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Box dimension of stable sub-slices of fractal graphs over Anosov diffeomorphisms

Bernardo Carvalho and Rafael da Costa Pereira
Abstract

We consider fractal graphs invariant by a skew product F:𝕋k×𝕋k×F:\mathbb{T}^{k}\times\mathbb{R}\rightarrow\mathbb{T}^{k}\times\mathbb{R} of the form F(x,y)=(Ax,λy+p(x))F(x,y)=(Ax,\lambda y+p(x)) where 0<λ<10<\lambda<1, p:𝕋kp\colon\mathbb{T}^{k}\to\mathbb{R} is a Ck+1C^{k+1} function, and AA is an Anosov diffeomorphism of 𝕋k\mathbb{T}^{k} admitting kk distinct eigenvalues with respective eigenvectors forming a basis of k\mathbb{R}^{k}. We note that the stable sub-slices can give information of the fractal structure of the graph that is not captured by the box dimension of the graph. Using the results of Kaplan, Mallet-Paret, and Yorke [6], we exhibit conditions on the skew product that ensure the box dimension of the graph is smaller than the sum of the box dimensions of its stable/unstable sub-slices. We prove that these conditions hold for generic functions pCk+1p\in C^{k+1}.

1 Introduction

Fractal geometry, as a branch of mathematics, provides mathematical tools to understand fractals. It was inspired by the classical example of the Weierstrass function of a continuous but everywhere non-differentiable function (see Falconer [3] for more details). Though the structure of fractal sets can be really complicated, it is still possible to study their fractal properties, and concepts such as dimension (either box or Hausdorff) help the understanding of their structure. Fractal sets appear naturally in hyperbolic dynamics as important examples of hyperbolic sets, such as the classical examples of Smale’s horseshoe and the solenoid (among others). The hyperbolic dynamics, and especially the stable/unstable manifolds, can help us to calculate fractal properties of hyperbolic fractal sets. Indeed, the Hausdorff dimension of hyperbolic basic sets of surfaces is calculated in McCluskey and Manning [8] as the sum of the Hausdorff dimension of its stable and unstable slices at any point of the set (these are the intersections of the local stable/unstable manifolds with the hyperbolic set). Extend this result to higher dimensional (and non-conformal) hyperbolic sets seems to be a really difficult problem that is not explored enough in the literature. The lack of regularity on the stable/unstable holonomies and the possible difference between Hausdorff and box dimensions are difficulties that appear in this scenario. Two results in this direction are important to be noted: in the first, Hasselblatt and Schmeling [5] prove that the ideas of McCluskey and Manning [8] can be applied to the solenoid and conjecture that the dimension (either box or Hausdorff) of any hyperbolic set should be the sum of the dimension of their stable/unstable slices; in the second, Kaplan, Mallet-Paret, and Yorke [6] calculate the box dimension of fractal graphs invariant by a skew product over the cat map on 𝕋2\mathbb{T}^{2} using Fourier analysis of quasi-periodic functions, which are quite distinct techniques and will be explained in Section 2.4 of this paper. It is also noted in [6] that their techniques can be used to calculate the box dimension of fractal graphs over higher-dimensional linear Anosov diffeomorphisms of 𝕋k\mathbb{T}^{k} (see Theorem C in [6]). The argument is similar to the case of 𝕋2\mathbb{T}^{2} using the strongest stable eigenvalue of the base hyperbolic matrix to control the sizes of boxes covering the graph. One thing we observe and explore in this paper is that the other stable directions do not influence on the box dimension of the graph, but the associated stable/unstable slices can be either smooth or fractal, depending on the relation between the eigenvalues and the contraction rate of the skew product on the fibers. Thus, the box dimension of the graph does not give a full description of the fractal directions of these graphs. This motivates our study of fractal dimensions of stable sub-slices that we now describe.

2 Kaplan, Mallet-Paret, and Yorke techniques

In this section, we define the basic concepts of fractal geometry and hyperbolic dynamics that will be used in this article and explain the techniques of Kaplan, Mallet-Paret, and Yorke [6] to calculate the box dimension of the graph invariant by FF.

2.1 Box dimension

Definition 2.1.

Consider XX a non-empty totally bounded subset of a metric space MM and for each δ>0\delta>0 let Nδ(X)N_{\delta}(X) be the smallest number of sets of diameter at most δ\delta which union covers X. We define the lower box dimension of XX as

dim(X)=lim infδ0+logNδ(X)logδ\displaystyle\text{\text@underline{dim}}(X)=\liminf_{\delta\rightarrow 0^{+}}\frac{\log{N_{\delta}(X)}}{-\log{\delta}}

and the upper box dimension of XX as

dim¯(X)=lim supδ0+logNδ(X)logδ.\displaystyle\overline{\text{dim}}(X)=\limsup_{\delta\rightarrow 0^{+}}\frac{\log{N_{\delta}(X)}}{-\log{\delta}}.

If they are equal, this is the box dimension of AA, and we write dim(X)\text{dim}(X).

The following proposition exhibits an easy way to calculate the box dimension of a set and will be used a few times in the arguments.

Proposition 2.2.

If there are positive constants C1,C2C_{1},C_{2} and aa such that

C1δaNδ(X)C2δa,C_{1}\delta^{-a}\leq N_{\delta}(X)\leq C_{2}\delta^{-a},

then dim(X)=a\text{dim}(X)=a.

Proof.

We just need to note the following inequalities:

C1δaNδ(X)C2δa\displaystyle C_{1}\delta^{-a}\leq N_{\delta}(X)\leq C_{2}\delta^{-a} \displaystyle\Rightarrow log(C1δa)logδlogNδ(X)logδlog(C2δa)logδ\displaystyle\frac{\log(C_{1}\delta^{-a})}{-\log\delta}\leq\frac{\log N_{\delta}(X)}{-\log\delta}\leq\frac{\log(C_{2}\delta^{-a})}{-\log\delta}
\displaystyle\Rightarrow logC1logδ+alogNδ(X)logδlogC2logδ+a.\displaystyle\frac{\log C_{1}}{-\log\delta}+a\leq\frac{\log N_{\delta}(X)}{-\log\delta}\leq\frac{\log C_{2}}{-\log\delta}+a.

Then dim(X)=a\text{dim}(X)=a follows by letting δ0\delta\to 0 in the last inequalities. ∎

In the special case that XX is the graph of a continuous function f:I=[c,d]f\colon I=[c,d]\to\mathbb{R}, Proposition 2.2 can be applied to calculate the box dimension considering similar inequalities for the variation of the function ff defined by

var𝐼(f)=suptIf(t)inftIf(t).\underset{I}{\text{var}}(f)=\sup_{t\in I}f(t)-\inf_{t\in I}f(t).
Proposition 2.3.

If there are L0(0,1)L_{0}\in(0,1), a(0,1)a\in(0,1), C1,C2>0C_{1},C_{2}>0 such that

C1Lavar𝐽(f)C2LaC_{1}L^{a}\leq\underset{J}{\text{var}}(f)\leq C_{2}L^{a}

for every interval JJ with length(J)=LL0(J)=L\leq L_{0}, then

dim(graph(f))=2a.\text{dim(graph}(f))=2-a.
Proof.

Given δ<min{L0,dc,1}\delta<\text{min}\{L_{0},d-c,1\}, let

|I|δ:=dcδ,|I|_{\delta}:=\left\lceil\frac{d-c}{\delta}\right\rceil,

where x\lceil x\rceil is the smaller integer greater than xx. For each j{1,,|I|δ}j\in\{1,\dots,|I|_{\delta}\}, let

Ij:=[c+(j1)δ,c+jδ],|f|j:=varIJ(f)δ,I_{j}:=[c+(j-1)\delta,c+j\delta],\,\,\,\,\,\,|f|_{j}:=\left\lceil\frac{\underset{I_{J}}{\text{var}}(f)}{\delta}\right\rceil,

and for each k{1,,|fj|}k\in\{1,\dots,|f_{j}|\}, let

Ijk=[inftIjf(t)+(k1)δ,inftIjf(t)+kδ].I^{k}_{j}=\left[\inf_{t\in I_{j}}f(t)+(k-1)\delta,\inf_{t\in I_{j}}f(t)+k\delta\right].

Consider the set

I={Ij×Ijk;j{1,,|I|δ},k{1,,|fj|}}I=\{I_{j}\times I^{k}_{j};j\in\{1,\dots,|I|_{\delta}\},k\in\{1,\dots,|f_{j}|\}\}

and let

A:=#I=k=1|I|δ|fj|.A:=\#I=\sum_{k=1}^{|I|_{\delta}}|f_{j}|.

The set II covers graph(f)\text{graph}(f) with sets of diameter 2δ\sqrt{2}\delta, so

N2δ(graph(f))A.N_{\sqrt{2}\delta}(\text{graph}(f))\leq A.
Refer to caption
Figure 1: Example of the cover II for δ=0.13\delta=0.13 and f(x)=0.2cos(7πx)+0.1cos(4πx)f(x)=0.2\cos(7\pi x)+0.1\cos(4\pi x) on [0,1][0,1].

Note that |I|δdcδ+1|I|_{\delta}\leq\frac{d-c}{\delta}+1 and that by hypothesis

varIJ(f)C2δafor everyj{1,,|I|δ}.\underset{I_{J}}{\text{var}}(f)\leq C_{2}\delta^{a}\,\,\,\,\,\,\text{for every}\,\,\,\,\,\,j\in\{1,\dots,|I|_{\delta}\}.

It follows that

|f|jC2δa1+1for everyj{1,,|I|δ}|f|_{j}\leq C_{2}\delta^{a-1}+1\,\,\,\,\,\,\text{for every}\,\,\,\,\,\,j\in\{1,\dots,|I|_{\delta}\}

and, hence,

N2δ(graph(f))A\displaystyle N_{\sqrt{2}\delta}(\text{graph}(f))\leq A (dcδ+1)(C2δa1+1)\displaystyle\leq\left(\frac{d-c}{\delta}+1\right)\left(C_{2}\delta^{a-1}+1\right)
=δa2(C2(dc)+(dc)δ1a+C2δ+δ2a)\displaystyle=\delta^{a-2}(C_{2}(d-c)+(d-c)\delta^{1-a}+C_{2}\delta+\delta^{2-a})
δa2(C2(dc)+(dc)+C2+1)\displaystyle\leq\delta^{a-2}(C_{2}(d-c)+(d-c)+C_{2}+1)
(2)a2δa2K2\displaystyle\leq(\sqrt{2})^{a-2}\delta^{a-2}K_{2}

for some positive constant K2K_{2}. Also note that

N2δ(graph(f))A9N_{\sqrt{2}\delta}(\text{graph}(f))\geq\frac{A}{9}

since any set of diameter 2δ\sqrt{2}\delta intercepts no more than 9 distinct sets of the form Ij×IkjI_{j}\times I^{j}_{k}. By hypothesis we have

varIJ(f)C1δafor everyj{1,,|I|δ}\underset{I_{J}}{\text{var}}(f)\geq C_{1}\delta^{a}\,\,\,\,\,\,\text{for every}\,\,\,\,\,\,j\in\{1,\dots,|I|_{\delta}\}

and since we similarly have |I|δdcδ|I|_{\delta}\geq\frac{d-c}{\delta} and

|f|jC1δa1for everyj{1,,|I|δ},|f|_{j}\geq C_{1}\delta^{a-1}\,\,\,\,\,\,\text{for every}\,\,\,\,\,\,j\in\{1,\dots,|I|_{\delta}\},

it follows that

N2δ(graph(f))A9(dc9δ)C1δa1(2)a2δa2K1N_{\sqrt{2}\delta}(\text{graph}(f))\geq\frac{A}{9}\geq\left(\frac{d-c}{9\delta}\right)C_{1}\delta^{a-1}\geq(\sqrt{2})^{a-2}\delta^{a-2}K_{1}

for some positive constant K1K_{1}. Thus,

K1(2δ)a2N2δ(X)K2(2δ)a2K_{1}(\sqrt{2}\delta)^{a-2}\leq N_{\sqrt{2}\delta}(X)\leq K_{2}(\sqrt{2}\delta)^{a-2}

and Proposition 2.2 ensures that dim(graph(f))=2a\text{dim}(\text{graph}(f))=2-a. ∎

The essence of the proof above is that the number of intervals of size δ\delta needed to cover the domain are proportional to δ1\delta^{-1} and on each of these intervals the number of intervals of size δ\delta needed to cover the image is, by hypothesis, proportional to δa1\delta^{a-1}, so the number of squares of size δ\delta needed to cover the graph is proportional to δa2\delta^{a-2}. This can also be done for a nn-variable function with the only difference being that the number of nn-cubes needed to cover the domain is proportional to δn\delta^{-n}. Then we can state the following result.

Proposition 2.4.

If f:Inf\colon I^{n}\to\mathbb{R} is a function satisfying the same hypothesis as in Proposition 2.3, then

dim(graph(f))=n+1a.\text{dim(graph}(f))=n+1-a.

A formal proof is omitted since it is similar with the proof of Proposition 2.3 with the only adaptation explained above.

2.2 Hyperbolic Sets and Stable/Unstable Manifolds

Definition 2.5 (Hyperbolic set).

Let MM be a Riemannian manifold, UMU\subset M be an open set, and f:UMf\colon U\rightarrow M be a smooth embedding. A compact invariant subset ΛU\Lambda\subset U is called a hyperbolic set if the tangent bundle over Λ\Lambda splits as TΛM=EsEuT_{\Lambda}M=E^{s}\oplus E^{u} and there exist constants C>0C>0 and 0<λ<10<\lambda<1 such that Df|Esn<Cλn\|Df_{|_{E^{s}}}^{n}\|<C\lambda^{n} and Df|Eun<Cλn\|Df_{|_{E^{u}}}^{-n}\|<C\lambda^{n} for every nn\in\mathbb{N}.

The Stable Manifold Theorem ensures that if ϵ\epsilon is small enough, then the sets

Wϵs(x)\displaystyle W^{s}_{\epsilon}(x) ={yM| dist(fn(x),fn(y))ϵ for all n}\displaystyle=\{y\in M|\text{ dist}(f^{n}(x),f^{n}(y))\leq\epsilon\text{ for all }n\in\mathbb{N}\}
Wϵu(x)\displaystyle W^{u}_{\epsilon}(x) ={yM| dist(fn(x),fn(y))ϵ for all n}\displaystyle=\{y\in M|\text{ dist}(f^{-n}(x),f^{-n}(y))\leq\epsilon\text{ for all }n\in\mathbb{N}\}

are C1C^{1} embedded disks tangent to EsE^{s} and EuE^{u}, respectively. In particular, there is a local product structure close to any xΛx\in\Lambda: there exists δ>0\delta>0 and a map

[,]:(Wϵ,δs(x)Λ)×(Wϵ,δu(x)Λ)M,[,]\colon(W^{s}_{\epsilon,\delta}(x)\cap\Lambda)\times(W^{u}_{\epsilon,\delta}(x)\cap\Lambda)\to M,

where Wϵ,δs(x)=Wϵs(x)B(x,δ)W^{s}_{\epsilon,\delta}(x)=W^{s}_{\epsilon}(x)\cap B(x,\delta) and Wϵ,δu(x)=Wϵu(x)B(x,δ)W^{u}_{\epsilon,\delta}(x)=W^{u}_{\epsilon}(x)\cap B(x,\delta), defined by

[p,q]=Wϵu(p)Wϵs(q).[p,q]=W^{u}_{\epsilon}(p)\cap W^{s}_{\epsilon}(q).

Thus, for each xΛx\in\Lambda, the map [,][,] defines a homeomorphism between the product

(Wϵ,δs(x)Λ)×(Wϵ,δu(x)Λ)(W^{s}_{\epsilon,\delta}(x)\cap\Lambda)\times(W^{u}_{\epsilon,\delta}(x)\cap\Lambda)

and ΛB(x,δ)\Lambda\cap B(x,\delta^{\prime}) (for some δ(0,δ]\delta^{\prime}\in(0,\delta]). The sets in this product are called the local stable and local unstable slices of Λ\Lambda at xΛx\in\Lambda. If the map [,][,] is bi-Lipschitz, then the dimension of Λ\Lambda equals the sum of the dimensions of the stable and the unstable slices at any point, since Hausdorff and box dimensions are preserved by bi-Lipschitz functions. This fact is used by McCluskey and Manning [8] to calculate the Hausdorff dimension of two dimensional horseshoes.

Theorem 2.6 (Theorem 2 of [8]).

Let f:SSf\colon S\rightarrow S be a C2C^{2} axiom A diffeomorphism of a surface, and ΛS\Lambda\subset S be a basic set of ff. Then the Hausdorff dimension of Λ\Lambda is the sum of the Hausdorff dimension of the stable and unstable slices of any xΛx\in\Lambda. Also the Hausdorff dimension varies continuously with ff.

For hyperbolic sets on manifolds of higher dimensions, the map [,][,] is always Hölder continuous but not necessarily Lipschitz (see Shub [9]). Thus, it is really difficult to compute the dimension in this case. Hasselblatt and Schmeling [5] stated the following conjecture:

Conjecture 2.7.

The fractal dimension of a hyperbolic set is (at least generically or under mild hypotheses) the sum of those of its stable and unstable slices, where “fractal” can mean either Hausdorff or upper box dimension.

They prove this conjecture for the Smale Solenoid, that is a hyperbolic set on a manifold of dimension three (see [5] for more details). We hope we have motivated the importance of the stable/unstable slices and the calculus of their dimensions, and finish this sub-section.

2.3 The graph of a skew product and its sub-slices

Following Kaplan et al. [6] we consider the skew product F:𝕋k×𝕋k×F\colon\mathbb{T}^{k}\times\mathbb{R}\rightarrow\mathbb{T}^{k}\times\mathbb{R}, where 𝕋k\mathbb{T}^{k} is the kk-torus, defined by

F(x,y)=(Ax,λy+p(x))F(x,y)=(Ax,\lambda y+p(x))

where AA is a linear Anosov diffeomorphism, 0<λ<10<\lambda<1, and p:𝕋kp\colon\mathbb{T}^{k}\to\mathbb{R} is a smooth function. This skew-product has an attractor that can be seen as a graph over 𝕋k\mathbb{T}^{k} (see [6]) such that any point (x,y)(x,y) in the attractor satisfies

y=i=1λi1p(Aix).y=\sum_{i=1}^{\infty}\lambda^{i-1}p(A^{-i}x).

For simplicity, consider the function ϕ:𝕋k\phi\colon\mathbb{T}^{k}\to\mathbb{R} defined by

ϕ(x)=n=0λnp(Anx).\phi(x)=\sum_{n=0}^{\infty}\lambda^{n}p(A^{-n}x). (1)

Thus, y(x)=ϕ(A1x)y(x)=\phi(A^{-1}x), and since AA is a linear Anosov diffeomorphism, the box dimensions of the graphs of yy and ϕ\phi are the same. Assume that AA has kk distinct eigenvalues B1,,BkB_{1},\dots,B_{k} and that the respective eigenvectors v1,,vkv_{1},\dots,v_{k} form a basis of k\mathbb{R}^{k}. Write points of 𝕋k\mathbb{T}^{k} on this basis as t¯=(t1,,tk)\underline{t}=(t_{1},\ldots,t_{k}) and write ϕ\phi on this basis as

ϕ(t¯)=n=0+λnp(t1B1nv1++tkBjnvk)=n=0+λnp(t1B1n,,tkBjn).\phi(\underline{t})=\sum_{n=0}^{+\infty}\lambda^{n}p(t_{1}B_{1}^{-n}v_{1}+\cdots+t_{k}B_{j}^{-n}v_{k})=\sum_{n=0}^{+\infty}\lambda^{n}p(t_{1}B_{1}^{-n},\dots,t_{k}B_{j}^{-n}).

At any point of the graph (t¯,ϕ(t¯))(\underline{t},\phi(\underline{t})), each direction viv_{i} gives a slice of the graph given by the graph of the function ϕi\phi_{i} given by

ϕi(t):=ϕ(tvi+t¯)\phi_{i}(t):=\phi(tv_{i}+\underline{t})

(with tt sufficiently small). These slices are called the sub-slices of the graph ϕ\phi at the point (t¯,ϕ(t¯))(\underline{t},\phi(\underline{t})). If EsE^{s} denotes the space spanned by all the eigenvectors whose respective eigenvalues have modulus smaller than 1, and EuE^{u} denotes the space spanned by all the eigenvectors whose respective eigenvalues have modulus bigger than 1, then the slices of the graph given by EsE^{s} and EuE^{u} are exactly the stable and the unstable slices defined in the previous sub-section. In this paper we discuss how to calculate the box dimension of each sub-slice of the graph. The case we deal in this subsection is when the absolute value of the eigenvalue is bigger than the contraction rate λ\lambda of the skew product. The following proposition ensures that the box dimensions of these sub-slices equal one.

Proposition 2.8.

If λ<|Bi|\lambda<|B_{i}| then ϕ\phi is a C1C^{1} function of tit_{i} and |iϕ||\partial_{i}\phi| is bounded independent of (t1,,tk)(t_{1},\dots,t_{k}).

Proof.

The following function is the candidate for the derivative of ϕ\phi on (t1,,tk)(t_{1},\dots,t_{k}) in the direction viv_{i}:

h(t¯)=n=0+(λBi)npti(t1B1n,,tkBjn)h(\underline{t})=\sum_{n=0}^{+\infty}\left(\frac{\lambda}{B_{i}}\right)^{n}\frac{\partial p}{\partial t_{i}}(t_{1}B_{1}^{-n},\dots,t_{k}B_{j}^{-n})

Let us prove it is, indeed, the case. For each mm\in\mathbb{N}, let

hm(t¯)=n=0m(λBi)npti(t1B1n,,tkBjn).h_{m}(\underline{t})=\sum_{n=0}^{m}\left(\frac{\lambda}{B_{i}}\right)^{n}\frac{\partial p}{\partial t_{i}}(t_{1}B_{1}^{-n},\dots,t_{k}B_{j}^{-n}).

Note that gmg_{m} is converges uniformly to hh because pti\frac{\partial p}{\partial t_{i}} is bounded and λ<|Bi|\lambda<|B_{i}|. Therefore

0tihm(t¯)𝑑tim0tih(t¯)𝑑ti.\int_{0}^{t_{i}}h_{m}(\underline{t})dt_{i}\underset{m\to\infty}{\longrightarrow}\int_{0}^{t_{i}}h(\underline{t})dt_{i}.

The notation 0tig(t¯)𝑑ti\int_{0}^{t_{i}}g(\underline{t})dt_{i} means that we are integrating gg as a function of one variable where t¯\underline{t} varies only on the coordinate tit_{i}. We hope this does not cause confusion with the number tit_{i} that is fixed on the point (t1,,tk)(t_{1},\dots,t_{k}) we consider at the beginning. On the other hand we have

0tihm(t¯)𝑑ti=n=0mλnp(t1B1n,,tkBjn)mϕ(t¯),\int_{0}^{t_{i}}h_{m}(\underline{t})dt_{i}=\sum_{n=0}^{m}\lambda^{n}p(t_{1}B_{1}^{-n},\dots,t_{k}B_{j}^{-n})\underset{m\to\infty}{\longrightarrow}\phi(\underline{t}),

because pp is bounded and λ<1\lambda<1 it follows that

0tihm(t¯)𝑑timϕ(t¯),\int_{0}^{t_{i}}h_{m}(\underline{t})dt_{i}\underset{m\to\infty}{\longrightarrow}\phi(\underline{t}),

So we have that ϕ(t¯)=0tih(t¯)𝑑ti\phi(\underline{t})=\int_{0}^{t_{i}}h(\underline{t})dt_{i}, and, hence,

h(t¯)=ϕti(t1,,tk).h(\underline{t})=\frac{\partial\phi}{\partial t_{i}}(t_{1},\ldots,t_{k}).

The function ϕ\phi being C1C^{1} in the direction viv_{i} implies that the slice given by the graph of ϕi\phi_{i} is a C1C^{1} curve and, hence, has box dimension one. We end this subsection noting that this applies to all unstable sub-slices since, in this case, the eigenvalues have modulus bigger than 1, while λ<1\lambda<1.

2.4 Box dimension for the stable slice in 𝕋2×\mathbb{T}^{2}\times\mathbb{R}

In this section, we consider the case k=2k=2, that is, the skew product is of the form

F:𝕋2×𝕋2×.F\colon\mathbb{T}^{2}\times\mathbb{R}\rightarrow\mathbb{T}^{2}\times\mathbb{R}.

In this case, at any point of the graph there is only one stable slice and we discuss how to calculate its box dimension following Kaplan et al. [6]. This will be used in Section 3 to calculate the box dimension of all stable sub-slices in the higher-dimensional case associated to directions viv_{i} with |Bi|<λ|B_{i}|<\lambda. Using the notation of the previous subsection, when k=2k=2 the hyperbolic matrix AA has two eigenvalues B1<1B_{1}<1 and B2>1B_{2}>1. If v1v_{1} is the eigenvector associated to B1B_{1}, then the stable slice at any point t¯𝕋k\underline{t}\in\mathbb{T}^{k} is given by the graph of the function ϕ1\phi_{1} defined by

ϕ1(t):=ϕ(tv1+t¯).\phi_{1}(t):=\phi(tv_{1}+\underline{t}).

Letting q(t)=p(tv1+t¯)q(t)=p(tv_{1}+\underline{t}), the stable slice is given by the graph of the function

f(t)=n=0λnq((B11)nt).f(t)=\sum_{n=0}^{\infty}\lambda^{n}q((B_{1}^{-1})^{n}t).

The following theorem calculates the box dimension of this stable slice:

Theorem 2.9 (Theorem A in [6]).

Let

f(t)=n=0λnq(Bnt)f(t)=\sum_{n=0}^{\infty}\lambda^{n}q(B^{n}t)

where 0<λ<10<\lambda<1 and B>1/λB>1/\lambda and assume that

q(t)=i=1Nqicos(ait+θi),q(t)=\sum_{i=1}^{N}q_{i}\cos{(a_{i}t+\theta_{i})}, (2)

for some real numbers qi,ai,θiq_{i},a_{i},\theta_{i}. Then either ff is C1C^{1}, and hence dim(graph(f))=1\mathrm{dim(graph}(f))=1, or

dim(graph(f))=2|lnλlnB|.\mathrm{dim(graph}(f))=2-\left|\frac{\ln{\lambda}}{\ln{B}}\right|.

Actually, the conclusion holds for every function qq that is quasi-periodic and satisfies hypothesis (H1) (see Definitions 2.10 and 2.11). In this subsection, we explain the proof of this statement. First, we recall the necessary definitions and explain their consequences.

Definition 2.10.

A function q:q:\mathbb{R}\rightarrow\mathbb{R} is called almost periodic if for every ϵ>0\epsilon>0 there is l(ϵ)>0l(\epsilon)>0 such that any real interval of length l(ϵ)l(\epsilon) contains a number τ\tau satisfying

|q(x)q(x+τ)|<ϵfor everyx.|q(x)-q(x+\tau)|<\epsilon\,\,\,\,\,\,\text{for every}\,\,\,\,\,\,x\in\mathbb{R}.

Even though qq is not periodic it is possible to define a formal Fourier series (see Corduneanu [2]) such that

q(t)aqaeiatq(t)\sim\sum_{a}q_{a}e^{iat}

where

qa=limT12TT+Tq(t)eiat𝑑t.q_{a}=\lim_{T\rightarrow\infty}\frac{1}{2T}\int_{-T}^{+T}q(t)e^{-iat}\hskip 5.0ptdt.

There is only a countable set of aa\in\mathbb{R} such that qa0q_{a}\neq 0.111Notice that if qq was periodic this definition would coincide with the standard definition of Fourier Series. The main idea to prove Theorem 2.9 is to formally consider

g(t):=n=λnq(Bnt),g(t):=\sum_{n=-\infty}^{\infty}\lambda^{n}q(B^{n}t), (3)

that contains the function ff but also the negative part of the series. Ignoring the fact that the function gg may not be well defined, if we try to calculate its Fourier series coefficients we get:

gσ\displaystyle g_{\sigma} =limT12TT+Tn=λnq(Bnt)eiσtdt\displaystyle=\lim_{T\rightarrow\infty}\frac{1}{2T}\int_{-T}^{+T}\sum_{n=-\infty}^{\infty}\lambda^{n}q(B^{n}t)e^{-i\sigma t}\hskip 5.0ptdt
=limTn=λn2TT+Tq(Bnt)eiσt𝑑t\displaystyle=\lim_{T\rightarrow\infty}\sum_{n=-\infty}^{\infty}\frac{\lambda^{n}}{2T}\int_{-T}^{+T}q(B^{n}t)e^{-i\sigma t}\hskip 5.0ptdt
=limTn=λn2TTBn+TBnq(s)eiσsBnBn𝑑s\displaystyle=\lim_{T\rightarrow\infty}\sum_{n=-\infty}^{\infty}\frac{\lambda^{n}}{2T}\int_{-TB^{n}}^{+TB^{n}}\frac{q(s)e^{-i\sigma sB^{-n}}}{B^{n}}\hskip 5.0ptds
=n=λnlimTBn12TBnTBn+TBnq(s)eiσBns𝑑s\displaystyle=\sum_{n=-\infty}^{\infty}\lambda^{n}\lim_{TB^{n}\rightarrow\infty}\frac{1}{2TB^{n}}\int_{-TB^{n}}^{+TB^{n}}q(s)e^{-i\sigma B^{-n}s}\hskip 5.0ptds
=n=λnqσBn.\displaystyle=\sum_{n=-\infty}^{\infty}\lambda^{n}q_{\sigma B^{-n}}.

Regardless of the lack of rigor in the calculation above, all that is truly necessary is that

gσ:=n=λnqσBng_{\sigma}:=\sum_{n=-\infty}^{\infty}\lambda^{n}q_{\sigma B^{-n}} (4)

converges. The Hypothesis (H1) of Kaplan et al. [6] gives a sufficient condition to ensure this convergence.

Definition 2.11.

We say that an almost periodic function q:q\colon\mathbb{R}\to\mathbb{R} satisfies the hypothesis (H1) if

a|qa||a|α<\sum_{a}|q_{a}||a|^{\alpha}<\infty (H1)

where α=logλ/logB\alpha=-\log{\lambda}/\log{B} and qaq_{a} are its Fourier Coefficients.

Lemma 2.12.

If qq is an almost periodic function satisfying (H1)(\mathrm{H1}), 0<λ<10<\lambda<1, and B>1/λB>1/\lambda, then the series (4)(\ref{gsigma}) converges.

Proof.

Given |σ|1|\sigma|\geq 1 and nn\in\mathbb{N}, let an:=σBna_{n}:=\sigma B^{-n}, notice that |an|α=|σ|αBnα|a_{n}|^{\alpha}=|\sigma|^{\alpha}B^{-n\alpha}, and since α=logλ/logB\alpha=-\log{\lambda}/\log{B} we have Bnα=λnB^{-n\alpha}=\lambda^{n}, so

|λnqσBn|=|(anσ)αqan||an|α|qan|.|\lambda^{n}q_{\sigma B^{-n}}|=\left|\left(\frac{a_{n}}{\sigma}\right)^{\alpha}q_{a_{n}}\right|\leq|a_{n}|^{\alpha}|q_{a_{n}}|.

Thus, Hypothesis (H1) ensures that

n=|λkqσBn|n=|an|α|qan|a|a|α|qa|<.\sum_{n=-\infty}^{\infty}|\lambda^{k}q_{\sigma B^{-n}}|\leq\sum_{n=-\infty}^{\infty}|a_{n}|^{\alpha}|q_{a_{n}}|\leq\sum_{a}|a|^{\alpha}|q_{a}|<\infty.

If |σ|<1|\sigma|<1, then consider kk\in\mathbb{N} such that |σBk|>1|\sigma B^{k}|>1 and note that

gσ\displaystyle g_{\sigma} =n=λnqσBn=n=λnq(σBk)Bnk\displaystyle=\sum_{n=-\infty}^{\infty}\lambda^{n}q_{\sigma B^{-n}}=\sum_{n=-\infty}^{\infty}\lambda^{n}q_{(\sigma B^{k})B^{-n-k}}
=1λkn=λn+kq(σBk)Bnk=1λkm=λmqσ~Bm,\displaystyle=\frac{1}{\lambda^{k}}\sum_{n=-\infty}^{\infty}\lambda^{n+k}q_{(\sigma B^{k})B^{-n-k}}=\frac{1}{\lambda^{k}}\sum_{m=-\infty}^{\infty}\lambda^{m}q_{\tilde{\sigma}B^{-m}},

where σ~=σBk\tilde{\sigma}=\sigma B^{k}, so we are in the previous case. ∎

Although this condition may appear to be too technical, Kaplan et al. [6] proved that if pp is a C3C^{3} function then qq will satisfy (H1) (see Proposition 3.3 for our version in higher dimensions). So now that gσg_{\sigma} is well defined, there are two possible cases: either 1) gσ=0g_{\sigma}=0 for every σ\sigma, and in this case hypothesis (H1) ensures that g(t)0g(t)\equiv 0 as shown in Proposition 2.2 from Kaplan et al. [6], and, hence, that

f(t)=n=1λnq(Bnt).f(t)=-\sum^{-1}_{n=-\infty}\lambda^{n}q(B^{n}t).

This ensures that ff is C1C^{1} by a similar argument as in Proposition 2.8 (see Lemma 2.1 in [6]); or 2) there is a Fourier coefficient gσ0g_{\sigma}\neq 0 and it can be used to ensure the existence of C1>0C_{1}>0 such that

var𝐽(f)C1Lα\underset{J}{\text{var}}(f)\geq C_{1}L^{\alpha}

for every interval JJ with L=length(J)L=\text{length}(J) small enough. Indeed, the following proposition is the main step in proving this.

Proposition 2.13 (Proposition 2.5 in [6]).

Given a continuous real function γ(t)\gamma(t), let

I=ρ2πn02πnργ(t)cos(ρt+ϕ)𝑑t.I=\frac{\rho}{2\pi n}\int_{0}^{\frac{2\pi n}{\rho}}\gamma(t)\cos(\rho t+\phi)dt.

where ρ(0,)\rho\in(0,\infty), ϕ\phi\in\mathbb{R} and nn is a positive integer. Then

var𝐽(γ)π|I|,\underset{J}{\mathrm{var}}(\gamma)\geq\pi|I|,

where J=[0,2πn/ρ]J=[0,2\pi n/\rho].

Kaplan et al. [6] use this proposition for any translation of the form γ(t)=f(t+θ)\gamma(t)=f(t+\theta), since the variation of f(t+θ)f(t+\theta) on [0,2πn/ρ][0,2\pi n/\rho] equals the variation of f(t)f(t) on [θ,2πn/ρ+θ][\theta,2\pi n/\rho+\theta], so they can cover any interval of the domain. Using Lemma 2.6 and Lemma 2.7 in [6] they shown that for large enough k,nk,n\in\mathbb{N} there is 0<C0<|gσ0|0<C_{0}<|g_{\sigma_{0}}| such that

|I|=σ0Bk2πn02πnσ0Bkf(t+θ)cos(σ0Bk(t+θ))𝑑tλk(|gσ0|C0)π|I|=\frac{\sigma_{0}B^{k}}{2\pi n}\int_{0}^{\frac{2\pi n}{\sigma_{0}B^{k}}}f(t+\theta)\cos(\sigma_{0}B^{k}(t+\theta))dt\geq\frac{\lambda^{k}(|g_{\sigma_{0}}|-C_{0})}{\pi}

Setting C1C_{1} as |gσ0|C0|g_{\sigma_{0}}|-C_{0} and using Proposition 2.13 we obtain

var𝐽(f)\displaystyle\underset{J}{\text{var}}(f) \displaystyle\geq λkC1=BαkC1\displaystyle\lambda^{k}C_{1}=B^{-\alpha k}C_{1}
\displaystyle\geq (length(J)(2πn/σ0))αC1\displaystyle\left(\frac{\text{length}(J)}{(2\pi n/\sigma_{0})}\right)^{\alpha}C_{1}
=\displaystyle= C1(length(J))α,\displaystyle C_{1}(\text{length}(J))^{\alpha},

whenever length(J)(2πn/σ0)Bk\text{length}(J)\leq(2\pi n/\sigma_{0})B^{-k}. For the upper bound the fact that qq and qq^{\prime} are bounded let us obtain C2>0C_{2}>0 such that

var𝐽(f)C2Lα\underset{J}{\text{var}}(f)\leq C_{2}L^{\alpha}

if L=length(J)L=\text{length}(J) is small enough (see Proposition 2.9 in [6]). Then Proposition 2.3 ensures that

dim(graph(f))=2α.\mathrm{dim(graph}(f))=2-\alpha.

After calculating the box dimensions of the stable and unstable slices, it is possible to calculate the box dimension of the graph of ϕ\phi. We expand some details in the proof given in [6].

Theorem 2.14 (Theorem B in [6]).

If pp is C3C^{3} and λ(B1,1)\lambda\in(B_{1},1), then either ϕ\phi is nowhere differentiable and

dim(graph(ϕ))=3|logλlogB1|\mathrm{dim(graph}(\phi))=3-\left|\frac{\log\lambda}{\log B_{1}}\right|

or ϕ\phi is C1C^{1} and dim(graph(ϕ))=2\mathrm{dim(graph}(\phi))=2.

Proof.

Recall that ϕ\phi restricted to the unstable manifold is a C1C^{1} function (see Proposition 2.8) and when all Fourier coefficients of g1g_{1} are null, ϕ1\phi_{1} is C1C^{1}, so in this case ϕ\phi is C1C^{1} and dim(graph(ϕ))=2\mathrm{dim(graph}(\phi))=2. If there exists a non-zero Fourier coefficient of g1g_{1}, then there exist C1,C2>0C_{1},C_{2}>0, as in the proof of Theorem 2.9, and K1>0K_{1}>0 given by Proposition 2.8, such that

C1Lαvar|t|L2ϕ1C2Lαandsup𝕋2|ϕs|<K1,C_{1}L^{\alpha}\leq\underset{|t|\leq\frac{L}{2}}{\text{var}}\phi_{1}\leq C_{2}L^{\alpha}\,\,\,\,\,\,\text{and}\,\,\,\,\,\,\underset{\mathbb{T}^{2}}{\sup}\left|\frac{\partial\phi}{\partial s}\right|<K_{1},

if LL is sufficiently small. If (t0,s0)𝕋2(t_{0},s_{0})\in\mathbb{T}^{2} and EL:=[t0L2,t0+L2]×[s0L2,s0+L2]E_{L}:=\left[t_{0}-\frac{L}{2},t_{0}+\frac{L}{2}\right]\times\left[s_{0}-\frac{L}{2},s_{0}+\frac{L}{2}\right], then the above inequalities ensure that

C1Lα2K1LvarELϕC2Lα+2K1LC_{1}L^{\alpha}-2K_{1}L\leq\underset{E_{L}}{\text{var}}\phi\leq C_{2}L^{\alpha}+2K_{1}L

that is equivalent to

Lα(C12K1L1α)varELϕLα(C2+2K1L1α)L^{\alpha}(C_{1}-2K_{1}L^{1-\alpha})\leq\underset{E_{L}}{\text{var}}\phi\leq L^{\alpha}(C_{2}+2K_{1}L^{1-\alpha})

that, in turn, implies

12C1LαvarELϕ2C2Lα\frac{1}{2}C_{1}L^{\alpha}\leq\underset{E_{L}}{\text{var}}\phi\leq 2C_{2}L^{\alpha}

if L1αmin{C2/2K1,C1/4K1}L^{1-\alpha}\leq\text{min}\{C_{2}/2K_{1},C_{1}/4K_{1}\}. Proposition 2.4 ensures that

dim(graph(ϕ))=3α=3|logλlog(1/B1)|=3|logλlogB1|.\mathrm{dim(graph}(\phi))=3-\alpha=3-\left|\frac{\log\lambda}{\log(1/B_{1})}\right|=3-\left|\frac{\log\lambda}{\log B_{1}}\right|.

3 Fractal structure of the graph and its sub-slices

In this section, we consider the case k>2k>2, where AA is a linear Anosov diffeomorphism with kk distinct eigenvalues B1,,BkB_{1},\dots,B_{k} satisfying

|B1|<|B2|<<|Bj|<1<|Bj+1|<<|Bk||B_{1}|<|B_{2}|<\cdots<|B_{j}|<1<|B_{j+1}|<\cdots<|B_{k}|

and such that the respective eigenvectors v1,,vkv_{1},\dots,v_{k} form a basis of k\mathbb{R}^{k}.

3.1 Box dimension for the stable slices in 𝕋k×\mathbb{T}^{k}\times\mathbb{R}

In Proposition 2.8 we proved that if λ<|Bi|\lambda<|B_{i}| then the box dimension of the graph of ϕi\phi_{i} is 1. When |Bi|<λ|B_{i}|<\lambda, the function ϕi\phi_{i} can be written in the form of the function ff in Theorem 2.9. Indeed, letting

qi(t)=p(tvi+t¯)q_{i}(t)=p(tv_{i}+\underline{t})

it follows that

ϕi(t)=n=0λnqi((Bi1)nt).\phi_{i}(t)=\sum_{n=0}^{\infty}\lambda^{n}q_{i}((B_{i}^{-1})^{n}t).

Thus, the argument of subsection 2.4 can also be applied for ϕi\phi_{i}, using the function

gi(t):=n=λnqi((Bi1)nt),g_{i}(t):=\sum_{n=-\infty}^{\infty}\lambda^{n}q_{i}((B_{i}^{-1})^{n}t),

associated to qiq_{i} as in (3), and we obtain the following theorem.

Theorem 3.1.

If qiq_{i} is a C1C^{1} function, almost periodic, and satisfies the hypothesis (H1)(\mathrm{H1}), then either all the Fourier coefficients of gig_{i} are zero, ϕi\phi_{i} is C1C^{1}, and dim(graph(ϕi))=1\mathrm{dim(graph}(\phi_{i}))=1, or there is at least one non-zero Fourier coefficient of gig_{i} and

dim(graph(ϕi))=2lnλln|Bi|.\mathrm{dim(graph}(\phi_{i}))=2-\frac{\ln{\lambda}}{\ln{|B_{i}|}}.

The hypothesis on qiq_{i} are satisfied with enough regularity on pp. Indeed, qq will be almost periodic as long as pp is continuous and the Hypothesis (H1) is satisfied when pp is Ck+1C^{k+1}. This is proved in the next results but analogous results in the case k=2k=2 are announced in [6].

Lemma 3.2.

If pp is uniformly continuous, then qiq_{i} is almost periodic.

Proof.

Given ϵ>0\epsilon>0, because pp is uniformly continuous, there is δ>0\delta>0 such that

d(x,y)δ|p(x)p(y)|<ϵ.d(x,y)\leq\delta\Rightarrow|p(x)-p(y)|<\epsilon.

Let αi(t)=t¯+tvi\alpha_{i}(t)=\underline{t}+tv_{i} be a parametrization of the stable manifold on the direction of viv_{i}. Since AA is linear, αi\alpha_{i} parametrizes an orbit of a linear flow on the Torus 𝕋k\mathbb{T}^{k}, that is a recurrent flow without singularities. Thus, if αi(t)B(t¯,δ)¯\alpha_{i}(t)\in\overline{B(\underline{t},\delta)}, we can consider the smaller s>0s>0 satisfying: αi(t+s)B(t¯,δ)¯\alpha_{i}(t+s)\in\overline{B(\underline{t},\delta)} and such that there exists s(t,s)s^{\prime}\in(t,s) with αi(t+s)B(t¯,δ)¯\alpha_{i}(t+s^{\prime})\notin\overline{B(\underline{t},\delta)}. We let s=τ(αi(t))s=\tau(\alpha_{i}(t)) and call it the first-return time of αi(t)\alpha_{i}(t) to B(t¯,δ)¯\overline{B(\underline{t},\delta)}. Let

L=sup{τ(αi(t));αi(t)B(t¯,δ)¯}L=\sup\{\tau(\alpha_{i}(t));\,\,\alpha_{i}(t)\in\overline{B(\underline{t},\delta)}\}

and note that L<L<\infty. Every interval II of length LL contains sIs\in I satisfying

d(αi(0),αi(s))δ,d(\alpha_{i}(0),\alpha_{i}(s))\leq\delta,

since d(αi(0),αi(0+s))>δd(\alpha_{i}(0),\alpha_{i}(0+s))>\delta for every sIs\in I implies the existence of tt\in\mathbb{R} such that

αi(t)B(t¯,δ)¯andτ(αi(t))>L\alpha_{i}(t)\in\overline{B(\underline{t},\delta)}\,\,\,\,\,\,\text{and}\,\,\,\,\,\,\tau(\alpha_{i}(t))>L

contradicting the definition of LL. Now given r,s,tr,s,t\in\mathbb{R}, by linearity we have

d(αi(t),αi(t+s))=d(αi(r),αi(r+s)),d(\alpha_{i}(t),\alpha_{i}(t+s))=d(\alpha_{i}(r),\alpha_{i}(r+s)),

and this implies that for each interval II of length LL, there is sIs\in I such that

d(αi(t),αi(t+s))δfor everyt.d(\alpha_{i}(t),\alpha_{i}(t+s))\leq\delta\,\,\,\,\,\,\text{for every}\,\,\,\,\,\,t\in\mathbb{R}.

Hence,

|qi(t)qi(t+s)|=|p(αi(t))p(αi(t+s))|<ϵ.|q_{i}(t)-q_{i}(t+s)|=|p(\alpha_{i}(t))-p(\alpha_{i}(t+s))|<\epsilon.

for every tt\in\mathbb{R}. Since this can be done for each ϵ>0\epsilon>0, the proof is complete. ∎

Proposition 3.3.

If p:𝕋kp\colon\mathbb{T}^{k}\rightarrow\mathbb{R} is Ck+1C^{k+1}, then qiq_{i} is almost periodic and satisfies (H1)(\mathrm{H1}) for every i{1,,j}i\in\{1,\dots,j\}.

Proof.

As pp is periodic and Ck+1C^{k+1}, the Fourier Series of pp is convergent, let

p(x¯)=j¯kpj¯e2πi(x¯j¯).p(\underline{x})=\sum_{\underline{j}\in\mathbb{Z}^{k}}p_{\underline{j}}e^{2\pi i(\underline{x}\cdot\underline{j})}.

Because pp is Ck+1C^{k+1}, we have that there is a positive KK such that

|pj¯|K|j¯|k+1.|p_{\underline{j}}|\leq\frac{K}{|\underline{j}|^{k+1}}.

Indeed, the coefficient of k+1p/ik+1\partial^{k+1}p/\partial i^{k+1} are 2π(ji)k+1pj¯2\pi(j_{i})^{k+1}p_{\underline{j}} and they are all limited (see Corduneanu [2]) therefore

|pj¯|1ki=1kKi2π|ji|k+1i=1kKi2πki=1k|ji|k+1|p_{\underline{j}}|\leq\frac{1}{k}\sum_{i=1}^{k}\frac{K_{i}}{2\pi|j_{i}|^{k+1}}\leq\frac{\sum_{i=1}^{k}K_{i}}{2\pi k\prod_{i=1}^{k}|j_{i}|^{k+1}}

because |ji|1|j_{i}|\geq 1 we have

ki=1k|ji|i=1k|ji|2k\prod_{i=1}^{k}|j_{i}|\geq\sum_{i=1}^{k}\sqrt{|j_{i}|^{2}}

and so

|pj¯|i=1kKi2πi=1k|ji|2K|j¯|k+1.|p_{\underline{j}}|\leq\frac{\sum_{i=1}^{k}K_{i}}{2\pi\sum_{i=1}^{k}\sqrt{|j_{i}|^{2}}}\leq\frac{K}{|\underline{j}|^{k+1}}.

With this the Fourier series of qi(t)q_{i}(t) can be written as

j¯kpj¯e2πi((tvi+t¯)j¯)=j¯ke2πi(t¯j¯)pj¯e2πi((tvi)j¯)=a(qi)aeiat\sum_{\underline{j}\in\mathbb{Z}^{k}}p_{\underline{j}}e^{2\pi i((tv_{i}+\underline{t})\cdot\underline{j})}=\sum_{\underline{j}\in\mathbb{Z}^{k}}e^{2\pi i(\underline{t}\cdot\underline{j})}p_{\underline{j}}e^{2\pi i((tv_{i})\cdot\underline{j})}=\sum_{a}(q_{i})_{a}e^{iat} (5)

where a=2π(j¯vi)a=2\pi(\underline{j}\cdot v_{i}). Thus, using the Cauchy-Schwarz inequality and the one above,

a|(qi)a||a|α\displaystyle\sum_{a}|(q_{i})_{a}||a|^{\alpha} =(2π)αj¯k|e2πit¯j¯pj¯||j¯vi|α=(2π)αj¯k|pj¯||j¯vi|α\displaystyle=(2\pi)^{\alpha}\sum_{\underline{j}\in\mathbb{Z}^{k}}|e^{2\pi i\underline{t}\cdot\underline{j}}p_{\underline{j}}||\underline{j}\cdot v_{i}|^{\alpha}=(2\pi)^{\alpha}\sum_{\underline{j}\in\mathbb{Z}^{k}}|p_{\underline{j}}||\underline{j}\cdot v_{i}|^{\alpha}
(2π)αj¯kK|j¯|α|vi|α|j¯|k+1K2j¯k1|j|k+1α.\displaystyle\leq(2\pi)^{\alpha}\sum_{\underline{j}\in\mathbb{Z}^{k}}\frac{K|\underline{j}|^{\alpha}|v_{i}|^{\alpha}}{|\underline{j}|^{k+1}}\leq K_{2}\sum_{\underline{j}\in\mathbb{Z}^{k}}\frac{1}{|j|^{k+1-\alpha}}.

This converges because k+1α>kk+1-\alpha>k (see Bromwich [1]222This fact is proved for the double sum, but also works for any finite number sum.). ∎

The following is a direct corollary of the above results.

Theorem 3.4.

If pp is a Ck+1C^{k+1} function and iji\leq j, then either all the Fourier coefficients of gig_{i} are zero, ϕi\phi_{i} is C1C^{1}, and dim(graph(ϕi))=1\mathrm{dim(graph}(\phi_{i}))=1, or there is at least one non-zero Fourier coefficient of gig_{i} and

dim(graph(ϕi))=2lnλln|Bi|.\mathrm{dim(graph}(\phi_{i}))=2-\frac{\ln{\lambda}}{\ln{|B_{i}|}}.

This calculates the box dimension of all stable sub-slices of the graph.

3.2 Dimension of the graph and dimension of the sub-slices

In this subsection, we discuss how to calculate the dimension of the graph using the dimension of the sub-slices calculated in the previous section and compare the dimension of the graph with the sum of the dimensions of its sub-slices. We obtain sufficient conditions that ensure the dimension of the graph is smaller than the sum of the dimension of the sub-slices (see Theorem 3.6). This means that there exist fractal structure in each sub-slice that is not captured by the box dimension of the graph. The box dimension of the graph in the case k>2k>2 can be calculated as in Theorem C of Kaplan et al. [6]. Indeed, if |B1|<λ<1|B_{1}|<\lambda<1 and there is σ\sigma such that (g1)σ0(g_{1})_{\sigma}\neq 0, then the variation of ϕ\phi is proportional to the variation of ϕ1\phi_{1} and so

dim(graph(ϕ))=k+1logλlog|B1|=k1+dim(ϕ1).\mathrm{dim(graph}(\phi))=k+1-\frac{\log\lambda}{\log|B_{1}|}=k-1+\mathrm{dim}(\phi_{1}).

The above number is also the Lyapunov dimension of the graph(ϕ)\mathrm{graph}(\phi) (see Frederickson et al. [4] for more details), and it is conjectured that this is usually the case for an attractor.333You can find more results in this direction in Ledrappier [7]. If (g1)σ=0(g_{1})_{\sigma}=0 for every σ\sigma and |B2|<λ<1|B_{2}|<\lambda<1, we can try to see if there is fractal dimension for the sub-slice ϕ2\phi_{2} by seeing if there is σ\sigma such (g2)σ0(g_{2})_{\sigma}\neq 0. If this is the case, then

dim(graph(ϕ))=k+1logλlog|B2|=k1+dim(ϕ2).\mathrm{dim(graph}(\phi))=k+1-\frac{\log\lambda}{\log|B_{2}|}=k-1+\mathrm{dim}(\phi_{2}).

and if not, we can repeat the argument till the sub-slice ϕl\phi_{l} as long as |Bl|<λ|B_{l}|<\lambda. The idea is that the variation of ϕ\phi and the variation of the sub-slice with the highest variation are proportional to the same value, while the sub-slice with the highest variation is given by the smallest ii admitting a (gi)σ0(g_{i})_{\sigma}\neq 0.

Theorem 3.5.

If pp is Ck+1C^{k+1}, λ(Bl,Bl+1)(0,1)\lambda\in(B_{l},B_{l+1})\cap(0,1) with ljl\leq j, and there is (gi)σ0(g_{i})_{\sigma}\neq 0 with ili\leq l, then

dim(graph(ϕ))=k+1logλlog|Bi0|,\mathrm{dim(graph}(\phi))=k+1-\frac{\log\lambda}{\log|B_{i_{0}}|},

where i0=min{il|σ,(gi)σ0}i_{0}=\mathrm{min}\{i\leq l\,\,|\,\,\exists\,\,\sigma,(g_{i})_{\sigma}\neq 0\}. If (gi)σ=0(g_{i})_{\sigma}=0 for every σ\sigma and every i{1,,j}i\in\{1,\dots,j\}, then ϕ\phi is C1C^{1} and dim(graph(ϕ))=k\mathrm{dim(graph}(\phi))=k.

Proof.

For each i{1,,l}i\in\{1,\dots,l\}, let

αi={logλlog|Bi|,if there is σ such that (gi)σ01,otherwise\alpha_{i}=\begin{cases}\frac{\log\lambda}{\log|B_{i}|},&\text{if there is $\sigma$ such that }(g_{i})_{\sigma}\neq 0\\ 1,&\text{otherwise}\end{cases}

and if i>li>l let αi=1\alpha_{i}=1. Consider the kk-cube I=[L2,L2]kI=\left[-\frac{L}{2},\frac{L}{2}\right]^{k}, so we have that for each i{1,,j}i\in\{1,\dots,j\} there are positive constants Ci,KiC_{i},K_{i} such that

KiLαivar[L2,L2](ϕi)CiLαi.K_{i}L^{\alpha_{i}}\leq\underset{\left[-\frac{L}{2},\frac{L}{2}\right]}{\text{var}}(\phi_{i})\leq C_{i}L^{\alpha_{i}}.

Since

var𝐼(ϕ)var[L2,L2](ϕi)for everyi{1,,k}\underset{I}{\text{var}}(\phi)\geq\underset{\left[-\frac{L}{2},\frac{L}{2}\right]}{\text{var}}(\phi_{i})\,\,\,\,\,\,\text{for every}\,\,\,\,\,\,i\in\{1,\dots,k\}

and

var𝐼(ϕ)i=1kvar[L2,L2](ϕi),\underset{I}{\text{var}}(\phi)\leq\sum_{i=1}^{k}\underset{\left[-\frac{L}{2},\frac{L}{2}\right]}{\text{var}}(\phi_{i}),

it follows that

Ki0Lαi0var𝐼(ϕ)i=1kCiLαi.K_{i_{0}}L^{\alpha_{i_{0}}}\leq\underset{I}{\text{var}}(\phi)\leq\sum^{k}_{i=1}C_{i}L^{\alpha_{i}}.

Note that αiαi0>0\alpha_{i}-\alpha_{i_{0}}>0 for every ii0i\neq i_{0}. Indeed, this is clear if αi=1\alpha_{i}=1, and otherwise, i0<ili_{0}<i\leq l and

αi=logλlog|Bi|>logλlog|Bi0|=αi0.\alpha_{i}=\frac{\log\lambda}{\log|B_{i}|}>\frac{\log\lambda}{\log|B_{i_{0}}|}=\alpha_{i_{0}}.

Thus, if LL is small enough such that

Lαiαi0Ci0Cifor everyii0,L^{\alpha_{i}-\alpha_{i_{0}}}\leq\frac{C_{i_{0}}}{C_{i}}\,\,\,\,\,\,\text{for every}\,\,\,\,\,\,i\neq i_{0},

then

Ki0Lαi0var𝐼(ϕ)kCi0Lαi0.K_{i_{0}}L^{\alpha_{i_{0}}}\leq\underset{I}{\text{var}}(\phi)\leq kC_{i_{0}}L^{\alpha_{i_{0}}}.

So by the Proposition 2.4 we have

dim(graph(ϕ))=k+1logλlog|Bi0|=k1+dim(graph(ϕi0)).\mathrm{dim(graph}(\phi))=k+1-\frac{\log\lambda}{\log|B_{i_{0}}|}=k-1+\mathrm{dim(graph}(\phi_{i_{0}})).

It is interesting that the only fractal structure captured by the box dimension of the graph is the one given by the sub-slice ϕi0\phi_{i_{0}}. Thus, if there are more fractal sub-slices ϕi\phi_{i} for i>i0i>i_{0}, then the sum of the box dimensions of the sub-slices will be greater than the box dimension of the graph.

Theorem 3.6.

If pp is Ck+1C^{k+1}, λ(Bl,Bl+1)(0,1)\lambda\in(B_{l},B_{l+1})\cap(0,1) with ljl\leq j, and there exist i1{i0+1,,l}i_{1}\in\{i_{0}+1,\dots,l\} and σ1\sigma_{1}\in\mathbb{R} with (gi1)σ10(g_{i_{1}})_{\sigma_{1}}\neq 0, then

dim(graph(ϕ))<i=1kdim(graph(ϕi)).\mathrm{dim(graph}(\phi))<\sum_{i=1}^{k}\mathrm{dim(graph}(\phi_{i})).
Proof.

Theorem 3.5 ensures that

dim(graph(ϕ))=k1+dim(graph(ϕi0)),\mathrm{dim(graph}(\phi))=k-1+\mathrm{dim(graph}(\phi_{i_{0}})),

Proposition 2.8 ensures that

dim(graph(ϕm))=1for everym{l+1,,k},\mathrm{dim(graph}(\phi_{m}))=1\,\,\,\,\,\,\text{for every}\,\,\,\,\,\,m\in\{l+1,\dots,k\},

and by the definition of i0i_{0}, Theorem 3.4 ensures that the same is true for m<i0m<i_{0}. The hypothesis and Theorem 3.4 ensure that

dim(graph(ϕi1))=2logλlog|Bi1|>1.\mathrm{dim(graph}(\phi_{i_{1}}))=2-\frac{\log\lambda}{\log|B_{i_{1}}|}>1.

Since

dim(graph(ϕm))1wheneveri0<ml,\mathrm{dim(graph}(\phi_{m}))\geq 1\,\,\,\,\,\,\text{whenever}\,\,\,\,\,\,i_{0}<m\leq l,

it follows that

mi0dim(graph(ϕm))>k1\sum_{m\neq i_{0}}\mathrm{dim(graph}(\phi_{m}))>k-1

and, hence,

dim(graph(ϕ))=k1+dim(graph(ϕi0))<i=1kdim(graph(ϕl)).\mathrm{dim(graph}(\phi))=k-1+\mathrm{dim(graph}(\phi_{i_{0}}))<\sum_{i=1}^{k}\mathrm{dim(graph}(\phi_{l})).

3.3 Examples and generic conditions

In this subsection, we prove that the hypothesis on Theorem 3.6 about the existence of two distinct i0,i1{1,,l}i_{0},i_{1}\in\{1,\dots,l\} with (gi0)σ00(g_{i_{0}})_{\sigma_{0}}\neq 0 and (gi1)σ10(g_{i_{1}})_{\sigma_{1}}\neq 0 is always satisfied if we choose correctly the function p:𝕋kp\colon\mathbb{T}^{k}\to\mathbb{R}. Actually, we prove that it is possible to choose pp such that this holds for every i{1,,l}i\in\{1,\dots,l\} and that the set of Ck+1C^{k+1} functions satisfying this is generic in the Ck+1C^{k+1} topology. We begin with an explicit example illustrating Theorem 3.6.

Example 3.7.

Let A be the following hyperbolic matrix

(651100010).\displaystyle\begin{pmatrix}6&-5&1\\ 1&0&0\\ 0&1&0\end{pmatrix}.

The characteristic polynomial is given by

PA(x):=\displaystyle P_{A}(x):= det(6x511x001x)\displaystyle\text{det}\begin{pmatrix}6-x&-5&1\\ 1&-x&0\\ 0&1&-x\end{pmatrix}
=\displaystyle= (6x)det(x01x)(5)det(100x)+det(1x01)\displaystyle(6-x)\text{det}\begin{pmatrix}-x&0\\ 1&-x\end{pmatrix}-(-5)\text{det}\begin{pmatrix}1&0\\ 0&-x\end{pmatrix}+\text{det}\begin{pmatrix}1&-x\\ 0&1\end{pmatrix}
=\displaystyle= (6x)x25x+1\displaystyle(6-x)x^{2}-5x+1
=\displaystyle= x3+6x25x+1.\displaystyle-x^{3}+6x^{2}-5x+1.

This polynomial has three distinct real positive roots, which are the eigenvalues of AA, and satisfy

μ1<μ2<0.65<1<5<μ3.\mu_{1}<\mu_{2}<0.65<1<5<\mu_{3}.
Refer to caption
Figure 2: The graph of y=x3+6x25x+1y=-x^{3}+6x^{2}-5x+1.

Moreover, if μ\mu is an eigenvalue of AA, then

(651100010)(μ2μ1)=(6μ25μ+1μ2μ)=(μ3μ2μ)=μ(μ2μ1),\begin{pmatrix}6&-5&1\\ 1&0&0\\ 0&1&0\end{pmatrix}\begin{pmatrix}\mu^{2}\\ \mu\\ 1\end{pmatrix}=\begin{pmatrix}6\mu^{2}-5\mu+1\\ \mu^{2}\\ \mu\end{pmatrix}=\begin{pmatrix}\mu^{3}\\ \mu^{2}\\ \mu\end{pmatrix}=\mu\begin{pmatrix}\mu^{2}\\ \mu\\ 1\end{pmatrix},

that is, vμ=(μ2,μ,1)v_{\mu}=(\mu^{2},\mu,1) is an eigenvector associated to μ\mu. For each i{1,2,3}i\in\{1,2,3\} let vi=(μi2,μi,1)v_{i}=(\mu_{i}^{2},\mu_{i},1). For each i{1,2}i\in\{1,2\}, let ai>0a_{i}>0, θi(0,π/2)\theta_{i}\in(0,\pi/2) and

qi(t)=aicos(μi1t+θi).q_{i}(t)=a_{i}\cos{(\mu_{i}^{-1}t+\theta_{i})}. (6)
Proposition 3.8.

In the coordinate system given by (v1,v2,v3)(v_{1},v_{2},v_{3}), if

p(x1,x2,x3):=q1(x1)+q2(x2)+q3(x3),p(x_{1},x_{2},x_{3}):=q_{1}(x_{1})+q_{2}(x_{2})+q_{3}(x_{3}),

where q3q_{3} is any C4C^{4} function, then for each i{1,2}i\in\{1,2\} we have

(gi)1=λ(qi)μi10.(g_{i})_{1}=\lambda(q_{i})_{\mu_{i}^{-1}}\neq 0.
Proof.

This stands from the fact that the Fourier coefficient (qi)μi1(q_{i})_{\mu_{i}^{-1}} is the only non zero coefficient of qiq_{i}. Indeed, it is known that

limT12TTTcos(bt+θ)sin(at)𝑑t={0 if basin(θ)2 if b=a\lim_{T\rightarrow\infty}\frac{1}{2T}\int_{-T}^{T}\cos{(bt+\theta)}\sin(at)\hskip 5.0ptdt=\begin{cases}0&\text{ if }b\neq a\\ \frac{-\sin(\theta)}{2}&\text{ if }b=a\end{cases}

and

limT12TTTcos(bt+θ)cos(at)𝑑t={0 if bacos(θ)2 if b=a.\lim_{T\rightarrow\infty}\frac{1}{2T}\int_{-T}^{T}\cos{(bt+\theta)}\cos(at)\hskip 5.0ptdt=\begin{cases}0&\text{ if }b\neq a\\ \frac{\cos(\theta)}{2}&\text{ if }b=a.\end{cases}

Therefore,

(qi)a=limT12TTTaicos(μi1t+θi)eiat𝑑t\displaystyle(q_{i})_{a}=\lim_{T\rightarrow\infty}\frac{1}{2T}\int^{T}_{-T}a_{i}\cos{(\mu_{i}^{-1}t+\theta_{i})}e^{-iat}\hskip 5.0ptdt

is non zero if, and only if, a=μi1a=\mu_{i}^{-1}, and in this case, (qi)μi1=aieiθi2(q_{i})_{\mu_{i}^{-1}}=\frac{a_{i}e^{i\theta_{i}}}{2}. By Definition (4) it follows that

(gi)1=λ(qi)μi10.(g_{i})_{1}=\lambda(q_{i})_{\mu_{i}^{-1}}\neq 0.

Thus, if λ(μ2,1)\lambda\in(\mu_{2},1), then

dim(graph(ϕ1))=2logλlogμ1,dim(graph(ϕ2))=2logλlogμ2,\mathrm{dim(graph}(\phi_{1}))=2-\frac{\log\lambda}{\log\mu_{1}},\,\,\,\,\,\,\mathrm{dim(graph}(\phi_{2}))=2-\frac{\log\lambda}{\log\mu_{2}},

and, hence,

dim(graph(ϕ))=4logλlogμ1<i=13dim(graph(ϕi))=(2logλlogμ1)+(2logλlogμ2)+1.\mathrm{dim(graph}(\phi))=4-\frac{\log\lambda}{\log\mu_{1}}<\sum_{i=1}^{3}\mathrm{dim(graph}(\phi_{i}))=\left(2-\frac{\log\lambda}{\log\mu_{1}}\right)+\left(2-\frac{\log\lambda}{\log\mu_{2}}\right)+1.

If λ<μ2\lambda<\mu_{2}, then

dim(graph(ϕ))=i=13dim(graph(ϕi)).\mathrm{dim(graph}(\phi))=\sum_{i=1}^{3}\mathrm{dim(graph}(\phi_{i})).

Indeed, if λ(μ1,μ2)\lambda\in(\mu_{1},\mu_{2}), then

dim(graph(ϕ1))=2logλlogμ1,dim(graph(ϕ2))=1,\mathrm{dim(graph}(\phi_{1}))=2-\frac{\log\lambda}{\log\mu_{1}},\,\,\,\,\,\,\mathrm{dim(graph}(\phi_{2}))=1,

and, hence,

i=13dim(graph(ϕi))=(2logλlogμ1)+1+1=4logλlogμ1=dim(graph(ϕ)),\sum_{i=1}^{3}\mathrm{dim(graph}(\phi_{i}))=\left(2-\frac{\log\lambda}{\log\mu_{1}}\right)+1+1=4-\frac{\log\lambda}{\log\mu_{1}}=\mathrm{dim(graph}(\phi)),

and if 0<λ<μ10<\lambda<\mu_{1}, then ϕ\phi is C1C^{1} and dim(graph(ϕ))=3\mathrm{dim(graph}(\phi))=3.

Now we return to the general case where AA is any linear Anosov diffeomorphism of the Torus as in the beginning of this section.

Theorem 3.9.

If λ(|Bl|,|Bl+1|)(0,1)\lambda\in(|B_{l}|,|B_{l+1}|)\cap(0,1) with l2l\geq 2, then there is a Ck+1C^{k+1} function pp satisfying: for each i{1,,l}i\in\{1,\dots,l\} there exists σi\sigma_{i}\in\mathbb{R} such that (gi)σi0(g_{i})_{\sigma_{i}}\neq 0. Moreover, the set of functions satisfying this is an open and dense subset in Ck+1C^{k+1} (in the Ck+1C^{k+1} topology).

Proof.

The first part of this Theorem follows the idea of Proposition 3.8. If pp is any Ck+1C^{k+1} function, let II be the set of i{1,,l}i\in\{1,\dots,l\} such that (gi)σ=0(g_{i})_{\sigma}=0 for every σ\sigma\in\mathbb{R}. For each ϵ>0\epsilon>0, the function

p(x¯)+ϵ(iIcos(μi1xi))p(\underline{x})+\epsilon\left(\sum_{i\in I}\cos(\mu_{i}^{-1}x_{i})\right)

satisfies (gi)10(g_{i})_{1}\neq 0 for every iIi\in I (by Proposition 3.8). Also, if iIi\notin I, it satisfies (gi)σi0(g_{i})_{\sigma_{i}}\neq 0 for some σi\sigma_{i}\in\mathbb{R}. Letting ϵ0\epsilon\to 0 we obtain that pp is approximated by Ck+1C^{k+1} functions satisfying: for each i{1,,l}i\in\{1,\dots,l\} there exists σi\sigma_{i}\in\mathbb{R} such that (gi)σi0(g_{i})_{\sigma_{i}}\neq 0. Because of the relation (5) we have that the Fourier coefficients of qiq_{i} depend continuously on the Fourier coefficients of pp and so (gi)σ(g_{i})_{\sigma} also varies continuously with pp. This concludes the proof. ∎

We conclude noting that for a generic function pp the box dimension of a stable sub-slice ϕi\phi_{i} will depend only if λ<|Bi|\lambda<|B_{i}| or λ>|Bi|\lambda>|B_{i}|. In the first case the dimension is one and in the second is 2logλ/log|Bi|2-\log{\lambda}/\log{|B_{i}|}.

Corollary 3.10.

If pp is a generic Ck+1C^{k+1} function and λ(|Bl|,|Bl+1|)(0,1)\lambda\in(|B_{l}|,|B_{l+1}|)\cap(0,1) for some ljl\leq j, then

i=1kdim(graph(ϕi))=(kl)+2li=0llogλlog|Bi|.\sum_{i=1}^{k}\mathrm{dim(graph}(\phi_{i}))=(k-l)+2l-\sum_{i=0}^{l}\frac{\log{\lambda}}{\log{|B_{i}|}}.

Acknowledgments. Bernardo Carvalho was supported by Progetto di Eccellenza MatMod@TOV grant number PRIN 2017S35EHN, by CNPq grant number 405916/2018-3, and Rafael Pereira was also supported by Fapemig grant number APQ-00036-22.

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B. Carvalho

Dipartimento di Matematica,

Università degli Studi di Roma Tor Vergata

Via Cracovia n.50 - 00133

Roma - RM, Italy

R. C. Pereira

Departamento de Matemática,

Universidade Federal de Minas Gerais - UFMG

Av. Antônio Carlos, 6627 - Campus Pampulha

Belo Horizonte - MG, Brazil.