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Ergodic properties of the measure r/+

Ipl-- ~ IpAI

7. Ergodic properties of the measure r/+

Combining all the previous bounds yields (6.3).

T h e equation (6.3) holds, in particular, for l = 0 and B = Ri, 1 <

i < I. Since M_,~ = U~/=l(/~i N M-m), we i m m e d i a t e l y obtain

lira -- 1

m,~-~oo #~(M-m)

thus completing t h e proof of (6.2) and Proposition 6.2.

The first part of T h e o r e m 1.8 is t h e n established.

Proposition 6.3. For any G > 0 and # E ~ ( G ) the limit (see (1.15)) lira I[ ;,mll


exists, and c[#] is the same as in Proposition 5.6.

P r o o f . We have lira

f/% t n---~ OO

9 ) - n - m , T ~ " ' M "

II nmll= lira + -m)

= lim

~TLln --+ OO

which is equal to e[#] due to Proposition 5.6. Proposition 6.3 is proved.

T h e proof of T h e o r e m 1.8 is completed.

7. Ergodic properties of the measure r/+

Here we prove t h e ergodic and fractal properties of the invariant measure r]+ on the repeller, given by s t a t e m e n t s 1.9-1.14.

Let k, I >_ 0, and take a r b i t r a r y a t o m s / 3 E ~z and D E Tg_k. Assume t h a t int(B N D) r ~ and pick a point x E B n D.

Lemma7.1. There is a constant C7 > 1 independent of x, B, D, k, 1 such that

C71 <_ )~k++l J~+l(T-lx ) 9 rl+(B n D) <_ C7

P r o o f . T h e set E = T k ( B N D) is an a t o m of 7~k+t, and due to (1.13)

~+(B n D) = rl+iTk(B N D)) = f e(g) df~+(U)

d M E

Bol. Soc. Bras. Mat., Vol. 28, ~L 2, 1997


In virtue of (1.9) and (5.11) we have

fl < inf e(U) < sup e(U) _< fl-1

UcS/ U c U

so t h a t

8 < v+(B n D) <_ fl_~

Next, the conditional invariance of # + implies t h a t

= +k- fu



where F = T - I ( B • D) is an a t o m of " ] ' ~ - k - I and U ( F ) = {U 6 L/ :


To estimate this last integral, recall t h a t the measures u~ on unstable fibers U E /g have densities uniformly b o u n d e d away from zero and infinity, and note t h a t for all U C b/(F)

0 < const < m u ( F A U) 9 J~+l(T-Zx) < const < oc

which follows from the absolute continuity of stable and unstable folia- tions, Sect. 2. This completes the proof of L e m m a 7.1.

This l e m m a immediately implies t h a t ~+ is a Gibbs measure with the potential g+(x) = - l o g JU(x) and the topological pressure P(r]+) = log I + = - 7 + , see [2].

T h e o r e m s 1.9 and 1.11 are now proved. Corollary 1.10 m o s t l y fol- lows from [2], for more advanced limit theorems t h a n the central limit t h e o r e m see,

e.g., [13].

T h e o r e m 1.12 is self-evident.

We now t u r n to T h e o r e m 1.13.

The measure % is also a Gibbs measure, with potential g_(x) = log J S ( T - l x )

and topological pressure P(r/_) -- - l o g A_-I = - 7 - . The next lemma is a direct consequence of P r o p o s i t i o n 4.5, [2].

L e m m a 7.2. The following three conditions are equivalent:

(i) ~+ = r]_;

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(ii) there is a c o n s t a n t Z > 0 such that f o r a n y periodic p o i n t x E ~2, T k x = x, we have J ~ ( x ) . J ~ ( x ) = Z k ;

(iii) the f u n c t i o n s g + ( x ) a n d g _ ( x ) are cohomoIogous, i.e. there is a c o n s t a n t R a n d a H61der c o n t i n u o u s f u n c t i o n u ( x ) such that g+ (x) - g _ ( z ) = R + u ( T x ) - u ( z ) .

I f those c o n d i t i o n s are satisfied, t h e n

- i n Z = R = P ( r ] + ) - P ( • _ ) = ~/_ - 7 +

T h e o r e m 1.13 now follows immediately. This theorem, combined with P r o p o s i t i o n 4.14 from [2], gives Corollary 1.14.

Possible applications of T h e o r e m 1.13 and Corollary 1.14 cover hy- perbolic repellers c o n s t r u c t e d on the base of Hamiltonian systems (those preserve Liouville measures t h a t are absolutely continuous). In partic- ular, these include billiard systems, like the open billiard with three circular scatterers studied

in [16],

w h e r e repellers are thus always time- symmetric.

A n o t h e r interesting class of repellers are linear repellers. Let the rectangles RI,... , R I be subset in ]R d a n d all E ~ a n d all E s be paral- lel. Let the m a p T be linear o n each Ri, with the constant derivative D T =const on M , so t h a t the functions JU(x) = j u and j S ( x ) = j s are constant on M . In this case the measures r/+ and r/_ always coincide, and b o t h coincide with the measure of maximal entropy on ~2, see [2]

for definitions and details. In this case the repeller ~2 is, however, time s y m m e t r i c if and only if det D T = J~ 9 j s = 1, i.e. if T preserves the Lebesgue measure in ]R d.

8 . G e n e r a l i z a t i o n s a n d o p e n p r o b l e m s

In our arguments, we never essentially relied on the fact t h a t T was a diffeomorphism of a connected manifold, in fact the action of T on H = M ' \ M never came into play. All our results hold true under the following, more general assumptions:

Let M be a finite union of disjoint closed domains R 1 , . . . , R1 in a s m o o t h R i e m a n n i a n manifold AA. Let T : M -+ 3 4 be a diffeomorphism of M onto its image, which is C 1+~ up to the b o u n d a r y O M . We

Bol. Soc. Bras. Mat., VoL 28, N. 2, 1997


assume the Anosov splitting (1.1) at every x E M , and require (1.2) if the corresponding iterations of T are defined. Let the bundles E~ 's be HSlder continuous and integrable over every Ri, so that Ri is foliated by HSlder continuous families of C 1+~ submanifolds W~ ,s such t h a t T W ~ '8 = E~ 's at every x E Ri. Assume t h a t every Ri is a rectangle and {R1 .. 9 , R r} is a Markov partition of M in the sense of Section 2.

Under these assumptions our results remain true. The above setting is very convenient for horseshoe-like maps, studied in [4,21].

We now discuss w h a t h a p p e n s if we relax the mixing assumption in Section 2. First, we can classify the rectangles like one does states of Markov chains. We call a rectangle/~i recurrent if its points come back to itself under T, i.e. intRi N Tn(Ri A M-n) r 2J for some n _> 1. In the trivial case, where all the rectangles are nonrecurrent (transient), the sets M + , M _ and f~ are empty, and the phase space M 'escapes' entirely.

The recurrent rectangles can be grouped, in each group points from any rectangle can be m a p p e d into any other rectangle, so t h a t the sym- bolic dynamics within every group is transitive.

Let us assume first t h a t there is only one transitive group of rectan- gles R 1 , ' " , Rz0, and p u t M0 = R1 U . . . t_J Ri0. This group is periodic if there is a k _> 1 such t h a t the periods of all the periodic points in M 0 are multiples of k. In t h a t case this group can be divided into k s u b g r o u p s eyelicly p e r m u t e d by T, and the restriction of T k to any subgroup is topologically mixing. The s t u d y of the m a p T admits a s t a n d a r d reduction to t h a t of T k, well known in the t h e o r y of Axiom A diffeomorphisms [2], so t h a t we can restrict ourselves to the case k = 1.

T h e n the repeller fl belongs in M0. The nonrecurrent rectangles Ri, i > I0, can be of three types: isolated (such t h a t intTnRi N M 0 = 2~ for all n E Z), incoming (such that intTnRi N M0 r ~ for some n > 0) and outgoing (such t h a t i n t T n R / ~ M 0 ~ ~ for some n < 0). The set M + in- tersects only recurrent and outgoing rectangles, M _ only recurrent and incoming ones. The measures #-L conditioned on M0 coincide with the corresponding measures for the restriction of T to M0. The measures

Bol. Soc. Bras. Mat., Vol. 28, N. 2, 1997

E R G O D I C P R O P E R T I E S O F A N O S O V M A P S 3 0 9

W+ and the escape rates 7+ will be t h e same for



TIM o.

So, non- recurrent rectangles do not really affect the properties of t h e repeller ft studied in this paper, t h e y only m a y enlarge t h e sets M• and 'stretch' the measures #• accordingly.

A more involved situation occurs when there are two or more groups

9 . . j ~ /

of recurrent rectangles. For simplicity, consider two groups, R~, , I0

ll II


R~,... ,Rjo,

and put M(~ = UR~ and M(~' = URn. If there is no connection between these groups, i.e. int(T~M(~ N

T'~M~ t)

= 2~ for all m, n E Z, t h e n we have two trivially independent repellers in M D and M~ t, respectively. On the contrary, if there is a route from M~ to M~', i.e.

int(TnM~NM~ ') r rg

for some n >_ 1, t h e n the picture gets intricate.

T h e rate of escape from M(~ is still the same as for t h e m a p



if M(~' did not exist. T h e escape from M(~ ~, however, is combined with t h e influx of points from M(~. T h e resulting escape rate from M will be t h a n influenced by three factors: t h e escape rates from

M~, M~'

and by t h e fraction of M(~ t r a n s m i t t e d to M(~' after escaping from M(~. We did not investigate here these interesting phenomena.

A n o t h e r n a t u r a l extension would be to s t u d y Axiom A diffeomor- phisms r a t h e r t h a n Anosov ones. Let T :

M t ~ M'

be an Axiom A diffeomorphism with t h e basic set ft. Let M C M ' be a proper closed s u b d o m a i n such t h a t ft =


T h e n it might be possible to con- struct conditionally invariant measures on M + =


and invariant measures on ft in t h e same way as we did for Anosov diffeomorphisms.

We leave this for future researches.

Lastly, there are nonuniformly hyperbolic diffeomorphisms and hy- perbolic maps with singularities, like billiards, which have countable Markov partitions and t h e derivatives growing to infinity at singulari- ties. Extension of our results to those models is the most challenging problem at present.

A p p e n d i x

This appendix contains the Perron-Frobenius theorem on positive ma- trices and related results9 Most of these results are taken from [4]9

BoL Soc. Bras. Mat., Vol. 28, N. 2, 1997

3 t 0 N. C H E R N O V a n d R. MARKARIAN

Let Vm be the space of row m-vectors, and Vr~ the space of column m-vectors. We equip t h e m with norms


la] = ~

lail, Ib*l = maxl<i<m ]bil (A.1)

i = 1

and scalar p r o d u c t

(a, b*) = albl + ' " + ambm

for all a E V,~ and b* E V~. We call vectors a E V,~ and b* E Vr~ positive if their c o m p o n e n t s are all positive.

Note t h a t I(a,b*)l <_ lallb*l .

Let 79,rn be the set of m x m matrices with positive entries:

A = ( A i j ) ET),~ i f A i j > O Y l < _ i , j < _ m

Stochastic ( E j Aij = 1) and substochastic ( E j Aij <_ 1) matrices are the best studied classes of matrices in 79,~.

T h e o r e m A l . [ P e r r o n - F r o b e n i u s t h e o r e m ] Every positive matrix A E T'm has a positive row eigenvector p and a positive column eigenvector

p * :

pA = Ap and Ap* = Ap*

where A > 0 is the largest (in absolute value) eigenvalue of the matrix A. These vectors are unique up to a scalar multiple, i.e. the multiplicity of A is one.

We p u t p and p* for the Perron eigenvectors of A normalized so t h a t

m m

~ P i = lpl = 1 and ~ P i P ~ = (P*,P)= 1 (A.2)

i = 1 i = 1

For a fixed matrix A E Pro, we introduce other norms in Vm and V~



Ilall~ = ~ la~lp~, IIb*ll~ = maxl~j~m(IbSI/P~)

If the c o m p o n e n t s of a are non-negative, then


= (a,p*) and

IlaAIl~ =.~llall~


N o t e t h a t I(a,b*)l < llallrllb*llc.

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We will say t h a t P _> 1 is an estimate of the ratio of rows of A C 7)m p - 1 < A i j / A k j < P V l < i , j , k <_ m

If A satisfies this estimate, we write A E 7).~(P).

If A c 7)m (P), t h e n t h e components of its Perron eigenvectors satisfy p - 1 ~_ p~/p~ ~_ p, )kP -1 ~_ A i j / p j ~_ )~P, p - 1 ~_ p~ <_ p for all 1 < i , j <_ m. T h e norms defined by (A.1) and (A.3) are t h e n equivalent:

P - l i d I < [[al[~ <



P-11b*] <

[Ib*]l~ < P]b]

for all a E Vm and b* E V,~.

The following estimate on t h e so called coefficients of ergodicity is also satisfied if A E Pro(P):


> a p

j = l

Denote by Lm and L~n the orthogonal complements to the P e r r o n eigenvectors:

Lm = {a E Vm : (a,p*) = O} and T h e n we have the decompositions


L ~ = {b* e V ~ : (p, b*) = O}

a = (a,p*)p + ao w i t h ao E L.~

b* = (p, b*);* + b~ w i t h b~ c L ~

L e m m a A . 2 . I f A E 7),~(P), then f o r any a E Lm we have IlaAll~ <_ ~(1 - P-~lllall~

and f o r any b* C L m we have

[IAb*IIr ~_ A(1 - P-2)ilb*IIr

If A < 1 (this is t h e case if A is a proper substochastic matrix), t h e n this l e m m a says t h a t the contraction in the orthogonal subspaces Lm

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312 N . C H E R N O V a n d R. M A R K A R I A N

and L m is stronger t h a n t h a t in the eigenspaces spanned by the Perron eigenvectors.

CorollaryA.3. I f A E 72re(P) and 0 = 1 - p - l , then lira A - h A n = p* |

Tb - ~ (X9

where (p* | p ) ~ j = p~pj is the tensor product of p* and p. Moreover, II(A- A n - p * op) ll _<

where Bk, f o r a matrix B , means the k-th row.

R e m a r k . If A is a stochastic matrix ( ~ j A~j = 1), then A = 1 and p~ = 1 for all 1 _< j < n, and we recover a well known ergodic theorem for finite Markov chains.

We now compare the action on positive row vectors by two positive matrices which are close to each other. We say t h a t B G P,~ is close to A E 7)m, with the constant of proximity R > 1 if

R -1 <_ B i j / A i j <_ R V 1 <_ i, j <_ m (A.4) In t h e following statements, A E 7P,~(P) is a fixed matrix, p is its Perron row eigenvector normalized by (A.2) and A is the corresponding eigenvalue. We also set 0 = 1 - p - 1 .

L e m m a A . 4 . Let q be an arbitrary positive row vector such that iiqI[r = 1.

Let B E 7)ra be another matrix close to A with the constant of proximity R > 1. T h e n

R-11[pA[I, < [[qBIIr _< R[]pd]]~


]]qB - pAiI r <_ AO][q- plir + A ( R - 1)

L e m m a A.5. Let B E 7)m be as in L e m m a


For any positive row vector q E V,~ we have

q B p A r

iiqBil ~ ilpAll ~ <- ORIlq - pllr + 2 R ( R - 1)

T h e o r e m A.6. Let B 1 , B 2 , . . . , B n E 7)m be matrices, all close to A with the same constant of proximity JR >_ 1. For any positive row vector

Bol. Soc. Bras. Mat., Vol. 28, N. 2, 1997


[4] N.N. Cencova, Statistical properties of smooth Smale horseshoes~ in: Mathematical Problems of Statistical Mechanics and Dynamics, R.L. Dobrushin, Editor, pp.

199-256, Reidel, Dordrecht, 1986. malism, and transport coefficients, preprint, 1995.

[11] P. Gaspard and G. Nicolis, Transport properties, Lyapunov exponents, and en- tropy per unit time, Phys. Rev. Lett. 65 (1990), 1693-1696.

[12] P. Gaspard and S. Rice, Scattering from a classically chaotic repellor, J. Chem.

Phys. 90 (1989), 2225-2241.

[13] Y. Guivarc'h and J. Hardy, Thdor&mes limites pour une classe de chaines de Markov et applications aux diffdomorphismes d~Anosov, Ann. Inst. H. Poincar4, 24 (1988), 73 98.

E14] M. Keane and M. Mori, Dynamical systems on the Cantor sets associated with piecewise linear transformations, manuscript.

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[15] O. Legrand and D. Sornette, Coarse-grained properties of the chaotic trajectories in the stadium, Phys. D 44 (1990), 229 247.

[16] A. Lopes and R. Markarian, Open billiards: Cantor sets, invariant and condi- tionally invariant probabilities, SIAM J. of Applied Math, 56 (1996)~ 651-680.

[17] R. Mafi~, Ergodie Theory and Differentiable Dynamics, Springer-Verlag, Berlin, 1987.

[18] A. Manning, A relation between Lyapunov exponents, Hausdorff dimension and entropy, Ergod. Th. L~ Dynam. Sys. 1 (1981), 451-459.

[19] S. Martinez and M.E. Vares, Markov chain associated to the minimal Q.S.D. of birth-rate chains, to appear in J. Applied Probab.

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N. C h e r n o v

Department of Mathematics

University of Alabama in Birmingham Birmingham, AL 35294, USA

E-maih chernov@vor teb.math.uab.edu R . M a r k a r i a n

Instituto de Matem~itica y Estadistica "Prof. Ing. Rafael Laguardia"

Facuhad de Ingenierfanewline Universidad de la Repfiblica C.C. 30, Montevideo, Uruguay

E-maih roma@fing.edu.uy

BoL Soc. BTas. Mat., VoL 28, N. 2, 1997