Introduction Categories Entropy memento Entropy and invariant measures of weakly hyperbolic diffeomorphisms Wandering Seminar, Wroclaw Lecture 1 J´erˆome Buzzi (CNRS & Universit´e Paris-Sud) http://jbuzzi.wordpress.com/ [email protected] April 24, 2015 Exercices Introduction Categories Entropy memento Exercices Entropy and invariant measures of diffeomorphisms Entropy fundamental invariant (Kolmogorov 1958, Shannon 1948) • counts the orbits • classifies (Ornstein 1971, Adler-Weiss 1979) Our goal: Theorem (Boyle & B 2014) Any C 1+ -diffeo of a compact surface is conjugate to a Markov shift, ”up to zero entropy invariant probability measures” Corollary In particular, such diffeomorphisms with a unique mixing measure maximizing the entropy are classified by their topological entropy Introduction Categories Entropy memento Exercices Entropy and invariant measures of diffeomorphisms Our route for the mini-course: • positive entropy hyperbolicity in the sense of Pesin • converse: Katok’s horseshoe theorem • symbolic model for non-uniformly hyperbolic dynamics: Markov shifts (Gureviˇc) • hyperbolicity on surfaces Markov shift extension of Sarig • Markov shifts have a universal property by Hochman If time allows, study of the invariants: • (non)existence of measures maximizing the entropy • value of the entropy (continuity, local constancy, formula) Introduction Categories Entropy memento Plan for lecture 1 • Categories of dynamical systems • Entropy memento • Exercises Exercices Introduction Categories Entropy memento Exercices Categories of dynamical systems Definition dynamical system (d.s.) = isomorphism of a space up to relevant identifications. conjugacy of d.s. (X , S), (Y , T ) = isomorphism of spaces ψ : X → Y that intertwines the maps: ψ ◦ S = T ◦ ψ factor = epimorphism of spaces ψ : X → Y that intertwines the maps. • smooth: a compact C r manifold; • topological : a compact metric space (t.d.s.); • probability: a probability space up to subsets of zero measure (p.d.s.); • (standard) Borel: σ-algebra generated by topology defined by complete distance; • almost Borel: Borel up to subsets with zero measure wrt any aperiodic ergodic invariant probability measure Introduction Categories Entropy memento Categories of dynamical systems Our focus – interplay between these points of view: • one can forget • one can enrich, selecting from: • P(S, X ) := { S-invariant Borel probability measures of X } • Perg (S, X ) := {µ ∈ P(S, X ) : µ S-ergodic} • P0erg (S, X ) := {µ ∈ Perg (S, X ) : µ({periodic points}) = 0} Theorem (for t.d.s.) • (Krylov-Bogolioubov) P(T ) and Perg (T ) are not empty • (Dinaburg, Goodman) htop (S) = sup h(S, µ) = sup µ∈P(S) µ∈Perg (S) h(S, µ) (definitions are on the way...) More importantly:smoothness constrains entropy of measures Exercices Introduction Categories Entropy memento Exercices Entropy Memento Definition The (Kolmogorov-Sinai) entropy of a p.d.s. is h(T , µ) := supP h(T , µ, P) Wn−1 −k h(T , µ, P) = limn→∞ n1 Hµ P k=0 T P Hµ (P) := A∈P −µ(A) log µ(A) (0 log 0 = 0) The (Borel) entropy is h(T ) := supµ∈Perg (T ) h(T , µ) Proposition R For µ ∈ P(S), Borel d.s., ∃ ergodic decomposition µ = µx dµ(x). Then: x 7→ h(T , µx ) defined on a Borel set of total µ-measure and: Z h(T , µ) = h(T , µx ) dµ(x). Introduction Categories Entropy memento Entropy Memento Theorem (Goodman-Dinaburg Variational principle) For a t.d.s. (X , S), h(S) := supµ∈Perg (S) h(S, µ) is equal to the topological entropy defined as: htop (S) := lim→0 h(S, ) htop (S, ) := lim supn→∞ n1 log rS (X , , n) where S rS (Y , , n) := min{#C ⊂ Y : Y ⊂ x∈C BS (x, , n)} BS (x, , n) := {y ∈ X : ∀0 ≤ k < n d(S k y , S k x) < }. One can replace lim inf n→∞ by lim supn→∞ . Theorem (Katok entropy formula) If (X , S) t.d.s., µ ∈ Perg (S), 0 < t < 1, then: h(S, µ) = lim→0 ht (S, µ, ) ht (S, µ, ) := lim inf n→∞ n1 log inf µ(Y )>t rS (Y , , n) One can replace lim inf n→∞ by lim supn→∞ . Exercices Introduction Categories Entropy memento Exercices Entropy Memento Proof (Misiurewicz) of the variational principle:htop (S) = h(S) Katok formula h(S) ≤ htop (S) Converse: measures equidistributed on minimal (, n)-covers Remark. Limits of µ may have small entropy! Definition µ measure maximizing entropy (m.m.e.) if h(T , µ) = h(T ) Both existence and uniqueness can fail: Theorem (Misiurewicz 1973) ∀1 ≤ r < ∞ ∃ f , g ∈ Diff r (T4 ): • f has no m.m.e. • g has infinitely many m.m.e. (trivial) Question What about dimension 2? Theorem (Newhouse 1988) Any C ∞ -diffeomorphism of a compact manifold has some m.m.e. Introduction Categories Entropy memento Exercises Facts 1. For (S, µ) p.d.s., n ∈ Z: h(S n , µ) = |n| · h(S, µ) 2. If π : (S, µ) → (T , ν) is a factor of p.d.s. h(S, µ) ≥ h(T , ν) Exercice Prove them. Extend them to t.d.s. and Borel d.s. (Ledrappier-Walters variational principle gives a formula for t.d.s.) Box dimension of compact metric space: dimB (X ) := lim→0 log N(X , )/| S log | where N(X , ) = min{#C : C ⊂ X and x∈C B(x, ) ⊃ X } Exercice (Kushnirenko 1965) X is a compact metric with dimB X < ∞ S : X → X Lipschitz Then htop (S) ≤ dimB X · log+ (Lip(S)) (x + := max(x, 0)) Exercices Introduction Categories Entropy memento Exercices Exercises Exercice Show htop = 0 for (1) isometries of compact metric spaces; (2) homeomorphisms of the circle. (ΣN , σN ) shift on {1, . . . , N}. Prove/recall: htop (ΣN ) := log N A Misiurewicz (T , N)-horseshoe for f ∈ C 0 (I ): open ∅ = 6 U1 , . . . , UN ⊂ I s.t.: ∀1 ≤ i 6= j ≤ N Ui ∩ Uj = ∅ ∀1 ≤ i, j ≤ N int(f T (Ui )) ⊃ Uj Exercice T Show K := n≥0 g −n (U1 ∪ . . . UN ) is compact g := f T -invariant Use π : K → ΣN s.t. g n x ∈ Uπ(x)n to show htop (f ) ≥ T1 log N Generic (residual, fat) = dense Gδ set (Baire: stable by countable intersection in complete metric spaces) Exercice (Continued) Find a generic G ⊂ C 0 (I ) such that, for all g ∈ G, htop (g ) = ∞.
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