By connecting dynamical systems and number theory, this graduate textbook on ergodic theory acts as an introduction to a
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Table of contents :
Contents
Mathematical symbols
1 Numbertheoretical dynamical systems
1.1 Continued fractions and Diophantine approximation
1.1.1 Continued fractions
1.1.2 Elementary Diophantine approximation: Hurwitz’s Theorem and badly approximable numbers
1.2 Topological Dynamical Systems
1.2.1 The Gauss map
1.2.2 Symbolic dynamics
1.2.3 A return to the Gauss map
1.2.4 Elementary metrical Diophantine analysis
1.2.5 Markov partitions for interval maps
1.3 The Farey map: definition and topological properties
1.3.1 The Farey map
1.3.2 Topological properties of the Farey map
1.4 Two further examples
1.4.1 The aLüroth maps
1.4.2 The aFarey maps
1.4.3 Topological properties of Fa
1.4.4 Expanding and expansive partitions
1.4.5 Metrical Diophantinelike results for the aLüroth expansion
1.5 Notes and historical remarks
1.5.1 The Farey sequence
1.5.2 The classical Lüroth series
1.6 Exercises
2 Basic ergodic theory
2.1 Invariant measures
2.1.1 Invariant measures for the Gauss and aLüroth system
2.2 Recurrence and conservativity
2.3 The transfer operator
2.3.1 Jacobians and the change of variable formula
2.3.2 Obtaining invariant measures via the transfer operator
2.3.3 The Ruelle operator
2.3.4 Invariant measures for F , Fa, G and La
2.3.5 Invariant measures via the jump transformation
2.4 Ergodicity and exactness
2.4.1 Ergodicity of the systems G and La
2.4.2 Ergodic theorems for probability spaces and consequences for the Gauss and aLüroth systems
2.4.3 Ergodic theorems for infinite measures
2.4.4 Inducing
2.4.5 Uniqueness of the invariant measures for F and Fa
2.4.6 Proof of Hopf’s Ratio Ergodic Theorem
2.5 Exactness revisited
2.6 Exercises
3 Renewal theory and asumlevel sets
3.1 Sumlevel sets
3.2 Sumlevel sets for the aLüroth expansion
3.2.1 Classical renewal results
3.2.2 Renewal theory applied to the asumlevel sets
3.3 Exercises
4 Infinite ergodic theory
4.1 The functional analytic perspective and the Chacon–Ornstein Ergodic Theorem
4.2 Pointwise dual ergodicity
4.3 ψmixing, Darling–Kac sets and pointwise dual ergodicity
4.4 Exercises
5 Applications of infinite ergodic theory
5.1 Sumlevel sets for the continued fraction expansion, first investigations
5.2 ψmixing for the Gauss map and the Gauss problem
5.3 Pointwise dual ergodicity for the Farey map
5.4 Uniform and uniformly returning sets
5.5 Finer asymptotics of Lebesgue measure of sumlevel sets
5.6 Uniform distribution of the even Stern–Brocot sequence
5.7 Exercises
Bibliography
Index
Marc Kesseböhmer, Sara Munday, Bernd Otto Stratmann Inﬁnite Ergodic Theory of Numbers De Gruyter Graduate
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Marc Kesseböhmer, Sara Munday, Bernd Otto Stratmann
Inﬁnite Ergodic Theory of Numbers 
Mathematics Subject Classiﬁcation 2010 37Axx, 11B57, 11J70, 11J83, 60K05 Authors Dr. Sara Munday Università di Bologna Department of Mathematics Professor Dr. Marc Kesseböhmer Universität Bremen Department of Mathematics Professor Dr. Bernd O. Stratmann†
ISBN 9783110439410 eISBN (PDF) 9783110439427 eISBN (EPUB) 9783110430851 Library of Congress CataloginginPublication Data A CIP catalog record for this book has been applied for at the Library of Congress. Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliograﬁe; detailed bibliographic data are available on the Internet at http://dnb.dnb.de. © 2016 Walter de Gruyter GmbH, Berlin/Boston Typesetting: Konvertus, Haarlem, NL Printing and binding: CPI books GmbH, Leck ♾ Printed on acidfree paper Printed in Germany www.degruyter.com
Preface This book is intended to serve as an introduction to Inﬁnite Ergodic Theory for advanced undergraduate and PhD students and should be appropriate as a text for a seminar or reading course. We hope that some aspects of the presented material will be of interest also to researchers. The prerequisites we have assumed are a certain familiarity with measure theory and (to a lesser extent) basic concepts from functional analysis. For the rest, we have attempted to be as selfcontained as possible. One of the fundamental objectives of ergodic theory is to investigate dynamical systems from a measuretheoretic perspective. Central to this conception are invariant measures – measures which remain unchanged under the dynamics. In classical ergodic theory these measures are probability measures, whereas the topic of inﬁnite ergodic theory is systems which preserve inﬁnite measures. This seemingly small change engenders radically different results, as we will see. A systematic approach to this part of ergodic theory has been provided in the foundational work of Jon Aaronson [Aar97], which is still a major source for a coherent presentation and also guided us in the preparation of this graduate text. In addition to Aaronson’s book, we also found helpful the book by Dajani and Kraaikamp [DK02] and the survey article by Stefano Isola [Iso11], as well as unpublished lecture notes by Omri Sarig, Maximilian Thaler, and Roland Zweimüller. Our aim with this book is to illuminate various aspects of inﬁnite ergodic theory by using several concrete examples of dynamical systems that are strongly linked to number theory. We will use these examples to analyse some explicit questions (like the asymptotic behaviour of sumlevel sets) to illustrate not only the powerful methods from inﬁnite ergodic theory but also the strong connection between inﬁnite ergodic theory and renewal theory. Another theme in the book is elementary Diophantine approximation. We give some classical results for continued fractions in Chapter 1, and, with the intention of closing a circle of ideas, in Chapter 5 we show how the analysis of the sumlevel sets for the continued fraction expansion also gives rise to some further Diophantinetype results. The ﬁnal application in Chapter 5 is to establish a uniform law for the SternBrocot sequence contrasting a famous analogous result for the Farey sequence. The book is organised with chapters containing general theory sandwiched between chapters devoted more to our examples and to applications. The ﬁrst chapter consists of a little necessary background material and the introduction of all our main examples. It is followed by a chapter containing some standard results in ergodic theory and the beginnings of the theory for inﬁnite systems. Chapter 3 is then devoted to renewal theory and its application to certain piecewiselinear systems. We return to more general inﬁnite ergodic theory for Chapter 4, and ﬁnally, in Chapter 5 we see some applications of this theory to the Gauss and Farey systems.
VI  Preface
Unfortunately, our good friend, coauthor and colleague Bernd O. Stratmann died before the manuscript of this book was complete – we have tried our best to ﬁnalise the book also in his spirit. We would like to extend our heartfelt thanks for helpful conversations and for reading various sections to Kathryn Lorenz, Marco Lenci, Giampaolo Cristadoro, Henna Koivusalo, Andrei Ghenciu, Mauro Artigiani, Tushar Das, Niclas Technau, Arne Mosbach and Konstantin Schäfer. August 2016, Marc Kesseböhmer and Sara Munday
Contents Mathematical symbols  IX
1.2 1.2.1 1.2.2 1.2.3 1.2.4 1.2.5 1.3 1.3.1 1.3.2 1.4 1.4.1 1.4.2 1.4.3 1.4.4 1.4.5 1.5 1.5.1 1.5.2 1.6
Numbertheoretical dynamical systems  1 Continued fractions and Diophantine approximation  1 Continued fractions  1 Elementary Diophantine approximation: Hurwitz’s Theorem and badly approximable numbers  7 Topological Dynamical Systems  12 The Gauss map  15 Symbolic dynamics  16 A return to the Gauss map  19 Elementary metrical Diophantine analysis  21 Markov partitions for interval maps  26 The Farey map: deﬁnition and topological properties  30 The Farey map  30 Topological properties of the Farey map  34 Two further examples  40 The αLüroth maps  40 The αFarey maps  44 Topological properties of F α  48 Expanding and expansive partitions  51 Metrical Diophantinelike results for the αLüroth expansion  53 Notes and historical remarks  58 The Farey sequence  58 The classical Lüroth series  59 Exercises  61
2 2.1 2.1.1 2.2 2.3 2.3.1 2.3.2 2.3.3 2.3.4 2.3.5
Basic ergodic theory  64 Invariant measures  64 Invariant measures for the Gauss and αLüroth system  66 Recurrence and conservativity  69 The transfer operator  75 Jacobians and the change of variable formula  75 Obtaining invariant measures via the transfer operator  76 The Ruelle operator  79 Invariant measures for F , F α , G and L α  82 Invariant measures via the jump transformation  86
1 1.1 1.1.1 1.1.2
VIII  Contents
2.4 2.4.1 2.4.2 2.4.3 2.4.4 2.4.5 2.4.6 2.5 2.6
Ergodicity and exactness  88 Ergodicity of the systems G and L α  92 Ergodic theorems for probability spaces and consequences for the Gauss and αLüroth systems  96 Ergodic theorems for inﬁnite measures  103 Inducing  105 Uniqueness of the invariant measures for F and F α  109 Proof of Hopf’s Ratio Ergodic Theorem  112 Exactness revisited  115 Exercises  120
3 3.1 3.2 3.2.1 3.2.2 3.3
Renewal theory and αsumlevel sets  122 Sumlevel sets  122 Sumlevel sets for the αLüroth expansion  123 Classical renewal results  124 Renewal theory applied to the αsumlevel sets  132 Exercises  135
4 4.1
Inﬁnite ergodic theory  137 The functional analytic perspective and the Chacon–Ornstein Ergodic Theorem  137 Pointwise dual ergodicity  147 ψmixing, Darling–Kac sets and pointwise dual ergodicity  151 Exercises  157
4.2 4.3 4.4 5 5.1 5.2 5.3 5.4 5.5 5.6 5.7
Applications of inﬁnite ergodic theory  159 Sumlevel sets for the continued fraction expansion, ﬁrst investigations  159 ψmixing for the Gauss map and the Gauss problem  161 Pointwise dual ergodicity for the Farey map  167 Uniform and uniformly returning sets  168 Finer asymptotics of Lebesgue measure of sumlevel sets  173 Uniform distribution of the even Stern–Brocot sequence  177 Exercises  181
Bibliography  183 Index  188
Mathematical symbols ∀ ∃
#E :=
‘for all’. ‘exists’. n consecutive appearances of the symbol 0. 32 operator norm. 76 pnorm for p ∈ [1, ∞]. 137 empty set. ‘implies’. ‘equivalent’. cardinality of E. 18 ‘equal by deﬁnition’.
A⊆B A* [a, b] [a, b) (a, b] (a, b) A B α˚ αD αH Aut(C)
topological closure of A in R. 26 set of recurrent points in A. 105 closed interval. right open interval. left open interval. open interval. symmetric difference of the sets A and B. 70 Markov partition for L α . 40 dyadic partition for L α . 43 harmonic partition for L α . 42 182 group of automorphisms acting on C.
B BA Bα,N Bα,φ BN Bn
badly approximable numbers. 11 σalgebra B restricted to the set A. 106, 108 Nbadly αapproximable numbers. 54 φbadly αapproximable numbers. 56 subset of the set of badly approximable numbers. 11 nth member of the Stern–Brocot sequence. 33 n := ∞ n=0 b n s . 131
0n ·  · p ∅ =⇒ ⇐⇒
B(s) C C α (1 , . . . , k ) Cb (R) Cn
set of complex numbers. αLüroth cylinder set. 44 realvalued bounded continuous functions. 177 sumlevel set for the continued fraction expansion. 122
X  Mathematical symbols
CT CV C(x1 , . . . , x n ) 1 , . . . , xn ) C(x α (x1 , . . . , x n ) C T C
∆μ δx diam(A) dimH (A) dν /dμ d(ω, τ) DT dv E
E* EN ε F
conservative part with respect to the measurepreserving transformation T. 72 conservative part with respect to the contracting operator V. 141 Gauss cylinder. 19 Farey cylinder. 33 αFarey cylinder. 48 for a nonsingular conservative part with respect to T transformation T. 78 distribution function of a measure μ with support in [0, 1]. 36 Dirac measure in x. 177 Euclidian diameter of the set A. 28 Hausdorff dimension of the set A. 55 Radon–Nikodým derivative of ν with respect to μ. 76 distance between ω and τ with respect to the metric d. 17 dissipative part with respect to the measurepreserving transformation T. 72, 78, 141 greatest common divisor of {n > 0 : v n > 0}. 125 set of exceptional points of a countable Markov partition. 26 set of all ﬁnite nonempty words over the alphabet E. 17 set of all inﬁnite nonempty words over the alphabet E. 17 empty word; word having no letters. 17
Fn F+
Farey map given by x → x/(1 − x) on [0, 1/2], x → (1 − x)/x on (1/2, 1]. 30 jump transformation on (1/2, 1] of the Farey map F. 31 := φ(x)−1 f ◦ T j (x); induced version of f on A. 112 j=0 αFarey map. 44 jump transformation on A1 of F α . 46 inverse branches of the αFarey map F α , j = 0, 1. 45 inverse branches of F, j = 0, 1. 31 nth Farey sequence. 58 subset of nonnegative functions from the function space F . 66
G gcd GL2 (Z) Gn
Gauss map given by x → 1/x − 1/x . 15 greatest common divisor. 125 general linear group. 179 nth inverse branch of G. 15
hF h Fα
invariant density of νF with respect to λ. 82 invariant density of νF α with respect to λ. 83
F* f A (x) Fα F *α F α,j Fj
Mathematical symbols 
hG h Lα
invariant density of m G with respect to λ. 85 invariant density of m α with respect to λ. 85
I Iα Int(A)
set of irrational numbers. set of αirrational numbers. 42 interior of the set A. 40
Ji
:= d(λ ◦ T i )/dλ; Jacobian for the ith inverse branch of the interval map T. 81
κ±
Hölder and subHölder exponent. 49
L1 (μ) [ 1 , 2 , . . . , k ] α [1 , 2 , 3 , . . .]α Lα L α,n λ L∞ (μ) L(x)
space of μintegrable functions. 66, 137 αLüroth expansion. 42 αLüroth coding. 47 αLüroth map. 40 inverse branches of L α . 67 Lebesgue measure. 67 space of μessentially bounded functions. 66, 137 Lüroth map. 60
M m A M(B) M(B ) mG
Markov partition. 27, 40 measure m restricted to BA . 106 equivalence classes of Bmeasurable functions. 65 set of Bmeasurable functions. 65 Gauss measure; invariant λabsolutely continuous measure for G. 67 measure with density g with respect to the measure μ. 75, 140
μg N N0 ν (x) νF νF α ν μ O(x) ω n ω
[ω] ω∧τ
set of natural numbers. set of natural numbers including 0. Hurwitz constant for x. 10 invariant λabsolutely continuous measure for F. 82 invariant λabsolutely continuous measure for F α . 83 ν absolutely continuous with respect to μ. 75 forward orbit of x. 12 initial block (ω1 , ω2 , . . . , ω n ) of length n of ω ∈ E* . 17 length of a word ω. 17 cylinder set with respect to the ﬁnite word ω ∈ E* . 17 longest common initial block of two words ω and τ. 17
XI
XII  Mathematical symbols
p(x) PF P Fα PG φ φn p k /q k PT
ﬁrst passage time of x. 86 Ruelle operator for the map F. 82 Ruelle operator for the map F α . 84 Ruelle operator for the map G. 85 return time to the set A ∈ B. 105 := S An (φ). 112 kth convergent to the continued fraction. 2 Ruelle operator for the differential Markov interval map T. 81
Q Qα Q
set of rational numbers. set of αrational numbers. 42 Minkowski’s questionmark function. 35
R ρ(x) ρ α (x) (x) r(α) k rn
set of real numbers. hitting time under F of x to the interval (1/2, 1]. 31 hitting time under F α of x to the interval A1 . 45 := [1 , . . . , k ]α ; αLüroth convergent of x. 44 nth remainder. 4
sn σ
:= q n−1 /q n . 4 shift map of EN . 18 = Bn \ Bn−1 ; vertices of the Stern–Brocot Tree. 34 ergodic sums for the induced system with respect to h : A → R. 112
Sn
S An (h) T E* θ α (x)
T:X→Y
jump transformation on T with respect to E. 86 conjugating homeomorphism between ([0, 1], F α ) and ([0, 1], T). 49 measurable inverse branches for an interval map T. 80 denotes the map x → kx mod 1, k > 2, on R/Z. 14 transfer operator of T with respect to the measure μ. 76 Stern–Brocot intervals of order n. 33 a map from X to Y.
U ∨V Uε (x) Un
join of two collections of subsets of X. 27 open neighbourhood of x with diameter ε. 179 nth reﬁnement of a collection U . 27
V* var[a,b] f var f Vn ((v n )n≥1 , (w n )n≥0 )
dual operator of V. 138 variation of f over the interval [a, b]. 162 total variation of f on [0, 1]. 162 := ∞ k=n v k . 129 renewal pair. 124
Ti Tk μ = T T Tn
Mathematical symbols 
Wα Wα,φ
wlimn μ n WT
well αapproximable numbers. 55 φwell αapproximable numbers. 56 weak* limit of the sequence of measures (μ n ). 177 collection of all wandering sets for a map T. 69
X X* x 1 , x 2 , . . . α [x1 , x2 , x3 , . . .] x1 , x2 , x3 , . . . [x1 , x2 , . . . , x n ] [x1 , . . . , x k ] (X, B , μ, T) x {x} (X, B , μ) (X, T)
nonempty metric space. dual space; set of all continuous linear functionals f : X → R. 77 αFarey coding. 47 regular continued fraction expansion of an irrational number. 1 Farey coding. 32 regular continued fraction expansion of a rational number. 2 periodic continued fraction expansion. 16 measuretheoretic dynamical system. 64 the greatest integer not exceeding x. 5 fractional part of x. 5 σﬁnite measure space. 64 (topological) dynamical system. 1
Z
set of integers.
XIII
1 Numbertheoretical dynamical systems Throughout this book, a (topological) dynamical system (X, T) means simply a nonempty metric space X and a continuous map T : X → X. In this chapter, we introduce various dynamical systems that generate real number expansions. Such systems will be referred to as numbertheoretic dynamical systems. The examples we will consider here are mainly constructed over the unit interval. For further simple examples of numbertheoretic dynamical systems, including that of the map that gives rise to the familiar decimal expansion, we refer to Dajani and Kraaikamp [DK02].
1.1 Continued fractions and Diophantine approximation In this chapter, the main goal is to introduce two wellstudied numbertheoretic dynamical systems, namely, the Gauss map and the Farey map. Both of these maps are related to the continued fraction expansion, which we introduce below. We give here a very succinct introduction, but for more details there are several good books available, for instance the classical text by Khintchine [Khi64] or the more modern approach given by Rockett and Szűsz [RS92].
1.1.1 Continued fractions An expression of the form 1
[x1 , x2 , x3 , . . .] :=
1
x1 + x2 +
,
(1.1)
1 x3 + · · ·
where each x i , for i ∈ N, is a positive integer, is called a regular continued fraction expansion (or simply a continued fraction). We will refer to the numbers x1 , x2 , x3 , . . . as the elements of the continued fraction. The number of elements of the continued fraction may be ﬁnite or inﬁnite. In the ﬁrst case, we will say we have a ﬁnite continued fraction and in the second case we will say that we have an inﬁnite continued fraction. A ﬁnite continued fraction is the result of a ﬁnite number of rational operations and
2  1 Numbertheoretical dynamical systems
hence it represents the rational number given by 1
[x1 , . . . , x n ] := x1 +
.
1 x2 + · · · +
(1.2)
1 xn
Notice that for x n ≥ 2, the continued fractions [x1 , . . . , x n − 1, 1] and [x1 , . . . , x n ] represent the same number. It is typical to use only the latter expression, but on occasion we ﬁnd it helpful to have the option of using either. We cannot immediately assign a value to an inﬁnite continued fraction, so, for the time being, it should be thought of only as a formal notation, akin to that for an inﬁnite series. In the theory of continued fractions, a particularly important role is played by the initial segments of each (ﬁnite or inﬁnite) continued fraction. For a given continued fraction [x1 , x2 , x3 , . . .], we consider the sequence of rational numbers ([x1 , . . . , x k ])k≥1 . For each k ∈ N, we will write p k /q k := [x1 , . . . , x k ], where the positive integers p k and q k are required to be coprime. Then p k /q k will be called the kth convergent to the continued fraction [x1 , x2 , x3 , . . .]. In particular, p 1 x2 p1 1 = and 2 = = . q1 x1 q2 x1 + 1/x2 x1 x2 + 1 For a ﬁnite continued fraction [x1 , . . . , x n ] with n elements, we have pn = [x1 , . . . , x n ]. qn In this case, there are only n convergents. Each inﬁnite continued fraction has an inﬁnite sequence of convergents. The justiﬁcation for this terminology will come in Proposition 1.1.3, but ﬁrst let us give the recurrence relations that describe the formation of the convergents. Here, we make the further deﬁnition that p0 := 0 and q0 := 1. Theorem 1.1.1. For each n ∈ N, we have that (a) p n+1 = x n+1 p n + p n−1 , (b) q n+1 = x n+1 q n + q n−1 , (c) q n p n−1 − p n q n−1 = (−1)n . Proof. The statements in (a) and (b) can be proved by induction; we leave this as an exercise for the reader. For part (c), multiplying part (a) by q n and part (b) by p n , then subtracting the ﬁrst from the second yields q n+1 p n − p n+1 q n = −(q n p n−1 − p n q n−1 ).
1.1 Continued fractions and Diophantine approximation
 3
It then suffices to notice that q1 p0 − p1 q0 = −1 to complete the proof of the theorem. Part (c) of Theorem 1.1.1 has the following immediate and useful corollary. Corollary 1.1.2. (a) For all n ≥ 1, (−1)n p n−1 p n − = . q n−1 q n q n q n−1 (b) For all n ≥ 2, p n−2 p n (−1)n−1 x n − = . q n−2 q n q n q n−2 This corollary allows us to reach important conclusions about the sequence of convergents to a continued fraction. Speciﬁcally, we have the following result. Proposition 1.1.3. The sequence of convergents p n /q n n≥1 of the continued fraction [x1 , x2 , x3 , . . .] satisﬁes the following four properties: (a) The sequence p2n /q2n n≥1 of even convergents is increasing. (b) The sequence p2n−1 /q2n−1 n≥1 of odd convergents is decreasing. (c) Every convergentof odd order is greater than every convergent of even order. p n+1 p n = 0. − (d) lim qn n→∞ q n+1 Proof. The statements in parts (a) and (b) follow directly from Corollary 1.1.2 (b). For part (c), observe that by Corollary 1.1.2 (a) we have that every odd convergent is greater than the directly preceding even convergent. Thus, p2k+1 /q2k+1 is greater than all of the smallerindex even convergents: p2 /q2 , p4 /q4 , . . . , p2k /q2k . Suppose that there exists some convergent p2m /q2m , where m > k, such that p2m p2k+1 > . q2m q2k+1 But then, p2m p2m+1 p2k+1 < < , q2m q2m+1 q2k+1 where the last inequality comes again from Corollary 1.1.2 (a). This contradiction ﬁnishes the proof of part (c). Finally, for part (d), it suffices to show that qn ≥ 2
n−1 2
.
Indeed, directly from Theorem 1.1.1 (b), we have that q n = x n q n−1 + q n−2 ≥ q n−1 + q n−2 ≥ 2q n−2 .
(1.3)
4  1 Numbertheoretical dynamical systems
Thus, repeated applications of the above inequality yields q2n ≥ 2n q0 = 2n and q2n+1 ≥ 2n q1 ≥ 2n . This ﬁnishes the proof. Using the above proposition, it then makes sense to say that the value of the inﬁnite continued fraction [x1 , x2 , x3 , . . .] is equal to the real number that is the limit of the sequence of convergents associated with it. Deﬁnition 1.1.4. For x = [x1 , x2 , x3 , . . .] ∈ (0, 1] and n ∈ N, let r n and s n be deﬁned as follows: 1 q r n := and s n := n−1 . qn [x n , x n+1 , x n+2 , . . .] The number r n is called the nth remainder of x. Note that for x = [x1 , x2 , x3 , . . .] and n ∈ N, the following recurrence relation holds: rn = xn +
1 . r n+1
Since q n = x n q n−1 + q n−2 , we have that s−1 n =
qn 1 = xn + . q n−1 q n−1 /q n−2
Clearly, this process may be continued until q1 /q0 = x1 is reached. Therefore, s n = [x n , x n−1 , . . . , x1 ]. Theorem 1.1.5. For x = [x1 , x2 , x3 , . . .] ∈ (0, 1] and n ∈ N, we have that x=
p n r n+1 + p n−1 . q n r n+1 + q n−1
Proof. We will prove the theorem by induction. For n = 1, on recalling the deﬁnitions p0 := 0 and q0 := 1, we deduce that r +0 p1 r2 + p0 1 = 2 = x. = q1 r2 + q0 x1 r2 + 1 x1 + 1/r2 Now assume that the statement is true for some n ∈ N. Then, in light of Theorem 1.1.1 (c), we obtain that p n r n+1 + p n−1 p n (x n+1 + 1/r n+2 ) + p n−1 = q n r n+1 + q n−1 q n (x n+1 + 1/r n+2 ) + q n−1 p n x n+1 r n+2 + p n + p n−1 r n+2 = q n x n+1 r n+2 + q n + q n−1 r n+2 p n+1 r n+2 + p n = . q n+1 r n+2 + q n
x=
1.1 Continued fractions and Diophantine approximation
 5
This theorem allows us to calculate the distance between a real number x = [x1 , x2 , x3 , . . .] and any of its convergents. Corollary 1.1.6. For x = [x1 , x2 , x3 , . . .] ∈ (0, 1] and n ∈ N, we have that 1 1 1 x − p n = ≤ . < q n q2n (r n+1 + s n ) x n+1 q2n q2n Proof. Using Theorem 1.1.5 and Theorem 1.1.1 (c), we infer that x − p n = p n r n+1 + p n−1 − p n q n q n r n+1 + q n−1 q n p n q n r n+1 + p n−1 q n − p n q n r n+1 − p n q n−1 = (q n r n+1 + q n−1 )q n qn p − p n q n−1 1 . = = 2n−1 q (r + s n ) q2 (r + sn ) n
n+1
n
n+1
The ﬁrst inequality follows since x n+1 < r n+1 , the second since x n+1 ≥ 1. We end this section with the important result that every real number admits a continued fraction expansion. The basis of the proof is simply the Euclidean algorithm, the algorithm for ﬁnding the greatest common divisor of two integers. Before stating the theorem, recall that for each positive real number x the notation x denotes the greatest integer not exceeding x and {x} denotes the fractional part of x, that is, x = x + { x }. Theorem 1.1.7. To every real number x ∈ (0, 1] there corresponds a continued fraction with value equal to x. This continued fraction is inﬁnite if and only if x is irrational. Moreover, every irrational number has a unique continued fraction expansion. Proof. If x = 1, then the continued fraction [1] := 1/1 is the one sought. So, suppose that x ∈ (0, 1). Then set r1 := 1/x and deﬁne x1 := r1 , so that x=
1 . x1 + {r1 }
If r1 is an integer, we are ﬁnished. Otherwise, if {r1 } ∈ (0, 1), then we set r2 := 1/{r1 }, so that x=
1 . x1 + 1/r2
Suppose that the numbers r1 , r2 , . . . , r n have been deﬁned and, if r n is not an integer, let x n := r n and set r n+1 := 1/{r n }. Then we obtain the relation rn = xn +
1 . r n+1
6  1 Numbertheoretical dynamical systems
If the number x happens to be a rational number, then each r n as deﬁned above will also be rational. In this case, the process must stop after a ﬁnite number of steps. Indeed, if r n = a/b and r n is not already an integer, then r n+1 =
1 1 b = = . r n − x n a/b − x n a − x n b
Therefore, r n+1 has a smaller denominator than r n and it follows that if we consider the sequence r1 , r2 , r3 , . . ., we must eventually come to an integer. If that integer is r k , then the number x is represented by the ﬁnite continued fraction [x1 , x2 , . . . , x k ], where x k := r k > 1 (if r k = 1, then r k−1 must also be an integer, so we replace the two ﬁnal terms x k−1 and 1 by the single integer x k−1 + 1). If x is irrational, then each r n must also be irrational and the abovedescribed process will not terminate. Then, by the deﬁnition of x k and Proposition 1.1.3, where p n /q n := [x1 , x2 , . . . , x n ] as before, we have for each n ≥ 1 that p p2n < x < 2n+1 . q2n q2n+1 Thus, it follows by Proposition 1.1.3 (d) that lim
n→∞
pn = x. qn
This means that the continued fraction [x1 , x2 , x3 , . . .] has as its value the given irrational number x. It only remains to show that each inﬁnite expansion is unique (recall that the ﬁnite expansions are not unique, since the value of [x1 , . . . , x n ] is equal to the value of [x1 , . . . , x n − 1, 1], for x n ≥ 2). So, ﬁx x := [x1 , x2 , x3 , . . .] and x := [x1 , x2 , x3 , . . .], and suppose that n + 1 := min{k ∈ N : x k = xk }. Since the ﬁrst n approximants of x and x must coincide, but the remainders r n+1 of x and rn+1 of x differ, it follows from Theorem 1.1.5 and the strict monotonicity of r → (p n r + p n−1 )/(q n r + q n−1 ) that x=
p n r n+1 + p n−1 p n rn+1 + p n−1 = = x . q n r n+1 + q n−1 q n rn+1 + q n−1
This proves uniqueness of the continued fraction expansion for all irrational numbers from the unit interval.
1.1 Continued fractions and Diophantine approximation
 7
1.1.2 Elementary Diophantine approximation: Hurwitz’s Theorem and badly approximable numbers Let us now turn to some numbertheoretic applications of continued fractions. One useful property of the continued fraction expansion of an irrational number is that it allows us to approximate the value of this number by rational numbers to within any desired degree of accuracy. These approximations are given by the convergents (this is why the convergents are sometimes also referred to as approximants). The larger n ∈ N is chosen, the closer the rational number p n /q n comes in value to x = [x1 , x2 , x3 , . . .]. The results presented below all concern the closeness, in absolute value, of the convergents to the irrational number they are approximating. These sorts of questions fall into the area of mathematics known as Diophantine approximation. This is a vast and extremely active research area with many interesting and deep open problems. Theorem 1.1.8. For all irrational numbers x = [x1 , x2 , x3 , . . .] and for all n ∈ N, we have that the inequality x − p i < 1 q i 2q2i is fulﬁlled for at least one element i ∈ {n, n + 1}. Proof. By way of contradiction, suppose that the statement in the theorem is false. This means that there exists some n ∈ N such that the inequality x − p i ≥ 1 q i 2q2i holds simultaneously for i = n and i = n + 1. Since, in light of Corollary 1.1.6, we have that x − p i /q i  = (q2i (r i+1 + s i ))−1 , this is equivalent to r i+1 + s i ≤ 2, for i = n, n + 1. Let us consider each case separately. (i) For i = n, we can rewrite this as 2 ≥ r n+1 + s n = x n+1 + 1/r n+2 + s n and hence, 1 1 ≤ 2 − (x n+1 + s n ) = 2 − . r n+2 s n+1 (ii) For i = n + 1, we obtain that r n+2 ≤ 2 − s n+1 .
8  1 Numbertheoretical dynamical systems Combining (i) and (ii), we infer that 1 ≤ 4 − 2s n+1 − 2s−1 n+1 + 1. It therefore follows that 0 ≤ 2 − s n+1 − s−1 , which ﬁnally implies that n+1 0 ≥ (s n+1 − 1)2 . Since for each n ∈ N we have that s n+1 = 1, this contradiction ﬁnishes the proof. Theorem 1.1.9 (Hurwitz’s Theorem I). For all irrational numbers x = [x1 , x2 , x3 , . . .] and for all n ∈ N, we have that the inequality x − p i < √ 1 qi 5 q2i is fulﬁlled for at least one element i ∈ {n, n + 1, n + 2}. Proof. As in the proof of the previous theorem (with 2 replaced by way of contradiction that for each i ∈ {n, n + 1, n + 2} we have that r i+1 + s i ≤
√
√
5), assume by
5.
Proceeding for i = n and i = n + 1 as in (i) and (ii) in the previous proof, we derive the inequality √
s2n+1 − 5s n+1 + 1 ≤ 0.
(1.4)
Analogously, by considering in turn i = n + 1 and i = n + 2, we deduce that √
s 2n+2 − 5s n+2 + 1 ≤ 0.
(1.5) √
By the quadratic formula, (1.4) and (1.5) yield, where γ := ( 5 + 1)/2 and γ * := √ ( 5 − 1)/2, γ * < s i < γ , for i = n + 1, n + 2.
(1.6)
The strict inequality follows from the fact that γ and γ * are both irrational and s n is rational. Using this, we obtain that s n+2 =
1 1 1 ≤ < = γ* , x n+2 + s n+1 1 + s n+1 1 + γ *
which contradicts (1.6). This ﬁnishes the proof. √
Remark 1.1.10. In this context, the number 1/ 5 is called the Hurwitz number. Sometimes this number is called the Hurwitz constant for the golden mean, and thus there are many others in the sense of Deﬁnition 1.1.13 (b). √
The number γ := ( 5 + 1)/2 which appears in the above proof is known as the golden mean (or, sometimes, the golden ratio). There are a great many commonlyheld beliefs
1.1 Continued fractions and Diophantine approximation
 9
about the golden mean and why it is supposed to be so interesting and/or important; unfortunately, a lot of these are simply wrong (see [Mar92]). However, the continued fraction expansion of γ does give a clue as to why this particular number turns up so often. Observe that γ is one of the two roots of the equation x2 − x − 1 = 0. Writing this another way, we have that γ = 1+
1 γ
.
Then, substituting for the γ which appears on the righthand side of the above equality, we obtain that γ = 1+
1 1+
1
.
γ
Clearly this process can be repeated inﬁnitely often, to yield the continued fraction expansion γ = 1 + [1, 1, 1, . . .], where the fractional part consists of inﬁnitely many ones. The other number γ * appearing in the proof of Theorem 1.1.9 is simply equal to γ − 1 = [1, 1, 1, . . .]. Since we are mostly concerned in this book with numbers in the unit interval, we shall, in a slight abuse of terminology, also refer to this number as the golden mean. √ The next theorem shows that the constant 1/ 5 that appears in Theorem 1.1.9 cannot be improved for arbitrary irrational numbers. The proof again relies upon the golden mean. Theorem 1.1.11 (Hurwitz’s Theorem II). For the golden mean γ * we have that the inequality * pn C γ − ≤ q n q2 n
√
is satisﬁed for at most ﬁnitely many convergents p n /q n if and only if C < 1/ 5. Proof. Firstly, note that since γ * = [1, 1, 1, . . .], we have for the remainders that r n := x n + [x n+1 , x n+2 , . . .] is in this case given by r n = 1 + [1, 1, 1, . . .] = 1/γ * , for all n ∈ N. Secondly, note that s n = [x n , x n−1 , . . . , x1 ] = γ * + ([x n , . . . , x1 ] − [x n+1 , x n+2 , . . .]) = γ * + δ n , where for δ n we have that limn→∞ δ n = 0. Hence, from these two observations it follows that r n+1 + s n =
1 γ*
√
+ γ* + δn =
5+1 + 2
√
√ 5−1 + δn = 5 + δn . 2
10  1 Numbertheoretical dynamical systems
Now, if C
0, then
1 1 1 γ − p n = √ √ ≤ , = q n q2n (r n+1 + s n ) q2n ( 5 + δ n ) q2n ( 5 + ρ) where the last inequality can only be fulﬁlled for ﬁnitely many n, due to the fact that √ √ 5 + ρ < 5 + δ n can be satisﬁed for at most ﬁnitely many n. Corollary 1.1.12. For each irrational number x ∈ (0, 1], the inequality x − p n ≤ K q n q2n √
is fulﬁlled for inﬁnitely many reduced p n /q n as long as K ≥ 1/ 5. We now want to investigate some further results in the vein of Hurwitz’s Theorems and Corollary 1.1.12 given above. Before that, it will be useful to make some further deﬁnitions. Deﬁnition 1.1.13. (a) Let c denote a ﬁxed positive real number. An irrational number x is said to be capproximable if and only if the inequality x − p < c q q2 is satisﬁed for inﬁnitely many reduced p/q. (b) To each irrational number x we associate a nonnegative real number ν (x), deﬁned by ν (x) := inf {c > 0 : x is capproximable},
and called the Hurwitz constant for x. (c) Two irrational numbers x and y are called equivalent if and only if there exist k, ∈ N such that the kth remainder of x is equal to the th remainder of y. In other words, the continued fraction expansions of x and y are the same if we discard a ﬁnite initial block from each. (d) Irrational numbers equivalent to the golden mean are called noble numbers. So, for every irrational number x it follows directly from Theorem 1.1.9 (Hurwitz’s √ Theorem I) that ν (x) ≤ 1/ 5. From Hurwitz’s Theorem II and the fact that if x and y are equivalent then ν (x) = ν (y) (see Exercise 1.6.6), it follows that if an irrational number √ x is noble, then ν (x) = 1/ 5.
1.1 Continued fractions and Diophantine approximation
 11
Theorem 1.1.14. Let N be some ﬁxed positive integer. If x = [x1 , x2 , x3 , . . .] is irrational such that for some n ∈ N we have that the inequality x − p i > √ 1 q i q2 N 2 + 4 i is fulﬁlled for all i ∈ {n, n + 1, n + 2}, then it follows that x n+2 < N. √
Proof. We proceed as in the proofs of Theorems 1.1.8 and 1.1.9, with 2 and 5, √ respectively, now replaced by N 2 + 4. In this way, considering in sequence i = n and i = n + 1, we derive s2n+1 − N 2 + 4 s n+1 + 1 < 0. Also, by considering i = n + 1 and i = n + 2, we similarly derive the inequality s 2n+2 − N 2 + 4 s n+2 + 1 < 0. Then, using the quadratic formula, we obtain that √
N2 + 4 − N < s i and s−1 i < 2
√
N2 + 4 + N , for i = n + 1, n + 2. 2
Using this, we then have that x n+2 = s n+1 + x n+2 − s n+1 = s−1 n+2 − s n+1 √
0
In other words, an irrational number x belongs to the set BN if and only if it is equivalent to some y = [y1 , y2 , . . .] such that y i < N for all i ∈ N. The following proposition clariﬁes why the elements in B are called “badly approximable”.
12  1 Numbertheoretical dynamical systems
Proposition 1.1.16. (a) Fix N ∈ N. If x is an irrational number in [0, 1] such that x ∈/ BN , then x − p n ≤ √ 1 q n q2n N 2 + 4 √
is fulﬁlled for inﬁnitely many n ∈ N. In other words, we have that ν (x) ≤ 1/ N 2 + 4. (b) For each x ∈ B there exists a constant C > 0 such that for all n ∈ N we have x − p n > C . q n q2n Proof. The ﬁrst part is an immediate consequence of Theorem 1.1.14. For the second part, ﬁx x = [x1 , x2 , x3 , . . .] ∈ B. Then there exist numbers M and m0 such that x n < M for all n ≥ m0 . Using this, for such an n we derive the inequality r n+1 + s n = x n+1 + [x n+2 , x n+3 , . . .] + [x n , . . . , x1 ] < M + 1 + 1 = M + 2 and hence
1 x − p n > , for all n ≥ m0 . q n q2n (M + 2)
For each of the ﬁnitely many n < m0 we have that there exists a number c n > 0 such that x − p n > c n . q n q2n If we then deﬁne C := min{1/(M + 2), c0 , c1 , . . . , c m0 −1 }, the result follows. Notice that in the above proof, it can be seen that the constant C depends on the value of N for which x ∈ BN . In that sense, one might say that the noble numbers are the “worst approximable” numbers.
1.2 Topological Dynamical Systems Throughout this book, a (topological) dynamical system (X, T) is a continuous map T : X → X of a nonempty metric space X. The ﬁrst objective in the study of a dynamical system is the consideration of the orbits of points of X. The (forward) orbit O(x) is the set O(x) := {T n (x) : n ≥ 0}.
For certain types of point, which we deﬁne below, the orbit is very easy to determine.
1.2 Topological Dynamical Systems

13
Deﬁnition 1.2.1. Let T : X → X be a dynamical system. (a) If T(x) = x, then x is called a ﬁxed point for T. (b) A point x ∈ X is said to be periodic for T if T n (x) = x for some n ≥ 1. Then n is called a period of x. The smallest period of a periodic point x is called the prime period of x. Note that the ﬁxed points for T are the periodic points with prime period n = 1. (c) A point x ∈ X is said to be preperiodic for T, if T k (x) is a periodic point for some k ≥ 1. In other words, x is preperiodic if T k+n (x) = T k (x) for some n ≥ 1. Suppose that we are given two dynamical systems (X, T) and (Y , S). It is desirable to have conditions under which these two systems should be considered dynamically equivalent, that is, as in some dynamical sense, “the same”. The sense we are after is that their orbits should behave in the same way. The following deﬁnition does exactly this job. Deﬁnition 1.2.2. Two dynamical systems (X, T) and (Y , S) are said to be topologically conjugate if there exists a homeomorphism h : X → Y, called a conjugacy map, such that h ◦ T = S ◦ h. In other words, (X, T) and (Y , S) are topologically conjugate if there exists a homeomorphism h such that the following diagram commutes:
X
T
X
h Y
h S
Y
Remark 1.2.3. 1. Topological conjugacy deﬁnes an equivalence relation on the space of all topological dynamical systems. 2. If two dynamical systems (X, T) and (Y , S) are topologically conjugate via a conjugacy map h, then all of their corresponding iterates are topologically conjugate by means of h. That is, h ◦ T n = S n ◦ h for all n ≥ 1. Therefore, there exists a onetoone correspondence between the orbits of T and those of S. Suppose that we are given two topologically conjugate dynamical systems, (X, T) and (Y , S). If T(x) = x, it follows that h(x) = h ◦ T(x) = S(h(x)) and so h(x) is a ﬁxed point of the map S. Thus, there is a onetoone correspondence between the ﬁxed points of T and the ﬁxed points of S. In particular, if the number of
14  1 Numbertheoretical dynamical systems
ﬁxed points of T and S are not equal, the systems cannot be topologically conjugate. The number of ﬁxed points is an example of a topological conjugacy invariant. The number of periodic points of each prime period is similarly seen to be a topological conjugacy invariant. Example 1.2.4. (a) Consider the two maps of the unit circle T2 : R/Z → R/Z and T3 : R/Z → R/Z, deﬁned by setting T 2 (x) := 2x (mod 1) and T3 (x) := 3x (mod 1). The map T2 has a single ﬁxed point in 0 and the set of ﬁxed points of the map T3 is {0, 1/2}. Therefore, the number of ﬁxed points is not the same and so, the sytems (R/Z, T2 ) and (R/Z, T3 ) cannot be topologically conjugate. √ (b) Let f : [0, 1] → [0, 1] be given by f (x) := x and g : [0, 1] → [0, 1] be given by g(x) := 3x(1 − x). Then the set of ﬁxed points of f is {0, 1}, whereas the set of ﬁxed points of g is {0, 2/3}. However, although they have the same number of ﬁxed points, there is no topological conjugacy map between ([0, 1], f ) and ([0, 1], g). This can be seen by noting that every homeomorphism h : [0, 1] → [0, 1] is either strictly increasing or strictly decreasing. In order to have h be a conjugating homeomorphism between f and g, we would have to have h(0) := 0 and h(1) := 2/3 or vice versa. But this is simply not possible for a strictly monotonic function that also has to map [0, 1] onto [0, 1]. In the next deﬁnition, we give a weaker notion than that of topological conjugacy. Deﬁnition 1.2.5. Let (X, T) and (Y , S) be two dynamical systems. If there exists a continuous surjection h : X → Y which satisﬁes h ◦ T = S ◦ h, then S is called a (topological) factor of T. The map h is thereafter called a factor map. In general, the existence of a factor map between two systems is not sufficient to make them topologically conjugate. Nonetheless, if (Y , S) is a factor of (X, T), then every orbit of T is projected to an orbit of S. As every factor map is by deﬁnition surjective, this means that all of the orbits of S have an analogue in T. However, as a factor map may not be injective, more than one orbit of T may be projected to the same orbit of S. In other words, some orbits of S may have more than one analogue in T. Therefore, the dynamical system (X, T) can in this sense be thought of as being more “complicated” than the factor (Y , S). In the following subsection, we introduce the ﬁrst of the examples of numbertheoretic dynamical systems that will be used to illustrate various concepts throughout the book. We will soon see that this map is related to the continued fraction expansion introduced in Section 1.1.1.
1.2 Topological Dynamical Systems

15
1.2.1 The Gauss map Deﬁnition 1.2.6. Let G : [0, 1] → [0, 1] be deﬁned by ⎧ ⎨1 − 1 for 0 < x ≤ 1; x G(x) := x ⎩ 0 for x = 0. The map G is referred to as the Gauss map and its graph is shown in Fig. 1.1. Let us also deﬁne here the inverse branches G n : (0, 1) → (1/(n + 1), 1/n) of the Gauss map. These are given, for each n ∈ N, by G n (x) :=
1 . x+n
In the following proposition, we shall show how the map G acts on points in the unit interval written in terms of their continued fraction expansion. Proposition 1.2.7. If x = [x1 , x2 , x3 , . . .] ∈ [0, 1], where the continued fraction expansion of x is either inﬁnite or ﬁnite and consists of at least two elements, then G(x) = [x2 , x3 , x4 . . .]. Moreover, if x = [x1 ], then G(x) = 0. Proof. If x = [x1 , x2 , x3 , . . .], then directly from the deﬁnition of G, we have that 1 1 1 = x1 + G(x) = − − x1 = [x2 , x3 , . . .]. 1 x x x2 + x3 + . . . If x = [x1 ] = 1/x1 , then G(x) = x1 − x1 = 0. G(x) 1
0
1 2
1
x
Fig. 1.1. The Gauss map G : [0, 1] → [0, 1], G(x) :=
1 1 − for x ∈ (0, 1] and G(0) := 0. x x
16  1 Numbertheoretical dynamical systems
Using the latter proposition, we can very easily identify the ﬁxed points and periodic points for the map G. First of all, by deﬁnition, G has a ﬁxed point at 0. There are countably many more ﬁxed points, given by the points x = [n, n, n, . . .], for n ∈ N. For n = 1, this is the golden mean, γ * . For n = 2, we have the ﬁxed point √ 2 − 1 = [2, 2, 2, . . .]. The periodic points for G are simply the periodic continued fractions, that is, points with continued fraction expansions of the form [x1 , . . . , x k ] := [x1 , . . . , x k , x1 , . . . , x k , x1 , . . .], with the block x1 , . . . , x k repeating inﬁnitely many times. It remains to describe the preperiodic points. The preperiodic points of the trivial ﬁxed point 0 are the rational numbers. The preperiodic points for all other ﬁxed points are numbers of the form x = [x1 , . . . , x k , n, n, n, . . .], that is, with ﬁnitely many elements that can take any value, before inﬁnitely many of the same element n, for some n ∈ N. In particular, the preperiodic points for the ﬁxed point γ * are the set of noble numbers (cf. Deﬁnition 1.1.13 (d)). Finally, the preperiodic points for the remaining periodic points are those points in the unit interval with eventually periodic continued fraction expansions, that is, numbers of the form x = [x1 , . . . , x m , x m+1 , . . . , x m+k ]. These eventually periodic expansions are the subject of the following theorem. Before stating the theorem, recall that a quadratic surd is an irrational root of a quadratic equation with integer coefficients. These are also called algebraic numbers of degree two. Theorem 1.2.8 (Lagrange’s Theorem). Every quadratic surd has an eventually periodic continued fraction expansion; conversely, every eventually periodic continued fraction represents a quadratic surd. Proof. See, for instance, Theorem 28 in Khintchine [Khi64]. From this point on, we are most of the time no longer interested in the rational numbers, as they are simply the countable set of preperiodic points for the trivial ﬁxed point 0. In other words, from here on all continued fraction expansions are assumed to be inﬁnite. We will denote the irrational points in the unit interval by I := [0, 1] \ Q.
1.2.2 Symbolic dynamics Recall Proposition 1.2.7, where we showed how the Gauss map acts on the continued fraction expansions. In this section, we will introduce some more general theory which will illuminate the idea behind this proposition, namely, the beginnings of the theory of symbolic dynamics and its connections to topological dynamical systems which admit a Markov partition (see Section 1.2.5 for the deﬁnition of Markov partitions in the context of interval maps). We will only provide a very short introduction, and for more details we refer, for example, to Lind and Marcus [LM95].
1.2 Topological Dynamical Systems

17
Deﬁnition 1.2.9. (a) Let E be a countable, possibly inﬁnite set containing at least two elements. The set E will be referred to as an alphabet. The elements of E will be called letters or symbols. (b) For each n ∈ N we shall denote by E n the set of all words comprising n letters from the alphabet E. For later convenience, we also denote the empty word (that is, the word having no letters) by ε. For instance, if E = {0, 1} then E1 = E and E2 = {(0, 0), (0, 1), (1, 0), (1, 1)}, whereas E3 = {(0, 0, 0), (1, 0, 0), (0, 1, 0), (0, 0, 1), (1, 1, 0), (1, 0, 1), (0, 1, 1), (1, 1, 1)}. (c) We will denote by E* := n∈N E n the set of all ﬁnite nonempty words over the alphabet E. The set of all inﬁnite words will be denoted by EN . In other words, EN := ω = (ω i )∞ i=1 : ω i ∈ E for all i ∈ N . (d) We deﬁne the length ω of a word ω to be the number of letters of which it consists. That is, for every ω ∈ E* , the length of ω is the unique n ∈ N such that ω ∈ E n . For ω ∈ EN , we have that ω = ∞. Furthermore, ε = 0. (e) If ω ∈ E* ∪ EN and n ∈ N does not exceed the length of ω, we deﬁne the initial block ωn to be the initial nlength word of ω, that is, the subword ω1 ω2 . . . ω n . (f) Given two words ω, τ ∈ E* ∪ EN , we deﬁne their wedge ω ∧ τ ∈ {ε} ∪ E* ∪ EN to be their longest common initial block. For example, if we again let E = {0, 1} and we have words ω = (0, 0, 1, 0, 1, . . .) and τ = (0, 0, 1, 1, 0, . . .), then ω ∧ τ = (0, 0, 1). On the other hand, if γ = (1, 0, 1, 0, 1, . . .) then ω ∧ γ = ε. Of course, if two (ﬁnite or inﬁnite) words ω and τ are equal, then ω ∧ τ = ω = τ. Let us now introduce a metric on the space EN which reﬂects the idea that two words are close if they share a long initial block. In other words, the longer their common initial subword, the closer two words are. We leave the proof that this genuinely deﬁnes a metric, in fact an ultrametric, as an exercise (see Exercise 1.6.10). Deﬁnition 1.2.10. Let the metric d : EN × EN → [0, 1] be deﬁned by d(ω, τ) := 2−ω∧τ . If ω and τ have no common initial block, then ω ∧ τ = ε. Thus, ω ∧ τ = 0 and d(ω, τ) = 1. On the other hand, if the two inﬁnite words ω and τ are such that ω = τ, then ω ∧ τ = ∞ and we deﬁne (1/2)+∞ := 0. Deﬁnition 1.2.11. Given a ﬁnite word ω ∈ E* , the cylinder set [ω] generated by ω is the set of all inﬁnite words with initial block ω, that is, [ω] := {τ ∈ EN : τω = ω} = {τ ∈ EN : τ i = ω i for all 1 ≤ i ≤ ω}. We now introduce the shift map, which is deﬁned by dropping the ﬁrst letter of each word and shifting all the remaining letters one place to the left.
18  1 Numbertheoretical dynamical systems Deﬁnition 1.2.12. The shift map σ : EN → EN is deﬁned by setting σ(ω) = σ((ω i )i≥1 ) := (ω i+1 )i≥1 . That is, σ((ω1 , ω2 , ω3 , . . .)) := (ω2 , ω3 , ω4 , . . .) The shift map is # Etoone on EN , where # E denotes the cardinality of E. In other words, each inﬁnite word has # E preimages under the shift map. In particular, if E is countably inﬁnite, it follows that σ is countabletoone. Indeed, given any letter e ∈ E and any inﬁnite word ω ∈ EN , the concatenation eω = (e, ω1 , ω2 , ω3 , . . .) of e with ω is a preimage of ω under the shift map, since σ(eω) = ω. Note that the shift map is continuous, since two words that are close share a long initial block, and thus their images under the shift map, which result from dropping their ﬁrst letters, will also share a long initial block. More precisely, whenever d(ω, τ) < 1, that is, whenever ω ∧ τ ≥ 1, we have that d(σω, στ) = 2−σω∧ στ = 2−ω∧τ+1 = 2 · 2−ω∧τ = 2d(ω, τ). It is evident that if ω = τ, then both d(ω, τ) and d(σ(ω), σ(τ)) are equal to zero. The system (EN , σ) will be referred to as the full shift system. Let us now consider another useful construction, that of subshifts. These are restrictions of the shift map to certain closed and shiftinvariant subsets of EN and are easiest to deﬁne in terms of an incidence matrix, that is, an (E × E)matrix consisting entirely of 0s and 1s. Deﬁnition 1.2.13. Let A = A ij i,j∈E be an incidence matrix. The set of all inﬁnite Aadmissible words is deﬁned to be N EN A := { ω ∈ E : A ω n ω n+1 = 1, for all n ∈ N} .
Note that if the nth row of A does not contain any 1, then no word can contain the letter n. This letter then may as well be thrown out of the alphabet. We will therefore impose the condition that every row of A contains at least one 1. Further, notice that N if all the entries of the incidence matrix A are equal to 1, then EN A = E . However, if A N N has at least one 0 entry, then E A is a proper subset of E , called a subshift (of ﬁnite type). In particular, if A is the identity matrix then EN A = {(e, e, e, . . .) : e ∈ E }, that is, EN is the set of all constant words, which are the ﬁxed points of σ in EN . We will now A consider a more interesting subshift. Example 1.2.14. Let E = {0, 1} and let
1 A= 1
1 0
.
Then EN A consists of all those points where a 0 can be followed by either a 0 or a 1, but a 1 can only be followed by a 0. This example is known as the golden mean
1.2 Topological Dynamical Systems

19
shift. One reason for this name can be discovered on considering the cardinality of the sets E nA , for n ∈ N. We have (and the reader is advised to draw a diagram of the Aadmissible words to see why) that the sequence (# E nA )n≥1 coincides with the sequence (2, 3, 5, 8, 13, 21, 34, . . .). This latter sequence is, of course, the sequence of Fibonacci numbers starting with f 2 = 2 and f3 = 3 instead of f0 = 1 and f1 = 1. √ Observe that we have for the sequence of convergents (p n /q n )n≥1 to γ = (1 + 5)/2 that p n /q n = f n+2 /f n+1 for each n ∈ N. In order to deﬁne the shift map on a subshift space EN A , we must ﬁrst verify that these N N spaces are σinvariant, which means that σ(EN ) ⊆ E . A A So, let ω ∈ E A . Then A ω n ω n+1 = 1 for every n ∈ N. In particular, A(σω)n (σω)n+1 = A ω n+1 ω n+2 = 1 for all n ∈ N. Thus, σω ∈ EN A N N and it therefore follows that EN is σinvariant. Therefore, the restriction σ : E → E is A A A well deﬁned.
1.2.3 A return to the Gauss map Let us now return to the Gauss map. In light of the above description of symbolic dynamics and the shift map, we can now rephrase Proposition 1.2.7 in the following way: The Gauss map acts on the irrational points of the unit interval like the full shift map acts on the space NN . More precisely, we have that the topological dynamical systems (I, G) and (NN , σ) are topologically conjugate under the map h : I → NN deﬁned by h([x1 , x2 , x3 , . . .]) = (x1 , x2 , x3 , . . .) ∈ NN . In other words, we obtain the following commuting diagram: G h
h σ
We leave it as an exercise for the reader to verify that the map h is genuinely a conjugacy map between the Gauss and shift systems (see Exercise 1.6.12). Recall the sequence of convergents to each irrational number x ∈ [0, 1] introduced in Subsection 1.1.1; these are the equivalent of initial blocks for the Gauss map and so we can use them to deﬁne Gauss cylinder sets. Deﬁnition 1.2.15. For each choice x1 , . . . , x k ∈ N, deﬁne the kth level Gauss cylinder set C(x1 , . . . , x k ) by C(x1 , . . . , x k ) := {[y1 , y2 , . . .] : y i = x i for 1 ≤ i ≤ k}.
20  1 Numbertheoretical dynamical systems The ﬁrst level of these cylinder sets are given by the sets, for n ∈ N, C(n) = {[n, x2 , x3 , . . .] : x i ∈ N, i ≥ 2}. For ﬁxed n ∈ N, any point x ∈ C(n) is of the form x=
1 , n + [y1 , y2 , . . .]
for some y1 , y2 , . . . ∈ N. Therefore, since the values of [y1 , y2 , . . .] lie between 0 and 1, we see that such a point is bigger than 1/(n +1) and smaller than 1/n. Therefore, C(n) = (1/(n + 1), 1/n) ∩ I. In general, if we ﬁx some x1 , . . . , x k ∈ N and let x ∈ C(x1 , . . . , x k ), we have that 1
x= x1 +
1 ..
.+
1 x k + [y1 , y2 , . . .]
and again, since the values of [y1 , y2 , . . .] range from 0 to 1, we infer that x can take any value in the set ([x1 , . . . , x k ], [x1 , . . . , (x k + 1)])± ∩ I, where the notation (·, ·)± indicates that the rational number [x1 , . . . , x k ] may be the left or right endpoint of C(x1 , . . . , x k ) depending upon whether k is even or odd, respectively. Thus, for the Lebesgue measure λ(C(x1 , . . . , x k )) of this set, we ﬁnd that p p + p k−1 1 . λ(C(x1 , . . . , x k )) = k − k = q k q k + q k−1 q q2k 1 + k−1 qk Here, the last equality comes from Theorem 1.1.1 (c). From this, we immediately obtain that 1 1 ≤ λ(C(x1 , . . . , x k )) ≤ 2 . 2q2k qk
(1.7)
It is of interest to calculate the approximate proportion of the kth level cylinder set C(x1 , . . . , x k ) that is occupied by each of the (k + 1)th level cylinder sets C(x1 , . . . , x k , n). Notice that the endpoints of the interval C(x1 , . . . , x k , n) are given by [x1 , . . . , x k , n] and [x1 , . . . , x k , n + 1] and, with the aid of Theorem 1.1.1, we can write these in terms of the rational numbers p k−1 /q k−1 = [x1 , . . . , x k−1 ] and p k /q k = [x1 , . . . , x k ] as follows: [x1 , . . . , x k , n] =
np k + p k−1 (n + 1)p k + p k−1 and [x1 , . . . , x k , n + 1] = . nq k + q k−1 (n + 1)q k + q k−1
1.2 Topological Dynamical Systems

21
Utilising Theorem 1.1.1 (c) once more, we obtain that np + p k−1 (n + 1)p k + p k−1 − λ(C(x1 , . . . , x k , n)) = k nq k + q k−1 (n + 1)q k + q k−1 n(p k q k−1 − q k p k−1 ) + (n + 1)(p k−1 q k − p k q k−1 ) = (nq k + q k−1 )((n + 1)q k + q k−1 ) 1 = q 1 q k−1 1 + + k−1 n2 q2k 1 + nq k n nq k Thus, it follows that q 1 + k−1 q k . q k−1 1 q 1+ 1 + + k−1 nq k n nq k
λ(C(x1 , . . . , x k , n)) 1 = 2· n λ(C(x1 , . . . , x k ))
One easily veriﬁes that the second ratio on the righthand side of the above equality is bounded above by 2 and below by 1/3, so ﬁnally we ﬁnd that λ(C(x1 , . . . , x k , n)) 2 1 ≤ ≤ 2. 3n2 n λ(C(x1 , . . . , x k ))
(1.8)
1.2.4 Elementary metrical Diophantine analysis Let us now consider another area of elementary number theory related to the continued fraction expansion. In this subsection, we will prove some results concerning the Lebesgue measure λ of various sets of numbers, beginning with the set of badly approximable numbers that was introduced in Deﬁnition 1.1.15. The proofs will utilise the Gauss cylinder sets deﬁned above. Theorem 1.2.16. Where B denotes the set of badly approximable numbers, we have that λ(B) = 0. Proof. For each N and n ∈ N, deﬁne the sets A(n) N := {[x 1 , x 2 , . . .] ∈ I : x i < N for all 1 ≤ i ≤ n } .
We aim to show that = 0. lim λ A(n) N
n→∞
22  1 Numbertheoretical dynamical systems To begin, note that A(n+1) ⊂ A(n) N N for each n ∈ N and
(x1 ,...,x n ) x i 0. = ∞ and n log n n(log n)1+ε n=1
1.2.5 Markov partitions for interval maps In this subsection, we introduce the idea of a Markov partition. This notion will be used repeatedly to build symbolic codings for our various examples. We will restrict the discussion to maps deﬁned on the closed unit interval [0, 1] which are either already continuous or become continuous when considered as functions on the circle R/Z [0, 1) identifying the points 0 and 1, with the exception of a countable set of points (denoted by E ) where the map can be discontinuous. We will refer to such maps as Markov interval maps. To make this clearer, all the possibilities are covered by considering the tent map, the doubling map (modulo 1), as in Example 1.2.4 (see also Fig. 1.2), and the Gauss map (modulo 1) (see Fig. 1.1), for which the exceptional set is E = {0}. Deﬁnition 1.2.21. Let T : J → J be a map as described above, that is J := [0, 1] or R/Z. Further, let M := {A i : i ∈ E ⊂ N} be a countable collection of open nonempty subintervals of J and deﬁne E := J \ i∈E A i , where we suppose that the set E of exceptional points is at most countable. In here, A denotes the topological closure
M1(x)
M2(x)
1
1
0
1
x
0
1
x
Fig. 1.2. The graph of two Markov interval maps. The map M1 is continuous on [0, 1] whereas M2 has to be considered as a continuous function on the circle R/Z [0, 1).
1.2 Topological Dynamical Systems

27
of the subset A in J. Then, the collection M is said to be a Markov partition for T if it satisﬁes the following properties: (a) T A i : A i → T(A i ) deﬁnes a homeomorphism for each i ∈ E. (b) A i ∩ A j = ∅ for all i, j ∈ E with i = j. (c) If T(A i ) ∩ A j = ∅ for some i, j ∈ E, then A j ⊂ T(A i ). Example 1.2.22. We give here the most natural Markov partition for the Gauss map, G : R/Z → R/Z. For each i ∈ N, let A i := (1/(i + 1), 1/i), so that M is the collection of sets M = {(1/(i + 1), 1/i) : i ∈ N}. Then the set E is equal to the singleton {0}. Notice that M in fact coincides with the family of ﬁrst level Gauss cylinder sets, up to sets of measure zero. We must check that the three properties deﬁning a Markov partition are satisﬁed for this choice of M. Property (a) is clearly satisﬁed, by the deﬁnition of the Gauss map. Property (b) is also straightforward to check. For property (c), it is enough to notice that for all i ∈ N we have G (A i ) = (0, 1). Let us now consider collections U of subsets of X and deﬁne some operations on these collections. Deﬁnition 1.2.23. Let T : X → X be some given map. (a) The join U ∨ V of two collections U and V of X is deﬁned to be U ∨ V := {U ∩ V : U ∈ U , V ∈ V} .
This deﬁnition is extended to ﬁnitely many collections in the natural way. (b) We deﬁne the preimage of a collection U under the map T to be the collection consisting of all the preimages of the elements of U under T, that is, T −1 U := {T −1 (U) : U ∈ U }. The collection T −n (U ) is deﬁned inductively to be the preimage of the collection T −(n−1) (U ), in other words, T −n (U ) := T −1 (T −(n−1) (U )). (c) For each n ∈ N, we deﬁne the nth reﬁnement U n of a collection U to be U n :=
n−1 "
T −k (U ) = U ∨ T −1 (U ) ∨ · · · ∨ T −(n−1) (U ).
k=0
These operations on collections of sets can now be used to deﬁne a certain type of Markov partition. Deﬁnition 1.2.24. A Markov partition M for a dynamical system ([0, 1], T) is said to be shrinking if the diameter of the largest element in each reﬁnement Mn shrinks to zero
28  1 Numbertheoretical dynamical systems as n tends to inﬁnity. That is, M is shrinking provided that lim sup diam(M) : M ∈ Mn = 0.
n→∞
Each shrinking Markov partition gives rise to a canonical coding, in the following way. (Here we are following [Adl98], and we refer to this paper for further discussion.) Deﬁnition 1.2.25. Let M := {A i : i ∈ E ⊆ N} be a shrinking Markov partition for a dynamical system ([0, 1], T) and let A = A ij be the E × E incidence matrix deﬁned, ∅. Then, where EN for each i, j ∈ E, by A ij = 1 if and only if T(A i ) ∩ A j = A is the subshift consisting of all Aadmissible words, the coding associated with M is given by the map π : EN A → [0, 1] \
∞
T −n (E )
n=0
deﬁned by ∞
A ω1 ∩ T −1 A ω2 ∩ · · · ∩ T −n A ω n+1 .
π((ω1 , ω2 , ω3 , . . .)) := n=0
Note that this map is well deﬁned, since the intersection on the righthand side above is a singleton, due to Cantor’s Intersection Theorem (since the sequence (A ω1 ∩ T −1 A ω2 ∩ · · · ∩ T −n A ω n+1 )n≥0 is a nested sequence of compact sets with diameters shrinking to zero; see Exercise 1.6.14 if you have not encountered this useful result before). Due to the restriction that the exceptional set E be countable, we obtain a unique coding for all but countably many points. Theorem 1.2.26. The map π is a factor map from the system EN A , σ to the system ∞ −n [0, 1] \ n=0 T (E ), T . Proof. Recall that the deﬁnition of a factor map is that π should be a continuous ∞ −n surjection from E∞ A to [0, 1] \ n=0 T (E ) such that π ◦ σ = T ◦ π. It is easily demonstrated that π is uniformly continuous. Indeed, for this it is enough to notice that diam(π([x1 , . . . , x n ])) ≤ sup diam(M) : M ∈ Mn → 0, for n → ∞, and uniformly for all admissible ncylinders. −n To see that π is surjective, we argue inductively. Let x ∈ [0, 1] \ ∞ n=0 T (E ) be an element of A ω1 ∩ T −1 A ω2 ∩ · · · ∩ T −(n−1) A ω n for some n ∈ N. Since the collection {M : M ∈ Mn+1 } covers the set A ω1 ∩ T −1 A ω2 ∩ · · · ∩ T −(n−1) A ω n \
∞
T −n (E ),
n=0
there must exist (at least) one element A ω1 ∩ T −1 A ω2 ∩ · · · ∩ T −n A ω n+1 containing x with T(A ω n ) ∩ A ω n+1 = ∅. In this way we construct a point (ω1 , ω2 , ω3 , . . .) lying in EN A and one quickly veriﬁes that π((ω 1 , ω 2 , ω 3 , . . .)) is equal to x.
1.2 Topological Dynamical Systems

29
Finally, it remains to show that π ◦ σ = T ◦ π. So, let ω = (ω1 , ω2 , ω3 , . . .) ∈ EN A and observe that ∞
π ◦ σ(ω) = π((ω2 , ω3 , ω4 , . . .)) =
A ω2 ∩ T −1 A ω3 ∩ · · · ∩ T −(n−1) A ω n+1 . n=0
On the other hand, using the continuity of the restriction of T to A ω1 we have that ∞ T ◦ π(ω) = T
A ω1 ∩ T −1 A ω2 ∩ · · · ∩ T −n A ω n+1 n=0
∞
TA ω1 ∩ A ω2 ∩ · · · ∩ T −(n−1) A ω n+1
= n=0 ∞
A ω2 ∩ T −1 A ω3 ∩ · · · ∩ T −(n−1) A ω n+1
= n=0
where the ﬁnal equality comes from the fact that T(A ω1 ) ⊃ A ω2 , since ω belongs to the set EN A . This ﬁnishes the proof. Example 1.2.27. Let us return once more to the Gauss map and to its canonical Markov partition given in Example 1.2.22. We can easily see that the Markov partition M given in that example is shrinking. Indeed, the reﬁnement Mn coincides with the collection of nth level Gauss cylinder sets, up to sets of measure zero, and the largest element of this collection is C(1, . . . , 1). We saw in (1.7) that 1 1 ≤ λ(C(x1 , . . . , x n )) ≤ 2 , 2q2n qn and since for C(1, . . . , 1) we have that q n is equal to the (n + 1)th Fibonacci number, it is clear that as n tends to inﬁnity, the diameter of the cylinder set with code consisting of n 1s shrinks to zero. Since M is shrinking, we can therefore obtain a coding from it. Recall that the set E in this case is equal to the singleton {0}. Therefore, the set of points where the coding is undeﬁned is here simply equal to the set of preperiodic points of 0, that is, the rational numbers in [0, 1]. So, for any point ω = (x1 , x2 , x3 , . . .) ∈ NN , we have that {π(ω)} = A x1 ∩ G−1 (A x2 ) ∩ G−2 (A x3 ) ∩ · · · .
In other words, we have that π(ω) lies in A x1 = (1/(x1 + 1), 1/x1 ), G(π(ω)) lies in A x2 = (1/(x2 + 1), 1/x2 ), G2 (π(ω)) lies in A x3 = (1/(x3 + 1), 1/x3 ) and so on. Thus, the ﬁrst continued fraction element of π(ω) is equal to x1 , the second is equal to x2 , the third is equal to x3 and so on. Therefore, we have shown that the coding that arises from the Markov partition M coincides with the continued fraction expansion of each irrational number.
30  1 Numbertheoretical dynamical systems Note that in this case the map π : NN → I is actually a topological conjugacy map, since the endpoints of all the intervals in M lie in the set G−1 (0) and hence, the map is injective as well as surjective. Remark 1.2.28. The ideas of this section can extended much further, to the setting of graphdirected Markov systems. For this, we refer the reader to the textbook by Mauldin and Urbanski [MU03].
1.3 The Farey map: deﬁnition and topological properties In this section, we introduce the second of our main examples, namely, the Farey map. First we give the deﬁnition and show how the Farey map is related to the Gauss map. We then deﬁne a Markov partition for the Farey map and describe the coding associated with it. Finally, in the second subsection, we give some basic topological properties of the Farey map. 1.3.1 The Farey map Let us now deﬁne a transformation on the unit interval that is closely related to the Gauss map. Deﬁnition 1.3.1. The Farey map F : [0, 1] → [0, 1] is deﬁned by # x/(1 − x) for 0 ≤ x ≤ 12 ; F(x) := (1 − x)/x for 12 < x ≤ 1. The graph of the Farey map is shown in Fig. 1.3.
F (x) 1
0
1 2
1
x
Fig. 1.3. The Farey map, F : [0, 1] → [0, 1]. In the ﬁxed point at 0 the map F has slope 1. The second √ 5 − 1 /2. ﬁxed point lies in γ * =
1.3 The Farey map: deﬁnition and topological properties

31
For later use, let us also give the inverse branches of F. These are the functions F0 : (0, 1) → (0, 1/2) and F1 : (0, 1) → (1/2, 1) which are deﬁned by F0 (x) :=
x 1 and F1 (x) := . 1+x 1+x
It is easily shown that the action of the map F on a point x = [x1 , x2 , x3 , . . .] ∈ (0, 1) is as follows: # [x1 − 1, x2 , x3 , . . .] if x1 > 1; F(x) = [x2 , x3 , x4 , . . .] if x1 = 1. For this reason, the Farey map is sometimes referred to as the slow continued fraction map, whereas the Gauss map is referred to as the fast continued fraction map. We can describe the relationship between the Farey and Gauss maps more precisely. To do this, we introduce the idea of a jump transformation, which is also often referred to as Schweiger’s jump transformation [Sch95]. Deﬁnition 1.3.2. Let the map ρ : (0, 1] → N ∪ {0} be deﬁned by ρ(x) := inf {n ≥ 0 : F n (x) ∈ (1/2, 1]}. Note that ρ(x) is ﬁnite for all x ∈ (0, 1]. Then, let the map F * : [0, 1] → [0, 1] be deﬁned by # F ρ(x)+1 (x) if x = 0; * F (x) := 0 if x = 0. The map F * is said to be the jump transformation on (1/2, 1] of F. Lemma 1.3.3. The jump transformation F * of the Farey map coincides with the Gauss map. Proof. Fix n ≥ 2 and let x = [n, x2 , x3 , . . .], so x ∈ (1/(n + 1), 1/n]. Then, we have that F * (x) = F n ([n, x2 , x3 , . . .]) = F n−1 ([n − 1, x2 , x3 , . . .]) = · · · = F([1, x2 , x3 , . . .]) = [x2 , x3 , . . .] = G(x). On the other hand, if x = [1, x2 , x3 , . . .] ∈ (1/2, 1], then ρ(x) is equal to zero and so we have that F * (x) = F(x), which is again equal to G(x), since G(1/2,1] = F (1/2,1] . Our next aim is to describe a coding generated by the Farey map. The two open sets {B0 := (0, 1/2), B1 := (1/2, 1)} form a Markov partition for F. In this case, the exceptional set E is the empty set. That the three conditions for a Markov partition are satisﬁed is easy to check, so we leave this as an exercise. The above Markov partition for the Farey map yields a coding of the numbers in [0, 1] as follows. Each element ω = (x1 , x2 , x3 , . . .) ∈ {0, 1}N is mapped by π to the
32  1 Numbertheoretical dynamical systems point x ∈ [0, 1] given by ∞
{x } :=
B x1 ∩ F −1 B x2 ∩ · · · ∩ F −n B x n+1 . n=0
We will use the notation π(ω) = x =: x1 , x2 , x3 , . . . ∈ [0, 1]. This coding will be referred to as the Farey coding. The Farey coding is related to the continued fraction expansion of x in a straightforward way. Indeed, if x = [x1 , x2 , x3 , . . .], then the Farey coding of x is given by x = 0x1 −1 , 1, 0x2 −1 , 1, 0x3 −1 , 1, . . . , where 0n denotes the sequence of n ∈ N consecutive appearances of the symbol 0 and 00 is understood to mean the appearance of no zeros between two consecutive 1s. This follows directly from the fact that the Gauss map is the jump transformation on (1/2, 1] of the Farey map. We have described the coding for irrational numbers; let us now consider the rational numbers. We can still deﬁne a code for these points, it will just no longer be unique. So, let x = [x1 , . . . , x k ]. In this case we obtain two inﬁnite codings, namely, we have that x = 0x1 −1 , 1, 0x2 −1 , 1, . . . , 0x k −1 , 1, 0, 0, 0, . . . and x = 0x1 −1 , 1, 0x2 −1 , 1, . . . , 0x k −2 , 1, 1, 0, 0, 0, . . . . Indeed, take for example the point 1/2. Observe that 1/2 ∈ B1 but also 1/2 ∈ B0 , so the ﬁrst entry in the Farey coding for 1/2 can be either 1 or 0. Then F(1/2) = 1 and F n (1/2) = 0 for all n ≥ 2, therefore we obtain that 1/2 = 1, 1, 0, 0, 0, . . . and 1/2 = 0, 1, 0, 0, 0, . . .. The coding for any rational number can be deduced from this example, since every rational number is eventually mapped to 1/2 by the map F (or, to put it another way, the set of rational numbers coincides with the iteration of the point 1/2 under the inverse branches of the Farey map). Recall that F acts on x = [x1 , x2 , x3 , . . .] in the following way: # [x1 − 1, x2 , x3 , . . .] for x1 ≥ 2; F(x) := [x2 , x3 , x4 . . .] for x1 = 1. In particular, this means that if we instead write x in its Farey coding, so x = y1 , y2 , . . . , then
F(x) = y2 , y3 , . . .. So, again, the system (I, F) can be thought of as acting like the shift map σ on the shift space EN , where this time the alphabet E = {0, 1} is ﬁnite. This is a potential advantage of the map F over the map G in certain situations, as the space {0, 1}N is a compact metric space, whereas NN is not.
1.3 The Farey map: deﬁnition and topological properties

33
We are now in a position to deﬁne the cylinder sets with respect to the Farey map. Deﬁnition 1.3.4. For each ntuple (x1 , . . . , x n ) ∈ {0, 1}n , deﬁne the Farey cylinder set 1 , . . . , x n ) by setting C(x 1 , . . . , x n ) := {y1 , y2 , . . . : y k = x k , for all 1 ≤ k ≤ n}. C(x Note that the Farey cylinder sets coincide with the reﬁnements of the Markov partition {B 0 , B1 } under the inverse branches F0 and F 1 of F. That is, the cylinder set 1 , . . . , x n ) coincides with the set F x1 ◦ F x2 ◦ · · · ◦ F x n ((0, 1)). We will refer to these C(x successive reﬁnements as the Farey decomposition. There is another way to describe these cylinder sets, in terms of the classical construction of Stern–Brocot intervals (cf. [Ste58], [Bro61]). For each n ≥ 0, the elements of the nth member of the Stern–Brocot sequence % $ s n,k Bn := : k = 1, . . . , 2n + 1 t n,k are deﬁned recursively as follows: – s0,1 := 0 and s0,2 := t0,1 := t0,2 := 1; – s n+1,2k−1 := s n,k and t n+1,2k−1 := t n,k , for k = 1, . . . , 2n + 1; – s n+1,2k := s n,k + s n,k+1 and t n+1,2k := t n,k + t n,k+1 , for k = 1, . . . 2n . From this arises the set Tn of Stern–Brocot intervals of order n which is given by ' % $& s n,k s n,k+1 : k = 1, . . . , 2n . Tn := , t n,k t n,k+1 It is straightforward to check that these intervals are precisely the same intervals as the set 1 , . . . , x n ) : x i ∈ {0, 1}, 1 ≤ i ≤ n}. {C(x Returning to the Stern–Brocot sequence, the nth member of this sequence consists of 2n + 1 proper fractions and the nth member of the sequence can be obtained from the (n − 1)th member by adding in the mediant of each neighbouring pair, where we remind the reader that the mediant of two rational numbers a/b and a /b is by deﬁnition the rational number (a + a )/(b + b ). The ﬁrst few of these sequences are given by: % % % $ $ $ 0 1 0 1 1 0 1 1 2 1 , B1 = , B2 = , B0 = , , , , , , , 1 1 1 2 1 1 3 2 3 1 % $ 0 1 1 2 1 3 2 3 1 , B3 = , , , , , , , , 1 4 3 5 2 5 3 4 1 % $ 0 1 1 2 1 3 2 3 1 4 3 5 2 5 3 4 1 ,... B4 = , , , , , , , , , , , , , , , , 1 5 4 7 3 8 5 7 2 7 5 8 3 7 4 5 1
34  1 Numbertheoretical dynamical systems 1 2 1 3 1 4 1 5
1 4
1 2
1 3 2 7
1 3
2 5 3 8
2 5
2 3
1 2 3 7
1 2
3 5 4 7
3 5
2 3 5 8
2 3
3 4 5 7
3 4
4 5
Fig. 1.4. The dyadic Stern–Brocot tree with root s1,2 /t1,2 = 1/2. For each s n,2k /t n,2k , n ∈ N and k = 1, . . . , 2n−1 we have the two offspring s n+1,2k /t n+1,2k and s n+1,2k+2 /t n+1,2k+2 . For each n, the missing elements from Bn \{0, 1} are marked in grey.
This sequence also gives rise to the Stern–Brocot Tree as shown in Fig. 1.4. The vertices of the nth generation of the Stern–Brocot Tree will be denoted by Sn := Bn \ Bn−1 , for n ∈ N and the sequence (Sn )n∈N is called the even Stern–Brocot sequence. Remark 1.3.5. The Markov partition {B0 , B1 } given above is not the only reasonable choice for the Farey map. We might instead choose to use the partition M, that is, the partition deﬁned in Example 1.2.22 for the Gauss map. It is not hard to verify that this is also a shrinking Markov partition for the Farey map, and so yields a different coding for −n the points of [0, 1]. In this case, we obtain a coding map π : NN A → [0, 1] \ n∈N F (0), N where the subshift NA is determined by the inﬁnite transition matrix given by ⎧ ⎪ ⎨1 if i = 1 and j ∈ N; A ij = 1 if i = n and j = n − 1, for n ≥ 2; ⎪ ⎩0 otherwise. This subshift is known as the (inﬁnite) renewal shift. We will return to the subject of renewal theory in Chapter 3.
1.3.2 Topological properties of the Farey map We now come to the study of the Farey map from a topological point of view. Let us ﬁrst introduce another wellstudied dynamical system, which we shall shortly show to be topologically conjugate to the Farey map. Deﬁnition 1.3.6. Deﬁne the map T : [0, 1] → [0, 1] by setting # 2x for x ∈ [0, 1/2); T(x) := 2 − 2x for x ∈ [1/2, 1].
1.3 The Farey map: deﬁnition and topological properties

35
T (x) 1
0
1 2
1
x
Fig. 1.5. The tent map, T : [0, 1] → [0, 1].
The map T is referred to as the tent map; the reason for this name is clear on inspection of the graph of T, shown in Fig. 1.5. It turns out, and we shall prove this shortly, that the conjugating homeomorphism between the Farey map and the tent map coincides with Minkowski’s questionmark function, which we shall denote by Q : [0, 1] → [0, 1]. This remarkable function was originally introduced by Minkowski [Min10] and later investigated by Denjoy [Den38] and Salem [Sal43], amongst others. Minkowski’s original motivation behind the deﬁnition of the function that now bears his name was to highlight the intriguing property of continued fractions that was described in Lagrange’s Theorem (see Theorem 1.2.8). Recall that this theorem states that the set of irrational algebraic numbers of degree two corresponds precisely to the set of real numbers that admit an eventually periodic continued fraction expansion. In other words, if x ∈ [0, 1] can be written as a continued fraction of the form [x1 , . . . , x m , x m+1 , . . . , x m+k ], then x is an irrational root of some quadratic polynomial and, moreover, the converse statement also holds. Minkowski designed the function Q to map the quadratic surds into the nondyadic rationals in a continuous and orderpreserving way (we leave the proof of this to Exercise 1.6.15). The questionmark function is constructed in the following way. First, deﬁne Q(0) = Q(0/1) := 0 and Q(1) = Q(1/1) := 1. Then, deﬁne p + p Q(p/q) + Q(p /q ) := Q . q+q 2 In other words, the function Q is successively deﬁned on all the rational numbers in the unit interval by taking mediants of those that have already been deﬁned. The deﬁnition of Q is extended to all of [0, 1] by continuity (since any uniformly continuous
36  1 Numbertheoretical dynamical systems Q n (x )
Q (x )
1
1
7/8 3/4 5/8 1/2 3/8 1/4 1/8 0
1 4
1 2 3 5
1 2
3 2 5 3
3 4
1
x
0
1
x
Fig. 1.6. On the left, the graphs of the functions Q n , n = 1, 2, 3, and on the right, an approximation to the graph of the Minkowski questionmark function, Q : [0, 1] → [0, 1].
function from a dense set of a metric space E into another metric space can be uniquely extended to a continuous function on all of E). Another way to think about the questionmark function is as a uniform limit of the sequence of piecewise linear functions (Q n )n∈N , where each Q n : [0, 1] → [0, 1] is deﬁned by mapping the nth level Stern–Brocot fractions, arranged in increasing order, onto the set {p/2n : 0 ≤ p ≤ 2n } and then joining these image points by straight line segments. Above, in Fig. 1.6, can be found an illustration of the ﬁrst few of these functions and also an approximation of the graph of Q itself. Denjoy demonstrated that the function Q is given by the following formula: Q([x1 , x2 , x3 , . . .]) = −2
∞ k (−1)k 2− i=1 x i . k=1
Later, Salem derived the most important properties of Q from this formula, including the facts that Q is strictly increasing and singular with respect to Lebesgue measure, which means that the derivative of Q is equal to zero, Lebesguealmost everywhere. The function Q is for this reason referred to as a slippery Devil’s staircase, a term coined by Gutzwiller and Mandelbrot in [GM88]. The (multifractal) fractal nature of Q is investigated in [KS08b], see also [KS07]. Also, recall that the distribution function ∆ μ of a measure μ with support in [0, 1] is deﬁned for each x ∈ [0, 1] by ∆ μ (x) := μ([0, x)).
1.3 The Farey map: deﬁnition and topological properties

37
Note that a distribution function is always nondecreasing and rightcontinuous (see Theorem 9.1.1 in Dudley [Dud89]) and in the case of the measure μ having no atoms, the function ∆ μ is continuous. Proposition 1.3.7. The dynamical systems ([0, 1], F) and ([0, 1], T) are topologically conjugate and the conjugating homeomorphism Q is given by Q([x1 , x2 , x3 , . . .]) := −2
∞ k (−1)k 2− i=1 x i . k=1
That is, the conjugating homeomorphism between the Farey map and the tent map is Minkowski’s questionmark function. Moreover, the map Q is equal to the distribution function ∆ μ0 of the measure of maximal entropy μ0 := λ ◦ Q for the Farey map. Remark 1.3.8. The reader unfamiliar with the concept of measures of maximal entropy should not be unduly alarmed by this terminology. For our purposes, it is enough to know that μ0 assigns mass 2−n to each nth level Farey cylinder set. For further reading we refer to [Wal82]. Also note that since Q is a homeomorphism the measure λ ◦ Q is well deﬁned. Proof of Proposition 1.3.7. We will ﬁrst show that the map Q is the conjugating homeomorphism from F to the tent system. For this, suppose ﬁrst that x ∈ [0, 1/2]. Then, Q(x) is an element of [0, 1/2] and we have that ∞ ∞ k − ki=1 x i k −(x1 −1)− ki=2 x i T Q(x) = 2 −2 (−1) 2 (−1) 2 = −2 k=1
= Q [x 1 − 1, x2 , x3 , . . .] = Q(F(x)).
k=1
Now, suppose that x ∈ (1/2, 1], that is, x = [1, x2 , x3 , . . .]. Then, it follows that Q(x) ∈ (1/2, 1] and we have that ∞ −1 k −1− ki=2 x i T(Q(x)) = 2 − 2 2 · 2 − 2 (−1) 2 =2
k=2 ∞
k −
(−1) 2
k i=2
xi
= Q [x2 , x3 , . . .] = Q(F(x)).
k=2
It only remains to show that Q is equal to the distribution function of μ 0 . Indeed, for each x ∈ [0, 1] we have ∆ μ0 (x) = μ0 ([0, x]) = λ ◦ Q([0, x]) = λ([Q(0), Q(x)]) = λ([0, Q(x)]) = Q(x). This ﬁnishes the proof. In preparation for the next proposition, we recall the deﬁnition of a Hölder continuous function.
38  1 Numbertheoretical dynamical systems
Deﬁnition 1.3.9. A map S : (X, d) → (X, d) of a metric space (X, d) into itself is said to be Hölder continuous with exponent κ > 0 if there exists a positive constant C > 0 such that d(S(x), S(y)) ≤ C d(x, y)κ , for all x, y ∈ X. We will show now that Minkowski’s questionmark function, the conjugating homeomorphism between the Farey and tent systems, is Hölder continuous with exponent √ log 2/(2 log γ ), where we recall that γ := (1 + 5)/2 denotes the golden mean. This result was also originally proved by Salem [Sal43]. First, we give a useful lemma. Lemma 1.3.10. For the denominator q n of the nth convergent of x = [x1 , x2 , x3 , . . .], we have that q n < γ x1 +···+x n , for all n ∈ N. Proof. We will prove this by induction. Certainly, for p1 /q1 = 1/x1 , the inequality x1 = q1 < γ x1 holds. So, ﬁx n ∈ N and suppose that q k < γ x1 +···+x k for all k < n. Recall that q n = x n q n−1 + q n−2 . Therefore, q n < x n γ x1 +···+x n−1 + γ x1 +···+x n−2 . Thus, it suffices to show that x n γ x n−1 + 1 ≤ γ x n−1 +x n , or, that x n + 1/γ ≤ γ x n . This last inequality becomes the equality 1 + 1/γ = γ when x n = 1; it is also straightforward to check that 2 + 1/γ = γ 2 . As the function n → γ n − n is increasing for n ≥ 2, the proof is ﬁnished. Proposition 1.3.11. The map Q is sHölder continuous for s = log 2/(2 log γ ), but not for any s > log 2/(2 log γ ). Proof. In order to calculate the Hölder exponent of Q, ﬁrst note that Q(C(x1 , x2 , . . . , x n )) = Q([x1 , x2 , . . . , x n ]) − Q([x1 , x2 , . . . , x n , 1]) = 2−s n , where we have put, as in the proof of Proposition 1.3.7, s n := ni=1 x i . This can be seen by simply calculating the image of the endpoints of this cylinder, or by noting that every Gauss cylinder C(x1 , x2 , . . . , x n ) is a s n th level Farey cylinder. Now, we have that λ(C(x1 , . . . , x n )) = p n /q n − pn+1 /qn+1 , where pn+1 /qn+1 = [x1 , . . . , x n , 1].
1.3 The Farey map: deﬁnition and topological properties

39
Recalling that p n q n−1 − p n−1 q n = (−1)n+1 , it follows, in light of Lemma 1.3.10, that λ(C(x1 , . . . , x n )) =
1 > γ −(2s n +1) . q n qn+1
Rearranging this expression, we obtain that λ(C(x1 , . . . , x n )) > (2log γ/ log 2 )−(2s n +1) = 2− log γ/ log 2 · (2−s n )2 log γ/ log 2 2 log γ/ log 2 = c · Q(C(x1 , . . . , x n )) , where c := 2− log γ/ log 2 = γ −1 . In other words, Q(C(x1 , . . . , x n )) ≤ c · λ(C(x1 , . . . , x n ))log 2/(2 log γ) . Now, let x and y be two arbitrary different irrational numbers in [0, 1]. There must be a ﬁrst time during the backwards iteration of [0, 1] under the inverse branches of F in which a Farey cylinder set appears between the numbers x and y. Say that this cylinder set appears in the pth stage of the Farey decomposition. If we iterate one more time, it is clear that there are two (p + 1)th level Farey cylinder sets fully contained in the interval (x, y); moreover, one of these also has to be a Gauss cylinder set. Let this Gauss cylinder set be denoted by C(z 1 , z2 , . . . , z k ), where kj=1 z j = p + 1. This leads to the observation that, as C(z1 , z2 , . . . , z k ) is contained in the interval (x, y), we have x − y log 2/(2 log γ) > λ(C(z 1 , z2 , . . . , z k ))log 2/(2 log γ)
≥ c−1 · Q(C(z1 , z2 , . . . , z k )) = c−1 · 2−(p+1) . Consider the interval (x, y) again. By construction, it is contained inside two neighbouring (p − 1)th level Farey intervals, and so Q(x) − Q(y) < 2−(p−1) + 2−(p−1) = 2−(p−2) = 8 · 2−(p+1) .
Combining these observations, we obtain that Q(x) − Q(y) ≤ 8c x − y log 2/(2 log γ) .
Now ﬁx s > log(2)/(2 log(γ )) and let x n , y n be the left and right boundary point of the Gauss cylinder C(1, . . . , 1) of length n ∈ N. Then there exists a constant c > 0 such that on the one hand x n − y n  = λ(C(1, . . . , 1)) =
1 ≤ cγ −2n . f n f n+1
(cf. Exercise 1.6.4). On the other hand we have Q(x n ) − Q(y n ) = λ(Q(C(1, . . . , 1))) ≥ c−1 2−n .
40  1 Numbertheoretical dynamical systems
Combining these two observations gives Q(x n ) − Q(y n ) 2−n ≥ c−s−1 −2sn = c−s−1 exp(n(− log(2) + 2s log(γ ))) → ∞. s x n − y n  γ
This ﬁnishes the proof.
1.4 Two further examples In this section we will introduce and study the properties of our other main examples. These are two families of dynamical systems, the αLüroth and αFarey systems, which are both indexed by partitions α of the unit interval. We shall ﬁrst introduce the class of partitions of [0, 1] we are interested in and then deﬁne the αLüroth map L α . We then describe the expansion of real numbers that can be derived from this map, again in terms of a Markov partition. Next, we introduce the family of αFarey maps and develop topological properties for these maps similar to those described in Section 1.3.2 for the Farey map.
1.4.1 The αLüroth maps Let us begin by letting α := {A n : n ∈ N} denote a countably inﬁnite partition of the unit interval [0, 1], consisting of nonempty, rightclosed and leftopen intervals. It is assumed throughout that the elements of α are ordered from right to left, starting from A1 , and that these elements accumulate only at the origin. We will denote the collection of all such partitions of [0, 1] by A. Further, we let a n denote the Lebesgue measure λ(A n ) of A n ∈ α and let t n := ∞ k=n a k denote the Lebesgue measure of the nth a = 1 for every partition α ∈ A. tail of α. It is clear that t1 = ∞ k=1 k Deﬁnition 1.4.1. For a given partition α ∈ A, the αLüroth map L α : [0, 1] → [0, 1] is given by # (t n − x)/a n for x ∈ A n , n ∈ N; L α (x) := 0 if x = 0. In other words, the map L α consists of countably many linear branches that send A n onto [0, 1), for each n ∈ N. We now deﬁne a Markov partition for the map L α . Let E := {0} and let α˚ := {B n : n ∈ N}, where B n := Int(A n ) denotes the interior of A n for each n ∈ N. It is easy to verify that the collection of sets α˚ constitutes a Markov partition. Indeed, the properties (a) and (b) of Deﬁnition 1.2.21 are obviously satisﬁed and property (c) follows from the observation that for all n ∈ N we have L α (B n ) = (0, 1).
1.4 Two further examples 
41
For later use, let us also deﬁne the inverse branches of L α . These are the countable family of maps L α,n : (0, 1) → B n deﬁned for each n ∈ N by L α,n (x) := t n − a n x. In order to construct a coding from this Markov partition, we must ﬁrst show that it is shrinking (cf. Deﬁnition 1.2.24). To do this, we calculate the Lebesgue measure of the ˚ First of all, the size of each element intervals that make up the reﬁnements α˚ n of α. B n := (t n+1 , t n ) of α˚ is equal to a n . The reﬁnement α˚ 2 is given by
˚ = L−1 ˚ = α˚ 2 = α˚ ∨ L−1 L α,n (B k ) α ( α) α ( α) =
k∈N n∈N
(t n − a n t k , t n − a n t k+1 ).
k∈N n∈N
Thus, the size of an interval B n,k := (t n − a n t k , t n − a n t k+1 ) in α˚ 2 is equal to a n a k , since t k − t k+1 = a k . Now, suppose that the endpoints of an interval B1 ,...,n in the reﬁnement α˚ n are given by t1 − a1 t2 + · · · + (−1)n−1 a1 . . . an−1 tn and t1 − a1 t2 + · · · + (−1)n−1 a1 . . . an−1 tn +1 , so that the size of B1 ,...,n is equal to a1 . . . an . To shorten this notation, let us write [1 , . . . , n ]α := t1 − a1 t2 + · · · + (−1)n−1 a1 . . . an−1 tn . Then,
˚ n) = α˚ n+1 = L−1 L α,k (B1 ,...,n ) α (α =
k∈N (1 ,...,n )∈Nn
t k − a k [1 , . . . , n ]α , t k − a k [1 , . . . , n + 1]α
k∈N (1 ,...,n )∈Nn
=
±
[1 , . . . , n+1 ]α , [1 , . . . , n+1 + 1]α ± ,
(1 ,...,n ,n+1 )∈Nn+1
where we recall that the notation (·, ·)± means that the endpoints are not necessarily in the correct order. Thus, the size of an interval B1 ,...,n+1 is equal to a1 . . . an+1 . From this, we can deduce that the partition α˚ is shrinking. Since ∞ k=1 a k = 1, it follows that there exists (at least) one of the a k with maximum size. Call it amax . Then, the largest element of α˚ n has size (amax )n and, as 0 < amax < 1, this clearly tends to zero as n tends to inﬁnity. Now we can utilise the shrinking Markov partition α˚ to obtain a coding for −n all the points in the set [0, 1] \ ∞ n=0 L α (0). Exactly as was the case for the Gauss map before, if x lies in this set, we ﬁnd a sequence (1 , 2 , . . .) ∈ NN such that
42  1 Numbertheoretical dynamical systems x∈
)∞
n=0 B 1
−n ∩ L −1 α B 2 ∩ · · · ∩ L α B n+1 . Thus, x ∈ B 1 and therefore,
L α (x) = (t1 − x)/a1 . So, x = t1 −a1 L α (x). Then, L α (x) ∈ B2 and a similar calculation leads us to the identity x = t1 − a1 t2 + a1 a2 L2α (x). We can continue this calculation indeﬁnitely to obtain an alternating series expansion −n of each x ∈ [0, 1] \ ∞ n=0 L α (0), which is given by x = t 1 +
∞ n=2
(−1)n−1
* i 1. By analogy with continued fractions, for which a number is rational if and only if it has a ﬁnite continued fraction expansion, we say that x ∈ [0, 1] is an αrational number when x has a ﬁnite αLüroth expansion  that is, whenever x is a preimage of 0 under the map L α  and say that x is an αirrational number otherwise. We denote the set of αrational numbers by Qα and the set of αirrational numbers by Iα . The set Qα is, of course, a countable dense set in [0, 1]. The reader should also notice that the αrationals are not necessarily equal to actual rational numbers, unless the partition α is chosen to consist solely of intervals with rational endpoints. Example 1.4.2. (a) Deﬁne the harmonic partition α H by setting ' % $ 1 1 :n≥1 . α H := A n := , n+1 n
1.4 Two further examples
The map L α H : [0, 1] → [0, 1] is then given by ⎧ ⎨−n(n + 1)x + (n + 1) L α H (x) := ⎩ 0
for x ∈

43
' 1 1 ; , n+1 n
for x = 0.
This map can be found in the literature where it is often referred to as the alternating Lüroth map. For references and more on the historical background to this, see Section 1.5. With respect to the map L α H , in exactly the way outlined above using the Markov partition α˚ H , the corresponding series expansion of some arbitrary x ∈ [0, 1] turns out to be ∞ n ! n−1 −1 x= (−1) (n + 1) (k (k + 1)) n=1
k=1
1 1 + − ··· , = − 1 1 (1 + 1)2 1 (1 + 1)2 (2 + 1)3 1
where each n ∈ N and the expansion can, as usual, be ﬁnite or inﬁnite. + (b) Deﬁne the partition α D := 1/2n , 1/2n−1 : n ∈ N . We will refer to α D as the dyadic partition. For this partition, we obtain the map L α D , which is given by # + 2 − 2n x for x ∈ 1/2n , 1/2n−1 ; L α D (x) := 0 for x = 0. Remark 1.4.3. 1. The name “αLüroth” for these maps is in honour of the German mathematician J. Lüroth, for his 1883 paper [Lür83] which develops a particular series expansion of real numbers which is related to the expansions derived above. For more details, we refer once more to Section 1.5. 2. Note that the αLüroth expansion is a particular type of generalised Lüroth series, a concept which was introduced by Barrionuevo et al. in [BBDK96] (also see [DK02]). Before going any further, let us describe the action of the map L α on the expansions it generates. For each x = [1 , 2 , 3 , . . .]α , we have, since x ∈ A1 , that L α (x) = (t1 − x)/a1 = (t1 − (t1 − a1 t2 + a1 a2 t3 + . . .))/a1 = t2 + a2 t3 + . . . = [2 , 3 , 4 , . . .]α . This shows that L α , just like the Gauss map, can be thought of as acting as the shift map on the space NN , at least for those points in [0, 1] with inﬁnite αLüroth expansions. That is, L α : Iα → Iα and σ : NN → NN are topologically conjugate via the conjugacy map h : NN → Iα given by h(1 2 3 . . .) = [1 , 2 , 3 , . . .]α .
44  1 Numbertheoretical dynamical systems For each x = [1 , 2 , 3 , . . .]α ∈ [0, 1], just as was done for the continued fraction expansion, if we truncate the αLüroth expansion of x after k entries, then we obtain the kth αLüroth convergent of x, that is, for each k ∈ N we obtain the ﬁnite αLüroth expansion r(α) (x), given by k := [1 , . . . , k ]α = t1 − a1 t2 + · · · + (−1)k−1 a1 · · · ak−1 tk . r(α) k (x) The behaviour of these convergents is exactly like those of the continued fraction convergents, as shown in the following proposition. Proposition 1.4.4. Let x = [1 , 2 , 3 , . . .]α ∈ Iα . Then, the sequence of αLüroth convergents of x satisﬁes the following four properties. (a) The sequence r(α) (x) n≥1 of even convergents is increasing. 2n (b) The sequence r(α) 2n−1 (x) n≥1 of odd convergents is decreasing. (c) Every convergent of oddorder is greater than every convergent of even order. (d) limn→∞ r(α) (x) − r(α) n (x) = 0. n+1 Proof. The proof is very similar to that of Proposition 1.1.3 and, as such, is left as an exercise. Deﬁnition 1.4.5. For each ktuple (1 , . . . , k ) of positive integers, deﬁne the αLüroth cylinder set C α (1 , . . . , k ) associated with the αLüroth expansion by C α (1 , . . . , k ) := {[y1 , y2 , . . .]α : y i = i for 1 ≤ i ≤ k}. Observe once again that these cylinder sets coincide up to sets of measure zero with ˚ the elements of the reﬁnements α˚ n of the Markov partition α.
1.4.2 The αFarey maps Let us now introduce a second family of maps, indexed by the same collection A of partitions of [0, 1] as were used in the deﬁnition of L α . We will soon see that these new maps are related to the maps L α in the same way the Farey map is related to the Gauss map. Deﬁnition 1.4.6. For a given partition α := {A n [0, 1] → [0, 1] is deﬁned by ⎧ ⎪ ⎨(1 − x)/a1 F α (x) := a n−1 (x − t n+1 )/a n + t n ⎪ ⎩0
: n ∈ N} ∈ A, the αFarey map F α : if x ∈ A1 ; if x ∈ A n , for n ≥ 2; if x = 0.
1.4 Two further examples 
45
Although the formula looks a bit cryptic, all that the transformation F α does is to map the set A1 linearly onto the interval [0, 1) and, for each n ≥ 2, map the interval A n linearly onto the interval A n−1 . In particular, notice that F α A1 = L α A1 . The action of F α on each point x = [1 , 2 , . . .]α ∈ [0, 1] is given by # for 1 = 1; [2 , 3 , . . .]α F α (x) := [1 − 1, 2 , 3 , . . .]α for 1 ≥ 2. Notice that the map F α acts on the αLüroth expansion of x in precisely the same way as the Farey map acts on the continued fraction expansion of each point x ∈ [0, 1]. Deﬁnition 1.4.7. Let the two inverse branches of the map F α be denoted by F α,0 : [0, 1] → [0, t2 ] and F α,1 : [0, 1] → [t2 , 1]. With the convention that F α,0 (0) = 0, these two branches are given by F α,0 (x) :=
a n+1 (x − t n+1 ) + t n+2 , for x ∈ A n , n ≥ 1 an
and F α,1 (x) := 1 − a1 x, for x ∈ [0, 1]. Note that F α,0 maps the interval A n into the interval A n+1 , for each n ∈ N. Example 1.4.8. + (a) For the harmonic partition α H := A n := 1/(n + 1), 1/n : n ∈ N , we obtain the α H Farey map F α H , which is given explicitly by ⎧ ⎨2 − 2x for x ∈ (1/2, 1]; F α H (x) := n + 1 1 ⎩ for x ∈ (1/(n + 1), 1/n]. x− n−1 n(n − 1) The graphs of the maps L α H and F α H are shown in Fig. 1.7 + (b) Consider again the dyadic partition α D := 1/2n , 1/2n−1 : n ∈ N . In this case the map F α D coincides with the tent map. To see this, it is enough to note that for each n ∈ N we have that a n = 2−n and t n = 2−(n−1) . The graphs of the maps L α D and F α D are shown in Fig. 1.8. We now show that the relationship between the maps L α and F α is exactly the same as the relationship between the maps G and F, that is, L α is a jump transformation of F α . More precisely, we make the following deﬁnition. Deﬁnition 1.4.9. Let the map ρ α : (0, 1] → N ∪ {0} be deﬁned by setting ρ α (x) := inf {n ≥ 0 : F αn (x) ∈ A1 }.
46  1 Numbertheoretical dynamical systems FαH (x)
LαH (x)
1
1
1 2 1 3 1 4
0
1 4
1 3
1 2
1
x
0
1 4
1 3
1 2
1
x
1 2
1
x
Fig. 1.7. The α H Lüroth and α H Farey map, where t n = 1/n, n ∈ N.
FαD (x)
LαD (x)
1
1
1 2
1 4
0
1 4
1 2
1
x
0
1 4
Fig. 1.8. The α D Lüroth and α D Farey map, which coincides with the tent map T , where t n = (1/2)n−1 , n ∈ N.
Notice that the map ρ α is ﬁnite everywhere on (0, 1]. Then, let the map F *α : [0, 1] → [0, 1] be deﬁned by # F αρ α (x)+1 (x) if x = 0; * F α (x) := 0 if x = 0. The map F *α is said to be the jump transformation on A1 of F α . We then obtain the following result. Note that the proof can be copied line by line from the proof of the corresponding result for the Farey and Gauss maps, so we omit it here.
1.4 Two further examples 
47
Lemma 1.4.10. The jump transformation F *α of F α coincides with the αLüroth map L α . Proof. This follows in precisely the same way as Lemma 1.3.3. Let us now describe how to construct a Markov partition for the αFarey map from the partition α and use it to obtain another coding of the points in [0, 1] from the map F α . The partition given by the two open sets {B0 := Int(A1 ), B1 := (0, 1] \ A1 } is a Markov partition for F α . Each αirrational number in [0, 1] has an inﬁnite coding x =: x1 , x2 , . . .α with (x1 , x2 , . . .) ∈ {0, 1}N such that x k = 1 if and only if F αk−1 (x) ∈ B1 for each k ∈ N. This coding will be referred to as the αFarey expansion or the αFarey coding. (Here we are skipping over the details, but this coding is obtained in precisely the way that the Farey coding was obtained in Section 1.3.) The αFarey coding is related to the αLüroth coding in exactly the same way as the Farey coding is related to the continued fraction expansion, namely, if an αirrational number x ∈ [0, 1] has αLüroth coding given by x = [1 , 2 , 3 , . . .]α , then the αFarey coding of x is given by x := 01 −1 , 1, 02 −1 , 1, 03 −1 , 1, . . . α , where we recall that 0n denotes the sequence of n ∈ N consecutive appearances of the symbol 0 and 00 is understood to mean the appearance of no zeros between two consecutive 1s. For each αrational number x = [1 , 2 , . . . , k ]α , one immediately veriﬁes that this number has an αFarey coding given by either x = 01 −1 , 1, 02 −1 , 1, . . . , 0k −1 , 1, 0, 0, 0, . . .α or x = 01 −1 , 1, 02 −1 , 1, . . . , 0k −2 , 1, 1, 0, 0, 0, . . . α . Recall that F α acts on x = [1 , 2 , . . .]α in the following way: # [1 − 1, 2 , 3 , . . .]α for 1 ≥ 2; F α (x) := for 1 = 1. [2 , 3 , . . .]α In particular, this means that if we instead write x in its αFarey coding, that is, x = x 1 , x2 , x3 , . . . α , then F α (x) := x2 , x3 , x4 , . . .α . Therefore, the map F α : [0, 1] → [0, 1] is a factor of the shift map σ on the shift space {0, 1}N , via the factor map h : {0, 1}N → [0, 1] deﬁned by h((x1 , x2 , x3 , . . .)) := x 1 , x 2 , x 3 , . . . α . Let us now deﬁne the cylinder sets associated with the map F α . These once more coincide with the reﬁnements of the Markov partition given above for F α .
48  1 Numbertheoretical dynamical systems
Deﬁnition 1.4.11. For each ntuple (x1 , . . . , x n ) of positive integers, deﬁne the αFarey α (x1 , . . . , x n ) by setting cylinder set C α (x1 , . . . , x n ) := {y1 , y2 , . . .α : y k = x k , for 1 ≤ k ≤ n}. C By analogy with the Farey decomposition described after Deﬁnition 1.3.4, we will α (x1 , . . . , x n ) : (x1 , . . . , x n ) ∈ {0, 1}n } the nth level call the set of cylinder sets {C α (x1 , . . . , x n ) = F α,x1 ◦ · · · ◦ αFarey decomposition. Observe that we have the relation C F α,x n ([0, 1]). Notice that every αLüroth cylinder set is also an αFarey cylinder set, whereas the converse of this statement is not true. The precise description of the correspondence α (01 −1 , 1, . . . , 0k −1 , 1) coincides is that any αFarey cylinder set which has the form C with the αLüroth cylinder set C α (1 , . . . , k ), but if an αFarey cylinder set is deﬁned by a ﬁnite word ending in the symbol 0, then it cannot be translated to a single αLüroth cylinder set. However, we do have the relation
α (01 −1 , 1, 02 −1 , 1, . . . , 0k −1 , 1, 0m ) = C C α (1 , 2 , . . . , k , n). n≥m+1
It therefore follows that for the Lebesgue measure of this interval we have that α (01 −1 , 1, 02 −1 , 1, . . . , 0k −1 , 1, 0m )) = λ(C α (1 , 2 , . . . , k , n)) λ(C n≥m+1
= a1 a2 · · · ak t m+1 . In addition, we can identify the endpoints of each αFarey cylinder set. If we consider α (01 −1 , 1, . . . , 0k −1 , 1), then we already know the endpoints of this interval the set C (since it is also equal to an αLüroth cylinder set). On the other hand, the endpoints α (01 −1 , 1, 02 −1 , 1, . . . , 0k −1 , 1, 0m ) are given by [1 , . . . , k , m + 1]α and of the set C [ 1 , . . . , k ] α .
1.4.3 Topological properties of F α Let us now consider the topological properties of the maps F α . Perhaps by now the reader will not be surprised to learn that they are essentially the same as the topological properties of the Farey map F. Again, the proofs can be closely modelled after the proofs of the corresponding results for the Farey map and so we leave many of them as exercises. Before stating the ﬁrst proposition, we remind the reader that the measure of maximal entropy μ α for the system F α is the measure that assigns mass 2−n to each nth level αFarey cylinder set, for each n ∈ N.
1.4 Two further examples 
49
Proposition 1.4.12. The dynamical systems ([0, 1], F α ) and ([0, 1], T) are topologically conjugate and the conjugating homeomorphism is given, for each x = [1 , 2 , 3 , . . .]α , by θ α (x) := −2
∞ k (−1)k 2− i=1 i . k=1
Moreover, the map θ α is equal to the distribution function of the measure of maximal entropy μ α for the αFarey map. Proof. See Exercise 1.6.17. Notice that the only difference between the map θ α , for a given partition α, and Minkowski’s questionmark function Q is that the summands in the power of 2 in the latter are the elements of the continued fraction expansion of the point x, whereas in the formula for the function θ α the elements of the αLüroth expansion turn up. Each function θ α is continuous and strictly increasing. It can also be shown that each of the functions θ α (with the obvious, trivial exception of the function θ α D which simply maps the tent map to the tent map), are singular with respect to the Lebesgue measure (for more on these functions, see [Mun14]). Our next aim is to determine the Hölder exponent and the subHölder exponent of the map θ α , for an arbitrary partition α. Recall that the deﬁnition of a Hölder continuous function was given in Deﬁnition 1.3.9. In a similar vein, we say that a map S : X → X of a metric space (X, d) into itself is subHölder continuous with exponent κ > 0 if there exists a positive constant C such that d(S(x), S(y)) ≥ Cd(x, y)κ , for all x, y ∈ X. In order to determine the Hölder and subHölder exponents of θ α , let us ﬁrst deﬁne κ(n) := −n log 2/(log a n ) and set κ+ := inf κ(n) : n ∈ N and κ− := sup κ(n) : n ∈ N . Proposition 1.4.13. We have that the map θ α is κ+ Hölder continuous and, provided that κ− is ﬁnite, κ− subHölder continuous. Proof. In order to calculate the Hölder exponent of θ α , ﬁrst note that θ α (C α (1 , 2 , . . . , k )) = θ α ([1 , 2 , . . . , k ]α ) − θ α ([1 , 2 , . . . , k + 1]α )
= 2−
k
j=1 j
.
This can be seen by simply calculating the image of the endpoints of this cylinder, or by noting that every αLüroth cylinder set C α (1 , 2 , . . . , k ) is an nth level αFarey cylinder set, where n = kj=1 j .
50  1 Numbertheoretical dynamical systems
Suppose ﬁrst that κ+ is nonzero. In this case we have that λ(C α (1 , 2 , . . . , k )) =
k ! i=1
= 2
a i =
k !
2
−i /κ(i )
i=1 1/κ+ k − i=1 i
≥
k !
1/κ+ 2
−i
i=1
= θ α (C α (1 , 2 , . . . , k ))1/κ+ .
Or, in other words, θ α (C α (1 , 2 , . . . , k )) ≤ λ(C α (1 , 2 , . . . , k ))κ+ .
From this point, the proof that θ α is κ+ Hölder continuous is completed in precisely the same way as the proof that Q is log 2/(2 log γ )Hölder continuous and we leave the details to the reader. Suppose now that κ+ is equal to zero. Then, we have that for each q ∈ N there exists m0 ∈ N with the property that for every m ≥ m0 , κ(m) =
m log 2 1 < , or, equivalently, a m < 2−qm . − log a m q
So we have that the sequence of partition elements are eventually exponentially decaying, and hence, the Hölder exponent of the map θ α is necessarily equal to zero. It remains to show that the map θ α is κ− subHölder continuous. Suppose that κ− is ﬁnite (otherwise the deﬁnition of subHölder continuity makes no real sense). Similarly to the κ+ case, we obtain the inequality θ α (C α (1 , . . . , k ))1/κ− ≥ λ(C α (1 , . . . , k )).
The proof can be completed analogously to the proof of the Hölder continuity of θ α from this point on and the details are left as an exercise for the reader (see Exercise 1.6.19). Example 1.4.14. For the conjugacy map θ α H between the map F α H , arising from the harmonic partition, and the tent map T, we have that θ α H is log 4/ log 6Hölder continuous. To show this, ﬁrst observe that κ(1) =
− log 2 2 log 2 2 log 2 3 log 2 =1> = κ(2) and < = κ(3). log 6 log 6 log 12 log 1/2
Then, since 6n > (n2 + n)2 for n ≥ 3, we have that for all n ≥ 3, κ(n) =
2 log 2 n log 2 > = κ(2). log 6 log(n(n + 1))
1.4 Two further examples 
51
Concerning the existence of a constant κ for which the map θ α H is κsubHölder continuous, recall that this means we have to ﬁnd κ > 0 such that for all x, y ∈ [0, 1], θ α H (x) − θ α H (y) x − y κ .
In particular, this inequality has to be satisﬁed for x = 0 and y given by [n]α H = 1/n successively, for each n ∈ N. But here we have that θ α H (0) − θ α H (1/n) = 2−(n−1) and 0 − 1/n  = 1/n, which implies that there can be no such κ. Notice that, in line with the fact that there is no subHölder continuity in this case, we also have that κ− is inﬁnite. Remark 1.4.15. The reasoning given above for why the map θ α H fails to be subHölder continuous also works for the map Q (the conjugacy map between the Farey and tent maps). In other words, there is no positive constant κ such that the map Q is κsubHölder continuous.
1.4.4 Expanding and expansive partitions Let us now introduce some particular classes of partitions that will be useful in the chapters that follow, particularly in Chapter 3. Before beginning this task, we ﬁrst recall the deﬁnition of a slowly varying function. Deﬁnition 1.4.16. A measurable function ψ : R+ → R+ is said to be slowly varying if lim
x→∞
ψ(xy) = 1, for all y > 0. ψ(x)
In the following proposition, we list some of the useful properties that slowly varying functions satisfy. From this list, it should be clear that the idea behind a slowly varying function is that it behaves like a logarithmic function. Proposition 1.4.17. Let ψ, φ : R+ → R+ be two slowly varying functions. Then the following three statements hold: (a) For any ε > 0, we have that lim x ε · ψ(x) = ∞ and lim x−ε · ψ(x) = 0.
x→∞
x→∞
log(ψ(x)) = 0. log(x) (c) For any −∞ < a < ∞, the functions ψ a , ψ · φ and ψ + φ are all slowly varying. (b) lim
x→∞
Proof. See the book by Seneta [Sen76].
52  1 Numbertheoretical dynamical systems Deﬁnition 1.4.18. Let α := {A n : n ∈ N} ∈ A. Then: (a) The partition α is said to be expanding provided that lim
tn
n→∞ t n+1
= ρ, for some ρ > 1.
(b) The partition α is said to be expansive of exponent θ ≥ 0 if the tails of the partition satisfy the power law t n = ψ(n) · n−θ , where ψ : R+ → R+ is a slowly varying function. (c) A partition α is said to be of ﬁnite type if for the sequence of tails t n of α, we have that ∞ n=1 t n converges. Otherwise, α is said to be of inﬁnite type. Notice that if α is expanding, one immediately veriﬁes that α is of ﬁnite type. This can be seen, for instance, by applying the ratio test for series convergence. The next proposition describes the situation for expansive partitions. Proposition 1.4.19. Suppose that α is expansive of exponent θ ≥ 0. Then we have the following classiﬁcation: (a) If θ ∈ [0, 1), then α is of inﬁnite type. (b) If θ > 1, then α is of ﬁnite type. (c) If θ = 1, then α can be either of ﬁnite or inﬁnite type. Proof. Suppose ﬁrst that α is expansive of exponent θ ∈ [0, 1). Then, by Proposition 1.4.17, for all ε > 0 there exists n0 ∈ N such that if n ≥ n0 , then we have that ψ(n) ≥ n−ε . Let ε > 0 be sufficiently small such that θ + ε ∈ (0, 1). Then, ∞
tn =
n=1
n 0 −1
ψ(n) · n−θ +
∞
ψ(n) · n−θ ≥
n=n0
n=1
∞
n−(θ+ε) ≥
n=n0
∞
n−1 .
n=n0
∞
Consequently, n=1 t n = ∞ and α is of inﬁnite type. Now suppose that α is expansive of exponent θ > 1. Then, again by Proposition 1.4.17, for all ε > 0 there exists n0 ∈ N such that if n ≥ n0 , then we have that ψ(n) ≤ n ε . For ε > 0 small enough such that θ − ε > 1, we then have that ∞ n=n0
tn =
∞ n=n0
ψ(n) · n−θ ≤
∞
n−(θ−ε) < ∞.
n=n0
Therefore, in this case, the partition α is of ﬁnite type. It only remains to prove the third assertion, which can be done by considering the following two examples. First, let t1 := 1 and for each n ≥ 2, let t n := (n log n)−1 . The partition α deﬁned in such a way
1.4 Two further examples 
53
is clearly expansive of exponent 1. For this partition, we have that ∞
tn = 1 +
n=1
∞ n=2
1 , n log n
which diverges. So, in this ﬁrst case, the partition is of inﬁnite type. On the other hand, if now we deﬁne a partition by setting t1 := 1 and t n := n−1 ·(log n)−2 for n ≥ 2, we obtain that ∞
tn = 1 +
n=1
∞ n=2
1 , n(log n)2
which is a convergent series, so in this case we have that the partition is of ﬁnite type. This ﬁnishes the proof. Fig. 1.9 illustrates two αFarey maps with α expansive. The graph on the lefthand side has α with exponent θ = 2, so satisﬁes the condition of the second part of Proposition 1.4.19. The graph on the righthand side has α with exponent θ = 1/2, so it satisﬁes the condition given in the ﬁrst part of Proposition 1.4.19.
1.4.5 Metrical Diophantinelike results for the αLüroth expansion In this section, we consider some easilyobtained results for the αLüroth expansion which are analogous to the metrical Diophantine results already given above
Fαʹ (x)
Fα (x)
1
1
1 2 1 3
1 4 1 9
0
1 1 16 9
1 4
1
x
0
1 1 1 52 3
1 2
1
x
Fig. 1.9. The graphs of two αFarey maps with α expansive. The partition on the left is of ﬁnite type with α given by t n := 1/n2 , n ∈ N and the partition on the right is of inﬁnite type with α given by √ t n := 1/ n, n ∈ N.
54  1 Numbertheoretical dynamical systems
for the continued fraction expansion. We will ﬁrst consider the equivalent of the badlyapproximable numbers. Deﬁnition 1.4.20. For each N ∈ N, let the set Bα,N be deﬁned by Bα,N := {x = [1 , 2 , . . .]α ∈ Iα : n ≤ N for all sufficiently large n ∈ N}
and set Bα :=
Bα,N .
N∈N
The set Bα will be referred to as the set of badly αapproximable numbers. Lemma 1.4.21. λ (Bα ) = 0. Proof. First notice that we can write the set of badly αapproximable numbers in the following way:
Bα = Aα,N , N∈N
where Aα,N := {x = [1 , 2 , . . .]α ∈ Iα : k ≤ N for all k ∈ N}.
Also, for each N, n ∈ N, deﬁne A(n) α,N := { x = [1 , 2 , . . .]α ∈ Iα : k ≤ N for all 1 ≤ k ≤ n } . (n+1) (n) It is clear that Aα,N ⊆ A(n) α,N and further that Aα,N ⊂ Aα,N for all N, n ∈ N. Notice that
we may also write A(n+1) α,N in the following way: A(n+1) α,N =
C α (1 , . . . , n+1 ) =
1 ,...,n+1 i ≤N, 1≤i≤n+1
1 ,...,n i ≤N, 1≤i≤n
k≤N
C α (1 , . . . , n , k).
Thus, for all n ∈ N, we have that N = λ A(n+1) a k λ A(n) α,N α,N . k=1
Hence, on applying this argument n − 1 more times, it follows that λ
A(n+1) α,N
=
N k=1
n ak
λ A(1) α,N .
1.4 Two further examples
Since the last term above is simply a constant and since 0 < that λ(Aα,N ) = 0, for any N ∈ N. Finally, we have that ∞ ∞
λ Aα,N ≤ λ(Aα,N ) = 0. N=1
N
k=1 a k

55
< 1, this shows
N=1
This ﬁnishes the proof of the lemma. Corollary 1.4.22. Let Wα be the set deﬁned by Wα := {x = [1 , 2 , . . .]α ∈ Iα : lim sup n = ∞}. n→∞
Then, Wα is of full Lebesgue measure. Proof. Notice that the complement of Wα is the set of all those αirrational numbers with bounded αLüroth elements. The corollary then follows directly from Lemma 1.4.21. Although the sets Aα,N deﬁned in the proof of Lemma 1.4.21 have Lebesgue measure zero for every N ∈ N, we can still distinguish between their sizes by calculating their Hausdorff dimension. Luckily, this is very easy to do, as the next lemma demonstrates. The reader unfamiliar with the Hausdorff dimension of a set can either refer, for instance, to the book by Falconer [Fal14], or can safely ignore the next two results, as they will not be needed for anything that follows. Lemma 1.4.23. For each N ∈ N, we have N a si = 1. dimH Aα,N = s, where s is given by i=1
Proof. All that is required to prove this statement is to notice that for each N ∈ N the set Aα,N is an invariant set for a ﬁnite iterated function system {L α,1 , . . . , L α,N }, where L α,n denotes the nth inverse branch of the map L α . Recall that these are given by L α,n (x) := t n − a n x. That Aα,N is an invariant set for this system means that Aα,N =
N
L α,i Aα,N .
i=1
Then, since these inverse branches are contracting similarities, that is, they satisfy the equality L α,i (x) − L α,i (y) = a i x − y for all x, y ∈ [0, 1], we have that the dimension of Aα,N can be deduced directly from an application of Hutchinson’s Formula (see [Fal14], Theorem 9.3). This observation can be used to calculate the Hausdorff dimension of the set Bα , as follows.
56  1 Numbertheoretical dynamical systems
Proposition 1.4.24. Let α be an arbitrary partition of [0, 1]. Then, dimH (Bα ) = 1. Proof. Since Bα :=
N∈N AN ,
we have that
dimH (Bα ) = sup dimH (AN ) : N ∈ N . N s Then, by Lemma 1.4.23, dimH Aα,N = s, where s is given by i=1 a i = 1 and N+1 t dimH Aα,N+1 = t, where t is given by i=1 a i = 1. Therefore, a1t + · · · + a tN < 1 and so s < t. In other words, dimH Aα,N < dimH Aα,N+1 . Furthermore, as
∞
i=1 a i
= 1, it follows that dimH (Bα ) = 1.
Note that similarly, the Hausdorff dimension of the set of badly approximable numbers (for the continued fraction expansion) is also known to be equal to 1. However, the proof is much more involved, so we simply refer to [Jar29]. Let us now consider the result analogous to Theorem 1.2.19. Theorem 1.4.25. (a) Let φ : N → N be a function such that the series ∞ n=1 t φ(n) diverges. Where the set Bα,φ is deﬁned by Bα,φ := {x = [1 , 2 , . . .]α ∈ Iα : k < φ(k) for all k ∈ N},
we have that λ (Bα,φ ) = 0. (b) Let φ : N → N be a function such that the series Wα,φ is deﬁned to be
∞
n=1 t φ(n)
converges. Where the set
Wα,φ := {x = [1 , 2 , . . .]α ∈ Iα : k > φ(k) inﬁnitely often},
we have that λ (Wα,φ ) = 0. Proof. For the proof of part (a), we proceed similarly to the proof of Lemma 1.4.21. n by setting Deﬁne the sets Bα,φ (n) := {x = [1 , 2 , . . .]α ∈ Iα : k < φ(k) for all 1 ≤ k ≤ n}. Bα,φ
1.4 Two further examples

57
(n+1) (n) (n) ⊂ Bα,φ and Bα,φ ⊂ Bα,φ for all n ∈ N. So, in order to prove that λ (Bα,φ ) = 0, Then, Bα,φ it suffices to show that (n) = 0. lim λ Bα,φ n→∞
To that end, notice that for arbitrary (1 , . . . , n ) ∈ Nn , we can write ⎞ ⎞ ⎛ ⎛ φ(n+1)−1
C α (1 , . . . , n , k)⎠ = ⎝ a k ⎠ λ(C α (1 , . . . , n )) λ⎝ 1≤k n} ∩ T −n (B)), for all B ∈ B .
,n=0 (b) μ(X) = φ dμ. A
Proof. By Lemma 2.4.3, every set A satisfying 0 < μ(A) < ∞ is a sweepout set for T. Therefore, the function φ is well deﬁned. Observe that for all n ≥ 0, T −1 (A c ∩ {φ > n}) = (A ∩ {φ > n + 1}) ∪ (A c ∩ {φ > n + 1}).
(2.10)
(The proof of this fact is left to Exercise 2.6.8.) Now suppose that for all B ∈ B and some ﬁxed n ∈ N, we have μ(B) =
n
μ(A ∩ {φ > k} ∩ T −k (B)) + μ(A c ∩ {φ > n} ∩ T −n (B)).
(2.11)
k=0
Then, using (2.10) and the Tinvariance of μ, we obtain that μ(B) =
n
μ(A ∩ {φ > k} ∩ T −k (B)) + μ(T −1 (A c ∩ {φ > n}) ∩ T −(n+1) (B))
k=0
=
n
μ(A ∩ {φ > k} ∩ T −k (B))
k=0
+ μ(((A ∩ {φ > n + 1}) ∪ (A c ∩ {φ > n + 1})) ∩ T −(n+1) (B)) =
n+1
μ(A ∩ {φ > k} ∩ T −k (B)) + μ(A c ∩ {φ > n + 1} ∩ T −(n+1) (B)).
k=0
Consequently, since the formula is obviously true for n = 0, we have shown by induction that (2.11) holds for all n ≥ 0. In order to ﬁnish the proof of part (a), we must show that limn→∞ μ(A c ∩ {φ > n} ∩ T −n (B)) = 0. In order to do this, we split the remainder of the proof up into three cases. (i) Let B = T −1 (A). Then, μ(A) = μ(T −1 (A)) n μ(A ∩ {φ > k} ∩ T −(k+1) (A)) = k=0
+ μ(A c ∩ {φ > n} ∩ T −(n+1) (A)) n μ(A ∩ {φ = k + 1}) + μ(A c ∩ {φ = n + 1}), = k=0
c and since we can write μ(A) = ∞ k=0 μ(A ∩{ φ = k }) < ∞, we have that lim n→∞ μ(A ∩ −n {φ > n } ∩ T (B)) = 0, as required.
2.4 Ergodicity and exactness
 111
Incidentally, notice that the above calculation also shows that, for all n ≥ 0, μ(A c ∩ {φ = n + 1}) = μ(A) − =μ A\
n
μ(A ∩ {φ = k + 1})
k=0 n
(A ∩ {φ = k + 1})
k=0
= μ(A ∩ {φ > n + 1}). This will be useful in case (iii), below. (ii) Suppose now that B ⊆ A. Then, A c ∩ {φ = n} ∩ T −n (A) = ∅, so in this case the proof is ﬁnished. (iii) Finally, let B ⊆ A c ∩ {φ = N }, for some ﬁxed N ∈ N. In this case, we have that A c ∩ {φ > n} ∩ T −n (B) ⊆ A c ∩ {φ = n + N }. Thus, we have that lim μ(A c ∩ {φ > n} ∩ T −n (B)) = lim μ(A c ∩ {φ = n + N })
n→∞
n→∞
= lim μ(A ∩ {φ > N + n}) = 0, n→∞
where the last two equalities are due to the observation made at the end of case (i) and the fact that the set A is assumed to be of ﬁnite measure. This ﬁnishes the proof of case (iii) and so completes the proof of part (a). For part (b), if we substitute X for B into part (a), we obtain that μ(X) =
∞
μ(A ∩ {φ > n} ∩ T −n (X)) =
n=0
∞
, μ(A ∩ {φ > n}) =
n=0
φ dμ. A
Remark 2.4.34. The result in Proposition 2.4.33 (b) is known as Kac’s formula. Theorem 2.4.35. Let (X, B , μ, T) be a conservative, ergodic, nonsingular (but not necessarily measurepreserving) system. Then, up to multiplication by a constant, there is at most one μabsolutely continuous σﬁnite Tinvariant measure. Proof. Let m1 and m2 be two nonzero, Tinvariant σﬁnite measures that are both absolutely continuous with respect to μ. Then, let B ∈ B with μ(B) > 0. Since T is conservative and ergodic, Lemma 2.4.3 implies that the set B is a sweepout set for T with respect to the measure μ. That is, we have ∞
T −n (B) = X mod μ.
n=0
Therefore, since μ(X \ m1
∞
n=0 T
−n
(B)) = 0, we also have that ∞ ∞
−n −n T (B) = 0 and m2 X \ T (B) = 0. X\ n=0
n=0
112  2 Basic ergodic theory
In other words, the set B is also a sweepout set for T with respect to m1 and m2 . In particular, m1 (B), m2 (B) > 0, so the measures m1 and m2 are in fact in the same measure class as μ. Now choose A ∈ B such that 0 < m1 (A) < ∞ and 0 < m2 (A) < ∞. We may assume, without loss of generality, that m1 (A) = m2 (A) = 1. Then, the measures m1 A and m2 A are equivalent ergodic Tinvariant probability measures for the dynamical system given by T A : (A, BA ) → (A, BA ). Thus, according to Proposition 2.4.8, we have that m1 = m2 on BA . The formula in Proposition 2.4.33 (a) then yields that m1 = m2 on all of B. Corollary 2.4.36. Up to multiplication by a constant, the invariant measures νF for the Farey system ([0, 1], B , F) and να for the αFarey system ([0, 1], B , F α ) are unique. Proof. First, both F and F α are nonsingular with respect to λ, since νF , να and λ are in the same measure class. Then, as F and F α are both conservative and ergodic (see Proposition 2.4.31 and Exercise 2.6.9), an application of Theorem 2.4.35 gives that both νF and να are unique.
2.4.6 Proof of Hopf’s Ratio Ergodic Theorem Now, we will turn our attention to a proof of Hopf’s Ratio Ergodic Theorem. The proof we will shortly present is due originally to Zweimüller [Zwe04]. It exploits the idea of inducing in a way that will allow us to apply the ﬁnite measure version of Birkhoff’s Pointwise Ergodic Theorem. Before we begin the proof, let us ﬁrst ﬁx some notation. Throughout, the system (X, B , μ, T) is assumed to be conservative, ergodic and measurepreserving. For f ∈ L1 (μ), we denote ergodic sums for the system T by S n (f ) :=
n−1
f ◦ Tj.
j=0
We let A be a sweepout set for T and consider the induced transformation T A : A → A on A. For a measurable function h : A → R, we denote the ergodic sums for the induced system by S An (h) :=
n−1
h ◦ T Aj .
j=0
A particularly important example is given by φ n := S An (φ) =
n−1 j=0
φ ◦ T Aj ,
(2.12)
2.4 Ergodicity and exactness  113
where φ : A → N is the return time function on A. Note that for a speciﬁc x ∈ A, the jth summand φ ◦ T Aj (x) inside this sum is equal to the length of the jth excursion of the orbit (T n (x))n≥0 to the set A. To have a more concrete idea of what this means, it helps to think in terms of continued fractions. So, if x = [1, x1 , x2 , x3 , . . .] ∈ A := [1/2, 1] and if F A denotes the Farey map induced on A, then φ1 (x) := φ(x) = x1 , φ2 (x) := φ(x) + φ(F A (x)) = x1 + x2 , and so on; in general, φ n (x) :=
n
xi .
i=1
Notice also that, trivially, we have S An (A) := S An (1 A ) = n, for all n ≥ 1. The idea of chopping up the orbits of points under T into pieces corresponding to each excursion to the set A is a useful one. We can also apply this idea to obtain the induced version of a function f : X → R, by adding up the values of the function observed during the ﬁrst excursion and then represent these as a single function. Deﬁnition 2.4.37. For f : X → R, let the function f A : A → R be deﬁned by
φ(x)−1 A
f (x) :=
f ◦ T j (x).
j=0
The function f A is referred to as the induced version of f on A. Lemma 2.4.38. For an integrable function f : X → R, the following hold. (a) S φ n (f ) = S An (f A ), for all n ∈ N. (b) ,
, f dμ = X
f A dμ.
A
Proof. To prove part (a), we observe that for any n ∈ N, the section of orbit x, T(x), . . . , T φ n (x)−1 (x) that determines the sum S φ n (f ) consists of n complete excursions to A (that is, T φ n (x) (x) ∈ A). Therefore, we have that S φ n (f ) = S φ1 (f ) + S φ2 −φ1 (f ◦ T A ) + · · · + S φ n −φ n−1 (f ◦ T An−1 ) = S φ (f ) + S φ◦T A (f ◦ T A ) + · · · + S φ◦T n−1 (f ◦ T An−1 ) A
= S φ (f ) + (S φ (f )) ◦ T A + · · · + (S φ (f )) ◦ T An−1 = S An (f A ).
114  2 Basic ergodic theory To prove part (b), let f := 1 B for some B ∈ B. Using Proposition 2.4.33 (a), we then have that , ∞ 1 B dμ = μ(B) = μ(A ∩ {φ > n} ∩ T −n (B)) n=0
X
=
, ∞ A
, = A
1 A∩{φ>n} · 1 B ◦ T
n
dμ
n=0
⎛ ⎞ , φ−1 n ⎝ 1 B ◦ T ⎠ dμ = (1 B )A dμ. n=0
A
Hence, the assertion in part (b) holds for characteristic functions. A standard argument from measure theory then ﬁnishes the proof; we leave the details as an exercise. Remark 2.4.39. The latter proposition also yields Kac’s formula (see Remark 2.4.34) as a corollary, by simply choosing f := 1 X . In particular, note that this formula implies that the Tinvariant measure μ is inﬁnite if and only if the returntime function to any set A of positive ﬁnite measure is nonintegrable. We are now in a position to provide a proof of Hopf’s Ratio Ergodic Theorem. Proof of Theorem 2.4.24. Let A be a sweepout set for T. First observe that it suffices to prove that for all f ∈ L1 (μ), we have that 0 f dμ S n (f ) lim (2.13) (x) = X , for μa.e. x ∈ A. n→∞ S n (1 A ) μ(A) Indeed, the set of points where this limit exists and is equal to the righthand side of the equality in (2.13) is Tinvariant and of strictly positive μmeasure (since μ(A) > 0). Therefore, the correct limit must be attained μa.e., by ergodicity. Then, if the same 0 assertion is made for g ∈ L1 (μ), with the extra conditions that g ≥ 0 and X g dμ > 0, the assertion of the theorem follows immediately. Therefore, we are left only to give a proof of (2.13). For this, consider the induced map T A . In light of Proposition 2.4.29, we have that T A is an ergodic measurepreserving transformation on the ﬁnite measure space (A, BA , μA ). We can therefore apply Birkhoff’s Pointwise Ergodic Theorem to T A and the induced function f A , which is integrable by Lemma 2.4.38, to deduce that 0 , f dμ S φ n (f ) S An (f A ) A lim = lim , μa.e. on A. (2.14) = f dμA = X n n→∞ S φ n (1 A ) n→∞ μ(A) This proves (2.13) for μa.e. x ∈ A for the subsequence φ n (x) n≥1 . It remains to demonstrate convergence for the full sequence.
2.5 Exactness revisited

115
By the linearity of the integral, we may assume without loss of generality that f ≥ 0. Then the sequence S n (f ) n≥1 is nondecreasing in n. Now, for a.e. x ∈ A we ﬁnd for every k ∈ N a positive integer n such that φ n−1 (x) ≤ k < φ n (x). Therefore, observing that S k (1 A )(x) = n − 1 and using Lemma 2.4.38 (a), we have S An−1 (f A )(x) S k (f )(x) n S An (f A )(x) ≤ ≤ . n−1 n S k (1 A )(x) n − 1 Observing that n tends to inﬁnity as k tends to inﬁnity, the proof is ﬁnished.
2.5 Exactness revisited Recall from Deﬁnition 2.4.10 that a nonsingular transformation T of a σﬁnite measure ) space (X, B , μ) is said to be exact if for each B in the tail σalgebra n∈N T −n (B) we have that either μ(B) or μ(X \ B) vanishes. In this section, our ﬁrst aim is to prove the exactness of the Farey map. The second aim will be to give a useful equivalent formulation of exactness, known as Lin’s criterion. Let us ﬁrst give a different characterisation of exactness, which we will employ to prove that the Farey map is exact. The origin of this characterisation is a paper by Miernowski and Nogueira [MN13] but it can also be found formulated more generally, in [Len14]. Similar, although not equivalent, ideas can be found already in a paper from the 1960s by Rokhlin [Rok64], where exactness was introduced for the ﬁrst time. We will show that exactness of a transformation is implied by the following intersection property. Deﬁnition 2.5.1. Let (X, B , μ) be a σﬁnite measure space and let T : (X, B , μ) → (X, B , μ) denote a bimeasurable map (that is, T is measurable and T(B) ∈ B for all B ∈ B). Then T is said to satisfy the intersection property with respect to the measure μ provided that for every A ∈ B with positive measure, there exists some k ≥ 1, depending on A, such that μ(T k (A) ∩ T k+1 (A)) > 0. Lemma 2.5.2. Let the map T : (X, B , μ) → (X, B , μ) be bimeasurable and ergodic with respect to μ. If T satisﬁes the intersection property, then T is exact. Proof. Suppose that T is bimeasurable, ergodic and satisﬁes the intersection prop) erty, and let A ∈ m∈N T −m (B). Suppose that μ(A) > 0. In order to show that T is exact, we have to show that the complement of A has μmeasure equal to zero. Since A belongs to the tail σalgebra, we have that T −m (T m (A)) = A, for all m ≥ 0. We then have for all m ≥ 0, T m+1 (T −1 (A) \ A) = T m+1 (T −1 (T −m (T m (A))) \ T −(m+1) (T m+1 (A))) = T m+1 (T −(m+1) (T m (A)) \ T −(m+1) (T m+1 (A))) = T m (A) \ T m+1 (A).
116  2 Basic ergodic theory
Using this, it follows for all m ≥ 1, that T m (T −1 (A) \ A) ∩ T m+1 (T −1 (A) \ A) = (T m−1 (A) \ T m (A)) ∩ (T m (A) \ T m+1 (A)) = ∅. In particular, this shows that λ T m T −1 (A) \ A ∩ T m+1 T −1 (A) \ A = 0, for all m ≥ 1. Hence, by the intersection property, we have that μ(T −1 (A) \ A) = 0. Proceeding similarly for the set A \ T −1 (A), we also obtain that μ(A \ T −1 (A)) = 0. Hence, it follows that T −1 (A) = A mod μ. From there, the result is an immediate consequence of the ergodicity of T. We will now begin to work towards the proof that F is exact. This will follow from a proposition given below, after we prove the following preparatory lemma. Let us remark that the main idea of the proof of exactness of F which we present here, including how to utilise the intersection property given in Deﬁnition 2.5.1, is inspired by the ideas of Lenci in [Len12], where one ﬁnds a similar proof valid for more general systems. Lemma 2.5.3. Consider the Farey system ([0, 1], B , F), and let A be given such that λ(A) > 0. Then lim sup λ F n (A) ∩ C(1) = λ(C(1)). n→∞
Proof. We always have λ F n (A) ∩ C(1) ≤ λ(C(1)). Hence we are left to show that there exists a strictly increasing sequence of positive integers (n k )k≥1 such that limk→∞ λ F n k (A) ∩ C(1) = λ(C(1)). For this let x = x1 , x2 , x3 , . . . be a 1 , . . . , x n ))n≥1 denotes the shrinking Lebesguedensity point² of A and recall that (C(x family of Farey cylinder sets each containing x. Note that, since F is ergodic, (or by using Halmos’s Recurrence Theorem), we can certainly choose x such that there exists a sequence (n k )k≥1 such that x n k +1 = 1, for all k ∈ N. To shorten the notation, let us deﬁne for all k ∈ N, 1 , . . . , x n , 1). 1 , . . . , x n , x n +1 ) = C(x D k := C(x k k k = C(1). Since x is a We have that F n k is bijective on D k and we have that F n k (D k ) = C(1) Lebesguedensity point of A, it follows that lim
k→∞
λ(A ∩ D k ) λ(D k \ A) = 1 and lim = 0. λ(D k ) k→∞ λ(D k )
(2.15)
2 A good reference for the Lebesgue density theorem and Lebesgue density points is Rudin [Rud87]; see in particular Theorem 7.2.
2.5 Exactness revisited

117
By partitioning C(1), we have 1=
λ(C(1)) λ(C(1) \ F n k (A)) λ(F n k (A) ∩ C(1)) = + . λ(C(1)) λ(C(1)) λ(C(1))
Here, the limit for k tending to inﬁnity of the ﬁrst summand in the above expression is equal to zero. Indeed, by ﬁrst using the fact that F n k (D k ) = C(1) and then Lemma 2.4.13 and (2.15), it follows that we have λ(D k \ A) λ(C(1) \ F n k (A)) λ(F n k (D k \ A)) λ(F n k (D k \ A)) = → 0, λ(C(1)) λ(C(1)) λ(D k ) for k tending to inﬁnity. This ﬁnishes the proof. Proposition 2.5.4. Let A be given such that λ(A) > 0. Then for the Farey map F we have that lim sup λ(F n (A) ∩ F n+1 (A) ∩ C(1)) = λ(C(1)). n→∞
Proof. Obviously, lim supn→∞ λ(F n (A) ∩ F n+1 (A) ∩ C(1)) ≤ λ(C(1)). As in the proof of Lemma 2.5.3, let x = x1 , x2 , . . . be a Lebesguedensity point of A. In light of Remark 2.4.18, we have that there exists a sequence (m k )k≥1 such that x m k +1 = 1 , . . . , x m , x m +1 ))k≥1 and x m k +2 = 1, for all k. Therefore, for both of the sequences (C(x k k (C(x1 , . . . , x m k , x m k +1 , x m k +2 ))k≥1 we can proceed as in the proof of Lemma 2.5.3, which yields that lim λ(F m k (A) ∩ C(1)) = lim λ(F m k +1 (A) ∩ C(1)) = λ(C(1)).
k→∞
k→∞
Using this observation we obtain for the lower bound λ(F m k (A) ∩ F m k +1 (A) ∩ C(1)) = λ(F m k (A) ∩ C(1)) + λ(F m k +1 (A) ∩ C(1)) − λ((F m k (A) ∪ F m k +1 (A)) ∩ C(1)) ≥ λ(F m k (A) ∩ C(1)) + λ(F m k +1 (A) ∩ C(1)) − λ(C(1)) → λ(C(1)), for k tending to inﬁnity. This proves the proposition. Corollary 2.5.5. For the Farey system ([0, 1], B , F), let A be given such that λ(A) > 0. Then there exists n ∈ N such that λ(F n (A) ∩ F n+1 (A)) > 0. In other words, we have that the Farey map F satisﬁes the intersection property with respect to the Lebesgue measure λ.
118  2 Basic ergodic theory
Theorem 2.5.6. The Farey map F is exact with respect to the inﬁnite invariant measure νF . Proof. Recalling that F is ergodic, this follows immediately on combining Corollary 2.5.5 with Lemma 2.5.2 and using the fact that λ and νF are in the same measure class. Let us now move on to our second goal, the statement and proof of Lin’s equivalent formulation of exactness [Lin71]. We will use this result in Chapter 5 to obtain information about certain sets deﬁned in terms of their continued fraction expansion. In the proof here, we will make use of the dual space of L1 (X, B , μ), which we already introduced in Remark 2.3.8, and the Banach–Alaoglu Theorem, which can be found, for instance, as Theorem V.4.2 in Dunford and Schwartz [DS88], if the reader has not encountered it before. Theorem 2.5.7 (Lin’s Criterion for Exactness). Let (X, B , μ, T) be a measurepreserving 0 system. Then, T is exact if and only if for all f ∈ L1 (X, B , μ) with X f dμ = 0 we have for that the transfer operator T 1 1 1 n 1 (f )1 = 0. lim 1T n→∞
1
Proof. First suppose that T is exact and that f ∈ L1 (X, B , μ) has zero expectation, that 0 = 1 (see Exercise 2.6.11), the sequence is, suppose that X f dμ = 0. Then, since T n (f )1 )n≥1 is bounded. To show that its limit is zero, ﬁx a subsequence (n k ) such (T k≥1 that 1 1 1 1 1 nk 1 1 n 1 lim 1T ( f )1 = lim sup 1T ( f )1 < ∞. k→∞
1
n→∞
1
If (g n )n≥1 is deﬁned by n ( f ) ∈ L∞ (X, B , μ) , g n := sign T then we have, for all n ∈ N, , 1 , 1 1 n 1 n ( f ) dμ = g n ◦ T n · f dμ. 1T ( f )1 = g n · T 1
1 1 Since 1g n ◦ T n 1∞ = 1, it follows from the Riesz Representation Theorem that we can identify each g n ◦ T n with a bounded linear functional on L1 (X, B , μ), that is, with an element in L1 (X, B , μ)* L∞ (X, B , μ). By the Banach–Alaoglu Theorem we know that the closed unit ball in L1 (X, B , μ)* is compact in the weak* topology. Since T −n−1 B ⊂ * * T −n B, it follows that L1 X, T −n−1 B , μ ⊂ L1 X, T −n B , μ . Now for each K ∈ N, we consider the nonempty and weak* compact sets G K of accumulation points of the sequence g n k ◦ T n k k≥K in L1 (X, B , μ)* , that is, . * / G K := g ∈ L1 X, T −n K B , μ : g is an accumulation point of g n k ◦ T n k k≥K ,
2.5 Exactness revisited

119
as subsets of the weak* compact unit ball in L1 (X, B , μ)* . Since G K ⊂ G K+1 for all ) K ∈ N, the intersection property of compact sets implies that K∈N G K = ∅. Fix * ) g ∈ K∈N G K . By deﬁnition, g ∈ L1 X, T −n K B , μ L∞ X, T −n K B , μ for all K ∈ N, so ) we have that g is measurable with respect to the tailσalgebra n∈N T −n B. Therefore, by the exactness of T, we have that g must be constant μa.e., that is, g = c ∈ [0, ∞) μa.e., for some c ∈ R. Since g is an accumulation point of the sequence g n k ◦ T n k k≥1 , 0 0 there exists a subsequence n k ≥1 such that lim→∞ g n k ◦ T n k · f dμ = g · f dμ. This gives that , 1 1 1 1 1 n 1 1 nk 1 n 0 ≤ lim sup 1T ( f )1 = lim 1T ( f )1 = lim g n k ◦ T k · f dμ 1 k→∞ 1 k→∞ n→∞ , , , = lim g n k ◦ T n k · f dμ = g · f dμ = c · f dμ = 0. →∞
In order to prove the converse, we assume that T 1is not exact and construct 1 0 1 n 1 f ∈ L1 (X, B , μ) with f dμ = 0 and lim inf n→∞ 1T ( f )1 > 0. To that end, choose 1 ) −n A ∈ n∈N T B such that 0 < μ (A) < μ (X ), which is possible by the σﬁniteness of μ. For the same reason there exists a measurable set B ⊂ X \ A such that 0 < μ (B) < ∞. For 0 0 f := 1 A − μ (A) /μ (B) 1 B , we have that f ∈ L1 (X, B , μ), f dμ = 0 and A f dμ = μ (A) > 0. ) Since A ∈ n∈N T −n B, there exists a sequence (A n )n≥1 in B such that A = T −n A n , for each n ∈ N. This yields that for all n ∈ N, we have , , 1 , 1 1 n 1 n n n f dμ 1T ( f )1 ≥ T f dμ ≥ T f dμ = 1 A n T 1
An
,
=
An
1 A n ◦ T n f dμ =
, f dμ > 0. A
This ﬁnishes the proof. We end this section by showing that if the underlying measure is ﬁnite, then exactness of a transformation implies the following mixing property. Discussion of mixing for inﬁnite systems, whilst certainly interesting, is a much trickier business and we shall not go into it here. The interested reader is referred to the work of Lenci, see [Len14], [Len13], and references therein. Deﬁnition 2.5.8. Let (X, B , μ, T) be a measurepreserving system such that μ(X) = 1. Then T is said to be mixing with respect to the measure μ provided that for every A, B ∈ B , we have that lim μ(A ∩ T −n (B)) = μ(A)μ(B).
n→∞
Corollary 2.5.9. Let (X, B , μ, T) be a measurepreserving system such that μ(X) = 1. If T is exact, then T is mixing with respect to the measure μ.
120  2 Basic ergodic theory
Proof. First note that since T preserves the probability measure μ, we have that 1 = 1. Using this, it μ ◦ T −1 = μ, which implies d(μ ◦ T −1 )/dμ = 1 and hence, T 0 0 − f dμ) = T(f ) − f dμ. follows that for each f ∈ L1 (X, B , μ) we have that T(f X X 0 0 0 Since (f − X f dμ) ∈ L1 (X, B , μ) and X (f − X f dμ)dμ = 0, we can apply Lin’s 0 n (f − f dμ)1 = 0. Using this and the fact Criterion, which gives that limn→∞ T X 0 that 1 A − X 1 A dμ ∈ L1 (X, B , μ), we obtain , , n 1 A )1 B dμ lim μ(A ∩ T −n (B)) = lim 1 A (1 B ◦ T n ) dμ = lim (T n→∞
n→∞
n→∞
X
, = lim
n→∞
X
n (1 A − μ(A))1 B dμ + μ(A)μ(B) (T
X
= μ(A)μ(B). This completes the proof.
2.6 Exercises Exercise 2.6.1. Show that the Lebesgue measure is not invariant under the Gauss map. Exercise 2.6.2. Show that the system (R, B , λ, T) deﬁned in Example 2.3.12 is really nonsingular and that its Hopf decomposition is nontrivial with conservative part T = [0, 1]. given by C Exercise 2.6.3. As before, let F0 : x → x/(1 + x) and F1 : x → 1/(1 + x) denote the two inverse branches of the Farey map F. For ω = (ω1 , . . . , ω n ) ∈ {0, 1}n , n ∈ N, deﬁne F ω := F ω1 ◦ . . . ◦ F ω n , En := {(ω1 , . . . , ω n ) ∈ {0, 1}n : # {i : ω i = 1} is even}, and On := {0, 1}n \ En .
(i) Show that for all n ∈ N and x ∈ (0, 1) we have that * F ω (x) x = * ω∈En . ω∈On F ω (x) (ii) Use the identity in (i) to obtain an alternative proof of the ﬁxed point equation P F (h) = h of the Ruelle operator P F for the map F. (See Proposition 2.3.19). Exercise 2.6.4. Give a proof of Proposition 2.3.21 (b), by verifying the eigenequation P L α h L α = h L α for the Ruelle operator P L α of an αLüroth map L α . Exercise 2.6.5. Let F A : A → A denote the induced map of the Farey map F on the set A := [1/2, 1]. Prove that F A ([x1 , x2 , x3 , x4 , . . .]) = [x1 , x3 , x4 , . . .], for all [x1 , x2 , x3 , x4 , . . .] ∈ A.
2.6 Exercises

121
Exercise 2.6.6. Let (X, B , μ, T) be an ergodic system with a ﬁnite invariant measure μ, and suppose that E ∈ B is such that μ(E) > 0. Let (n k )k≥0 be the sequence of occurrence times such that T n k (x) ∈ E for all k ≥ 0 (note that these are guaranteed to exist for μa.e. x by Halmos’s Recurrence Theorem). Show that if we assume that n0 = 0, so x ∈ E, then we have μa.e. that lim
k→∞
nk 1 . = k μ(E)
Exercise 2.6.7. Prove that in an inﬁnite ergodic system, the assumption of conservativity is necessary for the existence of sweepout sets of ﬁnite measure. (See Lemma 2.4.3.) Exercise 2.6.8. Prove that where φ(x) := inf {n ≥ 1 : T n (x) ∈ A} and A is a sweepout set for T, we have T −1 (A c ∩ {φ > n}) = (A ∩ {φ > n + 1}) ∪ (A c ∩ {φ > n + 1}), for all n ≥ 0. Exercise 2.6.9. Taking inspiration from the proof of Proposition 2.4.31, prove that the map F α is ergodic with respect to the measure να deﬁned in Proposition 2.3.20. Exercise 2.6.10. Using the duality (L1 (μ))* L∞ (μ) give a formal proof that the unitary operator U T : L∞ (μ) → L∞ (μ), f → f ◦ T, is the dual operator of T. = 1. Exercise 2.6.11. Prove that T Exercise 2.6.12. Prove the statement in (2.7). Exercise 2.6.13. Let μ and ν be two σﬁnite measures on (X, B) with μ ∼ ν . Show that the Radon–Nikodým density dμ/dν is almost everywhere positive and that dν /dμ = (dμ/dν )−1 Exercise 2.6.14. Show that if v/w is a reduced fraction in (0, 1), then for all p/q ∈ F −n v/w we have that n p q2 = (F ) . q w2 Exercise 2.6.15. Show that the statement in Proposition 2.5.4 still holds if we replace C(1) by any arbitrary Fareycylinder whose ﬁnal symbol is equal to 1. (Of course, the sequence (m k )k≥1 might be a different one). Exercise 2.6.16. Fill in the gap in the proof of Lin’s Criterion: Prove that if g is measurable with respect to the tail σalgebra of T and T is exact, then g is constant μa.e.
3 Renewal theory and αsumlevel sets In this chapter we will mainly investigate certain subsets of the unit interval which are deﬁned in terms of the αLüroth expansion. However, in order to motivate this exploration, in the ﬁrst section we will describe the analogous problem for the continued fraction expansion. In the second section, we ﬁrst deﬁne the sets we are interested in and then show how classical results in the ﬁeld of renewal theory can be used to obtain detailed information about the sets in question.
3.1 Sumlevel sets One of the goals of Chapter 5 will be to give a detailed measuretheoretical analysis of the following sets Cn , for n ∈ N, which we will call the sumlevel sets for the continued fraction expansion: # k Cn := [x 1 , x2 , x3 , . . .] ∈ [0, 1] : x i = n for some k ∈ N i=1
The ﬁrst few of these sets are shown in Fig. 3.1, below. Directly from the deﬁnition, we have that C1 = C(1) = [1/2, 1]. Likewise, it follows that for the next few sumlevel sets we have C2 = C(2) ∪ C(1, 1), C3 = C(3) ∪ C(2, 1) ∪ C(1, 1, 1) ∪ C(1, 2),
and so on. To begin the inspection of the sequence (Cn )n≥1 of these sets, let us consider the liminf set, which is deﬁned by
Cm = {x ∈ [0, 1] : x ∈ Cn for all sufficiently large n }. lim inf Cn := n→∞
n≥1 m≥n
In order for an irrational number x to lie in all of the sets CN , CN+1 , CN+2 , . . ., for some N ∈ N, we must have that x = [x1 , . . . , x k , 1, 1, 1, . . .], where ki=1 x i = N. In other words, the liminf set of the sequence (Cn )n≥1 is equal to the set of all noble numbers (see Deﬁnition 1.1.13 (d)), that is, irrational numbers whose continued fraction digits are from some point on always equal to 1. As we have already observed, this set is
3.2 Sumlevel sets for the αLüroth expansion  123
0
1 C1
1/2 1/3 1/4 1/5
C2
2/3 2/5
2/7 3/8 3/7
3/5
.. .
C3
3/4
4/7 5/8 5/7
4/5
C4
Fig. 3.1. The ﬁrst four sumlevel sets.
countable. On the other hand, one immediately veriﬁes that the limsup set¹
Cm = {x ∈ [0, 1] : x ∈ Cn for inﬁnitely many n } lim sup C n := n→∞
n≥1 m≥m
is equal to the set of all irrational numbers in [0, 1]. Hence, at ﬁrst sight, the sequence of sumlevel sets appears to be far away from being a canonical dynamical entity. (However, in Chapter 5 we will show that this is actually not the case.) For the Lebesgue measure of the ﬁrst four members of the sequence of the sumlevel sets (cf. Fig. 3.1) one immediately computes that λ(C1 ) = 1/2, λ(C2 ) = 1/3, λ(C3 ) = 3/10 and λ(C4 ) = 39/140. From this one might already start to suspect that the sequence (λ (Cn ))n≥1 is decreasing for n tending to inﬁnity. In fact, it was conjectured by Fiala and Kleban [FK10] that λ (Cn ) tends to zero, as n tends to inﬁnity. In Section 5.1, we will settle this conjecture affirmatively, as well as prove some much stronger results. Before this, though, we will consider the parallel, easier to analyse, situation for the αLüroth systems.
3.2 Sumlevel sets for the αLüroth expansion In this section, we will study the sequence of the Lebesgue measures of the αsumlevel sets for an arbitrary αLüroth map L α , for a given partition α. Analogous to the sumlevel sets for the continued fraction expansion, deﬁned in the previous section,
1 Recall that we have already encountered limsup sets in Chapter 1, speciﬁcally in Lemma 1.2.18 (the Borel–Cantelli Lemma).
124  3 Renewal theory and αsumlevel sets these sets are given, for each n ∈ N, by # Cn(α)
:=
x ∈ C α (1 , 2 , . . . , k ) :
k
i = n, for some k ∈ N .
i=1
Also, for later convenience, we deﬁne C0(α) := [0, 1]. Our toolkit for the investigation into the sequence (λ(Cn(α) ))n≥1 will consist of classical results from renewal theory.
3.2.1 Classical renewal results Our aim here is to state and give some ideas of the proofs of some strong renewal theorems due to Garsia/Lamperti [GL63] and Erickson [Eri70]. Before doing so, we ﬁrst state and prove the original discrete renewal theorem due to Erdős, Pollard and Feller [EFP49]. We begin by deﬁning a renewal pair. Deﬁnition 3.2.1. Let (v n )n≥1 be an inﬁnite probability vector, that is, a sequence of nonnegative real numbers for which ∞ k=1 v n = 1. Assume that associated to this vector there exists a sequence (w n )n≥0 , with w0 := 1, which satisﬁes the renewal equation: wn =
n
v m w n−m , for all n ∈ N.
m=1
A pair ((v n )n≥1 , (w n )n≥0 ) of sequences with these properties is referred to as a renewal pair. Let us give a brief sketch of the original probabilistic motivation for this deﬁnition. For further details and many examples, we refer the reader to Chapter XIII of Feller [Fel68a]. Consider a sequence of independent identically distributed random variables (T n )n∈N with values in N. (Just think of T n as the random discrete time between the occurrence of a ‘recurrent event’, like the successive renewal of a burnedout lightbulb.) For the distribution, we write v k := P(T1 = k) for each k ∈ N. Now the probability of the occurrence of the event at time k ∈ N is given by #  w k := P T i = k for some ∈ N0 . i=1
Since the empty sum is by deﬁnition equal to 0 we have w0 = 1. Using the fact that the sequence of random variables (T n ) are independent and identically distributed
3.2 Sumlevel sets for the αLüroth expansion  125
we ﬁnd # wn = P
 T i = n for some ∈ N0
i=1
=
n
# P
T1 = m and
m=1
 T i = n for some ∈ N
i=1
= P({T1 = n}) +
n−1
# P
T1 = m and
m=1
= vn +
n−1
# vm P
m=1
=
n
 T i = n − m for some ∈ N
i=2

T i+1 = n − m for some ∈ N0
i=1
v m w n−m .
m=1
This shows that the renewal equation w n = nm=1 v m w n−m for n ∈ N has its natural place in probability theory. In the following we will see how to determine the asymptotic behaviour of (w n ) in terms of (v n ) just by analysing the renewal equation. We are now almost in a position to state and prove the classical discrete renewal theorem. The proof we give here is essentially (with a few extra details inserted) the original proof given in [EFP49]. Before we start, we make the following deﬁnitions for a given renewal pair ((v n ), (w n )): d v := gcd{n ≥ 1 : v n > 0} and d w := gcd{n ≥ 1 : w n > 0}. Then, for all n with w n = 0 we also have that v n = 0, since using the renewal equation gives that w n = 0 = nm=1 v m w n−m and so each term in this sum must be equal to zero. In particular, v n w0 = 0, but since w0 = 1, it follows that v n = 0. This implies that d w is a factor of d v . It is also possible to show, using a fairly straightforward but somewhat ungainly induction argument, that d v is a factor of d w . Thus, these two quantities are always equal. We will also need the following elementary technical observation. Lemma 3.2.2. If d is the greatest common divisor of the sequence of natural numbers (n k )k≥1 , then there exist numbers K and M with the property that for each m ∈ N such that m ≥ M there exist c1 , . . . , c K ∈ N such that m·d=
K
ck nk .
k=1
Proof. We can assume that d = 1 (otherwise just divide each of the n k by d), and also that d is the greatest common divisor of the ﬁrst K of the given numbers, that is, g.c.d.(n1 , n2 , . . . , n K ) = 1. It is well known that there then exist integers b1 , b2 , . . . , b K
126  3 Renewal theory and αsumlevel sets
with the property that² b1 n1 + . . . + b K n K = 1. Letting b := max{b1 , b2 , . . . , b K } and M := bn1 (n1 + · · · + n K ), we have that each m ≥ M can be written in the form m = bn 1 (n1 + · · · + n K ) + in1 + r(b1 n1 + · · · + b K n K ), where i ≥ 0 and 0 ≤ r < n1 come from the division algorithm applied to m − M. Therein lie the factors c k and (since bn1 > b k r), they are clearly positive integers. Finally, before stating the theorem, we also need the following elementary lemma. We include the proof for completeness. Lemma 3.2.3. Let (b n )n≥1 and (bn )n≥1 be two sequences of real numbers with the property that limn→∞ (b n + bn ) exists. Then, provided that we do not have lim inf n→∞ b n = −∞ and lim supn→∞ bn = ∞, or vice versa, it follows that lim (b n + bn ) = lim inf b n + lim sup bn .
n→∞
n→∞
n→∞
Proof. On the one hand we have lim (b n + bn ) − lim sup(bn ) = lim inf (b n + bn ) + lim inf (−bn ) ≤ lim inf (b n ).
n→∞
n→∞
i→∞
i→∞
n→∞
On the other hand, lim (b n + bn ) − lim inf (b n ) = lim sup(b n + bn ) + lim sup(−b n ) ≥ lim sup(bn ).
n→∞
i→∞
n→∞
i→∞
n→∞
Combining these two inequalities, the lemma is proved. Theorem 3.2.4 (Discrete Renewal Theorem). Let ((v n )n≥1 , (w n )n≥0 ) be a renewal pair and suppose that d v = 1. Then 1 , m=1 m · v m
lim w n = ∞
n→∞
where the limit is understood to be equal to zero if the series in the denominator diverges. Proof. For ease of notation, throughout we denote s := ∞ m=1 m · v m . First, we show by induction that 0 ≤ w n ≤ 1, for each n ∈ N0 . To start, notice that w1 = v1 · w0 = v1 ≤ 1.
2 This can be seen, for instance, by considering moduli of integers. The interested reader is referred to Section 2.9 of The Theory of Numbers, by Hardy and Wright [HW08].
3.2 Sumlevel sets for the αLüroth expansion
 127
Now suppose that 0 ≤ w k ≤ 1 for all 0 ≤ k ≤ n − 1. Then w n = v1 · w n−1 + v2 · w n−2 + · · · + v n · w0 ≤
n
v k ≤ 1.
k=1
Let w := lim supn→∞ w n and pick a subsequence (w n k )k∈N with the property that limk→∞ w n k = w. Then, for all m ≥ 1, we have, via Lemma 3.2.3, that ⎛ ⎞ ⎜ ⎟ v s · w n k −s ⎠ w = lim w n k = lim ⎝v m · w n k −m + k→∞
k→∞
1≤s≤n k s =m
= lim inf v m · w n k −m + lim sup k→∞
k→∞
≤ v m lim inf w n k −m + k→∞
v s · w n k −s
1≤s≤n k s =m
v s lim sup w n k −s k→∞
s =m
≤ v m lim inf w n k −m + (1 − v m )w. k→∞
From this it follows immediately that v m w ≤ v m lim inf k→∞ w n k −m and therefore, provided that v m > 0, we obtain lim sup w n =: w ≤ lim inf w n k −m . k→∞
n→∞
Thus, lim w n k −m = w.
(3.1)
k→∞
Applying this argument many times over, one obtains that equation (3.1) holds for all m such that there exist positive integers m1 , . . . m j with each v m i > 0 so that m = m1 + . . . + m j . Given that d v = 1, from Lemma 3.2.1 it follows that every large enough m has this form (where we can do without the factors c i , as there is no reason that the integers m i have to be distinct). In other words, there exists some M ∈ N such that (3.1) holds for every m ≥ M. Now, for each n ∈ N, set r n :=
∞
vm .
m=n+1
Then r0 = 1 and ∞ m=1
m · vm =
∞ m=1
vm +
∞ m=2
vm +
∞ m=3
vm + . . . =
∞ n=0
rn .
128  3 Renewal theory and αsumlevel sets
From the renewal equation and the fact that r m − r m−1 = −v m , we deduce that r0 · w n = w n = −
n
(r m − r m−1 )w n−m
m=1
and, by bringing the negative terms to the lefthand side, we can write this in the following way: r0 · w n + r1 · w n−1 + · · · + r n · w0 = r0 · w n−1 + · · · + r n−1 · w0 .
(3.2)
If we deﬁne the lefthand side of Equation (3.2) to be equal to s n , then the righthand side of Equation (3.2) is equal to s n−1 . Note that s0 = r0 · w0 = 1. Thus, in light of Equation (3.2), we have that s n = 1, for all n ∈ N. In particular, n k −M
r i · w n k −(M+i) = 1.
(3.3)
i=0
We will now show that w = 1/s. First, suppose that s is ﬁnite. In that case, for all ε > 0 there exists N ∈ N with r0 + r1 + . . . r N ≥ s − ε. If k is sufficiently large such that n k − M ≥ N, then by (3.3) we have 1≥
N
r i · w n k −(M+i) .
i=0
It then follows from (3.1) that 1 ≥ w(r1 + . . . r N ) ≥ w(s − ε). Since ε was an arbitrary positive number, we obtain the inequality w ≤ 1/s. On the other hand, from (3.3) and from the pair of inequalities w n ≤ 1 and (r N+1 + r N+2 + . . .) ≤ ε, we deduce that 1≤ε+
N
r i · w n k −(M+i) .
i=0
Letting k tend to inﬁnity, the above equation yields that 1≤ε+w
∞
m · vm ,
m=1
and so we also have the opposite inequality, namely, w ≥ 1/s.
3.2 Sumlevel sets for the αLüroth expansion  129
If we are instead in the situation that s is inﬁnite, we have for all C > 0, that there exists an N ∈ N such that r0 + r1 + . . . r N > C, from which, in a similar way to the above, we obtain the inequality 1 ≥ Cw. Since C can be arbitrarily large, it follows that w = 0. Notice that if lim supn→∞ w n = 0, then we must have that limn→∞ w n = 0, as these are all positive numbers. Therefore, in the case where s is inﬁnite, the proof is ﬁnished. In the case where s is ﬁnite, we also have to show that lim inf n→∞ w n = 1/s. This proceeds analogously, starting by setting w := lim inf n→∞ w n and then choosing a subsequence that achieves this lower limit. Remark 3.2.5. In the above proof, if it so happens that v m > 0 for every m ∈ N, we could dispense with the slight complication of having to use Lemma 3.2.2, since in this situation we have that Equation (3.1) holds for every m ∈ N. We will now state some stronger renewal results obtained by Garsia and Lamperti [GL63], and by Erickson [Eri70]. Their results are for the case where the limit in the statement of the discrete renewal theorem is equal to zero. They study the manner in which the sequence (w n )n≥0 tends to zero, under a certain additional hypothesis which we will now describe. Let the sequences ((v n )n≥1 , (w n )n≥0 ) be a given renewal pair and let the two associated sequences (V n )n∈N and (W n )n∈N be deﬁned, for all n ∈ N, by V n :=
∞
v k and W n :=
k=n
n
wk .
(3.4)
k=1
Then the principal assumption in these strong renewal results is that V(n) satisﬁes V n = ψ(n)n−θ , for all n ∈ N, for some θ ∈ [0, 1] and for some slowly varying function ψ. (Recall that slowly varying functions were deﬁned in Section 1.4.4.) Before stating the theorem, let us also remind the reader that the notation “f (n) ∼ g(n)” means that limn→∞ f (n)/g(n) = 1. Finally, the constants appearing on the righthand side of the ﬁrst two statements are given in terms of the gamma function (which was originally introduced by Euler). The gamma function is an extension of the factorial function to complex arguments, so we have Γ(n) = (n − 1)!, and, considered as an extension to the open righthalf plane, it has no zeros. For more details, we refer the interested reader to the book Complex Analysis by Gamelin [Gam01].
130  3 Renewal theory and αsumlevel sets
Strong renewal results by Garsia/Lamperti and Erickson [GL63, Lemma 2.3.1], [Eri70, Theorem 5]: For θ ∈ [0, 1], we have that −1
W n ∼ (Γ(2 − θ)Γ(1 + θ))
·n·
n
−1 Vk
.
k=1
Also, if θ ∈ (1/2, 1], then −1
w n ∼ (Γ(2 − θ)Γ(θ))
·
n
−1 Vk
.
k=1
Finally, for θ ∈ (0, 1/2) we have that the limit in the latter formula does not have to exist in general. However, for θ ∈ (0, 1/2] it is shown in [GL63, Theorem 1.1] that one at least has lim inf n · w n · V n = n→∞
1 sin πθ = , π Γ(θ)Γ(1 − θ)
and that the limit exists if we restrict the indices to a set of integers whose complement is of zero density³ We will not rigorously prove these strong renewal results, as the proofs are decidedly nontrivial. However, we will provide a sketch of some of the main ideas. The proof of the ﬁrst statement in the strong renewal results by Garsia/Lamperti and Erickson is reasonably straightforward, although it does use some fairly heavy analytic machinery. The deep result underlying this statement is Karamata’s Tauberian Theorem, which we state below in the setting of power series (the proof can be found in [Fel68b]). Before stating this theorem, let us recall the following deﬁnitions: – A measurable function ψ : R+ → R+ is said to be slowly varying if lim
x→∞
–
ψ(xy) = 1, for all y > 0. ψ(x)
A function f : R+ → R+ is called regularly varying with exponent ρ (with ρ ∈ R) if for all x ∈ R+ we have f (x) = x ρ · ψ(x),
–
where ψ is slowly varying. A sequence (b n )n∈N is called regularly varying with exponent ρ if for all n ∈ N, we have that b n = f (n), with f : R+ → R+ regularly varying with exponent ρ.
3 If we set A(n) := {1, . . . , n} ∩ A, then the density of a set of integers A is given, where the limit √ exists, by d(A) := limn→∞ # A(n)/n. For example, if A := {n2 : n ∈ N}, then since # A(n) ≤ n we have that d(A) = 0.
3.2 Sumlevel sets for the αLüroth expansion  131
Theorem 3.2.6 (Karamata’s Tauberian Theorem). Let b n ≥ 0 for all n ∈ N0 and suppose that the series B(s) :=
∞
bn sn
n=0
converges for 0 ≤ s < 1. If ψ is slowly varying and 0 ≤ ρ < ∞, then the following two statements are equivalent: 1 1 , as s → 1− . · ψ (a) B(s) ∼ 1−s (1 − s)ρ (b)
n−1
bk ∼
k=0
n ρ · ψ(n) , as n → ∞. Γ(1 + ρ)
Furthermore, if the sequence (b n )n∈N is monotonic and 0 < ρ < ∞, then (a) is equivalent to (c) b n ∼
n ρ−1 · ψ(n) , as n → ∞. Γ(ρ)
Finally, if for a family of sequences (b xn )x∈X the asymptotic in (b) holds uniformly in x ∈ X then so does the asymptotic in (a). Proof. See [Fel68b], Theorem 5 in Section XIII.5 and, for the uniformity, a detailed inspection of the proof of the Extended Continuity Theorem (Section XIII.1 Theorem 2a) is needed (cf. Excercises 3.3.6 and 3.3.7). For the following discussion, we will need to use the notion of a generating function (see also Chapter XI of [Fel68b]). Deﬁnition 3.2.7. Let (c i )i≥0 be a sequence of real numbers and deﬁne C(s) :=
∞
cn sn .
n=0
Then, if C(s) is convergent in some interval −s0 < s < s0 , we say that C(s) is the generating function of the sequence (c i )i≥0 . Note that if the sequence (c i )i≥0 is bounded, then C(s) certainly converges in the interval s < 1. Now, for a given renewal pair ((v n )n≥1 , (w n )n≥0 ), and with W n and V n deﬁned as in (3.4), we wish to use Karamata’s Tauberian Theorem to prove that −1
W n ∼ (Γ(2 − θ)Γ(1 + θ))
·n·
n
−1 Vk
k=1
To begin, ﬁrst notice that n k=1
V k ∼ (1 − θ)−1 · n1−θ · ψ(n), as n → ∞.
.
132  3 Renewal theory and αsumlevel sets
Now, let us deﬁne two generating functions V(t) :=
∞
V n t n and W(t) :=
n=0
∞
wn tn .
n=0
Recalling both that V n = n−θ ψ(n), where θ ∈ [0, 1] and ψ is a slowly varying function, and that V n is monotonically decreasing, we can apply Theorem 3.2.6 (c) =⇒ (a) to obtain 1 V(t) ∼ Γ(1 − θ)(1 − t)θ−1 ψ . 1−t Multiplying out and gathering coefficients yields that V(t)W(t) = 1/(1 − t), or, in other words, W(t) =
1 . (1 − t)V(t)
Thus, W(t) ∼
1
1 Γ(1 − θ)ψ 1−t
(1 − t)−θ .
Finally, applying Theorem 3.2.6 (a) =⇒ (b), we have that 1 1 1 · nθ · · Γ(1 − θ) ψ(n) Γ(1 + θ) 1 1−θ · nθ · = Γ(1 + θ)Γ(2 − θ) ψ(n) n −1 1 Vk . ·n· = Γ(1 + θ)Γ(2 − θ)
Wn ∼
k=1
The proof of the other parts of the strong renewal results quoted above also rely on Theorem 3.2.6, but also on some intricate estimates of integrals. We leave the details to the intrepid reader.
3.2.2 Renewal theory applied to the αsumlevel sets We begin our discussion with the crucial observation that the sequence of the Lebesgue measures of these αsumlevel sets satisﬁes a renewal equation (as observed in [WX11], [Mun11] and [KMS12]). Here, the role of the probability vector is ﬁlled by the sequence of Lebesgue measures of the partition elements of α, that is, the sequence (a m )m≥1 .
3.2 Sumlevel sets for the αLüroth expansion
 133
Lemma 3.2.8. We have that a n , Cn(α) deﬁnes a renewal pair. That is, for each n ∈ N, we have n (α) . a m λ Cn−m λ Cn(α) = m=1
Proof. Since λ(C0(α) ) = 1 and λ(C1(α) ) = a1 , the assertion certainly holds for n = 1. For n ≥ 2, the following calculation ﬁnishes the proof. n−1 λ Cn(α) = λ C α (n) + m=1
= λ(C α (n)) +
n−1
λ(C α (1 , . . . , k , m))
(α) C α (1 ,...,k ,m)∈Cn k∈N
am
m=1
λ(C α (1 , . . . , k ))
(α) C α (1 ,...,k )∈Cn−m k∈N
n−1 n (α) (α) = a n λ C0(α) + = . a m λ Cn−m a m λ Cn−m m=1
m=1
We are now in a position to prove our main results. The ﬁrst of these is valid for arbitrary partitions, but for the second we must restrict ourselves to partitions that are either expansive of exponent θ ∈ [0, 1] or of ﬁnite type (recall that these were introduced in Deﬁnition 1.4.18). The proof of the ﬁrst statement of the ﬁrst main result again makes use of the notion of a generating function, which was deﬁned above (see Deﬁnition 3.2.7). Theorem 3.2.9. For the αsumlevel sets of an arbitrary given partition α ∈ A we have (α) that ∞ n=1 λ(Cn ) diverges, and that # 0 if F α is of inﬁnite type; (α) −1 lim λ Cn = ∞ n→∞ if F α is of ﬁnite type. k=1 t k Proof. The general form of the discrete renewal theorem given in Theorem 3.2.4 above can be applied directly to our speciﬁc situation. For this, ﬁx some partition α ∈ A, and set v n := λ(A n ) = a n , for each n ∈ N. Let us recall again that this is certainly a probability vector. Then, put w n := λ(Cn(α) ), for each n ∈ N0 . In light of Lemma 3.2.8 and the observation that w0 = λ(C0(α) ) = 1, we then have that these particular sequences (v n )n≥1 and (w n )n≥0 are indeed a renewal pair. Consequently, an application of the discrete renewal theorem immediately implies that ∞ −1 ∞ −1 (α) k · ak = tk , lim λ Cn = n→∞
k=1
∞
k=1
where this limit is equal to zero if k=1 t k diverges. Note that by Lemma 2.3.20, the divergence of the latter series is equivalent to the statement that the partition α is of inﬁnite type.
134  3 Renewal theory and αsumlevel sets
For the remaining assertion, let us consider the two generating functions V and W, which are given by V(s) :=
∞
v n s n and W(s) :=
n=1
∞
wm sm .
m=0
Using the Cauchy product formula for the two power series in tandem with the renewal equation provided in Lemma 3.2.8, we have that W(s)V(s) =
∞
sn
n=1
n
v m w n−m =
m=1
∞
w n s n = W(s) − 1
(3.5)
n=1
Hence, W(s) = 1/(1 − V(s)). Since a(1) = 1, this yields that lim W(s) = +∞,
s→1−
which shows that the series
∞
(α) n=0 λ(Cn )
diverges. This ﬁnishes the proof.
Theorem 3.2.10. For a given partition α which is either expansive of exponent θ ∈ [0, 1] or of ﬁnite type, we have the following estimates for the asymptotic behaviour of the Lebesgue measure of the αsumlevel sets. (a) With K θ := (Γ(2− θ)Γ(1+ θ))−1 for α expansive of exponent θ ∈ [0, 1] and with K θ := 1 for α of ﬁnite type, we have that n −1 n (α) λ Ck ∼ K θ · n · tk . k=1
k=1
(b) With k θ := (Γ(2 − θ)Γ(θ))−1 for α expansive of exponent θ ∈ (1/2, 1] and with k θ := 1 for α of ﬁnite type, we have that n −1 (α) tk . λ Cn ∼ k θ · k=1
(c) For an expansive partition α of exponent θ ∈ (0, 1), we have that sin πθ lim inf n · t n · λ Cn(α) = . π n→∞ Moreover, if θ ∈ (0, 1/2), then the corresponding limit does not exist in general. However, in this situation the existence of the limit is always guaranteed at least on the complement of some set of integers of zero density. Proof. The statements in the theorem concerning partitions α of ﬁnite type follow easily from Theorem 3.2.9. Indeed, given that ⎞ ⎛ n λ Cn(α) (α) ⎠ = lim · lim λ C t k = 1, lim ⎝ −1 n n n→∞ n→∞ n→∞ k=1 k=1 t k
3.3 Exercises
 135
the statement in part (b) follows immediately. The corresponding claim in part (a) follows directly on considering the Cesàro average of the sequence of Lebesgue measures of αsumlevel sets. Similarly to the proof of Theorem 3.2.9, the remainder of the proof (that is, those parts concerning partitions that are expansive of exponent θ), follow from straightforward applications of the strong renewal results of Garsia/Lamperti and Erickson to the setting of the αsumlevel sets. For this we must set v n := a n , V n := t n and w n := λ(Cn(α) ), and recall that the sodeﬁned pair of sequences ((v n )n≥1 , (w n )n≥0 ) satisﬁes the conditions of a renewal pair.
3.3 Exercises Exercise 3.3.1. Let (v n ) be a inﬁnite probability vector with generating function V (see Deﬁnition 3.2.7). Show that 1 − V(z) m · vm . = 1−z ∞
lim−
s→1
m=1
Exercise 3.3.2. Consider the renewal pair (v n , w n ) with the extra assumption that v1 := p ∈ (0, 1) and v2 := 1 − p. Determine the values of the sequence (w n ) explicitly with the help of the generating functions V and W, and verify that the speed of convergence in the renewal theorem is in fact exponential. (Hint: Use the relation (3.5) to show that W = 1/(1 − V) is a rational function with two poles, one in s1 = 1 and another one in s2 . Find the residues and determine the power series of W by using the identity (1 − s/s k ) = n≥0 (s/s k )n , for k = 1, 2.) Exercise 3.3.3. Generalise the ideas of Exercise 3.3.2 to the case that more than two, but only ﬁnitely many, of the v n are nonzero and such that d v = 1. (Hint: As an intermediate step show that 1− V(s) is a polynomial of ﬁnite degree which has a simple root in 1 and all the other roots are of modulus strictly greater than 1. Also make use of Exercise 3.3.1.) Exercise 3.3.4. In the following exercise we employ a very useful representation for slowly varying functions. Assume that L is a slowly varying function. Then there exist constants c ∈ R and A > 0, a bounded measurable function η and a continuous function δ, both deﬁned on [a, ∞), with limx→∞ η(x) = c and limx→∞ δ(x) = 0 such that for all x ≥ a we have , x δ(t) L(x) = exp η(x) + dt . t a Use this representation to prove: 1. For every 0 < r < s < ∞,
L(tx) − 1 = 0. lim sup x→∞ t∈[r,s] L(x)
136  3 Renewal theory and αsumlevel sets
2.
We have lim
x→∞
3.
log(L(x)) = 0. log(x)
We have for α > 0 lim x α L(x) = ∞ and lim x−α L(x) = 0.
x→∞
x→∞
4. For α ∈ R and for slowly varying functions L1 , L2 and L3 , where also limx→∞ L3 (x) = ∞ we have that each of the functions x → (L1 (x))α , L1 + L2 , and L1 ◦ L3 are also slowly varying. Exercise 3.3.5. Show that a measurable function f : R+ → R+ is regularly varying with exponent ρ ∈ R if and only if lim
x→∞
f (xy) = y ρ for every y > 0. f (x)
Exercise 3.3.6. Let us ﬁx a constant ρ ≥ 0, a slowly varying function ψ and a family of sequences of distribution functions t → U x (t), t ≥ 0, x ∈ X such that the corresponding measure – also denoted by U x – carries no mass in 0. The Laplace transform of U x is deﬁned to be ,∞ x + + ω : R → R , s → exp(−st) dU x (t). 0
Suppose that uniformly in x ∈ X we have U x (t) ∼ t ρ ψ(t), as t tends to inﬁnity, then we also have uniformly in x ∈ X, as τ tends to zero, ω x (τ) ∼ Γ(ρ + 1)τ−ρ ψ(1/τ). Hint: First show that for some δ ∈ (0, 1) the quotient ω x (δ · τ)/τ−ρ ψ(1/τ) stays uniformly bounded as τ tends to zero (for this split the domain of integration into the points 2k /τ, k ∈ N and use integration by parts). Then use Exercise 3.3.4 (1) with a > 0 sufficiently small and b < ∞ sufficiently large to split the domain of integration in the deﬁnition of the Laplace transform in a convergent part and two negligible parts. Exercise 3.3.7. Use Exercise 3.3.6 to prove the uniformity statement in Karamata’s Tauberian Theorem 3.2.6. t −1 x and make the Hint: Consider the distribution function U x (t) := k=0 b xk + (t − t )b t
change of variables y = exp(−t).
4 Inﬁnite ergodic theory In this chapter we will make a deeper journey into inﬁnite ergodic theory, taking up where we left off in Chapter 2. In the following chapter, we shall then see some applications of this general theory to continued fractions.
4.1 The functional analytic perspective and the Chacon–Ornstein Ergodic Theorem Our ﬁrst main result of this section will be the Chacon–Ornstein Ergodic Theorem [CO60], which is stated completely in terms of linear operators acting on L1 (μ). After having proved this powerful result, we will then see that it implies Birkhoff’s Pointwise Ergodic Theorem (which we have already seen in Theorem 2.4.16), Hopf’s Ergodic Theorem (cf. Theorem 2.4.24), as well as Hurewicz’s Ergodic Theorem (Corollary 4.1.18), which we will speciﬁcally need later in this chapter. The proof, as you might expect, is not trivial. Before stating and proving the Chacon–Ornstein Ergodic Theorem, we will ﬁrst collect a few useful observations concerning the functional analytic nature of this part of the theory. In particular, we shall now study the and Koopman operator U T : f → f ◦ T from a previouslydeﬁned transfer operator T broader functionalanalytic perspective (recall that the Koopman operator U T was ﬁrst mentioned in Remark 2.3.8). Let (X, B , μ) denote a σﬁnite measure space. We remind the reader that the space of integrable functions L1 (μ), in which functions are identiﬁed if they are a.e. equal, together with norm · 1 given by , f 1 := f  dμ deﬁnes a Banach space. The space L∞ (μ) equipped with the norm · ∞ given by f ∞ := inf {c ∈ R : f  ≤ c a.e.}
also deﬁnes a Banach space. Further, let L+p (μ) denote the set of nonnegative functions from L p (μ) and recall that the nonnegative part ψ+ of a measurable realvalued function ψ is given by the measurable function ψ+ := max{ψ, 0}. We shall now study bounded linear operators acting on L1 (μ); these are linear functions V : L1 (μ) → L1 (μ) with bounded operator norm, which is deﬁned to be 1
1
V := sup 1V(f )11 . f 1 =1
138  4 Inﬁnite ergodic theory
Deﬁnition 4.1.1. Let V : L1 (μ) → L1 (μ) be a linear operator. (a) V is said to be a contraction if V ≤ 1. (b) V is said to be positive if V L+1 (μ) ⊂ L+1 (μ). We have already seen that if (X, B , μ, T) is a nonsingular dynamical system then : L1 (μ) → L1 (μ) is well deﬁned and T ≤ 1 (see Exercise 2.6.11). In the next lemma, T we will consider positivity for T and both properties for the Koopman operator. Lemma 4.1.2. For the Koopman and the transfer operator we have: (a) If (X, B , μ, T) is a measurepreserving dynamical system, then U T : f → f ◦ T is a positive contraction on L1 (μ). is a positive contraction (b) If (X, B , μ, T) is a nonsingular dynamical system, then T on L1 (μ). Proof. Let (X, B , μ, T) be a measurepreserving dynamical system. Then for every f ∈ L1 (μ), we have , , , , U T f  dμ = f ◦ T  dμ = f  dμ ◦ T −1 = f  dμ < ∞, which shows that U T is well deﬁned and a contraction. It is clear that U T is positive, so the proof of part (a) is ﬁnished. has norm 1. Towards part (b), as we recalled above, we have already seen that T + Positivity follows from the fact that for a given f ∈ L1 (μ) we have, for all g ∈ L+∞ (μ), that , , Tf · g dμ = f · g ◦ T dμ ≥ 0. Deﬁnition 4.1.3. Let V : L1 (μ) → L1 (μ) be a bounded linear operator. Then the dual of V, which will be denoted by V * , is an operator acting on the dual space (L1 (μ))* L∞ (μ) which is uniquely determined by the identity , , f · V * (g) dμ := V(f ) · g dμ, for all f ∈ L1 (μ) and g ∈ L∞ (μ). In particular, as was already alluded to in Remark 2.3.8, if (X, B , μ, T) is a nonsingular we have by deﬁnition that T * = UT dynamical system, then for the transfer operator T as an operator acting on L∞ (μ). Note that if V is a positive linear operator acting on L1 (μ) then the dual operator 0 V * : L∞ (μ) → L∞ (μ) is also positive. Indeed, for every g ∈ L+∞ (μ) we have that V * (g) · 0 f dμ = g · Vf dμ ≥ 0 for all f ∈ L+1 (μ), and hence V * g ≥ 0. If V is a positive linear operator acting on L1 (μ) we will use the notation S n f :=
n−1 k=0
V k f for n ∈ N ∪ {∞}, and we set S0 f := 0.
4.1 The functional analytic perspective and the Chacon–Ornstein Ergodic Theorem

139
Note that for V = U T , this deﬁnition coincides with our deﬁnition in the context of dynamical systems as introduced in Section 2.4.6. We can now continue towards the proof of the Chacon–Ornstein Ergodic Theorem and its immediate corollaries, i.e., to Hopf’s, Birkhoff’s and Hurewicz’s Pointwise Ergodic Theorems. Lemma 4.1.4 (Chacon–Ornstein Lemma). Let V be a positive contraction acting on L1 (μ) and let g ∈ L1 (μ) be such that g > 0. For all φ ∈ L1 (μ), we then have μa.e. that Vnφ = 0. n→∞ S n g lim
Proof. Let ε > 0 be ﬁxed and deﬁne E n := {x ∈ X : V n φ(x) > εS n g }. The aim is to show that ∞ n=2 μ g (E n ) 0 as well as that ε > 0 was arbitrary, n this gives lim supn→∞ VS n gφ ≤ 0 μa.e. Applying this result to −φ instead of φ shows that n
μa.e. we also have lim inf n→∞ VS n gφ ≥ 0, giving ﬁnally the assertion. Now, since V is a positive operator and both 0 and ψ ∈ L1 (μ) are less than or equal + to ψ , we have 0 = V(0) ≤ V(ψ+ ) as well as V ( ψ) ≤ V ψ+ , and hence (Vψ)+ ≤ (V(ψ+ ))+ = V(ψ+ ). Using this and the fact that V(V n φ − εS n g) = V n+1 φ − S n+1 εg + εg, it follows that + V n+1 φ − εS n+1 g + 1 E n+1 εg = 1 E n+1 V n+1 φ − εS n+1 g + 1 E n+1 εg = 1 E n+1 V n+1 φ − S n+1 εg + εg + = 1 E n+1 V(V n φ − εS n g) + . ≤ V V n φ − εS n g + To shorten the notation below, let us set J n := V n φ − εS n g . Then the above inequality together with the fact that V is a contraction implies that , , , , ε 1 E n+1 gdμ ≤ (VJ n − J n+1 )dμ ≤ (V J n − J n+1 )dμ ≤ (J n − J n+1 )dμ. This shows that for all N ∈ N, we have that ε
, N n=1
1 E n+1 gdμ ≤
, N n=1
, (J n − J n+1 )dμ =
, J1 − J N+1 dμ ≤
J1 dμ < ∞.
140  4 Inﬁnite ergodic theory
Lemma 4.1.5 (Hopf’s maximal inequality). Let V be a positive contraction acting on L1 (μ) and let f ∈ L1 (μ). If we set f n := max0≤j≤n S j f for each n ∈ N0 , we then have , f dμ ≥ 0. {f n >0}
Proof. First, notice that f0 = 0 ≤ f + = f1 ≤ f2 ≤ · · · . Then, using the positivity of V, we have on the set {f n > 0} that ⎛ ⎞ j−1 j−1 j−2 f n = max V k f = max ⎝ f + V k f ⎠ = f + max V Vkf 0≤j≤n
1≤j≤n
k=0
≤ f + V max 1≤j≤n
j−2
1≤j≤n
k=1
k=0
V k f = f + V ( f n−1 ) ≤ f + V ( f n ) .
k=0
Since Vf n ≥ 0 and V is contracting, a rearrangement of the above inequality yields that , , , , f dμ ≥ Vf n dμ ( f n − Vf n ) dμ = f n dμ − {f n >0}
{f n >0}
,
,
≥
f n dμ −
{f n >0}
,
, Vf n dμ ≥
f n dμ − V
f n dμ ≥ 0.
Before stating the next result, let us ﬁx some notation. We let Q n (φ, g ) := S n φ/S n g and 2 (φ, g ) := sup deﬁne Q n∈N Q n ( φ, g ). Lemma 4.1.6 (Wiener’s maximal inequality). Let V be a positive contraction on L1 (μ) and let g ∈ L1 (μ) be such that g > 0 and such that μ g , given by dμ g := gdμ, is a probability measure. For each φ ∈ L1 (μ) and s > 0, we then have that . μg
2 (φ, g ) > s Q
/ ≤
φ 1
s
.
Proof. Hopf’s maximal inequality applied to f n := max0≤j≤n S j (φ − sg) gives that for each n ∈ N, we have that , , (φ − sg) dμ = φ dμ − sμ g ({f n > 0}) . 0≤ {f n >0}
{f n >0}
Since f n > 0 if and only if max1≤k≤n Q k (φ − sg, g ) > 0, it follows that $ % , 1 1 max Q k (φ − sg, g ) > 0 = μ g ({f n > 0}) ≤ φ dμ ≤ φ1 . μg s s 1≤k≤n {f n >0}
4.1 The functional analytic perspective and the Chacon–Ornstein Ergodic Theorem

141
φ − sg, g ) > 0})n∈N is an increasing sequence of sets with union Since ({max . 1≤k≤n Q k (/ 2 equal to Q (φ, g ) > s , using the continuity of μ g from below ﬁnishes the proof. Let g ∈ L1 (μ) such that g > 0. By letting s tend to inﬁnity in the previous lemma, we 2 ﬁnd that Q(φ, g) < ∞ μa.e., for each φ ∈ L1 (μ), and in particular for those φ such that φ > 0. In fact, this shows that {S∞ g = ∞} = {S∞ φ = ∞} mod μ and hence the set {S ∞ g = ∞} is μa.e. independent of g. Deﬁnition 4.1.7 (Hopf decomposition for operators). Let V be a positive contraction on L1 (μ). The above observation allows us to deﬁne the (μa.e. determined) Hopf decomposition of X with respect to the positive contraction V into the conservative part C V := {S∞ g = ∞} for some g ∈ L1 (μ) such that g > 0 and the dissipative part D V := X \ C V . If X = C V mod μ, then V is called conservative. Remark 4.1.8. This deﬁnition further generalizes our notion of Hopf decompositions. Let (X, B , μ, T) be a measuretheoretical dynamical system. Then the following hold: (a) If the system is measurepreserving, then C T = C UT
mod μ.
(b) If the system is nonsingular, then T = C C T
mod μ.
Lemma 4.1.9. Let V be a conservative positive contraction on L 1 (μ) and let φ ∈ L∞ (μ) be given such that either V * φ ≥ φ or V * φ ≤ φ. Then V * φ = φ. Proof. For the case that V * φ ≥ φ, let g ∈ L1 (μ) be ﬁxed such that g > 0. We then have that , 0≤
*
V φ−φ
n−1
k
V g dμ =
,
n
,
φ V g − g dμ ≤ 2 φ∞
g dμ < ∞.
k=0
k Since by assumption X∞ (g) = X, we have that S n g = n−1 k=0 V g is unbounded μa.e. on * X. Therefore, the latter inequality can be satisﬁed only if V φ = φ. The case V * φ ≤ φ can be treated in an analogous way and is left to the reader. Example 4.1.10. Since for all f ∈ L+1 (μ), , , , , * f · V 1 X dμ = Vf dμ ≤ f dμ = f · 1 X dμ, it follows that V * 1 X ≤ 1 X . Consequently, in light of Lemma 4.1.9, we deduce that V * 1X = 1X .
142  4 Inﬁnite ergodic theory Lemma 4.1.11. Let V be a positive conservative contraction on L1 (μ) and let φ ∈ L∞ (μ) be V *invariant, that is, φ = V * φ. Then φ+ and 1{a 0 was chosen arbitrarily, we can now conclude, by letting ε tend to zero, that μ g a.e. we have that (Q n (h, g ))n≥1 converges. This shows that h ∈ L and hence, L is closed. It remains to characterise the limiting function. In order to do so, let E ( · I ) = Eμ g ( · I ) denote the conditional expectation with respect to the measure μ g . Let us ﬁrst consider the special situation in which f = φg + ψ − Vψ is a ﬁxed element in L. We already know that in this case the limit of Q n (f , g) n≥1 is μa.e. equal to φ, which is I measurable, by Lemma 4.1.12. Let J denote the set of all ﬁnite intersections of subsets of the form {x : a < h(x) ≤ b}, for some a, b ∈ R and h ∈ L∞ (μ) with h = V * h. Then using Lemma 4.1.11 and Remark 4.1.13, we have for all F ∈ J , , , , , μ(g) 1 F · f /g dμ g = 1 F · f dμ = 1 F φg dμ + 1 F · ψ − V(ψ) dμ , , , = 1 F · φg dμ + 1 F · ψ dμ − V * (1 F ) · ψ dμ , = μ(g) 1 F · φ dμ g . Since the set J is closed under taking intersections and generates I and since φ is I measurable, it follows from the characterisation of the conditional expectation
146  4 Inﬁnite ergodic theory stated above that φ = E f /gI . For general f ∈ L1 (μ) the claim follows by approximating f with functions from L. Finally, if we additionally have that V is ergodic, then the σalgebra I generated by the V *invariant functions is trivial by deﬁnition and hence, the limiting function is μa.e. constant and equal to 0 , f dμ E f /g I = f /g dμ g = 0 . g dμ As already mentioned at the beginning, we end this section by ﬁrst showing how the Chacon–Ornstein Ergodic Theorem implies Hurewicz’s Ergodic Theorem, and then Hopf’s Ergodic Theorem, and, in turn, Birkhoff’s Pointwise Ergodic Theorem. Corollary 4.1.18 (Hurewicz’s Ergodic Theorem). Let (X, B , μ, T) be an ergodic conser0 vative dynamical system and let g ∈ L+1 (μ) be such that g dμ > 0. For each f ∈ L1 (μ), we then have μa.e. that 0 n−1 k f dμ k=0 T (f ) 0 lim n−1 = . k n→∞ gdμ T (g) k=0
: L1 (μ) → L1 (μ), the Proof. By the observations in Section 4.1 for V = T Chacon–Ornstein Ergodic Theorem is applicable. Hence, for all f ∈ L1 (μ) and for a particular g0 ∈ L+1 (μ) such that g0 > 0 the convergence holds as stated in the corollary. Note that such a function g0 exists because (X, B , μ) is σﬁnite; in fact, this is a necessary and sufficient condition for σﬁniteness. For general g ∈ L+1 (μ) with 0 g dμ > 0 we have that 0 n−1 k 7 n−1 k n−1 k f dμ k=0 T (f ) k=0 T (f ) k=0 T (g) = lim =0 . lim n−1 k (g) n→∞ n−1 T k (g0 ) n−1 T k (g0 ) n→∞ g dμ T k=0
k=0
k=0
Remark 4.1.19. We can also give a second proof of Hopf’s Ergodic Theorem (which we already discussed in Chapter 2, see Theorem 2.4.24), using the Chacon–Orstein Ergodic Theorem. The argument works in precisely the same way as that given above. First, we have seen in Section 4.1 that the Chacon–Ornstein Ergodic Theorem is applicable to the Koopman operator U T : L1 (μ) → L1 (μ), given by U T f := f ◦ T. Therefore, the assertion in Hopf’s Ergodic Theorem certainly holds for all f ∈ L1 (μ) and for a particular g0 ∈ L+1 (μ) such that g0 > 0. The proof is then completed in exactly 0 the same way as in Hurewicz’s Ergodic Theorem for any g ∈ L1 (μ) with g dμ > 0. Now Birkhoff’s Pointwise Ergodic Theorem (see Theorem 2.4.16) follows immediately by choosing g = 1 X in Hopf’s Ergodic Theorem. Recall that in the proof we gave of Hopf’s Ergodic Theorem in Chapter 2 using inducing, part of the argument was to use Birkhoff’s Theorem, so this deduction is only reasonable now.
4.2 Pointwise dual ergodicity

147
4.2 Pointwise dual ergodicity In this section we will study an ergodicity property of the transfer operator associated to a system. Deﬁnition 4.2.1. An ergodic, conservative measurepreserving system (X, B , μ, T) is said to be pointwise dual ergodic if there exists a sequence (r n )n≥1 such that μa.e. , n−1 1 i T f → f dμ for all f ∈ L1 (μ). rn i=0
The sequence (r n )n≥1 , which is unique up to asymptotic equivalence (see remark below), will be referred to as the return sequence of T. Remark 4.2.2. For what follows it will be important to ﬁnd a sequence (a n )n≥1 in the asymptotic class of (r n ) that is strictly increasing. First ﬁx f , g ∈ M(B) with f 0 a μintegrable function and g a bounded function with g > 0, g dμ = 1. Then ﬁx a μtypical point x ∈ X that witnesses both the Hurewicz Ergodic Theorem, in the sense that , n n k k lim T f (x)/ T g(x) = f dμ, n→∞
k=0
k=0
and the pointwise dual ergodicity for f , in the sense that , n−1 1 i T f (x) → f dμ. rn i=0
k and asymptotic to r n . In fact Then the sequence a n := n−1 k=0 T g(x) is strictly increasing k−1 g(x) we have shown that a n can be written as the sum a n = nk=1 b k where b k := T is strictly positive. Deﬁnition 4.2.3. For a set A ∈ B with 0 < μ(A) < ∞, the wandering rate of A with respect to T is given by the sequence (w n (A))n≥1 , where n
−k T (A) . w n (A) := μ k=0
Where φ is the return time function with respect to A (as in Deﬁnition 2.4.25), we also have that n n k−1
− −k μ(A ∩ {φ > k}) = μ(A) + μ T A\ T A . (4.2) w n (A) = k=0
k=1
The proof of this statement is left to Exercise 4.4.8
=0
148  4 Inﬁnite ergodic theory
Information about the wandering rate can be understood as information about “how inﬁnite” the system is, in terms of the size of X relative to E. That is, the increments μ(A ∩ {φ > n}) in the characterisation of the wandering rate quantify in some way how large X is relative to E. Deﬁnition 4.2.4. Let (X, B , μ, T) be pointwise dual ergodic with return sequence (r n ). Then, a set A ∈ B with positive, ﬁnite measure is called a uniform set for f ∈ L+1 (μ) if , n−1 1 k T f → f dμ uniformly (mod μ) on A. rn k=0
We will also call a set A ∈ B uniform if it is a uniform set for some f ∈ L+1 (μ) with 0 f dμ > 0. Here, uniform convergence (mod μ) on A means uniform convergence on a set A0 such that the symmetric difference A0 A has μmeasure zero. One can also think of this convergence as convergence in the L∞ (μA )norm. We also recall that the deﬁnition of a regularly varying sequence was given directly above Theorem 3.2.6. Lemma 4.2.5. Assume that the sequence (r n ) is regularly varying with exponent ρ ∈ [0, 1] and given by r n = nk=0 b k , for some nonnegative sequence (b n ). If for some A ∈ B with 0 < μ(A) < ∞ and f ∈ L+1 (μ) we have uniformly (mod μ) on A that , n−1 1 k lim T f = f dμ, n→∞ r n k=0
then with B(s) :=
∞
n=0 b n s
n
, s ∈ [0, 1), we have uniformly (mod μ) on A that 1 n n s T f= B(s) ∞
lim−
s→1
, f dμ.
n=0
Proof. Let r n = n ρ ψ(n) for some slowly varying function ψ. Since b n ≤ r n it follows that B(s) is ﬁnite for all s ∈ [0, 1). Then the claim follows by applying Karamata’s Tauberian Theorem (see Theorem 3.2.6) twice, ﬁrst with the sequence (b n ) and then with the n f ). More precisely, ﬁrst note that sequence (T n 1 1 n ρ ψ(n) 1 Γ(1 + ρ). bk ∼ =⇒ B(s) ∼ ψ 1−s Γ(1 + ρ) Γ(1 + ρ) (1 − s)ρ k=0
Thus, since by assumption uniformly (mod μ) on A n k=0
k f ∼ T
,
, f dμ · r n ∼
f dμ · n ρ ψ(n)
4.2 Pointwise dual ergodicity

149
it follows that uniformly (mod μ) on A 0
, B(s) ·
f dμ ∼
∞ f dμ 1 n f . Γ(1 + ρ) ψ ∼ sn T 1−s (1 − s)ρ n=0
Lemma 4.2.6 ([Aar81]). For f ∈ L1 (μ) or f ∈ M + (B), and A ∈ B such that 0 < μ(A) < ∞ we have for s ∈ (0, 1) ,
1 − sφ
where A n := T −n A \
k=0
∞
n f dμ = sn T
n=0
A
n−1
∞
sn
n=0
, f dμ, An
T −k A, for n ∈ N, and A0 = A.
Proof. We may restrict our attention to nonnegative measurable functions f only since the case f ∈ L1 (μ) can be deduced from this in the following way: First note that for f ∈ L+1 (μ) the right hand side of the equality is ﬁnite due to the fact that the sets A n are pairwise disjoint. For an arbitrary f ∈ L1 (μ) we consider its positive part max(f , 0) and its negative part max(−f , 0) separately and use the linearity and positivity of T together with the linearity of both the integral and the inﬁnite sum. For f ∈ M + (B) and with dν = f dμ we have , n f dμ = ν (T −n A) T A
= ν (A n ) +
n−1
ν T −k (A ∩ {φ = n − k })
k=0
= ν (A n ) +
, n−1
k f · 1{φ=n−k} dμ. T
A k=0
Using this gives , ∞
n f dμ = sn T
A n=0
=
∞
s n ν (A n ) +
sn
n=0
n=1
∞
, ∞
s n ν (A n ) +
n=0
=
∞
∞ n=0
sn
,
,
s k 1{φ=k}
sφ
A
k f · 1{φ=n−k} dμ T
A k=0
A k=1
f dμ + An
, n−1
∞
n−k f dμ s n−k T
n=k ∞
n f dμ sn T
n=0
Rearranging the last identity proves the claim. Proposition 4.2.7 (Asymptotic Renewal Equation [Aar81]). Let A be a uniform set with regular varying return sequence r n = nk=0 b k , for some nonnegative sequence (b k ).
150  4 Inﬁnite ergodic theory
Then
,
1 , for s → 1− , 1 − s φ dμ ∼ B(s)
A
where B(s) :=
∞
n=0 b n s
n
.
Proof. Let A be a uniform set for f ∈ L+1 (μ). By Lemma 4.2.6 and with A n as deﬁned therein, we have ,
1 − sφ
∞
n f dμ = sn T
n=0
A
∞ n=0
sn
,
, f dμ →
f dμ,
An
for s → 1− , where the convergence follows by Abel’s Theorem. On the other hand, making use now of Lemma 4.2.5 and the almost everywhere uniform convergence, we ﬁnd for s → 1− , , , ∞ k f dμ ∼ B(s) f dμ sk T 1 − sφ 1 − s φ dμ. k=0
A
A
These observations combined prove the proposition. Proposition 4.2.8. If T is pointwise dual ergodic with return sequence (r n ) and if A is a uniform set such that the wandering rate w n (A) is regularly varying with exponent α ∈ [0, 1], then we have r n w n (A) ∼
n . Γ(2 − α)Γ(1 + α)
In particular, we have that r n is regularly varying with exponent 1 − α and there exists a sequence W n ↗ ∞ such that for all uniform sets B ∈ B we have W n ∼ w n (B). Proof. As shown in Remark 4.2.2, pointwise dual ergodicity implies that the sequence (r n ) can be chosen to be r n = nk=0 b n for some strictly positive sequence (b n ). On the one hand, Lemma 4.2.6 for f = 1 X together with Proposition 4.2.7 implies, for s → 1− , ∞ k=0
with B(s) :=
∞
k=0 s
k
1 s μ (A k ) = 1−s k
,
(1 − s φ ) dμ ∼
1 , (1 − s)B(s)
A
b k . Since, as in (4.2), w n (A) = μ(A) +
n k=1
μ (A k ) ∼ n α ψ(n)
4.3 ψmixing, Darling–Kac sets and pointwise dual ergodicity

151
for α ∈ [0, 1] and ψ some slowly varying function, Karamata’s Tauberian Theorem gives on the other hand ∞
s k μ (A k ) ∼
k=1
Γ(1 + α) 1 . ψ 1−s (1 − s)α
Hence we have B(s) ∼
1 . (1 − s)1−α Γ(1 + α)ψ(1/(1 − s))
Applying in Karamata’s Tauberian Theorem the assertion (a) implies (b) to the generating function B(s) gives rn =
n k=0
b k ∼ n1−α
1 . Γ(2 − α)Γ(1 + α)ψ(n)
From this the claim follows.
4.3 ψmixing, Darling–Kac sets and pointwise dual ergodicity Deﬁnition 4.3.1. Let (X, B , μ, T) be a measurepreserving system. Then a set A ∈ B with positive, ﬁnite measure is called a Darling–Kac set if there exists a positive sequence (r n ) such that n−1 1 k T 1 A → μ(A) uniformly (mod μ ) on A. rn k=0
Let us now introduce a stronger mixing property than the one given in Deﬁnition 2.5.8. We recall that the reﬁnements U n of a collection of sets U were deﬁned in Deﬁnition 1.2.23(c) and σ(U ) denotes the σalgebra generated by U . Deﬁnition 4.3.2. Let (X, B , μ, T) be a dynamical system with μ(X) < ∞ and let M be a measurable partition of X. Then the system is said to be ψmixing with respect to M, if there exists a sequence (ψ m )m≥0 of positive real numbers which tends to zero for m tending to inﬁnity, such that for all n ∈ N, A ∈ σ Mn , B ∈ B and m ∈ N0 , we have that μ A ∩ T −(m+n) (B) ≤ (1 + ψ m ) μ (A) μ (B) , and for all m ∈ N large enough μ A ∩ T −(m+n) (B) ≥ (1 − ψ m ) μ (A) μ (B) .
152  4 Inﬁnite ergodic theory Proposition 4.3.3. Let (X, B , μ, T) be a conservative, measurepreserving and ergodic dynamical system. Let A ∈ B with 0 < μ(A) < ∞ and let M be a measurable partition of A such that the return time function φ with respect to A is Mmeasurable and the induced system (A, BA , μA , T A ) is ψmixing with respect to M. Then A is a Darling–Kac set. Proof. Without loss of generality we assume that μ(A) = 1. Recall that since T is k conservative and ergodic we have by Corollary 2.4.5 that ∞ k=0 T 1 A = ∞ and hence n the sequence (a n )n≥1 given for each n ∈ N by a n := k=1 μ(A ∩ T −k A) tends to inﬁnity. We will show that this sequence will witness the Darling–Kac property for A. To begin, by the assumed ψmixing condition there exists a positive sequence (ψ n )n≥0 with limn→∞ ψ n = 0 such that for B ∈ σ(Mk ) with k ∈ N, and for all n ∈ N0 we have Ak+n 1 B ≤ (1 + ψ n )μ(B) T and for all n ∈ N large enough Ak+n 1 B ≥ (1 − ψ n )μ(B). T The key observation in this proof will be that for B ∈ B with B ⊂ A, A ∩ T −n B =
n
{φ k = n } ∩ T A−k B,
(4.3)
k=1
where, as in (2.12), we deﬁne φ k := from (4.3) that n 1A = T
n
k−1 =0
φ ◦ T A . For the transfer operators, it follows
Ak 1{φ =n} and T k
n
k=1
In particular, we have a n = n k=1
k 1A = T
k 1A = T
k=1
n
n
k=1 μ A ({ φ k
Ak 1{φ ≤n} ≤ T k
k=1
≤m+
Ak 1{φ ≤n} . T k
k=1
≤ n}). Now, for the upper bound we have
n+m
Ak 1{φ ≤n} T k
k=1 n k=1
≤m+
n
n
Ak+m 1{φ ≤n} ≤ m + T k+m
n
Ak+m 1{φ ≤n} T k
k=1
(1 + ψ m )μA ({φ k ≤ n}) = m + (1 + ψ m )a n ,
k=1
where for the last inequality we used the fact that {φ k ≤ n} ∈ σ(Mk ). Since this inequality holds for every m, n ∈ N and since (a n ) is diverging it follows that
4.3 ψmixing, Darling–Kac sets and pointwise dual ergodicity

153
uniformly a.e. n 1 k T 1 A ≤ 1. an
lim sup n→∞
k=1
For the lower bound we calculate similarly n
k 1A ≥ T
k=1
n
Ak+m 1{φ ≤n} − m T k+m
k=1
≥
n
Ak+m 1{φ ≤n} − T k
k=1
≥
n
n
Ak+m 1{φ ≤n≤φ } − m T k k+m
k=1
(1 − ψ m )μA ({φ k ≤ n}) −
k=1
n
Ak+m 1{φ ≤n≤φ } − m T k k+m
k=1
≥ (1 − ψ m )a n − m −
n
Ak+m 1{φ ≤n≤φ } . T k k+m
k=1
Now we observe that n
Ak+m 1{φ ≤n≤φ } ≤ (1 + ψ0 ) T k k+m
k=1
= (1 + ψ0 )
n
μA ({φ k k=1 n n
≤ n ≤ φ k+m })
μA ({φ k = ; φ m ◦ T Ak > n − })
k=1 =1 n n 2
≤ (1 + ψ0 )
μA ({φ k = })μA ({φ m > n − }).
k=1 =1
Let us split up the last sum for = 1, . . . , n − p and = n − p + 1, . . . , n for a ﬁxed p < n. For the ﬁrst part we have n−p n
μA ({φ k = })μA ({φ m > n − }) ≤ μA ({φ m > p})
k=1 =1
n k=1
≤ μA ({φ m > p})a n . For the second part, using μA ({φ m > n − }) ≤ 1 we have n n
μA ({φ k = })μA ({φ m > n − })
k=1 =n−p+1
≤
n k=1
μA ({n − p ≤ φ k ≤ n})
μA ({φ k ≤ n − p})
154  4 Inﬁnite ergodic theory
≤
≤
n k=1 n
μA ({φ k ≤ n}) − μA ({φ k ≤ n}) −
n k=1 n−p
k=1
μA ({φ k ≤ n − p}) μA ({φ k ≤ n − p})
k=1
= a n − a n−p ≤ p. Combining these three inequalities we see that for all m, p ∈ N we have lim inf n→∞
n 1 k T 1 A ≥ 1 − ψ m − (1 − ψ0 )2 μA ({φ1 ≥ p}). an k=1
Since φ1 = φ is ﬁnite a.e. we have μA ({φ1 ≥ p}) → 0 for p → ∞. Also, ψ m → 0 for m → ∞, this proves the uniform lower bound lim inf n→∞
n 1 k T 1 A ≥ 1. an k=1
Combining these bounds ﬁnishes the proof. Lemma 4.3.4. For A, B ∈ B with 0 < μ(A), μ(B) < ∞ and A n := A \ ∞ k 1A = 1X . (a) T n k=0
(b)
n
k (1 B ) = T
n
n n− k k 1 k T 1 A · T (1 B ) + T B\
−m (A) m=0 T
=0
k=0
n
k=0
k=1 T
−k
(A) we have:
.
k=0
Proof. To see the ﬁrst claim, note that by Proposition 2.4.33 (a) we have for two measurable sets A, C with ﬁnite measure and μ(A) > 0 that ∞ ∞ μ A k ∩ T −k (C) = μ A ∩ T −k (C) ∩ {φ > k} = μ(C). k=0
k=0
Hence, using the Monotone Convergence Theorem, for every measurable set C with ﬁnite measure we have , ∞ , ∞ ∞ , k −k k (1 A ) dμ = 1 ( 1 ) dμ = μ A ∩ T (C) = 1 X dμ. T T Ak C k k C
k=0
k=0
k=0
C
k This means that ∞ k=0 T (1 A k ) = 1 X . The second claim follows in a similar way. For n ∈ N and two measurable sets A, C with ﬁnite measure we deﬁne C n := T −n (C) \
n
k=0
T −k (A).
4.3 ψmixing, Darling–Kac sets and pointwise dual ergodicity

155
and observe that T −1 (C n ) = C n+1 ∪ (A ∩ T −1 (C n )), where C n+1 ∩ (A ∩ T −1 (C n )) = ∅. Now with A, B, C measurable sets with ﬁnite measure we have n− n μ B ∩ T −k (A ) ∩ T −(k+) (C) =0 k=0
=
n− n
μ B∩T
−k
−
A ∩ T (C) \
=0 k=0
=
T
−m
(A)
m=1
n− n n μ B ∩ T −k (C ∩ A) + μ B ∩ T −k T −1 (C−1 ) \ B . =0 k=0
k=0
Now for the second summand we ﬁnd by a telescoping argument n− n μ B ∩ T −k T −1 (C−1 ) \ B =0 k=0
=
n−1 n−(+1)+1 =0
=
=
=
μ(B ∩ T −k (C ) −
μ(B ∩ T −k (C )) −
=0 k=1 n
n− n−1
μ(B ∩ T −k (C )) −
=1 k=1
μ(B ∩ T −k (C0 ) −
k=1 n
μ(B ∩ T −k (C )
=1 k=0
k=1
n− n−1
n− n
n
n
μ(B ∩ C )
=1
μ(B ∩ C )
=1
μ(B ∩ T −k (C \ A)) −
n
μ(B ∩ C )
=0
k=0
Combining these two calculations gives n
μ(B ∩ T −k (C)) =
n− n μ B ∩ T −k (A ) ∩ T −(k+) (C) =0 k=0
k=0
+
n
−
μ B∩T C\
=0
T
−m
A .
m=0
The remaining part of the proof is analogous to the ﬁrst part. Proposition 4.3.5. Let (X, B , μ, T) be a conservative ergodic measurepreserving system, and let A, B ∈ B with 0 < μ(A), μ(B) < ∞. Suppose that there exists an increasing positive sequence (a n )n≥1 tending to inﬁnity such that a.e. on A we have n 1 k T 1 B = μ(B). n→∞ a n
lim
k=0
(4.4)
156  4 Inﬁnite ergodic theory
Then the system is pointwise dual ergodic. In particular, the existence of a Darling–Kac set implies pointwise dual ergodicity. Proof. We are going to prove that the convergence in (4.4) in fact holds a.e. on X. Then Hurewicz’s Ergodic Theorem combined with this observation proves pointwise dual ergodicity. First note that by the Chacon–Ornstein Lemma 4.1.4 and the assumption stated in the proposition we have for all N ∈ N that n k n k a n−N a n−N k=0 T 1 B k=n−N+1 T 1 B = 1 − n → 1. k 1B k 1B an a n n−N T T k=0
k=0
n
As before, for n ≥ 0, let A n := A \ k=1 T −k A. By Egorov’s Theorem we may assume without loss of generality that the convergence in (4.4) holds uniformly on A. Fix ε > 0. Using the ﬁrst part of Lemma 4.3.4 we ﬁnd for almost every x ∈ X an n0 ∈ N such 0 k that nk=0 T 1 A k (x) ≥ (1 − ε). Further there exists n1 > n0 such that for all n ≥ n1 and uniformly on A we have n−n 0
1 a n−n0
k=0
k (1 B ) ≥ (1 − ε)μ(B) and a n−n0 ≥ (1 − ε). T an
Now using the second part of Lemma 4.3.4 gives for all n ≥ n1 n n n n−k k 1 k 1 k 1B + 1 k −j T 1 B (x) = T 1Ak · T T B\ j=0 T (A) (x) an an =0 k=0 k=0 k=0 n0 n−k 1 k T 1Ak · T (1 B ) (x) ≥ an =0 k=0 n0 n−n 0 1 k (1 B ) (x) ≥ T 1Ak · T an k=0
≥ (1 − ε)
a n−n0 μ(B) an
=0 n0
k 1 A (x) ≥ (1 − ε)3 μ(B). T k
k=0
k 1 B ≥ μ(B). This shows that a.e. we have lim inf n→∞ a1n nk=0 T Towards the upper bound for the limit superior, ﬁx ε > 0. By Hurewicz’s Ergodic Theorem we also have a.e. on A, n 1 k T 1 A = μ(A). n→∞ a n
lim
(4.5)
k=0
Again by Egorov’s Theorem we ﬁnd a set A ∈ B such that μ(A ) > (1 + ε)−1 μ(A) and the convergence in (4.5) holds uniformly on A . Hence there exists n0 ∈ N such that on A k and all n ≥ n0 we have a1n nk=0 T 1 A ≤ (1 + ε)μ(A) for all n ≥ n0 . Using the second part
4.4 Exercises

157
of Lemma 4.3.4 again with A in the place of B gives n n n−k 1 k 1 k 1 A T 1 A = T 1 Ak · T an an =0 k=0 k=0 n n 1 k ≤ T 1 Ak · T (1 A ) an =0
k=0
≤ (1 + ε)μ(A)
n
k 1 A T k
k=0
≤ (1 + ε)μ(A) ≤ (1 + ε)2 μ(A ). k Hence, we have a.e. lim supn→∞ a1n nk=0 T 1 A ≤ μ(A ). Another application of Hurewicz’s Ergodic Theorem allows us to replace 1 A by 1 B in the above inequality. This ﬁnishes the proof.
4.4 Exercises Exercise 4.4.1. Let X := (0, ∞) and λ denote the Lebesgue measure restricted to X. Consider V1 : L1 (λ) → L1 (λ) f → x → V1 (f )(x) := e−x f (x) . Is V1 a positive contractive operator? Is this operator conservative? Determine the sets {S ∞ g = ∞} and {S∞ g > 0} for some integrable g > 0. Is V1 ergodic?
Exercise 4.4.2. Let X := (0, ∞) and λ denote the Lebesgue measure restricted to X. Consider V2 : L1 (λ) → L1 (λ) , f → x → V2 (f )(x) := 1(0,1) f dλ . Is V2 a positive contractive operator? Is this operator conservative? Determine the sets {S ∞ g = ∞} and {S∞ g > 0} for some integrable g > 0. Is V2 ergodic? Exercise 4.4.3. Let f , g ∈ L1 (μ) with g ≥ 0. Prove with the help of Wiener’s Maximal Inequality that we have lim
n→∞
S n f S∞ f = S n g S∞ g
158  4 Inﬁnite ergodic theory exists μa.e. on {S∞ g > 0} \ {S∞ g = ∞}. Go back to Exercises 4.4.1 and 4.4.2 and consider the limit limn→∞ S n f /S n g on {S∞ g > 0} and {S∞ g = ∞}, respectively. Exercise 4.4.4. Let V be a positive conservative contraction on L1 (μ). Then a measurable set A is called V *invariant if 1 A is a V *invariant function. Do the V *invariant sets form a σalgebra? Compare this collection with the σalgebra generated by the V *invariant functions. Exercise 4.4.5. For g ∈ L+1 (μ) we have that the set {S∞ g = ∞} is V * invariant. Exercise 4.4.6. Prove Remark 4.1.15. Exercise 4.4.7. Let (X, B , μ) be a probability space. We recall that a subσalgebra F ⊂ B is said to be trivial if for all A ∈ F we have μ(F) ∈ {0, 1}. Show that for all f ∈ L 1 (μ) we have , Eμ (f F ) = f dμ. Exercise 4.4.8. Prove the statement in (4.2).
5 Applications of inﬁnite ergodic theory In this chapter, we shall ﬁrst consider the sumlevel sets for the continued fraction expansion and prove all the corresponding results to those obtained in Chapter 3 for the αsumlevel sets. In the continued fraction case investigated here, though, we will ﬁrst have to prove that the Gauss map has the ψmixing property, in order to apply the results from inﬁnite ergodic theory given in Chapter 4, as the renewal arguments given in Chapter 3 are no longer sufficient. We will also see an application of the ﬁne asymptotics of the Lebesgue measure of the sumlevel sets to Diophantine approximation. Finally, we employ inﬁnite ergodic theory to show that the even Stern–Brocot sequence is uniformly distributed with respect to certain canonical weightings.
5.1 Sumlevel sets for the continued fraction expansion, ﬁrst investigations To begin, let us recall the deﬁnition of the sumlevel sets (Cn )n≥1 for the continued fraction expansion: # k Cn := [x 1 , x2 , x3 , . . .] ∈ [0, 1] : x i = n for some k ∈ N i=1
=
n
k
k=1 (x1 ,...,x k ):
i=1
C(x1 , . . . , x k ). x i =n
We claimed in the introduction to this chapter that the renewal theory arguments as used in Chapter 3 are no longer sufficient to analyse the sumlevel sets for the continued fraction expansion. To see why, observe that if we wanted to prove a result equivalent to Lemma 3.2.8 in this situation, we would be doomed to failure, as the following calculation shows: 3 1 1 1 1 = λ(C3 ) = · λ(C2 ) + · λ(C1 ) + · λ(C0 ) = . 10 2 6 12 3 (Recall that we calculated the values of the Lebesgue measure of the ﬁrst four sumlevel sets at the beginning of Chapter 3.) Before stating the ﬁrst main theorem, we need the following lemma which provides the crucial link between the sequence of sumlevel sets and the Farey map. Note that this lemma contradicts our initial impression that the sequence of sumlevel sets is not a dynamical entity, despite its apparent strangeness.
160  5 Applications of inﬁnite ergodic theory Lemma 5.1.1. For all n ∈ N, we have that F −(n−1) (C1 ) = Cn . Proof. By computing the images of C1 under the inverse images F0 and F1 of the Farey map, one immediately veriﬁes that F −1 (C1 ) = C2 . We then proceed by way of induction as follows. Assume that for some n ∈ N we have that F −(n−1) (C1 ) = Cn . Since F −n (C1 ) = F −1 (F −(n−1) (C1 )) = F −1 (Cn ), it is then sufficient to show that F −1 (Cn ) = Cn+1 . For this, let x = [x1 , x2 , x3 . . .] ∈ Cn be given. Then there exists ∈ N such that x ∈ C(x1 , . . . , x ) and
x i = n.
i=1
By computing the images of x under the inverse branches F0 and F1 , one obtains that F −1 (x) = {[1, x1 , x2 , . . .], [x1 + 1, x2 , . . .]}. Since we have that 1+
x i = (x1 + 1) +
i=1
x i = n + 1,
i=2
this shows that F −1 (x) ⊂ Cn+1 , and hence, F −1 (Cn ) ⊂ Cn+1 . The reverse inclusion Cn+1 ⊂ F −1 (Cn ) can be established by counting the Stern–Brocot intervals contained in Cn+1 . Remark 5.1.2. Notice that an analogous result also holds for the αsumlevel sets, but that this observation was not necessary for the analysis given in Section 3.2.2. We are now almost ready to prove our ﬁrst main theorem. The proof will follow on combining Lemma 5.1.1 with the next result which depends on the fact that F is exact (as shown in Theorem 2.5.6), so that we can apply Lin’s criterion for exactness (see Theorem 2.5.7). Let us also recall that the unique absolutely continuous invariant 0 measure for the Farey map, denoted by νF , is given by νF (A) := A h F (x) dλ(x), where h F (x) := 1/x (see Proposition 2.3.19). Proposition 5.1.3. For each measurable set C which satisﬁes νF (C) < ∞, we have that lim λ F −n (C) = 0.
n→∞
Proof. Let C ∈ B be given as stated in the proposition. So, for each A ∈ B for which 0 < νF (A) < ∞, we then have λ F −n (C) = νF 1 F −n (C) · h F−1 = νF 1 C ◦ F n · h F−1 1A 1A = νF 1 C ◦ F n · h F−1 − + νF (A ) νF (A )
5.2 ψmixing for the Gauss map and the Gauss problem
 161
1 1 1 νF F −n (C) ∩ A 1 n 1 −1 A 1 ≤1 1F h F − νF (A ) 1 + νF (A ) 1 11 1 1 n C 1 ν −1 A F( ) 1 ≤1 1F h F − νF (A ) 1 + νF (A ) 1 ν (C) → F , for n tending to inﬁnity. νF (A ) Here, the limit follows from the fact that νF h F−1 − 1 A /νF (A) = 0 and F is exact, and hence, Lin’s criterion is applicable. Therefore, by choosing A ∈ B such that νF (A) is arbitrarily large, the proposition follows. We can now easily apply this result to determine the limit of the sequence (λ(Cn ))n≥1 , which was our ﬁrst objective. Theorem 5.1.4. lim λ(Cn ) = 0.
n→∞
Proof. The proof follows immediately by ﬁrst putting C = C1 in Proposition 5.1.3, and then using the fact that Cn = F −(n−1) (C1 ), for all n ∈ N, as shown in Lemma 5.1.1. Remark 5.1.5. 1. The above theorem (and proof) can be found in [KS12b]. Also in that paper, there is another proof of the same theorem, which is more elementary, in the sense that it uses less inﬁnite ergodic theory. The other proof depends upon ﬁrst showing that lim inf n→∞ λ(Cn ) = 0. This fact was ﬁrst established by Fiala and Kleban [FK10], but they did not provide a proof for the limit. 2. The arguments given above can be slightly modiﬁed to give a different proof of the inﬁnitetype part of Theorem 3.2.9. Note that this will not work for the ﬁnite measure case.
5.2 ψmixing for the Gauss map and the Gauss problem Recall that in Corollary 2.5.9, we used Lin’s criterion for exactness to show that if a map T is exact, then it is also mixing (cf. Deﬁnition 2.5.8). It follows, in light of Theorem 2.4.12, that the Gauss map G is mixing. Here, we want to show that G satisﬁes the stronger property of ψmixing which was introduced in Chapter 4 (see Deﬁnition 4.3.2). This property can sometimes, for instance in [Aar97], be found under the name continuedfraction mixing precisely because the Gauss map satisﬁes it. The proof that we will present here is very much inspired by the proof given in [Ios92]. Before proving that the Gauss map G is ψmixing, we must make some preliminary 1 = 1. Secondly, we remind the reader that remarks. First of all, recall that we have G −1 = h P G (h G f ) was established in Proposition 2.3.18, where there we the identity Gf G
162  5 Applications of inﬁnite ergodic theory understand the operator P G as acting on L1 (μ). Throughout this section we want to use the pointwise deﬁnition of h−1 G P G ( h G f ) for a concrete measurable function f (and in a later section we will do similarly for the Farey map F), but in order to shorten the for )(x) = h−1 P G (h G f ) (x). Thus, for the operator G notation, we will simply write G(f G all x ∈ [0, 1] and “suitable” functions f , where the class of suitable functions will be deﬁned below, we will write (x) = Gf
∞ i=1
p i (x)f
1 i+x
,
where we have set p i (x) := (1 + x)/((i + x) (i + 1 + x)), for all i ∈ N. (With the pointwise deﬁnition above kept in mind, the proof of this fact is simply a calculation; we leave it to Exercise 5.7.2.) Notice that (p i (x))i≥1 is a probability vector; this will turn out to be crucial later. Further, let us introduce the set of functions of bounded variation, BV := {f : [0, 1] → R : var f < ∞} , where var f is deﬁned to be var f := var[0,1] f and where var[a,b] f is deﬁned, for [a, b] ⊂ [0, 1], to be # n var[a,b] f := sup f (x i+1 ) − f (x i ) : a ≤ x 1 < · · · < x n+1 ≤ b, n ∈ N . i=1
Note that any function of bounded variation is in particular bounded. One can show that any function of bounded variation f can be written as the difference g − h of two bounded functions g and h, which are either both nondecreasing or both nonincreasing (you are asked to prove this in Exercise 5.7.1). More precisely, these functions can be chosen, for x ∈ [0, 1], in the nondecreasing case to be g (x) := var[0,x] f and h (x) := var[0,x] f − f (x) , and in the nonincreasing case to be g (x) := var[x,1] f and h (x) := var[x,1] f − f (x) . It is easy to see, and we leave it as an exercise, that in the nondecreasing case, var g = g (1) − g (0) = var f and var h = h(1) − h(0) = var f + f (0) − f (1), and in the nonincreasing case, var g = var f and var h = var f − f (0) + f (1). Lemma 5.2.1. If f : [0, 1] → R is bounded and either nondecreasing or nonincreasing, is bounded and nonincreasing or nondecreasing, respectively. then Gf is bounded follows directly from the fact that ∞ p i (x) = 1 for all Proof. That Gf i=1 x ∈ [0, 1]. For the proof of the remaining assertions, assume that f is nondecreasing
5.2 ψmixing for the Gauss map and the Gauss problem

163
(for the nonincreasing case consider −f instead) and let x, y ∈ [0, 1] be ﬁxed such that x < y. Then, ∞ 1 − p i (y) f y+i i=1 i=1 ∞ 1 = (p i (x) − p i (y)) f x+i i=1 ∞ 1 1 −f + p i (y) f x+i y+i i=1 ∞ 1 ≥ (p i (x) − p i (y)) f x+i i=1 ∞ 1 1 −f ≥ 0, = (p i (x) − p i (y)) f x+i x+2
(x) − Gf (y) = Gf
∞
p i (x) f
1 x+i
i=1
where, in the ﬁnal equality, and inequality, we have used the observations that ∞ i=1 ( p i ( x ) − p i ( y )) = 0, that p 1 is decreasing, and that p i is increasing for all i ≥ 3. Lemma 5.2.2. For each monotone and bounded function f : [0, 1] → R, we have that ≤ var Gf
1 var f . 2
Proof. Using Lemma 5.2.1 and assuming ﬁrst that f is nondecreasing, we obtain ∞ 1 1 − p i (1) f i 1+i i=1 i=1 ∞ 1 1 = f (1) − (p i−1 (1) − p i (0)) f 2 i i=2 ∞ 1 1 1 2 1 1 1 ≤ f (1) − f (0) = var f , = f (1) − f 2 2 i ( i + 1) i 2 2 2
:= Gf (0) − Gf (1) = var Gf
∞
p i (0) f
i=2
where we used the facts that f is nondecreasing and that 2/ (i (i + 1)) i≥2 is a probability vector. For the nonincreasing case, consider −f instead and observe that (−f ) = −Gf . var(−f ) = var f and G Remark 5.2.3. Note that the constant 1/2 in the latter lemma is optimal. In order to see this, choose f to be any nondecreasing function such that f [0,1/2] = 0 and 0 < f (1) < ∞. = 1/2 var f . For this choice, the above calculation immediately shows that var Gf Now we are in a position to take the next step towards the proof that G is ψmixing, namely, we will obtain a bound on the distance, arising from the supremum norm
164  5 Applications of inﬁnite ergodic theory applied to functions of bounded variation and the · ∞ , of the nth iterate of G integral of these functions. Lemma 5.2.4. For each f ∈ BV and n ∈ N, we have that 1 1 , 1 n 1 f − f dm G 1 ≤ 2−n (2 var f − f (0) − f (1)) . 1G 1 1 ∞
If f is monotone then we have 2 var f − f (0) − f (1) = var f . Proof. First observe that for each x ∈ [0, 1] and f ∈ BV, we have that , , f (u ) − f dm G ≤ f (u ) − f (x ) dm G (x ) ≤ var f , 0 and hence, f ∞ ≤ f dm G + var f . It follows from (2.4) by setting f := 1 that 0 n 0 f dm G = f dm G , and thus the above observation gives, for each n ∈ N, G 1 1 , , 1 1 n nf . n f − f dm G = var G f − f dm G 1 ≤ var G 1G 1 1 ∞
Let f = g − h, for the monotone bounded functions g and h as deﬁned prior to Lemma 5.2.1. Using Lemma 5.2.2, it follows that 1 1 1 1 1 1 , , , 1 1 1 1 n 1 n 1 n g − g dm G 1 + 1G h − h dm G 1 f − f dm G 1 ≤ 1G 1G 1 1 1 1 1 1 ∞
∞
∞
n g + var G nh ≤ var G ≤ 2−n (var g + var h) = 2−n min {2 var f − f (0) + f (1) , 2 var f + f (0) − f (1)} = 2−n (2 var f − f (0) − f (1)) Lemma 5.2.5. For each B ∈ B, all m, n ∈ N0 and either every Gauss cylinder set C := C(x1 , . . . , x n ) of level n > 0 or for n = 0 and C := [0, 1], we have that −n−m λ G (B) ∩ C − m G (B) λ (C) ≤ 2−m log 2 m G (B) λ (C) . and P G , we have Proof. Using Lemma 5.2.4 and, as before, the relationship between G that −n−m λ G (B) ∩ C − m G (B) λ (C) , B λ C dm 1 B ◦ G m+n · h−1 · 1 − m = C G G ( ) ( ) G , m+n h−1 − λ C dm 1B · G 1 1 = ( ) B G G C , 1 1 1 1 m n −1 G h G 1 C − λ ( C )1 ≤ 1 B dm G 1G ∞
5.2 ψmixing for the Gauss map and the Gauss problem

165
n −1 n −1 n h−1 − G h (0) − G h 1 1 1 ≤ m G (B) 2−m 2 var G G C G C G C (1) −1 n n −1 n = m G (B) 2−m 2 var h−1 G P G (1 C ) − h G P G (1 C ) (0) − h G P G (1 C ) (1) ≤ 2−m log 2 m G (B) λ (C) . Here, the ﬁnal inequality can be seen as follows: Directly from the deﬁnition of P G , we have that P nG (1 C ) is equal to the derivative of the inverse branch of G n that maps the unit interval onto the Gauss cylinder C. With p n /q n := [x1 , . . . , x n ], this derivative is given for y ∈ [0, 1] by 1/(q n + yq n−1 )2 (the proof of this fact is left to Exercise 5.7.3). Thus we obtain the formula log 2 (1 + y) n h−1 . G P G (1 C ) (y) = (q n + yq n−1 )2 n To shorten the notation, let us set f n := h−1 G P G (1 C ) . To obtain an upper bound on the variation of this function, we ﬁrst take the derivative: 2q n−1 (1 + y) 1 1 − , f n (y) = q n + yq n−1 (q n + yq n−1 )2 and then observe that if x n = 1, this derivative is always negative (so the function is monotonically decreasing), if x n ≥ 3, then the derivative is always positive (so the function is monotonically increasing), and if x n = 2 we have that the function has a maximum at the point y = q n /q n−1 −2 ∈ (0, 1). Therefore, in the cases x n = 1 and x n ≥ 3, we have immediately that log 2 2 log 2 var ( f n ) = f n (0) − f n (1) = 2 − (q n + q n−1 )2 qn (q n−1 /q n )2 + 2q n−1 /q n − 1 log 2 ≤ 2 1 + q n−1 /q n q n + q n q n−1 % $ 2 x + 2x − 1 : x ∈ [0, 1] ≤ log 2 λ(C) max 1+x = log 2 λ(C). If x n = 2, or, equivalently, if q n /q n−1 ∈ [2, 3], a similar calculation shows that var ( f n ) ≤ 2f n q n /q n−1 − 2 − f n (0) − f n (1) % $ 4 x − 4x3 + 3x2 + 2x + 2 ≤ log 2 λ(C) max : x ∈ [2, 3] 2x(x + 1)(x − 1) =
1 log 2 λ(C). 6
The case in which C := [0, 1] and n = 0 is more straightforward and is left to Exercise 5.7.4.
166  5 Applications of inﬁnite ergodic theory Corollary 5.2.6. For every set B ∈ B, we have that lim λ(G−n (B)) = m G (B).
n→∞
Proof. This is an immediate consequence of Lemma 5.2.5 with C = [0, 1]. Theorem 5.2.7. The system ([0, 1], B , m G , G) is ψmixing with respect to the partition α H . More precisely, for all positive integers m, n ∈ N, any Gauss cylinder set C := C(x1 , . . . , x n ) of level n and every set B ∈ B, we have that m G G−n−m B ∩ C − m G (B) m G (C) ≤ 2−m log 2 m G (B) m G (C) . Proof. Let B and C be given as stated in the theorem. For y := (y1 , . . . , y N ) ∈ NN , we use the notation [yC] to denote the Gauss cylinder set C(y1 , . . . , y N , x1 , . . . , x n ) of level n + N. With this notation, Lemma 5.2.5 implies that for each N ∈ N, we have −N −n−m B ∩ C − m G (B) λ G−N C G λ G λ [yC] ∩ G−n−m−N B − m G (B) λ ([yC]) = y∈NN λ ([yC]) ≤ 2−m log 2 m G (B) y∈NN
=2
−m
log 2 m G (B) λ G−N (C) .
Letting N tend to inﬁnity in the above inequality and invoking Corollary 5.2.6 ﬁnishes the proof of the theorem. Remark 5.2.8. The history of ψmixing for the Gauss map is a rather long one, and proving ψmixing for the Gauss map can be considered to be the ﬁrst problem in the metric theory of continued fractions. It originates in a letter which Gauss wrote to Laplace on the 30th of January 1812, asking him to give an estimate of the error term ρ n (x) := λ(G−n ([0, x])) − m G ([0, x]), for x ∈ [0, 1] and n ∈ N. This problem, known as the generalised Gauss problem¹, remained open for a long time, until R. O. Kuzmin [Kuz28] and P. Lévy [Lev29] independently and almost √ simultaneously gave a solution. Kuzmin showed that ρ n ≤ cκ n , for some positive constants κ < 1 and c, whereas Lévy obtained the result that ρ n ≤ cθ n , for positive constants θ < 0.68 . . . and c. These results of Kuzmin and Lévy were then followed by various improvements by several authors, among them W. Doeblin, F. Schweiger and P. Szüsz. The currently most satisfying estimate has been obtained
1 Note that the actual Gauss problem was to show what we have obtained in Corollary 5.2.6.
5.3 Pointwise dual ergodicity for the Farey map

167
by E. Wirsing [Wir74], who showed that the constant θ in Lévy’s estimate is equal to 0.30366300289873265860 . . ..
5.3 Pointwise dual ergodicity for the Farey map First let us recall from Chapter 2 the induced map FC1 of the Farey map on the interval C1 := [1/2, 1], which was deﬁned in Example 2.4.30. This map acts on points [1, x2 , x3 , . . .] in C1 by FC1 ([1, x2 , x3 , . . .]) = [1, x3 , x4 , . . .]. We showed in the proof of Proposition 2.4.31 that this induced system is measuretheoretically isomorphic to the Gauss system. Given that G is ψmixing, as was shown in the previous section (see Theorem 5.2.7), it follows immediately that the map FC1 is also ψmixing. Before stating the ﬁrst result, recall that pointwise dual ergodicity and Darling–Kac sets were introduced in Deﬁnitions 4.2.1 and 4.3.1, respectively. Lemma 5.3.1. The set C1 is a Darling–Kac set for the Farey map. Proof. This follows directly from Proposition 4.3.3, in combination with the discussion above. Theorem 5.3.2. The system ([0, 1], B , νF , F) is pointwise dual ergodic. Proof. Since, according to Lemma 5.3.1, the map F has a Darling–Kac set, the result follows directly from Theorem 4.3.5. Now we know that F is pointwise dual ergodic, we can obtain information about wandering rates and return sequences. Let us denote the return sequence associated to F by (v n )n≥1 . In order to determine the asymptotic type of (v n ), we must compute the wandering rate (w n (C1 ))n≥1 , (see Deﬁnition 4.2.3), where we recall that the wandering rate (w n (C)) for any C ∈ B is given by n
−(k−1) F (C) . w n (C) := νF k=1
So, for each n ∈ N we have n
−(k−1) w n (C 1 ) = ν F T (C1 ) = νF 1[1/(n+1),1] = log(n + 1) ∼ log(n). k=1
Next, observe that this wandering rate is slowly varying at inﬁnity, where we recall from Chapter 3 that this means lim w k·n (C1 ) /w n (C1 ) = 1, for each k ∈ N.
n→∞
168  5 Applications of inﬁnite ergodic theory
Lemma 5.3.3. For the map F, the return sequence (v n ) can be deﬁned by setting v n :=
n . log(n)
Proof. This follows on combining Proposition 4.2.8 with the fact that F is pointwise dual ergodic.
5.4 Uniform and uniformly returning sets In this section, it will turn out to be helpful to have a formula for the transfer operator in Section 5.2. Exactly as before, we have that for f ∈ L1 (νF ), := F νF , as we had for G F ) = h F−1 P F (h F f ). F(f It is then straightforward to check that this leads to the pointwise deﬁnition x x 1 )(x) = 1 f . + f F(f 1+x 1+x 1+x 1+x Now, we aim to obtain uniform sets for the system ([0, 1], B , νF , F). First recall the general deﬁnition of a uniform set stated in Deﬁnition 4.2.4: Let (X, B , μ, T) be pointwise dual ergodic with return sequence (r n ). Then, a set A ∈ B with positive, ﬁnite measure is called a uniform set for f ∈ L+1 (μ) if , n−1 1 k T f → f dμ uniformly (mod μ) on A. rn k=0
We introduce a set of functions D, in order to show that the set C1 is uniform for all of the elements of D. So, deﬁne D := {f ∈ L +1 (νF ) ∩ C2 ([0, 1]) : f > 0 and f ≤ 0}.
We remark that D is not empty, since id[0,1] ∈ D. D) ⊂ D. Lemma 5.4.1. We have F( ): By the monotonicity of f and f Proof. Let f ∈ D and consider the derivative of F(f we have x x 1 1 −f f f − xf x+1 x+1 x+1 x+1 ( f ) ( x ) = + , F 3 2 ( x + 1) ( x + 1) 3 45 6 3 45 6 >0
>0
5.4 Uniform and uniformly returning sets 
169
( f ) > 0. Furthermore, an easy calculation shows which implies F x x 1 1 f 2 f + xf −f x+1 x+1 x+1 x+1 ( f ) (x) = + F 5 3 ( x + 1) ( x + 1) x 1 − 2f 2 ( x + 1) ( x − 1) f x+1 x+1 + ≤ 0. 5 ( x + 1) This ﬁnishes the proof. Lemma 5.4.2. The set C1 is uniform for every f ∈ D. (1) = f (1/2). Lemma 5.4.1 implies that x → F n f (x) is Proof. Fix f ∈ D. Note that Ff monotone increasing. Thus, recalling from Lemma 5.3.3 that v n = n/ log(n), we have for every x ∈ C1 n n n 1 k 1 k 1 k F f (1/2) ≤ F f (x) ≤ F f (1) vn vn vn k=0
k=0
k=0
n+1 n 1 k 1 1 k ≤ F f (1) ≤ f (1) + F f (1/2) vn vn vn k=0
Since limn→∞ v n −1 f (1) = 0 the uniform convergence v n from pointwise dual ergodicity.
k=0
−1 n
k f →
k=0 F
0
f dνF follows
We will now introduce a somewhat stronger notion than uniform sets, namely, that of uniformly returning sets. Deﬁnition 5.4.3. Let (X, B , μ, T) be a conservative, ergodic, measurepreserving system. A set C ∈ B with 0 < μ(C) < ∞ is called uniformly returning for f ∈ L+1 (μ) if there exists an increasing sequence (w n ) := (w n (f , C))n≥1 of positive real numbers such that μalmost everywhere and uniformly on C we have that , n (f ) = f dμ. lim w n T n→∞
Remark 5.4.4. If A, B are sets of ﬁnite μmeasure and B is uniformly returning for 1 A , we immediately obtain a form of mixing for inﬁnite systems, in the sense that , n (1 A ) · 1 B dμ → μ(A)μ(B). w n μ(A ∩ T −n (B)) = w n T Note that this could be considered to be a little unsatisfactory as a deﬁnition, since it is basically considering an inﬁnite system by restricting it to a set of ﬁnite measure. The work of Lenci, mentioned directly before Deﬁnition 2.5.8, addresses this issue by
170  5 Applications of inﬁnite ergodic theory
looking at the problem of deﬁning inﬁnite mixing on the entire space from a more physical point of view. Let us now return to the Farey map. Proposition 5.4.5. For the Farey system ([0, 1], B , νF , F) we have that if v ∈ L1 (νF ) satisﬁes , n (v) = v dνF almost everywhere uniformly on C1 , lim w n F n→∞
then the same holds on any compact subset of (0, 1]. Proof. Let us ﬁrst recall that, for x ∈ (0, 1] and n ∈ N, n P n+1 F (h F · v) (x) = P F P F (h F · v) (x) = P nF (h F · v) (F0 (x)) · F0 (x) + P nF (h F · v) (F1 (x)) · F1 (x) , which gives
(P n+1 (h F · v))(x) − (P nF (h F · v))(F1 (x)) · F1 (x) . P nF (h F · v) (F0 (x)) = F F 0 (x)
(5.1)
We proceed by induction as follows. The start of the induction is given by the assumption in the theorem. For the inductive step, assume that the statement holds for ki=1 Ci , for some k ∈ N. Then consider some arbitrary y ∈ Ck+1 , and let x denote log(n +2) F ̂n φ(x ) 1
φ(x ) n n n n
0
1
=9 =4 =2 =1
x
Fig. 5.1. The iterates of the Farey transfer operator F n acting on φ : x → x and rescaled with the wandering rate approximate a.s. uniformly on compact subsets of (0, 1] the constant function of height 1 = φ dμ.
5.4 Uniform and uniformly returning sets
 171
= h−1 P F (h F · v) the unique element in A k such that F0 (x) = y. Using (5.1), the fact that F F and the inductive hypothesis in tandem with the assumption that lim w n /w n+1 = 1, we obtain that n n (v) (y) = w n F n (v) (F0 (x)) = w n (P F (h F · v))(F0 (x)) wn F h F (F0 (x)) w n (P n+1 (h · v))(x) −  F (x)  · w n (P nF (h α · v))(F1 (x)) F 1 F = h F (F0 (x)) · F0 (x) , , h (x) − h F (F1 (x)) · F1 (x) vd ∼ F ν = vdνF , F h α (F0 (x)) · F0 (x) where the last equality is a consequence of the eigenequation P F h F = h F . Let us now return to our collection D. To prove that C1 is indeed uniformly returning for every element of D we need the following observation. Lemma 5.4.6. On C1 , for every f ∈ D we have that n−1 f , for all n ∈ N. n f < F F (1) = f 1/2 . Then for every x ∈ C1 we Proof. Fix f ∈ D. Again we use the fact that Ff have / . n f (1) = F n f (x) : x ∈ C1 = F n−1 f 1/2 n f (x) ≤ max F F / . n−1 f (x) , n−1 f (x) : x ∈ C1 ≤ F = min F where at least one of the inequalities must be strict. Proposition 5.4.7. The set C1 is uniformly returning for every f ∈ D. That is for all f ∈ D we have , n ( f ) → fdνF , uniformly on C1 . log(n)F (See Fig. 5.1 for an illustration). Proof. Let λ, η ∈ R be arbitrary ﬁxed real numbers with 0 < λ < η < ∞. Putting V n :=
n
k ( f ) , F
k=0
n ( f ) C we have by the monotonicity of the sequence F 1
n∈N
that
nλ ( f ) nη ( f ) V nη − V nλ F F · (nη − nλ ) ≤ ≤ · (nη − nλ ) . Vn Vn Vn
172  5 Applications of inﬁnite ergodic theory Since nη − nλ ∼ n (η − λ) as n → ∞, we have for ﬁxed ε ∈ (0, 1) and all n sufficiently large n (1 − ε) (η − λ) ≤ nη − nλ ≤ n (1 + ε) (η − λ) . This implies for all n sufficiently large nλ ( f ) nη ( f ) V nη − V nλ n F nF · (1 − ε ) ( η − λ ) ≤ ≤ · (1 + ε ) ( η − λ ) . Vn Vn Vn Since V nη − V nλ
→ ηα − λα Vn
μa.e. uniformly on C1 ,
as n → ∞
we obtain on the one hand nλ ( f ) 1 nF ηα − λα · ≤ lim inf 1+ε η−λ Vn n→∞
μa.e. uniformly on C1 .
Letting η → λ and ε → 0, it follows that αλ α−1 ≤ lim inf n→∞
nλ ( f ) nF Vn
μa.e. uniformly on C1 .
On the other hand, we obtain similarly lim sup n→∞
nη ( f ) nF ≤ αη α−1 Vn
μa.e. uniformly on C1 .
Since λ and η are arbitrary, we have for any c > 0 nc ( f ) nF → αc α−1 Vn Finally using V nc ∼ c α V n m = nc
μa.e. uniformly on C1 .
μa.e. uniformly on C1 and nc ∼ cn, we obtain for
m ( f ) nc n F nc ( f ) V n mF = · →α · Vm n Vn V nc
μa.e. uniformly on C1 .
From this and Lemma 5.4.2 the assertion follows. Remark 5.4.8. The material developed here for the set of functions D can be found in greater generality in the context of operator renewal theory in various works, including [Gou11, Gou04, MT15, MT12, Sar02, KKSS15, KKS15].
5.5 Finer asymptotics of Lebesgue measure of sumlevel sets

173
5.5 Finer asymptotics of Lebesgue measure of sumlevel sets Using the results obtained in the previous sections, we are now in a position to state and prove our ﬁrst result on the ﬁner asymptotics of the sumlevel sets. Theorem 5.5.1. n
λ (C k ) ∼
k=1
n . log2 n
Proof. First, recall from Lemma 5.1.1 that Ck = F −(k−1) (C1 ). Therefore, , n n−1 1 1 · λ(Ck ) = 1C1 ◦ F k dλ vn vn k=1
k=0
, n−1 1 = 1C1 ◦ F k · h F−1 dνF vn k=0 n−1 , 1 k −1 = 1C1 F (h F ) dνF . vn k=0
−1 Since C1 is a uniform set for h−1 F ∈ D we have that limn→∞ v n 0 −1 h F dνF = 1 a.e. uniformly on C1 , and so
n−1 k −1 k=0 F (h F ) =
n 1 · λ(Ck ) = log 2. n→∞ v n
lim
k=1
Our second, and ﬁnal, theorem concerning the Lebesgue measure of the sumlevel sets gives a signiﬁcant improvement of Theorem 5.1.4 and Theorem 5.5.1. That is, by increasing the dosage of inﬁnite ergodic theory, we are able to obtain the following sharp estimate for the asymptotic behaviour of the Lebesgue measure of the sumlevel sets. Theorem 5.5.2. λ(Cn ) ∼
log 2 . log n
Proof. We use again the fact from Lemma 5.1.1 that Ck = F −(k−1) (C1 ). Therefore, , w n · λ(Cn ) = w n 1C1 ◦ F n dλ , = w n 1C1 ◦ F n · h F−1 dνF , n (h F−1 ) dνF . = 1C1 w n F
174  5 Applications of inﬁnite ergodic theory Since C1 is uniformly returning for h−1 F we have that , n (h F−1 ) = h F−1 dνF = 1 lim w n F n→∞
a. e. uniformly on C1 , and so lim w n · λ(Cn ) = log 2.
n→∞
Remark 5.5.3. We refer the interested reader to [Hee15] for an effective bound on the error term of this asymptotic. Let us now give an application of the above theorem to elementary metrical Diophantine analysis. We ﬁrst state the following result, which is a consequence of Theorem 1.2.19, given in Section 1.2.4. Theorem 5.5.4. For λalmost every x = [x1 , x2 , x3 , . . .] ∈ [0, 1], we have that lim sup n→∞
log(x n /n) = 1. log log n
Proof. In light of Corollary 1.2.20, for λa.e. x = [x1 , x2 , x3 , . . .] ∈ [0, 1] we have that for all ε > 0 and all sufficiently large n ∈ N, x n < n(log n)1+ε . Taking logarithms of each side, it follows that for all sufficiently large n ∈ N, log(x n ) < log(n) + (1 + ε) log log(n) or, rewriting this expression, again for all sufficiently large n ∈ N, we have log(x n /n) < 1 + ε. log log(n) Hence, lim sup n→∞
log(x n /n) ≤ 1 + ε. log log(n)
Since ε > 0 was arbitrary, we may conclude that lim sup n→∞
log(x n /n) ≤ 1. log log(n)
On the other hand, it also follows from Corollary 1.2.20 that for Lebesguealmost every x = [x1 , x2 , x3 , . . .] ∈ [0, 1], we have that for inﬁnitely many n ∈ N, x n > n log(n).
5.5 Finer asymptotics of Lebesgue measure of sumlevel sets 
175
Then, log(x n /n) log log(n)
1 ≤ lim sup n→∞
and the theorem is proved. In contrast to the almosteverywhere property of continued fraction digits stated in Theorem 5.5.4, we can now prove a similar statement associated to the Farey coding using Theorem 5.5.2. Note that ni=1 x i represents the word length associated with the Farey system, whereas the parameter n represents the word length associated with the Gauss system. Before stating the theorem, let us remind the reader that we use the notation A B to mean that there exists a constant c ≥ 1 such that c−1 A ≤ B ≤ cA. Proposition 5.5.5. For λalmost every x = [x1 , x2 , x3 , . . .] ∈ [0, 1], we have that log(x n+1 / ni=1 x i ) lim sup ≤ 0. n log log( i=1 x i ) n→∞ Proof. For each n ∈ N and ε > 0, let # k
ε ε x i = n, x k+1 ≥ n(log n) A n := C(x1 , . . . , x k+1 ) : i=1
k∈N
and deﬁne Aεn :=
C.
C∈A εn
Now recall from (1.8) that λ(C(x1 , . . . , x k , m)) m−2 λ(C(x1 , . . . , x k )). Therefore, for all k, ∈ N, we obtain that λ(C(x1 , . . . , x k , x k+1 )) −1 λ(C(x1 , . . . , x k )). x k+1 ≥
Using this estimate and Theorem 5.5.2, we deduce that λ(Aεn ) =
n k=1
(x1 ,...,x k ) k x =n i=1 i
λ(C(x1 , . . . , x k , x k+1 ))
x k+1 ≥n(log n)ε
n λ(C(x1 , . . . , x )) k ε n(log n) (x ,...,x ) k=1
1 k k x =n i=1 i
176  5 Applications of inﬁnite ergodic theory
=
n 1 λ(C(x1 , . . . , x k )) ε n(log n) (x ,...,x ) k=1
=
1 k k x =n i=1 i
λ(Cn ) log 2 ∼ . n(log n)ε n(log n)1+ε
ε Hence, since the above calculation implies that the series ∞ n=1 λ(An ) converges, a ε := straightforward application of the Borel–Cantelli Lemma then yields, where A∞ ε lim sup An , that n→∞
ε ) = 0, for each ε > 0. λ(A∞ ε in [0, 1], we have now shown that, for On considering the complement of the set A∞ each ε > 0 and for λalmost all x = [x1 , x2 , x3 , . . .],
k ε k x k+1 < xi x i , for all k ∈ N sufficiently large. log i=1
i=1
By taking logarithms on both sides of the above inequality, we obtain, for all sufficiently large k ∈ N, that k k x / x log(x k+1 ) − log log x k+1 i=1 i i=1 i < ε. = k k log log x log log x i i i=1 i=1 It therefore follows that log x k+1 / ki=1 x i ≤ ε. lim sup k k→∞ log log i=1 x i Finally, on letting ε tend to zero, the lemma follows. Remark 5.5.6. Using these ideas, there are other results related to continued fractions and Diophantine analysis that can be obtained. For instance, for the random variable # k k xi : x i ≤ n, k ∈ N0 , x ∈ I, X n (x) := max i=1
i=1
the process n − X n is investigated in [KS08a]. In that paper, a uniform law and large deviation law are derived. For further interesting results in the context of continued fraction digit sums we also refer to [GLJ93, GLJ96], wherein alternating sums of continued fraction digits are considered.
5.6 Uniform distribution of the even Stern–Brocot sequence 
177
5.6 Uniform distribution of the even Stern–Brocot sequence Let us begin this section by recalling the concept of weak convergence of probability measures. We say a sequence (μ n )n∈N of Borel probability measures on (R, B) converges weakly to a Borel probability measure μ if for all f ∈ Cb (R) we have , , lim f dμ n = f dμ. n→∞
For this we write wlimn μ n = μ. There are different effective ways to check weak convergence despite the difficulty of trying to directly use the deﬁnition. We will need the following two characterisations, which can be found in the standard literature on probability, for example in [Gut13]. We recall that ∆ μ : x → μ((−∞, x]) denotes the distribution function of μ. – (Distribution function) wlimn μ n = μ if and only if limn→∞ ∆ μ n (x) = ∆ μ for every x ∈ R that is a continuity point of ∆ μ . – (Method of Moments) For a probability measure μ, the function , t → exp(t · x) dμ is called the moment generating function (you will see why in Exercise 5.7.6). We 0 0 have that if exp(t · x) dμ n → exp(t · x) dμ ∈ R for n tending to inﬁnity and for all t in a neighbourhood of 0, then wlimn μ n = μ. Let δ x denote the Dirac measure in x, that is δ x (A) = 1 for x ∈ A and δ x (A) = 0 otherwise. Our aim in this section is to prove the following theorem: Theorem 5.6.1. For each rational number v/w ∈ (0, 1] we have that q−2 δ p/q = λ. wlim log(n vw ) n→∞
(5.2)
p/q∈F −n {v/w}
In order to prove this result, we ﬁrst prove the following proposition, and then one further lemma, which will allow us to transfer the result stated below for intervals to the atomic measures considered in Theorem 5.6.1. Proposition 5.6.2. For each interval [a, b] ⊂ (0, 1] we have that log n · λF −n ([a,b]) = λ. wlim n→∞ log b/a
178  5 Applications of inﬁnite ergodic theory
Proof. Consider the family of functions (φ t )t∈[−1,1] given by φ t : x → x · exp (t · x). The ﬁrst aim is to show that for all t ∈ [−1, 1] we have t ∈ D. Fφ (D) ⊂ D (see Lemma 5.4.1) Indeed, for t ∈ [−1, 0] this is an immediate consequence of F by noting that φ t is increasing, concave with φ t (0) = 0, that is φ t ∈ D. For t ∈ (0, 1], t at x ∈ [0, 1] is a straightforward computation shows that the ﬁrst derivative of Fφ given by x x 1 1 − φt − xφt φt φt x+1 x+1 x+1 x+1 t (x) = + . Fφ 3 2 ( x + 1) ( x + 1) For the second derivative we then obtain
t Fφ
−2 xt − 6x + 2t + xt 2 + 2x3 − 4tx2 − 4 exp
(x) =
tx x+1
6
( x + 1)
2tx − 6x − 2t + xt 2 + 2x3 + 4tx2 − 4 exp
+
6
( x + 1)
t x+1
.
t ≤ 0, for all t ∈ (0, 1]. Hence, Fφ t is concave and This immediately implies that Fφ t (1) = 0, this shows that on t is decreasing on [0, 1]. Since Fφ we have that Fφ t ≥ 0. Hence, Fφ t ∈ D, for all t ∈ [−1, 1]. [0, 1] we have that Fφ We proceed by noting that Proposition 5.4.7 combined with Proposition 5.4.5 guarantees that every compact interval contained in (0, 1] is a uniformly returning set for φ t , for each t ∈ [−1, 1]. In order to complete the proof of the proposition, we employ the method of moments as follows. For each [a, b] ⊂ (0, 1] and for each t ∈ [−1, 1], we have , log n · 1 F −n [a,b] (x) dλ(x) lim exp (tx) · ( ) n→∞ νF [a, b] log n log n n φt · 1 · νF φ t · 1 F −n [a,b] = lim · νF F = lim [a,b] ( ) n→∞ νF [a, b] n→∞ νF [a, b] , = νF (φ t ) = exp (tx) dλ(x). This shows that, restricted to [−1, 1], the moment generating functions for the log n · λF −n ([a,b]) converge to the moment generating sequence of measures log(b/a) n∈N
function for the Lebesgue measure λ. In turn, this shows the weak convergence in question and hence ﬁnishes the proof of Proposition 5.6.2.
5.6 Uniform distribution of the even Stern–Brocot sequence

179
For the next lemma we introduce the notation Φ for the free semigroup generated by the inverse branches F0 and F1 of the Farey map F. Note that for each rational number v/w ∈ (0, 1] we have that
{F −n {v/w} : n ∈ N} = g v/w : g ∈ Φ .
Moreover, note that the Φorbit of 1 is equal to the set of rational numbers contained in (0, 1). (Note that this is just a slightly different way of repeating what we already knew from Section 1.3, when obtaining the Farey coding of the rational numbers.) Then if we x 1 and F1 : x → 1+x , and observe associate matrices to the inverse branches F0 : x → 1+x that # 8 9 a b 1 0 0 1 : a, b, c, d ∈ Z, ad − bc = 1} , ⊂ GL2 (Z) := , c d 1 1 1 1 then to each g ∈ Φ we can associate a matrix ac db from GL2 (Z). The action of g on C is given by g : z → az+b cz+d . Thus g(1) = v/w, for some v, w ∈ N such that v < w and gcd(v, w) = 1. Furthermore, for the modulus of the derivative of g at x we have that g (x) = cx + d 2 . To make this more precise, see also Exercise 5.7.5. In the following we let Uε (x) denote the interval centred at x ∈ R of Euclidean diameter diam(Uε (x)) equal to ε > 0. Lemma 5.6.3. For each g ∈ Φ there exists a constant C g such that for all ε > 0 sufficiently small and for all h ∈ Φ, we have diam(h(Uε (g(1)))) − ε (h (g(1)) ≤ εC g diam(h(Uε (g(1)))). Proof. First we will prove the following bounded distortion property. Using Exercise 5.7.5, we know that for each g ∈ Φ we can ﬁnd m, n ∈ N such that g (z) = mz + n2 . Now ﬁx z ∈ (0, 1) and let 0 < ε < z/2 and x, y ∈ Uε (z). Then 2 2 2 g (x) (my + n)2 m (y − x ) + 2nm(y − x) sup − 1 ≤ sup − 1 ≤ sup 2 (mx + n)2 g∈Φ  g (y) m,n∈N (mx + n) m,n∈N 8 (2m2 + 2nm) m2 + mn y − x  ≤ 2 x − y  sup 2 2 z m,n∈N (mz/2 + n) m,n∈N (m + 2n)
≤ sup ≤
16 x − y. z2
Here, the last inequality can be seen by treating the two cases m ≤ n and m > n separately. Now, ﬁx g ∈ Φ. Then we have, for 0 < ε < g(1)2 /32 and each h ∈ Φ,
180  5 Applications of inﬁnite ergodic theory  h (g(1))  ε 1 − 1 diam(h(Uε (g(1)))) − 1 ≤ 1 0 g(1)+ε/2 h (η) ε g(1)−ε/2 h (g(1)) − 1 dη + 1 32ε 1 − 1 ≤ . ≤ 2 1 − 16ε/g(1) g(1)2 From this we deduce that diam(h(Uε (g(1)))) − εh (g(1)) < diam(h(Uε (g(1)))) 32ε g(1)2 Setting C g := 32/g(1)2 then ﬁnishes the proof. Proof of Theorem 5.6.1. Let g ∈ Φ be given and deﬁne, for ε > 0 sufficiently small,
Ug,ε,n := F −(n−1) Uε (g(1)) .
With u g,ε := 1/νF (Uε (g(1))) = 1/ log (g(1) + ε/2)/(g(1) − ε/2) , consider the scaled and restricted Lebesgue measure νg,ε,n which is given, for each n ∈ N, by νg,ε,n := u g,ε log n · λ Ug,ε,n .
By Proposition 5.6.2, we then have that wlimn→∞ νg,ε,n = λ. Then observe that lim εu g,ε = lim ε↘0
ε↘0
ε = g(1), g(1) + ε/2 log g(1) − ε/2
(5.3)
and consider the measures ρ g,n deﬁned, for each n ∈ N, by
ρ g,n := g(1) log n
f (1)∈F −(n−1) {g(1)}
f (1) ·δ . g (1) f (1)
Using Lemma 5.6.3, we now obtain the following for all x ∈ [0, 1], where ∆(ν) g,ε,n and ∆(ρ) g,n denote the distribution functions of the measures νg,ε,n , and ρ g,n , respectively. (ν) (ρ) ∆ g,ε,n (x) − ∆ g,n (x) 1 εu g,ε diam(h(Uε (g(1))) − εg(1) h (g(1)) + log n ≤ log n ε n2 h∈Φ:
hg(1)∈F −(n−1) {g(1)}
≤ ε g(1) C g + εu g,ε − g(1)
1 u g,ε log n εu g,ε
f ∈Φ: f (1)∈F −(n−1) {g(1)}
n→∞
1 → ε g(1) C g + εu g,ε − g(1) , g(1)
f (1)
5.7 Exercises

181
where the convergence follows from Proposition 5.6.2 and (5.3). This inequality holds for all x ∈ [0, 1], n ∈ N and the righthand side vanishes for ε → 0. Hence, using the condition for weak convergence in terms of distribution functions, we obtain that wlim ρ g,n = λ. n→∞
The proof of Theorem 5.6.1 now follows, if we use in the deﬁnition of ρ g,n the fact that g(1) can be written in the form of a reduced fraction v/w and that then g (1) = w−2 , as well as similarly, that f (1) can be written in the form of a reduced fraction p/q and that then f (1) = q−2 (cf. Exercise 5.7.8). Finally, let us recall here the even Stern–Brocot sequence which was ﬁrst deﬁned in Section 1.3.1. For each n ≥ 0, the nth member of the Stern–Brocot sequence is denoted by Bn and the nth member of the even Stern–Brocot sequence is deﬁned to be Sn := Bn \ Bn−1 . Recalling that Sn is exactly the set F −n ({1/2}), we obtain the following immediate corollary. Corollary 5.6.4. For the even Stern–Brocot sequence we have that −2 q δ p/q = λ. wlim log(n2 ) n→∞
(5.4)
p/q∈Sn
5.7 Exercises Exercise 5.7.1. Show that any function of bounded variation f can be written as the difference g − h of two bounded functions g and h, which are either both nondecreasing or both nonincreasing. Exercise 5.7.2. Show that if p i (x) := (1 + x)/((i + x) (i + 1 + x)) and f ∈ L1 (λ), then (x) = Gf
∞ i=1
p i (x)f
1 i+x
.
Exercise 5.7.3. Let n ∈ N and denote C := C(x1 , . . . , x n ). Show that P nG (1 C ) is equal to the derivative of the inverse branch of G n that maps the unit interval onto the cylinder set C. Exercise 5.7.4. Prove the case n = 0, C := [0, 1] of Lemma 5.2.5. Exercise 5.7.5. Let # GL2 (Z) :=
a c
b d
: a, b, c, d ∈ Z, ad − bc = 1}
182  5 Applications of inﬁnite ergodic theory denote denote the group of invertible 2 × 2 matrices over the integers and let Aut C Show the group of automorphism of C that is the set of biholomorphic mappings of C. that the map a b az + b , → z→ GL2 (Z) → Aut C cz + d c d deﬁnes a group homomorphism and determine its kernel. Further, show that for every −2 element φ : z → az+b cz+d in the image of this homomorphism we have φ ( z ) =  cz + d  . 0 Exercise 5.7.6. The function t → exp(t · x) dμ(x) is called the moment generating function of the probability distribution μ. Show that the kth derivative of this function 0 in 0 can be used to ﬁnd the kth moment M k (μ) := x k dμ(x), k ∈ N0 , of μ, if it exists. Exercise 5.7.7. For each g ∈ Φ there exists a constant ∆ g such that all ε > 0 sufficiently small and for all h ∈ Φ, we have diam(h(Uε (g(1)))) − ε (h (g(1)) ≤ ε2 (hg) (1)∆ g . Exercise 5.7.8. Show that for each p/q ∈ (0, 1) such that gcd(p, q) = 1, there exists a unique element f ∈ Φ with f (1) = p/q, and we have f (1) = q2 . Exercise 5.7.9. Prove that for all a, b ∈ (0, 1) with a < b we have lim log(n)λ F −n ([a, b]) = log(b/a).
n→∞
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Index absolutely continuous 75 admissible word 18 αFarey decomposition 48 alphabet 17 αFarey cylinder set 48 αFarey expansion 47 αFarey inverse branch 45 αFarey map 44 αirrational number 42 αLüroth convergent 44 αLüroth cylinder set 44 αLüroth expansion 42 αLüroth map 40 αrational number 42 alternating Lüroth map 61 approximants 7 badly approximable numbers 11 badly αapproximable numbers 54 Banach space 137 Borel σalgebra 65, 93 bounded variation 162 conditional expectation 143 conjugacy map 13 conservative operator 141 conservative part 72, 141 conservative transformation 69 continued fraction 1 – convergent 2 – elements of the 1 – expansion 1, 5, 29 – ﬁnite 1 – inﬁnite 1 – sumlevel set 122 continuedfraction mixing 161 convergent – αLüroth 44 – continued fraction 2 cover 73 cylinder set – αFarey 48 – αLüroth 44 – Farey 33 – Gauss 19 – symbolic dynamic 17
Darling–Kac set 151 decomposition – Farey 33 – Hopf 72 – Hopf’s (for operators) 141 Diophantine approximation 7 dissipative part 72, 141 distribution function 36, 177 Doeblin, Wolfgang 166 dual operator 138 dual space 77, 138 dyadic partition 43 dynamical system – conservative 69 – conservative measurepreserving 69 – ergodic 88 – measurepreserving 64 – measuretheoretic 64 – nonsingular 70 – numbertheoretic 1 – topological 1, 12 empty word 17 equivalent 75 ergodic 88 ergodic theorem – Birkhoff’s pointwise 96, 146 – Chacon–Ornstein 144 – Hopf’s 104, 146 – Hurewicz’s 146 eventually periodic 16 exact 115 exact transformation 94 expansion – αFarey 47 – αLüroth 42 – continued fraction 1, 5, 29 – Lüroth 59 factor 14 factor map 14 Farey coding 32 Farey decomposition 33 Farey map 30 Farey sequence 58 ﬁrst passage time 86
Index 
ﬁxed point 13 function – distribution 36 – distribution 37, 177 – generating 61, 131, 133 – Hölder continuous 38 – induced 113 – Minkowski’s questionmark 35, 38, 49 – moment generating 177, 182 – regularly varying 130 – singular 36 – slowly varying 51, 129, 130 Gauss map 15, 19 Gauss measure 67 Gauss, Carl Friedrich 166 generating function 61, 131, 133 golden mean 8 – shift 19 golden ratio 8 Hölder continuous function 38 Hölder exponent 38 harmonic partition 42 Hopf decomposition 72 – for operators 141 Hurwitz constant 10 Hurwitz number 8 incidence matrix 18 induced function 113 induced map 105, 146 initial block 17 intersection property 115 invariant – T  64 – V *  158 invariant measure 64 inverse branch – αFarey 45 – αLüroth 41 – Farey 31 – Gauss 15 join 27 jump transformation 31, 46, 86 Kac’s formula 111, 114 Koopman operator 77 Kuzmin, Rodion Ossijewitsch 166
Lüroth expansion 59 Lüroth map – alternating 43, 61 Lüroth series 43 Lévy, Paul 166 Laplace, PierreSimon 166 Laplace Transform 136 Lemma – Borel–Cantelli 23 – Chacon–Ornstein 139 – Hopf’s maximal inequality 140 – Wiener’s maximal inequality 140 length 17 letters 17 liminf set 122 limsup set 23 Lin’s Criterion 118 Lüroth map 60 map – αLüroth 40 – alternating Lüroth 43, 61 – conjugacy 13 – factor 14 – Farey 30 – Gauss 15, 19 – induced 105 – Lüroth 60 – shift 18 – tent 35 Markov partition 27, 40, 47 – coding 28 – shrinking 27 Markov partition coding 28 measurable union 73 measure – Dirac 177 – Gauss 67 – invariant 64 – restricted 105 measure of maximal entropy 37 measurepreserving 64 mediant 33 Minkowski’s questionmark function 35, 38, 49 mod μ 70 moment generating function 177, 182 Moments – Method of 177
189
190  Index
noble numbers 10 nonsingular 94 nth Farey sequence 58 numbertheoretic dynamical systems 1 occupation time 103 operator – bounded 137 – conservative 141 – contracting 138 – dual 138 – Koopman 77 – positive 138 – Ruelle 81 – transfer 76 operator norm 137 orbit 12 partition – dyadic 43 – expanding 52 – expansive 52 – ﬁnite type 52 – harmonic 42 – inﬁnite type 52 – Markov 27, 40, 47 – shrinking Markov 27 period 13 – prime 13 periodic – eventually 16 periodic point 13 Pigeonhole Principle 61 point – (pre)periodic 13 – exceptional 27 pointwise dual ergodic 147 (pre)periodic point 13 prime period 13 ψmixing 151, 161 quadratic surd 16 Radon–Nikodým derivative 76 reﬁnement 27 regularly varying 130, 148 renewal equation 124 – asymptotic 149 renewal pair 124
renewal shift 34 renewal theorem 124 return sequence 147 return time 105 Ruelle operator 81
saturate 73 Schweiger, Fritz 166 shift map 18 singular function 36 slippery Devil’s staircase 36 slowly varying function 51, 129, 130 stable distribution 100 Stern–Brocot intervals 33, 160 Stern–Brocot sequence 33, 58 – even 34, 181 Stern–Brocot tree 34, 82 subHölder continuous 49 subshift 18 subshifts 18 sumlevel set 159 – αLüroth expansion 123 – α 133 – continued fraction 122 sweepout set 73, 88, 105 symbolic dynamics 16 symbols 17 symmetric difference of two sets 70 system – induced 105 Szüsz, Peter 166
tail σalgebra 94 tent map 35 Theorem – Aaronson’s 104 – Birkhoff’s Pointwise Ergodic 96, 146 – Bogolyubov–Krylov 85 – Borel–Bernstein 24 – Dirichlet’s Approximation 61 – Discrete Renewal 126 – Hahn–Banach 144 – Halmos’s Recurrence 70 – Hopf’s Ratio Ergodic 104 – Hurewicz’s ergodic 146 – Hurwitz’s 8, 9 – Karamata’s Tauberian 131
Index
– Lagrange’s 16 – Maharam’s Recurrence 74 – Poincare’s Recurrence 74 – Radon–Nikodým 76 – Strong Renewal 130
– nonsingular 70 – Schweiger’s jump 31 trivial σalgebra 158 uniform set 148, 168 uniformly returning set 169
topological dynamical system 1 topologically conjugate 13
V * invariant set 158
transfer operator 76 transformation – ergodic 88 – exact 94 – jump 47, 86 – measurepreserving 64
wandering rate 147, 167 wandering sets 69 weak convergence 177 wedge 17 Wirsing, Eduard 167 words 17
 191