Assouad Dimension and Random Fractals (Contains joint work with Jonathan M. Fraser and Jun J. Miao)
Sascha Troscheit
University of St Andrews
October 3, 2014 Pure Postgraduate Seminar
Sascha Troscheit Assouad Dimension and Random Fractals Exponential ratio In particular it looks at the exponent α, called the dimension, such that content ∼ size−α
A glimmer of hope d Everything in the following slides extends to R euclidean space, 2 1 but I will only consider examples in R and R .
Dimension Theory
Dimension Theory Dimension theory is, broadly speaking, the study of the relationship between content of a set and its size.
Sascha Troscheit Assouad Dimension and Random Fractals A glimmer of hope d Everything in the following slides extends to R euclidean space, 2 1 but I will only consider examples in R and R .
Dimension Theory
Dimension Theory Dimension theory is, broadly speaking, the study of the relationship between content of a set and its size.
Exponential ratio In particular it looks at the exponent α, called the dimension, such that content ∼ size−α
Sascha Troscheit Assouad Dimension and Random Fractals Dimension Theory
Dimension Theory Dimension theory is, broadly speaking, the study of the relationship between content of a set and its size.
Exponential ratio In particular it looks at the exponent α, called the dimension, such that content ∼ size−α
A glimmer of hope d Everything in the following slides extends to R euclidean space, 2 1 but I will only consider examples in R and R .
Sascha Troscheit Assouad Dimension and Random Fractals Hausdorff dimension Similar idea, but not restricted to boxes. You can take any open set Uδ of diameter less than δ and consider the ‘best’ cover: X |U|s ∼ 1
Packing dimension I don’t care.
Classical Dimensions
Box counting dimension The box counting dimension s is the exponential factor between minimum number of boxes of side length δ, called Nδ to cover a set E. −s Nδ ∼ δ
Sascha Troscheit Assouad Dimension and Random Fractals Packing dimension I don’t care.
Classical Dimensions
Box counting dimension The box counting dimension s is the exponential factor between minimum number of boxes of side length δ, called Nδ to cover a set E. −s Nδ ∼ δ
Hausdorff dimension Similar idea, but not restricted to boxes. You can take any open set Uδ of diameter less than δ and consider the ‘best’ cover: X |U|s ∼ 1
Sascha Troscheit Assouad Dimension and Random Fractals Classical Dimensions
Box counting dimension The box counting dimension s is the exponential factor between minimum number of boxes of side length δ, called Nδ to cover a set E. −s Nδ ∼ δ
Hausdorff dimension Similar idea, but not restricted to boxes. You can take any open set Uδ of diameter less than δ and consider the ‘best’ cover: X |U|s ∼ 1
Packing dimension I don’t care.
Sascha Troscheit Assouad Dimension and Random Fractals Brace yourself! Definition Let (X , d) be a metric space and for any non-empty subset F ⊆ X and r > 0, let Nr (F ) be the smallest number of open sets with diameter less than or equal to r required to cover F . The Assouad dimension of a non-empty subset F of X , dimA F , is defined by
(
dimA F = inf α ∃ C, ρ > 0 such that, for all 0 < r < R ≤ ρ,
α ) R we have sup Nr B(x, R) ∩ F ≤ C x∈F r
Assouad dimension
Sascha Troscheit Assouad Dimension and Random Fractals Definition Let (X , d) be a metric space and for any non-empty subset F ⊆ X and r > 0, let Nr (F ) be the smallest number of open sets with diameter less than or equal to r required to cover F . The Assouad dimension of a non-empty subset F of X , dimA F , is defined by
(
dimA F = inf α ∃ C, ρ > 0 such that, for all 0 < r < R ≤ ρ,
α ) R we have sup Nr B(x, R) ∩ F ≤ C x∈F r
Assouad dimension
Brace yourself!
Sascha Troscheit Assouad Dimension and Random Fractals The Assouad dimension of a non-empty subset F of X , dimA F , is defined by
(
dimA F = inf α ∃ C, ρ > 0 such that, for all 0 < r < R ≤ ρ,
α ) R we have sup Nr B(x, R) ∩ F ≤ C x∈F r
Assouad dimension
Brace yourself! Definition Let (X , d) be a metric space and for any non-empty subset F ⊆ X and r > 0, let Nr (F ) be the smallest number of open sets with diameter less than or equal to r required to cover F .
Sascha Troscheit Assouad Dimension and Random Fractals (
dimA F = inf α ∃ C, ρ > 0 such that, for all 0 < r < R ≤ ρ,
α ) R we have sup Nr B(x, R) ∩ F ≤ C x∈F r
Assouad dimension
Brace yourself! Definition Let (X , d) be a metric space and for any non-empty subset F ⊆ X and r > 0, let Nr (F ) be the smallest number of open sets with diameter less than or equal to r required to cover F . The Assouad dimension of a non-empty subset F of X , dimA F , is defined by
Sascha Troscheit Assouad Dimension and Random Fractals Assouad dimension
Brace yourself! Definition Let (X , d) be a metric space and for any non-empty subset F ⊆ X and r > 0, let Nr (F ) be the smallest number of open sets with diameter less than or equal to r required to cover F . The Assouad dimension of a non-empty subset F of X , dimA F , is defined by
(
dimA F = inf α ∃ C, ρ > 0 such that, for all 0 < r < R ≤ ρ,
α ) R we have sup Nr B(x, R) ∩ F ≤ C x∈F r
Sascha Troscheit Assouad Dimension and Random Fractals dimH F ≤ dimB F ≤ dimB F ≤ dimA F
dimH F ≤ dimP F ≤ dimB F
For many settings with a lot of ‘regularity’, like self-similar fractals all notions coincide.
Dimensions summarised
In general we have:
Sascha Troscheit Assouad Dimension and Random Fractals dimH F ≤ dimP F ≤ dimB F
For many settings with a lot of ‘regularity’, like self-similar fractals all notions coincide.
Dimensions summarised
In general we have:
dimH F ≤ dimB F ≤ dimB F ≤ dimA F
Sascha Troscheit Assouad Dimension and Random Fractals For many settings with a lot of ‘regularity’, like self-similar fractals all notions coincide.
Dimensions summarised
In general we have:
dimH F ≤ dimB F ≤ dimB F ≤ dimA F
dimH F ≤ dimP F ≤ dimB F
Sascha Troscheit Assouad Dimension and Random Fractals Dimensions summarised
In general we have:
dimH F ≤ dimB F ≤ dimB F ≤ dimA F
dimH F ≤ dimP F ≤ dimB F
For many settings with a lot of ‘regularity’, like self-similar fractals all notions coincide.
Sascha Troscheit Assouad Dimension and Random Fractals Mandelbrot Percolation 1-Variable Random Iterated Function System Self-similar graph directed random
Random Fractals
We will introduce the following random models:
Sascha Troscheit Assouad Dimension and Random Fractals 1-Variable Random Iterated Function System Self-similar graph directed random
Random Fractals
We will introduce the following random models: Mandelbrot Percolation
Sascha Troscheit Assouad Dimension and Random Fractals Self-similar graph directed random
Random Fractals
We will introduce the following random models: Mandelbrot Percolation 1-Variable Random Iterated Function System
Sascha Troscheit Assouad Dimension and Random Fractals Random Fractals
We will introduce the following random models: Mandelbrot Percolation 1-Variable Random Iterated Function System Self-similar graph directed random
Sascha Troscheit Assouad Dimension and Random Fractals Mandelbrot Percolation
Notation Let F be the limit set of a Mandelbrot percolation of a d dimensional cube, dividing each side into n pieces with retaining probability p for each subcube in the construction.
Sascha Troscheit Assouad Dimension and Random Fractals Mandelbrot Percolation p = 0.85
Sascha Troscheit Assouad Dimension and Random Fractals Mandelbrot Percolation p = 0.85
Sascha Troscheit Assouad Dimension and Random Fractals Mandelbrot Percolation p = 0.85
Sascha Troscheit Assouad Dimension and Random Fractals Mandelbrot Percolation p = 0.85
Sascha Troscheit Assouad Dimension and Random Fractals Mandelbrot Percolation p = 0.85
Sascha Troscheit Assouad Dimension and Random Fractals Mandelbrot Percolation p = 0.85
Sascha Troscheit Assouad Dimension and Random Fractals Mandelbrot Percolation p = 0.85
Sascha Troscheit Assouad Dimension and Random Fractals Mandelbrot Percolation p = 0.85
Sascha Troscheit Assouad Dimension and Random Fractals Mandelbrot Percolation p = 0.65
Sascha Troscheit Assouad Dimension and Random Fractals Mandelbrot Percolation p = 0.65
Sascha Troscheit Assouad Dimension and Random Fractals Mandelbrot Percolation p = 0.65
Sascha Troscheit Assouad Dimension and Random Fractals Mandelbrot Percolation p = 0.65
Sascha Troscheit Assouad Dimension and Random Fractals Mandelbrot Percolation p = 0.65
Sascha Troscheit Assouad Dimension and Random Fractals Mandelbrot Percolation p = 0.65
Sascha Troscheit Assouad Dimension and Random Fractals Mandelbrot Percolation p = 0.65
Sascha Troscheit Assouad Dimension and Random Fractals Mandelbrot Percolation p = 0.65
Sascha Troscheit Assouad Dimension and Random Fractals Percolation Tree Structure, d = 2, n = 2, p = 1
Sascha Troscheit Assouad Dimension and Random Fractals Percolation Tree Structure, d = 2, n = 2, p = 0.7
Sascha Troscheit Assouad Dimension and Random Fractals Percolation Tree Structure, d = 2, n = 2, p = 0.3
Sascha Troscheit Assouad Dimension and Random Fractals Percolation Tree Structure, d = 2, n = 2, p = 0.3
Sascha Troscheit Assouad Dimension and Random Fractals Theorem (Fraser-Miao-T. ’14) Almost surely, conditioned on F being non-empty, we have
dimA F = d
Dimension of Mandelbrot percolation
Theorem (Kahane-Peyriere ’76, Hawkes ’81, Falconer ’86, Mauldin-Williams ’86) Almost surely the Hausdorff, box and packing dimension is given by
log nd p dim F = dim F = dim F = H B P log n conditioned on F being non-empty.
Sascha Troscheit Assouad Dimension and Random Fractals Dimension of Mandelbrot percolation
Theorem (Kahane-Peyriere ’76, Hawkes ’81, Falconer ’86, Mauldin-Williams ’86) Almost surely the Hausdorff, box and packing dimension is given by
log nd p dim F = dim F = dim F = H B P log n conditioned on F being non-empty.
Theorem (Fraser-Miao-T. ’14) Almost surely, conditioned on F being non-empty, we have
dimA F = d
Sascha Troscheit Assouad Dimension and Random Fractals Take a ‘nice’ compact set ∆, say the unit square ∆ = [0, 1]2, and define iteratively
F0 = ∆ n [ Fn+1 = Si (Fn) i=1 The ‘limit’ of these sets is the self-similar or self-affine fractal F , more precisely N \ F = lim Fn N→∞ n=1
Iterated Function Systems - (IFS)
2 2 Let I = {S1, S2,..., Sn} be a set of n contractions Si : R → R .
Sascha Troscheit Assouad Dimension and Random Fractals F0 = ∆ n [ Fn+1 = Si (Fn) i=1 The ‘limit’ of these sets is the self-similar or self-affine fractal F , more precisely N \ F = lim Fn N→∞ n=1
Iterated Function Systems - (IFS)
2 2 Let I = {S1, S2,..., Sn} be a set of n contractions Si : R → R . Take a ‘nice’ compact set ∆, say the unit square ∆ = [0, 1]2, and define iteratively
Sascha Troscheit Assouad Dimension and Random Fractals The ‘limit’ of these sets is the self-similar or self-affine fractal F , more precisely N \ F = lim Fn N→∞ n=1
Iterated Function Systems - (IFS)
2 2 Let I = {S1, S2,..., Sn} be a set of n contractions Si : R → R . Take a ‘nice’ compact set ∆, say the unit square ∆ = [0, 1]2, and define iteratively
F0 = ∆ n [ Fn+1 = Si (Fn) i=1
Sascha Troscheit Assouad Dimension and Random Fractals N \ F = lim Fn N→∞ n=1
Iterated Function Systems - (IFS)
2 2 Let I = {S1, S2,..., Sn} be a set of n contractions Si : R → R . Take a ‘nice’ compact set ∆, say the unit square ∆ = [0, 1]2, and define iteratively
F0 = ∆ n [ Fn+1 = Si (Fn) i=1 The ‘limit’ of these sets is the self-similar or self-affine fractal F , more precisely
Sascha Troscheit Assouad Dimension and Random Fractals Iterated Function Systems - (IFS)
2 2 Let I = {S1, S2,..., Sn} be a set of n contractions Si : R → R . Take a ‘nice’ compact set ∆, say the unit square ∆ = [0, 1]2, and define iteratively
F0 = ∆ n [ Fn+1 = Si (Fn) i=1 The ‘limit’ of these sets is the self-similar or self-affine fractal F , more precisely N \ F = lim Fn N→∞ n=1
Sascha Troscheit Assouad Dimension and Random Fractals An example: Sierpi´nskitriangle
Sascha Troscheit Assouad Dimension and Random Fractals An example: Sierpi´nskitriangle
Sascha Troscheit Assouad Dimension and Random Fractals An example: Sierpi´nskitriangle
Sascha Troscheit Assouad Dimension and Random Fractals An example: Sierpi´nskitriangle
Sascha Troscheit Assouad Dimension and Random Fractals An example: Sierpi´nskitriangle
Sascha Troscheit Assouad Dimension and Random Fractals An example: Sierpi´nskitriangle
Sascha Troscheit Assouad Dimension and Random Fractals An example: Sierpi´nskitriangle
Sascha Troscheit Assouad Dimension and Random Fractals An example: Sierpi´nskitriangle
Sascha Troscheit Assouad Dimension and Random Fractals Index elements by Λ = {1, 2,..., n} and consider the coding space Σ = ΛN, consisting of infinite sequences of entries in Λ. For x = (x1, x2,...) ∈ Σ we define
Sx = lim Sx ◦ Sx ◦ ... ◦ Sx (∆) k→∞ 1 2 k We can code F with Σ as every y ∈ F has at least one coding S x ∈ Σ such that Sx = y and Sx ⊆ F . We have
k \ [ F = lim Sx ◦ Sx ◦ ... ◦ Sx (∆) k→∞ 1 2 k i=1 x1∈Λ, x2∈Λ, ..., xk−1∈Λ, xk ∈Λ
Equivalent notion
Remember I?
Sascha Troscheit Assouad Dimension and Random Fractals For x = (x1, x2,...) ∈ Σ we define
Sx = lim Sx ◦ Sx ◦ ... ◦ Sx (∆) k→∞ 1 2 k We can code F with Σ as every y ∈ F has at least one coding S x ∈ Σ such that Sx = y and Sx ⊆ F . We have
k \ [ F = lim Sx ◦ Sx ◦ ... ◦ Sx (∆) k→∞ 1 2 k i=1 x1∈Λ, x2∈Λ, ..., xk−1∈Λ, xk ∈Λ
Equivalent notion
Remember I? Index elements by Λ = {1, 2,..., n} and consider the coding space Σ = ΛN, consisting of infinite sequences of entries in Λ.
Sascha Troscheit Assouad Dimension and Random Fractals We can code F with Σ as every y ∈ F has at least one coding S x ∈ Σ such that Sx = y and Sx ⊆ F . We have
k \ [ F = lim Sx ◦ Sx ◦ ... ◦ Sx (∆) k→∞ 1 2 k i=1 x1∈Λ, x2∈Λ, ..., xk−1∈Λ, xk ∈Λ
Equivalent notion
Remember I? Index elements by Λ = {1, 2,..., n} and consider the coding space Σ = ΛN, consisting of infinite sequences of entries in Λ. For x = (x1, x2,...) ∈ Σ we define
Sx = lim Sx ◦ Sx ◦ ... ◦ Sx (∆) k→∞ 1 2 k
Sascha Troscheit Assouad Dimension and Random Fractals We have
k \ [ F = lim Sx ◦ Sx ◦ ... ◦ Sx (∆) k→∞ 1 2 k i=1 x1∈Λ, x2∈Λ, ..., xk−1∈Λ, xk ∈Λ
Equivalent notion
Remember I? Index elements by Λ = {1, 2,..., n} and consider the coding space Σ = ΛN, consisting of infinite sequences of entries in Λ. For x = (x1, x2,...) ∈ Σ we define
Sx = lim Sx ◦ Sx ◦ ... ◦ Sx (∆) k→∞ 1 2 k We can code F with Σ as every y ∈ F has at least one coding S x ∈ Σ such that Sx = y and Sx ⊆ F .
Sascha Troscheit Assouad Dimension and Random Fractals Equivalent notion
Remember I? Index elements by Λ = {1, 2,..., n} and consider the coding space Σ = ΛN, consisting of infinite sequences of entries in Λ. For x = (x1, x2,...) ∈ Σ we define
Sx = lim Sx ◦ Sx ◦ ... ◦ Sx (∆) k→∞ 1 2 k We can code F with Σ as every y ∈ F has at least one coding S x ∈ Σ such that Sx = y and Sx ⊆ F . We have
k \ [ F = lim Sx ◦ Sx ◦ ... ◦ Sx (∆) k→∞ 1 2 k i=1 x1∈Λ, x2∈Λ, ..., xk−1∈Λ, xk ∈Λ
Sascha Troscheit Assouad Dimension and Random Fractals Construct F by ‘randomly choosing’ an IFS at each step of the construction. More formally we define the realisation ω ∈ Ω as an element of the set of all possible outcomes Ω = ΛN. The set F (ω) is then defined as
k \ [ F (ω) = lim Sx ◦ Sx ◦ ... ◦ Sx (∆) k→∞ 1 2 k i=1 x1∈Λω1 , x2∈Λω2 , ..., xk−1∈Λωk−1 , xk ∈Λωk
‘Randomly choosing’ ω gives us a random fractal F (ω).
(1-variable) Random Iterated Function System (RIFS)
Start with a collection of IFSs I = {I1, I2,..., IN }, indexed by Λ and each IFS is indexed by Λi .
Sascha Troscheit Assouad Dimension and Random Fractals More formally we define the realisation ω ∈ Ω as an element of the set of all possible outcomes Ω = ΛN. The set F (ω) is then defined as
k \ [ F (ω) = lim Sx ◦ Sx ◦ ... ◦ Sx (∆) k→∞ 1 2 k i=1 x1∈Λω1 , x2∈Λω2 , ..., xk−1∈Λωk−1 , xk ∈Λωk
‘Randomly choosing’ ω gives us a random fractal F (ω).
(1-variable) Random Iterated Function System (RIFS)
Start with a collection of IFSs I = {I1, I2,..., IN }, indexed by Λ and each IFS is indexed by Λi . Construct F by ‘randomly choosing’ an IFS at each step of the construction.
Sascha Troscheit Assouad Dimension and Random Fractals The set F (ω) is then defined as
k \ [ F (ω) = lim Sx ◦ Sx ◦ ... ◦ Sx (∆) k→∞ 1 2 k i=1 x1∈Λω1 , x2∈Λω2 , ..., xk−1∈Λωk−1 , xk ∈Λωk
‘Randomly choosing’ ω gives us a random fractal F (ω).
(1-variable) Random Iterated Function System (RIFS)
Start with a collection of IFSs I = {I1, I2,..., IN }, indexed by Λ and each IFS is indexed by Λi . Construct F by ‘randomly choosing’ an IFS at each step of the construction. More formally we define the realisation ω ∈ Ω as an element of the set of all possible outcomes Ω = ΛN.
Sascha Troscheit Assouad Dimension and Random Fractals ‘Randomly choosing’ ω gives us a random fractal F (ω).
(1-variable) Random Iterated Function System (RIFS)
Start with a collection of IFSs I = {I1, I2,..., IN }, indexed by Λ and each IFS is indexed by Λi . Construct F by ‘randomly choosing’ an IFS at each step of the construction. More formally we define the realisation ω ∈ Ω as an element of the set of all possible outcomes Ω = ΛN. The set F (ω) is then defined as
k \ [ F (ω) = lim Sx ◦ Sx ◦ ... ◦ Sx (∆) k→∞ 1 2 k i=1 x1∈Λω1 , x2∈Λω2 , ..., xk−1∈Λωk−1 , xk ∈Λωk
Sascha Troscheit Assouad Dimension and Random Fractals (1-variable) Random Iterated Function System (RIFS)
Start with a collection of IFSs I = {I1, I2,..., IN }, indexed by Λ and each IFS is indexed by Λi . Construct F by ‘randomly choosing’ an IFS at each step of the construction. More formally we define the realisation ω ∈ Ω as an element of the set of all possible outcomes Ω = ΛN. The set F (ω) is then defined as
k \ [ F (ω) = lim Sx ◦ Sx ◦ ... ◦ Sx (∆) k→∞ 1 2 k i=1 x1∈Λω1 , x2∈Λω2 , ..., xk−1∈Λωk−1 , xk ∈Λωk
‘Randomly choosing’ ω gives us a random fractal F (ω).
Sascha Troscheit Assouad Dimension and Random Fractals Examples
Sascha Troscheit Assouad Dimension and Random Fractals Examples
Sascha Troscheit Assouad Dimension and Random Fractals Examples
Sascha Troscheit Assouad Dimension and Random Fractals Examples
Sascha Troscheit Assouad Dimension and Random Fractals Examples
Sascha Troscheit Assouad Dimension and Random Fractals Examples
Sascha Troscheit Assouad Dimension and Random Fractals Examples
Sascha Troscheit Assouad Dimension and Random Fractals Examples
Sascha Troscheit Assouad Dimension and Random Fractals Examples
Sascha Troscheit Assouad Dimension and Random Fractals Examples
Sascha Troscheit Assouad Dimension and Random Fractals Examples
Sascha Troscheit Assouad Dimension and Random Fractals Examples
Sascha Troscheit Assouad Dimension and Random Fractals Examples
Sascha Troscheit Assouad Dimension and Random Fractals Examples
Sascha Troscheit Assouad Dimension and Random Fractals Dimension of Random Fractals
Theorem Assuming some ‘nice’ conditions (Uniform Open Set Condition), the almost sure Hausdorff, box and packing dimension for random self-similar sets is given by X dim F (ω) = E{dim F (i, i,...)} = pi dim F (i, i,...) i∈Λ
where pi is the probability of choosing the digit i.
Sascha Troscheit Assouad Dimension and Random Fractals Dimension of Random Fractals
Theorem (Fraser-Miao-T. ’14) Assuming some ‘nice’ conditions (Uniform Open Set Condition), the almost sure Assouad dimension for random self-similar sets is given by dimA F (ω) = max dimA F (i, i,...) i∈Λ
assuming pi > 0 for all digits i.
Sascha Troscheit Assouad Dimension and Random Fractals An interesting self-affine example
Sascha Troscheit Assouad Dimension and Random Fractals An interesting self-affine example
Sascha Troscheit Assouad Dimension and Random Fractals In particular take a finite directed strongly connected graph, where each of the edges represent maps. The fractal Ki is the limit set taking all possible infinite edge combinations of maps starting at vertex i. The limit set of an IFS is a graph directed fractal for the (almost) trivial graph consisting of one vertex and an edge for every map in the IFS.
Deterministic Graph Directed Construction
Take the infinite words idea and restrict the letter combinations.
Sascha Troscheit Assouad Dimension and Random Fractals The fractal Ki is the limit set taking all possible infinite edge combinations of maps starting at vertex i. The limit set of an IFS is a graph directed fractal for the (almost) trivial graph consisting of one vertex and an edge for every map in the IFS.
Deterministic Graph Directed Construction
Take the infinite words idea and restrict the letter combinations. In particular take a finite directed strongly connected graph, where each of the edges represent maps.
Sascha Troscheit Assouad Dimension and Random Fractals The limit set of an IFS is a graph directed fractal for the (almost) trivial graph consisting of one vertex and an edge for every map in the IFS.
Deterministic Graph Directed Construction
Take the infinite words idea and restrict the letter combinations. In particular take a finite directed strongly connected graph, where each of the edges represent maps. The fractal Ki is the limit set taking all possible infinite edge combinations of maps starting at vertex i.
Sascha Troscheit Assouad Dimension and Random Fractals Deterministic Graph Directed Construction
Take the infinite words idea and restrict the letter combinations. In particular take a finite directed strongly connected graph, where each of the edges represent maps. The fractal Ki is the limit set taking all possible infinite edge combinations of maps starting at vertex i. The limit set of an IFS is a graph directed fractal for the (almost) trivial graph consisting of one vertex and an edge for every map in the IFS.
Sascha Troscheit Assouad Dimension and Random Fractals My very own model.
Random Graph Directed Construction
There are two different construction I will introduce: The ‘Random Graph Directed Iterated Function Scheme’ (Lars’ version)
Sascha Troscheit Assouad Dimension and Random Fractals Random Graph Directed Construction
There are two different construction I will introduce: The ‘Random Graph Directed Iterated Function Scheme’ (Lars’ version) My very own model.
Sascha Troscheit Assouad Dimension and Random Fractals For my construction, ask me in a couple of months.
HD and AD of Random Graph Directed Construction
For Hausdorff, box and packing dimension of Lars’ version, see his book.
Sascha Troscheit Assouad Dimension and Random Fractals HD and AD of Random Graph Directed Construction
For Hausdorff, box and packing dimension of Lars’ version, see his book. For my construction, ask me in a couple of months.
Sascha Troscheit Assouad Dimension and Random Fractals