have

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \dim_H F\le \underline{\dim}_B F\le \overline{\dim}_B F} .

Comparison to the packing dimension

The shortcoming of the box dimension is that it is not countably stable. This can be enforced by modifying the definition in the following sense

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \underline\dim_{MB} F:= \inf \{\sup_i \underline\dim_B F_i: F\subset \bigcup_{i=1}^\infty F_i \}} .

Unfortunately the computation of the modified box dimension is now as complicated as of the Hausdorff dimension, however the modified box dimension is countably stable. It turns out that the modified box dimension can be easily computed for compact dimensionally homogeneous sets, i.e.: Suppose that SX is box measurable and for every point xS and every sufficiently small open set xOX we have dimB (OS) = dimB S, then

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \dim_{MB} S = \dim_B S} .

The main difference between the Hausdorff and box dimension is the definition of the underlying "measure". In the box dimension case we try to exhaust the set with balls of equal radius δ, while in the Hausdorff measure we cover with balls of radius at most δ and weigh corresponding to the radius. The Hausdorff analogue of the box dimension is the following packing measure

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P^{d*}_\delta(S) := \sup\{\sum_{i=1}^\infty r_i^d : \bigcup_{i=1}^\infty B_{r_i}(x_i)\text{ disjoint and }x_i\in S, r_i\le\delta \}} ,
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P^{d*}_0(S) := \lim_{\delta\to0+} P^{d*}_\delta(S)} ,
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P^{d*}(S) := \inf\{\sum_{i=1}^\infty P^{d*}_0(F_i): S\subset\bigcup_{i=1}^\infty F_i\}} .

The problem is that P0d* is not yet an (outer) measure, but Pd* is. The packing measure has properties analogue to the Hausdorff measure and the packing dimension dimP is defined analogue to the Hausdorff dimension as the border where the packing measures change from 0 to ∞.

Unfortunately the computation of the packing measure is even more difficult than that of the Hausdorff measure. However it is easy to relate the packing dimension to the upper modified box dimension as

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \dim_P = \overline\mathrm{dim}_{MB}} .

In total we obtain the following comparison between the Hausdorff, lower modified box dimension, and packing dimension for Hausdorff measurable sets FX

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \dim_H F\le \underline\mathrm{dim}_{MB} F\le \dim_P F\le \overline\mathrm{dim}_B F} .

Means of computing the Hausdorff dimension

Given the definition of (lower modified) box dimension it is often not too hard to find an upper bound for the Hausdorff dimension. It is however usually much harder to find a lower bound. The following method can be used to achieve that.

Let μ be any measure on FX such that μ(Br(x)) ≤ rd, then

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle H^d(F)\ge \frac{\mu(F)}{c}} .

The s-potential of a measure μ on RN is

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle I_s(\mu) := \int_{\R^N} \Phi_s(x)\mathrm{d}\mu(x) = \int_{\R^N\times\R^N} \frac{\mathrm{d}\mu(x)\,\mathrm{d}\mu(y)}{|x-y|^s}} .

If there is a positive measure μ on FRN with Is(μ) < ∞, then Hs(F) = ∞, and in particular dimH F ≥ s.

Conversely, if Hd(F) > 0, then there is a measure μ on FRN with Is(μ) < ∞ for all s < d.

It is also possible to capitalize from Fourier transforms in the estimate of Hausdorff measures in RN, see e.g. Triebel[2]

Behavior under projection

Given RN, then an orthogonal projection is a Lipschitz map with C=1. Therefore the dimension of a projected set cannot be bigger than the dimension of the original set. Note that the dimension of the image is also restricted by the dimension of the range of the projection.

Product formula

Remember that the Lebesgue measures (which are equivalent to the Hausdorff measures of integral dimension) fulfill the relation

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \lambda^m\times\lambda^n = \lambda^{m+n}} .

This is related to the dimension theory of embedded submanifolds M and N as

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \dim M\times N = \dim M+\dim N} . (n)

However for arbitrary Hausdorff measurable sets F and G, the weaker condition

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \dim_H F\times G\ge \dim_H F +\dim_H G}

holds.

We obtain an opposite estimate from the box dimension as follows

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \dim_H F\times G\le \dim_H F+\overline\mathrm{dim}_B\, G} ,

i.e. if we have a set G for which the upper box dimension and the Hausdorff dimension coincide, then the naive product formula (n) also holds.

Literature

  1. B.B. Mandelbrot: The fractal geometry of nature, Freemann (1983), ISBN 978-0-716-711-865
  2. H. Triebel: Fractals and spectra. Related to Fourier Analysis and function theory. Monographs in Mathematics. vol 91 (1997), Birkhäuser Verlag Basel. ISBN 978-3-0348-0033-4.

3. K.J. Falconer: Fractal geometry, Wiley & Sons (2003), 2nd edition, ISBN 0-470-84861-8.