Optimization of Lyapunov Exponents

100
Optimization of Lyapunov Exponents RDS and MET Workshop at BIRS, Banff, Canada Jairo Bochi (Catholic Univ. of Chile, Santiago) January 22, 2015 Jairo Bochi Optimization of Lyapunov Exponents 1 / 48

Transcript of Optimization of Lyapunov Exponents

Page 1: Optimization of Lyapunov Exponents

Optimization of Lyapunov Exponents

RDS and MET Workshop at BIRS, Banff, Canada

Jairo Bochi (Catholic Univ. of Chile, Santiago)

January 22, 2015

Jairo Bochi Optimization of Lyapunov Exponents 1 / 48

Page 2: Optimization of Lyapunov Exponents

Joint work with

Michał Rams

(Polish Academy of Sciences, Warsaw)

Jairo Bochi Optimization of Lyapunov Exponents 2 / 48

Page 3: Optimization of Lyapunov Exponents

Plan of the talk

1 Thanks to the organizers.2 “Classical” ergodic optimization3 Non-commutative ergodic optimization: what we would

like to do4 Setting: One-step cocycles and domination5 A useful tool: Barabanov functions6 The main result7 Strategy of the proof

Jairo Bochi Optimization of Lyapunov Exponents 3 / 48

Page 4: Optimization of Lyapunov Exponents

ERGODIC OPTIMIZATION:

FROM BIRKHOFF AVERAGES TO LYAPUNOV EXPONENTS

Ref.: Survey by O. Jenkinson (DCDS, 2006)

Jairo Bochi Optimization of Lyapunov Exponents 4 / 48

Page 5: Optimization of Lyapunov Exponents

“Classical” ergodic optimizationFix a continuous dynamics T : X→ X on a compact space X

Let M(T) be set of T-invariant Borel probabilities.

Given a continuous function f : X→ R, let

M(f ) := supμ∈M(T)

f dμ.

An invariant measure for which the sup is attained is calledmaximizing for f . (They always exist.)

“Classical” Ergodic Optimization is concerned aboutdetermining M(f ), describing the maximizing measures μ,their properties etc.

Rem.: Connections with Classical Mechanics(Aubry–Mather. . . ), Thermodynamical Formalism (zerotemperature limits), Multifractal Analysis . . .

Jairo Bochi Optimization of Lyapunov Exponents 5 / 48

Page 6: Optimization of Lyapunov Exponents

“Classical” ergodic optimizationFix a continuous dynamics T : X→ X on a compact space X

Let M(T) be set of T-invariant Borel probabilities.

Given a continuous function f : X→ R, let

M(f ) := supμ∈M(T)

f dμ.

An invariant measure for which the sup is attained is calledmaximizing for f . (They always exist.)

“Classical” Ergodic Optimization is concerned aboutdetermining M(f ), describing the maximizing measures μ,their properties etc.

Rem.: Connections with Classical Mechanics(Aubry–Mather. . . ), Thermodynamical Formalism (zerotemperature limits), Multifractal Analysis . . .

Jairo Bochi Optimization of Lyapunov Exponents 5 / 48

Page 7: Optimization of Lyapunov Exponents

The place where maximizing measures live

Theorem (Many authors(*), 90’s, 00’s)Suppose T : X→ X is “hyperbolic” and f is Hölder continuous.Then there exists a compact T-invariant set K ⊂ X (called a“Mather set”) such that:

μ ∈M(T) is maximizing ⇔ suppμ ⊂ K .

(*) Coelho, Conze – Guivarc’h, Savchenko, Mañé, Fathi, Contreras – Lopes –Thieullen, Bousch.

N.B.: The result is false for C0 functions; in this case there areexamples where the maximizing measure is unique and hasfull support.

Jairo Bochi Optimization of Lyapunov Exponents 6 / 48

Page 8: Optimization of Lyapunov Exponents

The place where maximizing measures live

Theorem (Many authors(*), 90’s, 00’s)Suppose T : X→ X is “hyperbolic” and f is Hölder continuous.Then there exists a compact T-invariant set K ⊂ X (called a“Mather set”) such that:

μ ∈M(T) is maximizing ⇔ suppμ ⊂ K .

(*) Coelho, Conze – Guivarc’h, Savchenko, Mañé, Fathi, Contreras – Lopes –Thieullen, Bousch.

N.B.: The result is false for C0 functions; in this case there areexamples where the maximizing measure is unique and hasfull support.

Jairo Bochi Optimization of Lyapunov Exponents 6 / 48

Page 9: Optimization of Lyapunov Exponents

Proof of the existence of Mather setsSuppose f̃ is cohomologous to f , i.e.,

f̃ = f + h ◦ T − h for some h

ThenM(f ) = sup

μ∈M(T)

f dμ = supμ∈M(T)

f̃ dμ = M(f̃ ).

μ is maximizing for f ⇐⇒ μ is maximizing for f̃ .

Theorem (Mañé(*) Lemma or Revelation Lemma)Suppose T : X→ X is “hyperbolic” and f is Hölder continuous.Then ∃ f̃ cohomologous to f such that f̃ ≤M(f̃ ).

Then the maximizing measures are exactly those who live inthe (Mather set) K := {x ∈ X; f̃ (x) = M(f̃ )}.

Jairo Bochi Optimization of Lyapunov Exponents 7 / 48

Page 10: Optimization of Lyapunov Exponents

Proof of the existence of Mather setsSuppose f̃ is cohomologous to f , i.e.,

f̃ = f + h ◦ T − h for some h

ThenM(f ) = sup

μ∈M(T)

f dμ = supμ∈M(T)

f̃ dμ = M(f̃ ).

μ is maximizing for f ⇐⇒ μ is maximizing for f̃ .

Theorem (Mañé(*) Lemma or Revelation Lemma)Suppose T : X→ X is “hyperbolic” and f is Hölder continuous.Then ∃ f̃ cohomologous to f such that f̃ ≤M(f̃ ).

Then the maximizing measures are exactly those who live inthe (Mather set) K := {x ∈ X; f̃ (x) = M(f̃ )}.

Jairo Bochi Optimization of Lyapunov Exponents 7 / 48

Page 11: Optimization of Lyapunov Exponents

TheoremIf T is “expanding” then for generic Lipschitz functions f :

(several authors) There is a unique maximizingmeasure.

(Morris 2008) This measure actually has zero entropy.

(Contreras, preprint 2013) This measure is actuallysupported on a periodic orbit.

Related results on periodicity of maximizing measures by:

Hunt–Ott’96 (experimental), Yuan–Hunt’99, Contreras–Lopes-Thieullen’01,Bousch’01, Quas–Siefken’12. The latter deals with spaces ofsuper-continuous functions over the shift.

Theorem (B.–Zhang, preprint ArXiv 2015)On spaces of sufficiently super-continuous functions, theconclusion of Quas–Siefken–Contreras is not only topologicallygeneric but prevalent (has “full probability”).

Jairo Bochi Optimization of Lyapunov Exponents 8 / 48

Page 12: Optimization of Lyapunov Exponents

TheoremIf T is “expanding” then for generic Lipschitz functions f :

(several authors) There is a unique maximizingmeasure.

(Morris 2008) This measure actually has zero entropy.

(Contreras, preprint 2013) This measure is actuallysupported on a periodic orbit.

Related results on periodicity of maximizing measures by:

Hunt–Ott’96 (experimental), Yuan–Hunt’99, Contreras–Lopes-Thieullen’01,Bousch’01, Quas–Siefken’12. The latter deals with spaces ofsuper-continuous functions over the shift.

Theorem (B.–Zhang, preprint ArXiv 2015)On spaces of sufficiently super-continuous functions, theconclusion of Quas–Siefken–Contreras is not only topologicallygeneric but prevalent (has “full probability”).

Jairo Bochi Optimization of Lyapunov Exponents 8 / 48

Page 13: Optimization of Lyapunov Exponents

TheoremIf T is “expanding” then for generic Lipschitz functions f :

(several authors) There is a unique maximizingmeasure.

(Morris 2008) This measure actually has zero entropy.

(Contreras, preprint 2013) This measure is actuallysupported on a periodic orbit.

Related results on periodicity of maximizing measures by:

Hunt–Ott’96 (experimental), Yuan–Hunt’99, Contreras–Lopes-Thieullen’01,Bousch’01, Quas–Siefken’12. The latter deals with spaces ofsuper-continuous functions over the shift.

Theorem (B.–Zhang, preprint ArXiv 2015)On spaces of sufficiently super-continuous functions, theconclusion of Quas–Siefken–Contreras is not only topologicallygeneric but prevalent (has “full probability”).

Jairo Bochi Optimization of Lyapunov Exponents 8 / 48

Page 14: Optimization of Lyapunov Exponents

TheoremIf T is “expanding” then for generic Lipschitz functions f :

(several authors) There is a unique maximizingmeasure.

(Morris 2008) This measure actually has zero entropy.

(Contreras, preprint 2013) This measure is actuallysupported on a periodic orbit.

Related results on periodicity of maximizing measures by:

Hunt–Ott’96 (experimental), Yuan–Hunt’99, Contreras–Lopes-Thieullen’01,Bousch’01, Quas–Siefken’12. The latter deals with spaces ofsuper-continuous functions over the shift.

Theorem (B.–Zhang, preprint ArXiv 2015)On spaces of sufficiently super-continuous functions, theconclusion of Quas–Siefken–Contreras is not only topologicallygeneric but prevalent (has “full probability”).

Jairo Bochi Optimization of Lyapunov Exponents 8 / 48

Page 15: Optimization of Lyapunov Exponents

TheoremIf T is “expanding” then for generic Lipschitz functions f :

(several authors) There is a unique maximizingmeasure.

(Morris 2008) This measure actually has zero entropy.

(Contreras, preprint 2013) This measure is actuallysupported on a periodic orbit.

Related results on periodicity of maximizing measures by:

Hunt–Ott’96 (experimental), Yuan–Hunt’99, Contreras–Lopes-Thieullen’01,Bousch’01, Quas–Siefken’12. The latter deals with spaces ofsuper-continuous functions over the shift.

Theorem (B.–Zhang, preprint ArXiv 2015)On spaces of sufficiently super-continuous functions, theconclusion of Quas–Siefken–Contreras is not only topologicallygeneric but prevalent (has “full probability”).

Jairo Bochi Optimization of Lyapunov Exponents 8 / 48

Page 16: Optimization of Lyapunov Exponents

Non-commutative Ergodic Optimization

What if instead of optimizing integrals (Birkhoff averages), weoptimize Lyapunov exponents instead?

Jairo Bochi Optimization of Lyapunov Exponents 9 / 48

Page 17: Optimization of Lyapunov Exponents

Lyapunov exponents (2× 2 case)Let T : X→ X be a homeomorphism of a compact space. LetA : X→ GL(2,R) be continuous. The pair (T,A) is called acocycle. Consider “Birkhoff-like” products:

A(n)(x) := A(Tn−1x) · · ·A(Tx)A(x).

The upper and lower Lyapunov exponents at the point x (ifthey exist) are:

λ1(x) := limn→+∞

1

nlog

A(n)(x)

λ2(x) := limn→+∞

1

nlog

[A(n)(x)]−1

−1

S1

A(n)(x)' enλ1(x)

enλ2(x) '

Jairo Bochi Optimization of Lyapunov Exponents 10 / 48

Page 18: Optimization of Lyapunov Exponents

Lyapunov exponents (2× 2 case)Let T : X→ X be a homeomorphism of a compact space. LetA : X→ GL(2,R) be continuous. The pair (T,A) is called acocycle. Consider “Birkhoff-like” products:

A(n)(x) := A(Tn−1x) · · ·A(Tx)A(x).

The upper and lower Lyapunov exponents at the point x (ifthey exist) are:

λ1(x) := limn→+∞

1

nlog

A(n)(x)

λ2(x) := limn→+∞

1

nlog

[A(n)(x)]−1

−1

S1

A(n)(x)' enλ1(x)

enλ2(x) '

Jairo Bochi Optimization of Lyapunov Exponents 10 / 48

Page 19: Optimization of Lyapunov Exponents

Oseledets Theorem

Theorem (Invertible 2× 2 Oseledets)Let μ be an ergodic probability measure for T. Then thereexist λ1(μ) ≥ λ2(μ) such that λi(x) = λi(μ) for μ-a.e. x.

Moreover, if λ1(μ) > λ2(μ) then there exists an “Oseledetssplitting” R2 = E1(x)⊕ E2(x), defined for μ-a.e. x and such thatfor each i = 1,2:

the spaces Ei vary measurably w.r.t. x;

the spaces Ei are equivariant: A(x)(Ei(x)) = Ei(Tx);

limn→±∞

1

nlog ‖A(n)(x)v‖ = λi(μ), ∀v ∈ Ei(x)r {0} (i = 1,2).

Jairo Bochi Optimization of Lyapunov Exponents 11 / 48

Page 20: Optimization of Lyapunov Exponents

Oseledets Theorem

Theorem (Invertible 2× 2 Oseledets)Let μ be an ergodic probability measure for T. Then thereexist λ1(μ) ≥ λ2(μ) such that λi(x) = λi(μ) for μ-a.e. x.

Moreover, if λ1(μ) > λ2(μ) then there exists an “Oseledetssplitting” R2 = E1(x)⊕ E2(x), defined for μ-a.e. x and such thatfor each i = 1,2:

the spaces Ei vary measurably w.r.t. x;

the spaces Ei are equivariant: A(x)(Ei(x)) = Ei(Tx);

limn→±∞

1

nlog ‖A(n)(x)v‖ = λi(μ), ∀v ∈ Ei(x)r {0} (i = 1,2).

Jairo Bochi Optimization of Lyapunov Exponents 11 / 48

Page 21: Optimization of Lyapunov Exponents

Optimization of Lyapunov exponents

Fixed cocycle (T,A) as above, let:

λ⊤1 := supμ∈Merg(T)

λ1(μ) , λ⊥1 := infμ∈Merg(T)

λ1(μ) .

(Rem.: The λ2 case can be reduced to the λ1 case by inverting time andsign.)

Questions:

Are the sup and inf above attained? That is, are thereμ ∈Merg(T) such that λ1(μ) = λ⊤1 or λ⊥1?How are those measures? What are their entropies,dimensions, etc?Are they unique?Are they characterized by their support?

Jairo Bochi Optimization of Lyapunov Exponents 12 / 48

Page 22: Optimization of Lyapunov Exponents

Optimization of Lyapunov exponents

Fixed cocycle (T,A) as above, let:

λ⊤1 := supμ∈Merg(T)

λ1(μ) , λ⊥1 := infμ∈Merg(T)

λ1(μ) .

(Rem.: The λ2 case can be reduced to the λ1 case by inverting time andsign.)

Questions:

Are the sup and inf above attained? That is, are thereμ ∈Merg(T) such that λ1(μ) = λ⊤1 or λ⊥1?How are those measures? What are their entropies,dimensions, etc?Are they unique?Are they characterized by their support?

Jairo Bochi Optimization of Lyapunov Exponents 12 / 48

Page 23: Optimization of Lyapunov Exponents

Optimization of Lyapunov exponents

Fixed cocycle (T,A) as above, let:

λ⊤1 := supμ∈Merg(T)

λ1(μ) , λ⊥1 := infμ∈Merg(T)

λ1(μ) .

(Rem.: The λ2 case can be reduced to the λ1 case by inverting time andsign.)

Questions:

Are the sup and inf above attained? That is, are thereμ ∈Merg(T) such that λ1(μ) = λ⊤1 or λ⊥1?

How are those measures? What are their entropies,dimensions, etc?Are they unique?Are they characterized by their support?

Jairo Bochi Optimization of Lyapunov Exponents 12 / 48

Page 24: Optimization of Lyapunov Exponents

Optimization of Lyapunov exponents

Fixed cocycle (T,A) as above, let:

λ⊤1 := supμ∈Merg(T)

λ1(μ) , λ⊥1 := infμ∈Merg(T)

λ1(μ) .

(Rem.: The λ2 case can be reduced to the λ1 case by inverting time andsign.)

Questions:

Are the sup and inf above attained? That is, are thereμ ∈Merg(T) such that λ1(μ) = λ⊤1 or λ⊥1?How are those measures? What are their entropies,dimensions, etc?

Are they unique?Are they characterized by their support?

Jairo Bochi Optimization of Lyapunov Exponents 12 / 48

Page 25: Optimization of Lyapunov Exponents

Optimization of Lyapunov exponents

Fixed cocycle (T,A) as above, let:

λ⊤1 := supμ∈Merg(T)

λ1(μ) , λ⊥1 := infμ∈Merg(T)

λ1(μ) .

(Rem.: The λ2 case can be reduced to the λ1 case by inverting time andsign.)

Questions:

Are the sup and inf above attained? That is, are thereμ ∈Merg(T) such that λ1(μ) = λ⊤1 or λ⊥1?How are those measures? What are their entropies,dimensions, etc?Are they unique?

Are they characterized by their support?

Jairo Bochi Optimization of Lyapunov Exponents 12 / 48

Page 26: Optimization of Lyapunov Exponents

Optimization of Lyapunov exponents

Fixed cocycle (T,A) as above, let:

λ⊤1 := supμ∈Merg(T)

λ1(μ) , λ⊥1 := infμ∈Merg(T)

λ1(μ) .

(Rem.: The λ2 case can be reduced to the λ1 case by inverting time andsign.)

Questions:

Are the sup and inf above attained? That is, are thereμ ∈Merg(T) such that λ1(μ) = λ⊤1 or λ⊥1?How are those measures? What are their entropies,dimensions, etc?Are they unique?Are they characterized by their support?

Jairo Bochi Optimization of Lyapunov Exponents 12 / 48

Page 27: Optimization of Lyapunov Exponents

Summary of the main result (B., Rams)

We will exhibit a natural open class of 2× 2 cocycles for whichthe Lyapunov-maximizing and -minimizing measures havezero entropy.

An important source of ideas:

paper by T. Bousch, J. Mairesse (JAMS, 2002).

Jairo Bochi Optimization of Lyapunov Exponents 13 / 48

Page 28: Optimization of Lyapunov Exponents

A SPECIAL SITUATION:

ONE-STEP COCYCLES

Jairo Bochi Optimization of Lyapunov Exponents 14 / 48

Page 29: Optimization of Lyapunov Exponents

One-step cocycles

A cocycle (T,A) is one-step if:

T : X→ X is the two-sided full shift on k ≥ 2 symbols, so

X =n

(ξi)i∈Z; ξi ∈ {1,2, . . . ,k}o

.

A : X→ GL(2,R) only depends on the zeroth symbol. Inother words, there is a list of matrices A1, . . . , Ak such that

ξ = (ξi)i∈Z ⇒ A(ξ) = Aξ0

(In particular, A is locally constant.)

Equivalent setting: Linear IFS’s (iterated function systems).

Jairo Bochi Optimization of Lyapunov Exponents 15 / 48

Page 30: Optimization of Lyapunov Exponents

One-step cocycles

A cocycle (T,A) is one-step if:

T : X→ X is the two-sided full shift on k ≥ 2 symbols, so

X =n

(ξi)i∈Z; ξi ∈ {1,2, . . . ,k}o

.

A : X→ GL(2,R) only depends on the zeroth symbol. Inother words, there is a list of matrices A1, . . . , Ak such that

ξ = (ξi)i∈Z ⇒ A(ξ) = Aξ0

(In particular, A is locally constant.)

Equivalent setting: Linear IFS’s (iterated function systems).

Jairo Bochi Optimization of Lyapunov Exponents 15 / 48

Page 31: Optimization of Lyapunov Exponents

One-step cocycles

A cocycle (T,A) is one-step if:

T : X→ X is the two-sided full shift on k ≥ 2 symbols, so

X =n

(ξi)i∈Z; ξi ∈ {1,2, . . . ,k}o

.

A : X→ GL(2,R) only depends on the zeroth symbol. Inother words, there is a list of matrices A1, . . . , Ak such that

ξ = (ξi)i∈Z ⇒ A(ξ) = Aξ0

(In particular, A is locally constant.)

Equivalent setting: Linear IFS’s (iterated function systems).

Jairo Bochi Optimization of Lyapunov Exponents 15 / 48

Page 32: Optimization of Lyapunov Exponents

Joint spectral radius and subradius

Equivalent elementary definitions of λ⊤1, λ⊥1 for one-stepcocycles:

λ⊤1 = limn→∞

1

nlog sup

i1,...,in

‖Ai1 . . .Ain‖ ,

λ⊥1 = limn→∞

1

nlog inf

i1,...,in‖Ai1 . . .Ain‖ .

For one-step cocycles, the numbers eλ⊤1 and eλ

⊥1 are known as

joint spectral radius (Rota, Strang 1960) and joint spectralsubradius (Gurvits, 1995) of the set of matrices {A1, . . . ,Ak}.

Applications in wavelets, control theory . . .

Jairo Bochi Optimization of Lyapunov Exponents 16 / 48

Page 33: Optimization of Lyapunov Exponents

Joint spectral radius and subradius

Equivalent elementary definitions of λ⊤1, λ⊥1 for one-stepcocycles:

λ⊤1 = limn→∞

1

nlog sup

i1,...,in

‖Ai1 . . .Ain‖ ,

λ⊥1 = limn→∞

1

nlog inf

i1,...,in‖Ai1 . . .Ain‖ .

For one-step cocycles, the numbers eλ⊤1 and eλ

⊥1 are known as

joint spectral radius (Rota, Strang 1960) and joint spectralsubradius (Gurvits, 1995) of the set of matrices {A1, . . . ,Ak}.

Applications in wavelets, control theory . . .

Jairo Bochi Optimization of Lyapunov Exponents 16 / 48

Page 34: Optimization of Lyapunov Exponents

The finiteness conjecture on the Joint spectralradius

Finiteness conjecture (1995): For one-step cocycles, therealways exists a Lyapunov-maximizing measure supported ona periodic orbit.

The conjecture was shown to be false by Bousch andMairesse (2002); their counterexamples are pairs of matricesin GL(2,R) such that the maximizing measures are sturmianbut not periodic.

Jairo Bochi Optimization of Lyapunov Exponents 17 / 48

Page 35: Optimization of Lyapunov Exponents

The finiteness conjecture on the Joint spectralradius

Finiteness conjecture (1995): For one-step cocycles, therealways exists a Lyapunov-maximizing measure supported ona periodic orbit.

The conjecture was shown to be false by Bousch andMairesse (2002); their counterexamples are pairs of matricesin GL(2,R) such that the maximizing measures are sturmianbut not periodic.

Jairo Bochi Optimization of Lyapunov Exponents 17 / 48

Page 36: Optimization of Lyapunov Exponents

Domination for one-step cocycles

An one-step cocycle is called dominated if the Lyapunovexponents are uniformly separated, for all invariant measures.In other words,

(λ1 − λ2)⊥(A) := inf

μ∈Merg(T)(λ1(A, μ)− λ2(A, μ))

is positive.

!4This definition applies only to one-step cocycles.

Example (Perron–Frobenius theorem)Each Ai is an (entrywise) positive matrix ⇒ the cocycle isdominated.

Jairo Bochi Optimization of Lyapunov Exponents 18 / 48

Page 37: Optimization of Lyapunov Exponents

Domination for one-step cocycles

An one-step cocycle is called dominated if the Lyapunovexponents are uniformly separated, for all invariant measures.In other words,

(λ1 − λ2)⊥(A) := inf

μ∈Merg(T)(λ1(A, μ)− λ2(A, μ))

is positive.

!4This definition applies only to one-step cocycles.

Example (Perron–Frobenius theorem)Each Ai is an (entrywise) positive matrix ⇒ the cocycle isdominated.

Jairo Bochi Optimization of Lyapunov Exponents 18 / 48

Page 38: Optimization of Lyapunov Exponents

Domination for one-step cocycles: notes

Recall: An one-step cocycle is called dominated if(λ1 − λ2)

⊥ > 0.

For one-step cocycles (only!), the definition above isequivalent to the more usual condition that the oneOseledets direction “dominates” the other.

In the area-preserving case (2× 2 matrices withdeterminant ±1), domination is equivalent to the familiarnotion of uniform hyperbolicity.Domination is a natural weakening of uniformhyperbolicity and is important smooth dynamics (e.g. inpartial hyperbolicity).It’s also weaker than exponential dichotomy.

Jairo Bochi Optimization of Lyapunov Exponents 19 / 48

Page 39: Optimization of Lyapunov Exponents

Domination for one-step cocycles: notes

Recall: An one-step cocycle is called dominated if(λ1 − λ2)

⊥ > 0.

For one-step cocycles (only!), the definition above isequivalent to the more usual condition that the oneOseledets direction “dominates” the other.In the area-preserving case (2× 2 matrices withdeterminant ±1), domination is equivalent to the familiarnotion of uniform hyperbolicity.

Domination is a natural weakening of uniformhyperbolicity and is important smooth dynamics (e.g. inpartial hyperbolicity).It’s also weaker than exponential dichotomy.

Jairo Bochi Optimization of Lyapunov Exponents 19 / 48

Page 40: Optimization of Lyapunov Exponents

Domination for one-step cocycles: notes

Recall: An one-step cocycle is called dominated if(λ1 − λ2)

⊥ > 0.

For one-step cocycles (only!), the definition above isequivalent to the more usual condition that the oneOseledets direction “dominates” the other.In the area-preserving case (2× 2 matrices withdeterminant ±1), domination is equivalent to the familiarnotion of uniform hyperbolicity.Domination is a natural weakening of uniformhyperbolicity and is important smooth dynamics (e.g. inpartial hyperbolicity).It’s also weaker than exponential dichotomy.

Jairo Bochi Optimization of Lyapunov Exponents 19 / 48

Page 41: Optimization of Lyapunov Exponents

Domination for one-step cocycles: geometricalviewpointLet P1 be the projective space (lines through the origin in R2).

A multicone for {A1, . . . ,Ak} is an open set M P1 such that

M has a finite number of connected components, and theyhave disjoint closures.∀ i, the closure of the image Ai(M) is contained in M.

ExampleEach Ai is an (entrywise) positive matrix ⇒ the first quadrantis a multicone.

Theorem (Avila, B., Yoccoz)An one-step cocycle is dominated IFF it has a multicone.

Consequence: domination is an open condition. (This is alsotrue for more general cocycles.)

Jairo Bochi Optimization of Lyapunov Exponents 20 / 48

Page 42: Optimization of Lyapunov Exponents

Domination for one-step cocycles: geometricalviewpointLet P1 be the projective space (lines through the origin in R2).

A multicone for {A1, . . . ,Ak} is an open set M P1 such that

M has a finite number of connected components, and theyhave disjoint closures.

∀ i, the closure of the image Ai(M) is contained in M.

ExampleEach Ai is an (entrywise) positive matrix ⇒ the first quadrantis a multicone.

Theorem (Avila, B., Yoccoz)An one-step cocycle is dominated IFF it has a multicone.

Consequence: domination is an open condition. (This is alsotrue for more general cocycles.)

Jairo Bochi Optimization of Lyapunov Exponents 20 / 48

Page 43: Optimization of Lyapunov Exponents

Domination for one-step cocycles: geometricalviewpointLet P1 be the projective space (lines through the origin in R2).

A multicone for {A1, . . . ,Ak} is an open set M P1 such that

M has a finite number of connected components, and theyhave disjoint closures.∀ i, the closure of the image Ai(M) is contained in M.

ExampleEach Ai is an (entrywise) positive matrix ⇒ the first quadrantis a multicone.

Theorem (Avila, B., Yoccoz)An one-step cocycle is dominated IFF it has a multicone.

Consequence: domination is an open condition. (This is alsotrue for more general cocycles.)

Jairo Bochi Optimization of Lyapunov Exponents 20 / 48

Page 44: Optimization of Lyapunov Exponents

Domination for one-step cocycles: geometricalviewpointLet P1 be the projective space (lines through the origin in R2).

A multicone for {A1, . . . ,Ak} is an open set M P1 such that

M has a finite number of connected components, and theyhave disjoint closures.∀ i, the closure of the image Ai(M) is contained in M.

ExampleEach Ai is an (entrywise) positive matrix ⇒ the first quadrantis a multicone.

Theorem (Avila, B., Yoccoz)An one-step cocycle is dominated IFF it has a multicone.

Consequence: domination is an open condition. (This is alsotrue for more general cocycles.)

Jairo Bochi Optimization of Lyapunov Exponents 20 / 48

Page 45: Optimization of Lyapunov Exponents

Domination for one-step cocycles: geometricalviewpointLet P1 be the projective space (lines through the origin in R2).

A multicone for {A1, . . . ,Ak} is an open set M P1 such that

M has a finite number of connected components, and theyhave disjoint closures.∀ i, the closure of the image Ai(M) is contained in M.

ExampleEach Ai is an (entrywise) positive matrix ⇒ the first quadrantis a multicone.

Theorem (Avila, B., Yoccoz)An one-step cocycle is dominated IFF it has a multicone.

Consequence: domination is an open condition. (This is alsotrue for more general cocycles.)

Jairo Bochi Optimization of Lyapunov Exponents 20 / 48

Page 46: Optimization of Lyapunov Exponents

The simplest example where the multicone cannot be takenas a cone is:

A1 = A, A2 = B.

Examples with complicated combinatorics: see [Avila–B.–Yoccoz]

Jairo Bochi Optimization of Lyapunov Exponents 21 / 48

Page 47: Optimization of Lyapunov Exponents

BARABANOV FUNCTIONS

FOR DOMINATED ONE-STEP COCYCLES:

An analogue of the Mañé revelation lemma

Jairo Bochi Optimization of Lyapunov Exponents 22 / 48

Page 48: Optimization of Lyapunov Exponents

Barabanov functionsAssume given an one-step dominated cocycle, with amulticone M. Let

−→M := {v ∈ R2; v = 0 or R · v ∈M} be the

“support” of M.

Proposition (Existence of a Barabanov function)

There exists a continuous function |||·||| :−→M → [0,∞) such that

for every v ∈−→M, t ∈ R we have

|||tv||| = |t| |||v||| (homogeneity);

maxi∈{1,...,k}

|||Aiv||| = eλ⊤1 |||v||| (main property).

We call |||·||| an upper Barabanov function.

Consequence: For any v ∈−→M , we can find recursively

symbols i1, i2, . . . ∈ {1, . . . ,k} such that

|||Ain · · ·Ai1(v)||| = enλ⊤1 |||v|||.

Jairo Bochi Optimization of Lyapunov Exponents 23 / 48

Page 49: Optimization of Lyapunov Exponents

Barabanov functionsAssume given an one-step dominated cocycle, with amulticone M. Let

−→M := {v ∈ R2; v = 0 or R · v ∈M} be the

“support” of M.

Proposition (Existence of a Barabanov function)

There exists a continuous function |||·||| :−→M → [0,∞) such that

for every v ∈−→M, t ∈ R we have

|||tv||| = |t| |||v||| (homogeneity);

maxi∈{1,...,k}

|||Aiv||| = eλ⊤1 |||v||| (main property).

We call |||·||| an upper Barabanov function.

Consequence: For any v ∈−→M , we can find recursively

symbols i1, i2, . . . ∈ {1, . . . ,k} such that

|||Ain · · ·Ai1(v)||| = enλ⊤1 |||v|||.

Jairo Bochi Optimization of Lyapunov Exponents 23 / 48

Page 50: Optimization of Lyapunov Exponents

Barabanov functionsAssume given an one-step dominated cocycle, with amulticone M. Let

−→M := {v ∈ R2; v = 0 or R · v ∈M} be the

“support” of M.

Proposition (Existence of a Barabanov function)

There exists a continuous function |||·||| :−→M → [0,∞) such that

for every v ∈−→M, t ∈ R we have

|||tv||| = |t| |||v||| (homogeneity);

maxi∈{1,...,k}

|||Aiv||| = eλ⊤1 |||v||| (main property).

We call |||·||| an upper Barabanov function.

Consequence: For any v ∈−→M , we can find recursively

symbols i1, i2, . . . ∈ {1, . . . ,k} such that

|||Ain · · ·Ai1(v)||| = enλ⊤1 |||v|||.

Jairo Bochi Optimization of Lyapunov Exponents 23 / 48

Page 51: Optimization of Lyapunov Exponents

Barabanov functionsAssume given an one-step dominated cocycle, with amulticone M. Let

−→M := {v ∈ R2; v = 0 or R · v ∈M} be the

“support” of M.

Proposition (Existence of a Barabanov function)

There exists a continuous function |||·||| :−→M → [0,∞) such that

for every v ∈−→M, t ∈ R we have

|||tv||| = |t| |||v||| (homogeneity);

maxi∈{1,...,k}

|||Aiv||| = eλ⊤1 |||v||| (main property).

We call |||·||| an upper Barabanov function.

Consequence: For any v ∈−→M , we can find recursively

symbols i1, i2, . . . ∈ {1, . . . ,k} such that

|||Ain · · ·Ai1(v)||| = enλ⊤1 |||v|||.Jairo Bochi Optimization of Lyapunov Exponents 23 / 48

Page 52: Optimization of Lyapunov Exponents

Barabanov functions

Upper Barabanov function:

|||tv||| = |t| |||v|||, maxi∈{1,...,k}

|||Aiv||| = eλ⊤1 |||v|||.

Actually (even without domination) there is a true norm|||·||| in R2 with the properties above; it is called Barabanovnorm (1988).

There is also a lower Barabanov function |||·|||′ such that

|||tv|||′ = |t| |||v|||′, mini∈{1,...,k}

|||Aiv|||′ = eλ⊥1 |||v|||′ .

In this case the domination hypothesis is really needed.Higher dimension: see [B.–Morris, Proc. LMS (to appear)].

Jairo Bochi Optimization of Lyapunov Exponents 24 / 48

Page 53: Optimization of Lyapunov Exponents

Barabanov functions

Upper Barabanov function:

|||tv||| = |t| |||v|||, maxi∈{1,...,k}

|||Aiv||| = eλ⊤1 |||v|||.

Actually (even without domination) there is a true norm|||·||| in R2 with the properties above; it is called Barabanovnorm (1988).There is also a lower Barabanov function |||·|||′ such that

|||tv|||′ = |t| |||v|||′, mini∈{1,...,k}

|||Aiv|||′ = eλ⊥1 |||v|||′ .

In this case the domination hypothesis is really needed.Higher dimension: see [B.–Morris, Proc. LMS (to appear)].

Jairo Bochi Optimization of Lyapunov Exponents 24 / 48

Page 54: Optimization of Lyapunov Exponents

Proof of existence of Barabanov functions

We apply Schauder fixed point theorem to an appropriatespace of Lispchitz functions (with respect to the Hilbertmetric) on the multicone . . .

BTW, this proof also gives a Lipschitz property for |||·|||, which isalso useful.

Jairo Bochi Optimization of Lyapunov Exponents 25 / 48

Page 55: Optimization of Lyapunov Exponents

Mather setsPropositionIf an one-step cocycle is dominated then there are nonemptycompact shift-invariant sets K⊤, K⊥ ⊂ kZ s.t. for everyshift-invariant measure μ,

suppμ ⊂ K⊤ ⇔ λ1(μ) = λ⊤1, suppμ ⊂ K⊥ ⇔ λ1(μ) = λ⊥1

(In particular, there exist Lyapunov-optimizing measures.)

Proof:

K⊤ :=n

ξ ∈ X; u ∈ E1(ξ), n ∈ Z ⇒ |||A(n)(ξ)(u)||| = enλ⊤1 |||u|||o

.

Rem.: The proposition above should also follow from the “classical”(commutative) theory applied to the function f := log ‖A|E1‖. Anyway, ourBarabanov functions with be fundamental for what we will do next.

Jairo Bochi Optimization of Lyapunov Exponents 26 / 48

Page 56: Optimization of Lyapunov Exponents

Mather setsPropositionIf an one-step cocycle is dominated then there are nonemptycompact shift-invariant sets K⊤, K⊥ ⊂ kZ s.t. for everyshift-invariant measure μ,

suppμ ⊂ K⊤ ⇔ λ1(μ) = λ⊤1, suppμ ⊂ K⊥ ⇔ λ1(μ) = λ⊥1

(In particular, there exist Lyapunov-optimizing measures.)

Proof:

K⊤ :=n

ξ ∈ X; u ∈ E1(ξ), n ∈ Z ⇒ |||A(n)(ξ)(u)||| = enλ⊤1 |||u|||o

.

Rem.: The proposition above should also follow from the “classical”(commutative) theory applied to the function f := log ‖A|E1‖. Anyway, ourBarabanov functions with be fundamental for what we will do next.

Jairo Bochi Optimization of Lyapunov Exponents 26 / 48

Page 57: Optimization of Lyapunov Exponents

OUR MAIN RESULT

Jairo Bochi Optimization of Lyapunov Exponents 27 / 48

Page 58: Optimization of Lyapunov Exponents

Non-overlapping conditionWe say that the matrices A1, . . . ,Ak satisfy forwardnonoverlapping condition (NOC+) if they admit a multicone Msuch that

i 6= j ⇒ Ai(M) ∩ Aj(M) =∅.

We say that the matrices A1, . . . ,Ak satisfy backwardsnonoverlapping condition (NOC−) if {A−1

1 , . . . ,A−1k } satisfy the

forward nonoverlapping condition.

If both conditions hold then we say that the matrices satisfythe nonoverlapping condition (NOC).

Example: The one in a previous figure:

Jairo Bochi Optimization of Lyapunov Exponents 28 / 48

Page 59: Optimization of Lyapunov Exponents

Non-overlapping conditionWe say that the matrices A1, . . . ,Ak satisfy forwardnonoverlapping condition (NOC+) if they admit a multicone Msuch that

i 6= j ⇒ Ai(M) ∩ Aj(M) =∅.

We say that the matrices A1, . . . ,Ak satisfy backwardsnonoverlapping condition (NOC−) if {A−1

1 , . . . ,A−1k } satisfy the

forward nonoverlapping condition.

If both conditions hold then we say that the matrices satisfythe nonoverlapping condition (NOC).

Example: The one in a previous figure:

Jairo Bochi Optimization of Lyapunov Exponents 28 / 48

Page 60: Optimization of Lyapunov Exponents

Non-overlapping conditionWe say that the matrices A1, . . . ,Ak satisfy forwardnonoverlapping condition (NOC+) if they admit a multicone Msuch that

i 6= j ⇒ Ai(M) ∩ Aj(M) =∅.

We say that the matrices A1, . . . ,Ak satisfy backwardsnonoverlapping condition (NOC−) if {A−1

1 , . . . ,A−1k } satisfy the

forward nonoverlapping condition.

If both conditions hold then we say that the matrices satisfythe nonoverlapping condition (NOC).

Example: The one in a previous figure:

Jairo Bochi Optimization of Lyapunov Exponents 28 / 48

Page 61: Optimization of Lyapunov Exponents

The main theorem

Theorem (B., Rams)If an one-step cocycle is dominated and satisfies thenon-overlapping condition

then the sets K⊤, K⊥ have zerotopological entropy.

In particular, the optimizing measures have zero entropy.

Jairo Bochi Optimization of Lyapunov Exponents 29 / 48

Page 62: Optimization of Lyapunov Exponents

The main theorem

Theorem (B., Rams)If an one-step cocycle is dominated and satisfies thenon-overlapping condition then the sets K⊤, K⊥ have zerotopological entropy.

In particular, the optimizing measures have zero entropy.

Jairo Bochi Optimization of Lyapunov Exponents 29 / 48

Page 63: Optimization of Lyapunov Exponents

Remarks

The zero entropy measures that appear in the theoremare not necessarily supported on periodic orbits(∃ examples by Bousch–Mairesse and others).Neither they are necessarily unique.The non-overlapping condition is indeed required: forexample, if A1 = A2 then there are optimal measures withpositive entropy.We think that a more general theorem holds, with T =subshift of finite type, A = locally constant cocycle, but wehaven’t checked the details.

Jairo Bochi Optimization of Lyapunov Exponents 30 / 48

Page 64: Optimization of Lyapunov Exponents

QuestionsCan we replace the non-overlapping condition by aweaker hypothesis (preferably “typical” amongdominated cocycles)?

Is there a result of this kind for cocycles that are notlocally constant?(We believe we know what should replace Barabanov functions andhow to construct them.)

What about the non-dominated case? (Maybe therestriction of the cocycle to K⊤ is typically dominated. . . )“Generic finiteness conjecture”: Is is typical forLyapunov-maximizing measures to be periodic?In other words, does a matrix version of Contrerastheorem hold?What about higher dimension?

There is much more to be done in this subject!

Jairo Bochi Optimization of Lyapunov Exponents 31 / 48

Page 65: Optimization of Lyapunov Exponents

QuestionsCan we replace the non-overlapping condition by aweaker hypothesis (preferably “typical” amongdominated cocycles)?Is there a result of this kind for cocycles that are notlocally constant?(We believe we know what should replace Barabanov functions andhow to construct them.)

What about the non-dominated case? (Maybe therestriction of the cocycle to K⊤ is typically dominated. . . )“Generic finiteness conjecture”: Is is typical forLyapunov-maximizing measures to be periodic?In other words, does a matrix version of Contrerastheorem hold?What about higher dimension?

There is much more to be done in this subject!

Jairo Bochi Optimization of Lyapunov Exponents 31 / 48

Page 66: Optimization of Lyapunov Exponents

QuestionsCan we replace the non-overlapping condition by aweaker hypothesis (preferably “typical” amongdominated cocycles)?Is there a result of this kind for cocycles that are notlocally constant?(We believe we know what should replace Barabanov functions andhow to construct them.)

What about the non-dominated case? (Maybe therestriction of the cocycle to K⊤ is typically dominated. . . )

“Generic finiteness conjecture”: Is is typical forLyapunov-maximizing measures to be periodic?In other words, does a matrix version of Contrerastheorem hold?What about higher dimension?

There is much more to be done in this subject!

Jairo Bochi Optimization of Lyapunov Exponents 31 / 48

Page 67: Optimization of Lyapunov Exponents

QuestionsCan we replace the non-overlapping condition by aweaker hypothesis (preferably “typical” amongdominated cocycles)?Is there a result of this kind for cocycles that are notlocally constant?(We believe we know what should replace Barabanov functions andhow to construct them.)

What about the non-dominated case? (Maybe therestriction of the cocycle to K⊤ is typically dominated. . . )“Generic finiteness conjecture”: Is is typical forLyapunov-maximizing measures to be periodic?In other words, does a matrix version of Contrerastheorem hold?

What about higher dimension?

There is much more to be done in this subject!

Jairo Bochi Optimization of Lyapunov Exponents 31 / 48

Page 68: Optimization of Lyapunov Exponents

QuestionsCan we replace the non-overlapping condition by aweaker hypothesis (preferably “typical” amongdominated cocycles)?Is there a result of this kind for cocycles that are notlocally constant?(We believe we know what should replace Barabanov functions andhow to construct them.)

What about the non-dominated case? (Maybe therestriction of the cocycle to K⊤ is typically dominated. . . )“Generic finiteness conjecture”: Is is typical forLyapunov-maximizing measures to be periodic?In other words, does a matrix version of Contrerastheorem hold?What about higher dimension?

There is much more to be done in this subject!Jairo Bochi Optimization of Lyapunov Exponents 31 / 48

Page 69: Optimization of Lyapunov Exponents

PROOF OF THE THEOREM

Jairo Bochi Optimization of Lyapunov Exponents 32 / 48

Page 70: Optimization of Lyapunov Exponents

Oseledets directions

Given a bi-infinite word ξ ∈ X = kZ, split it asξ = (ξ− , ξ+) ∈ kZ− × kZ+, where Z− = {. . . ,−2,−1} andZ+ = {0,1, . . .}.

The first Oseledets direction only depends on the past, whilethe second one only depends on the future:

E1(ξ) = E1(ξ−), E2(ξ) = E2(ξ+).

Jairo Bochi Optimization of Lyapunov Exponents 33 / 48

Page 71: Optimization of Lyapunov Exponents

Oseledets directions

Given a bi-infinite word ξ ∈ X = kZ, split it asξ = (ξ− , ξ+) ∈ kZ− × kZ+, where Z− = {. . . ,−2,−1} andZ+ = {0,1, . . .}.

The first Oseledets direction only depends on the past, whilethe second one only depends on the future:

E1(ξ) = E1(ξ−), E2(ξ) = E2(ξ+).

Jairo Bochi Optimization of Lyapunov Exponents 33 / 48

Page 72: Optimization of Lyapunov Exponents

Some facts about the sets of directions

C1 :=�

E1(ξ−); ξ ∈ kZ−

, C2 :=�

E2(ξ+); ξ ∈ kZ+

.

M is a multicone ⇒ C1 =∞⋂

n=1

i1,...,in

Ai1 · · ·Ain(M) (nested)

Proof: E1(ξ−) appears when we take ij = ξ−j.

forward non-overlapping condition (NOC+)

m

E1 : kZ− → C1 is a bijection.

C1 is a Cantor set.

Analogously for E2 : kZ+ → C1.

Jairo Bochi Optimization of Lyapunov Exponents 34 / 48

Page 73: Optimization of Lyapunov Exponents

Some facts about the sets of directions

C1 :=�

E1(ξ−); ξ ∈ kZ−

, C2 :=�

E2(ξ+); ξ ∈ kZ+

.

M is a multicone ⇒ C1 =∞⋂

n=1

i1,...,in

Ai1 · · ·Ain(M) (nested)

Proof: E1(ξ−) appears when we take ij = ξ−j.

forward non-overlapping condition (NOC+)

m

E1 : kZ− → C1 is a bijection.

C1 is a Cantor set.

Analogously for E2 : kZ+ → C1.Jairo Bochi Optimization of Lyapunov Exponents 34 / 48

Page 74: Optimization of Lyapunov Exponents

Main proposition

Proposition (E1 determines E2 a.s.)Suppose that the cocycle satisfies the forwardnon-overlapping condition (NOC+).Let μ be an ergodic measure such that λ1(μ) = λ⊤1 or λ⊥1.

Then for μ-a.e. ξ ∈ X, the direction E1(ξ−) is uniquelydetermined the direction E2(ξ+).

Jairo Bochi Optimization of Lyapunov Exponents 35 / 48

Page 75: Optimization of Lyapunov Exponents

Main proposition

Proposition (E1 determines E2 a.s.)Suppose that the cocycle satisfies the forwardnon-overlapping condition (NOC+).Let μ be an ergodic measure such that λ1(μ) = λ⊤1 or λ⊥1.Then for μ-a.e. ξ ∈ X, the direction E1(ξ−) is uniquelydetermined the direction E2(ξ+).

Jairo Bochi Optimization of Lyapunov Exponents 35 / 48

Page 76: Optimization of Lyapunov Exponents

Main Proposition ⇒ TheoremSuppose that the cocycle satisfies the non-overlappingcondition (NOC+ +NOC−). Let μ be an ergodic measuresupported in K⊤, that is, λ1(μ) = λ⊤1.

Then for μ-a.e. ξ ∈ X, the past ξ− uniquely the future ξ+:

ξ− ξ+

E1(ξ−) E2(ξ+)Proposition

NOC+

NOC−

This implies that h(μ) = 0, for all μ supported in K⊤.

By the Entropy Variational Principle, htop(K⊤) = 0. Analogouslyfor K⊥.

This proves the Theorem modulo the Proposition.

Jairo Bochi Optimization of Lyapunov Exponents 36 / 48

Page 77: Optimization of Lyapunov Exponents

Main Proposition ⇒ TheoremSuppose that the cocycle satisfies the non-overlappingcondition (NOC+ +NOC−). Let μ be an ergodic measuresupported in K⊤, that is, λ1(μ) = λ⊤1.

Then for μ-a.e. ξ ∈ X, the past ξ− uniquely the future ξ+:

ξ− ξ+

E1(ξ−) E2(ξ+)Proposition

NOC+

NOC−

This implies that h(μ) = 0, for all μ supported in K⊤.

By the Entropy Variational Principle, htop(K⊤) = 0. Analogouslyfor K⊥.

This proves the Theorem modulo the Proposition.

Jairo Bochi Optimization of Lyapunov Exponents 36 / 48

Page 78: Optimization of Lyapunov Exponents

Main Proposition ⇒ TheoremSuppose that the cocycle satisfies the non-overlappingcondition (NOC+ +NOC−). Let μ be an ergodic measuresupported in K⊤, that is, λ1(μ) = λ⊤1.

Then for μ-a.e. ξ ∈ X, the past ξ− uniquely the future ξ+:

ξ− ξ+

E1(ξ−) E2(ξ+)Proposition

NOC+

NOC−

This implies that h(μ) = 0, for all μ supported in K⊤.

By the Entropy Variational Principle, htop(K⊤) = 0. Analogouslyfor K⊥.

This proves the Theorem modulo the Proposition.Jairo Bochi Optimization of Lyapunov Exponents 36 / 48

Page 79: Optimization of Lyapunov Exponents

PROOF OF THE MAIN PROPOSITION

Jairo Bochi Optimization of Lyapunov Exponents 37 / 48

Page 80: Optimization of Lyapunov Exponents

Cross ratio

Given nonzero vectors u, v, u′, v′ ∈ R2, (no three of thembeing collinear), we define their cross ratio:

[u,v;u′,v′] :=u× u′

u× v′·v × v′

v × u′∈ R ∪ {∞} ,

where × is the cross product on R2 (that is, determinant).

Actually the value above only depends on the directionsdetermined by the vectors. So we can define the cross ratio offour points in P1.

The cross ratio is invariant by linear isomorphisms.

Jairo Bochi Optimization of Lyapunov Exponents 38 / 48

Page 81: Optimization of Lyapunov Exponents

Configurations of four points in P1

Let u, v, u′, v′ ∈ P1 be distinct. The configuration is calledco-parallel, crossing or anti-parallel according to the value ofthe cross ratio [u,v;u′,v′] as follows:

Co-parallel Crossing Anti-parallel

0 < [u,v;u′,v′] < 1 [u,v;u′,v′] > 1 [u,v;u′,v′] < 0

vv′

u′ u

vu′

v′ u

v′v

u′ u

Jairo Bochi Optimization of Lyapunov Exponents 39 / 48

Page 82: Optimization of Lyapunov Exponents

Restrictions among the Oseledets directionsLemma (Geometrical Lemma)K⊤, K⊥ = Mather sets of an 1-step dominated cocycle.

Then ξ, η ∈ K⊤ ⇒�

�[E1(ξ),E1(η);E2(ξ),E2(η)]�

� ≥ 1 ,

ξ, η ∈ K⊥ ⇒�

�[E1(ξ),E1(η);E2(ξ),E2(η)]�

� ≤ 1 .

In particular:

co-parallel configuration crossing configurationis forbidden en K⊤: is forbidden en K⊥:

E1(η)E2(η)

E2(ξ) E1(ξ)

E1(η)E2(ξ)

E1(η) E1(ξ)

Jairo Bochi Optimization of Lyapunov Exponents 40 / 48

Page 83: Optimization of Lyapunov Exponents

Restrictions among the Oseledets directionsLemma (Geometrical Lemma)K⊤, K⊥ = Mather sets of an 1-step dominated cocycle.

Then ξ, η ∈ K⊤ ⇒�

�[E1(ξ),E1(η);E2(ξ),E2(η)]�

� ≥ 1 ,

ξ, η ∈ K⊥ ⇒�

�[E1(ξ),E1(η);E2(ξ),E2(η)]�

� ≤ 1 .

In particular:co-parallel configuration crossing configuration

is forbidden en K⊤: is forbidden en K⊥:

E1(η)E2(η)

E2(ξ) E1(ξ)

E1(η)E2(ξ)

E1(η) E1(ξ)

Jairo Bochi Optimization of Lyapunov Exponents 40 / 48

Page 84: Optimization of Lyapunov Exponents

Consequences of the Geometrical Lemma

Consider the set G :=�

(E1(ξ),E2(ξ)); ξ ∈ K⊤

⊂ P1 × P1.

Since the co-parallel configuration is forbidden, the set G hasa monotonicity property: if E1 moves clockwise then sodoes E2.

This implies that G is a graph (aboveits projection), with the exception toan at most countable numbers of“plateaux” and “cliffs”:

Jairo Bochi Optimization of Lyapunov Exponents 41 / 48

Page 85: Optimization of Lyapunov Exponents

Consequences of the Geometrical Lemma

Consider the set G :=�

(E1(ξ),E2(ξ)); ξ ∈ K⊤

⊂ P1 × P1.

Since the co-parallel configuration is forbidden, the set G hasa monotonicity property: if E1 moves clockwise then sodoes E2.

This implies that G is a graph (aboveits projection), with the exception toan at most countable numbers of“plateaux” and “cliffs”:

Jairo Bochi Optimization of Lyapunov Exponents 41 / 48

Page 86: Optimization of Lyapunov Exponents

Consequences of the Geometrical Lemma

Consider the set G :=�

(E1(ξ),E2(ξ)); ξ ∈ K⊤

⊂ P1 × P1.

Since the co-parallel configuration is forbidden, the set G hasa monotonicity property: if E1 moves clockwise then sodoes E2.

This implies that G is a graph (aboveits projection), with the exception toan at most countable numbers of“plateaux” and “cliffs”:

Jairo Bochi Optimization of Lyapunov Exponents 41 / 48

Page 87: Optimization of Lyapunov Exponents

Geometrical Lemma ⇒ Main PropositionRecall the Main Proposition

PropositionNOC+ ⇒ E1 determines E2 almost surely with respect to anyλ1-maximizing or -minimizing measure.

Let μ be an ergodic non-periodic (and so non-atomic) measuresupported in K⊤. (The periodic case is trivial.)

Then the (countable) union of plateaux and cliffs in G is theimage under (E1,E2) of a set of zero μ-measure. (Here we needthe NOC+.)

Therefore each of the directions E1 and E2 uniquelydetermines the other μ-a.e., as we wanted to prove.

The case of K⊥ is similar (but extra care is needed:monotonicity is only local).

Jairo Bochi Optimization of Lyapunov Exponents 42 / 48

Page 88: Optimization of Lyapunov Exponents

Geometrical Lemma ⇒ Main PropositionRecall the Main Proposition

PropositionNOC+ ⇒ E1 determines E2 almost surely with respect to anyλ1-maximizing or -minimizing measure.

Let μ be an ergodic non-periodic (and so non-atomic) measuresupported in K⊤. (The periodic case is trivial.)

Then the (countable) union of plateaux and cliffs in G is theimage under (E1,E2) of a set of zero μ-measure. (Here we needthe NOC+.)

Therefore each of the directions E1 and E2 uniquelydetermines the other μ-a.e., as we wanted to prove.

The case of K⊥ is similar (but extra care is needed:monotonicity is only local).

Jairo Bochi Optimization of Lyapunov Exponents 42 / 48

Page 89: Optimization of Lyapunov Exponents

Geometrical Lemma ⇒ Main PropositionRecall the Main Proposition

PropositionNOC+ ⇒ E1 determines E2 almost surely with respect to anyλ1-maximizing or -minimizing measure.

Let μ be an ergodic non-periodic (and so non-atomic) measuresupported in K⊤. (The periodic case is trivial.)

Then the (countable) union of plateaux and cliffs in G is theimage under (E1,E2) of a set of zero μ-measure. (Here we needthe NOC+.)

Therefore each of the directions E1 and E2 uniquelydetermines the other μ-a.e., as we wanted to prove.

The case of K⊥ is similar (but extra care is needed:monotonicity is only local).

Jairo Bochi Optimization of Lyapunov Exponents 42 / 48

Page 90: Optimization of Lyapunov Exponents

SummaryAs explained before, we obtain zero entropy since the pastξ− uniquely the future ξ+ μ-a.e.:

ξ− ξ+

E1(ξ−) E2(ξ+)Proposition

NOC+

NOC−

Barabanov functions

Geometrical Lemma (cross-ratios, forbidden configurations)

Main Proposition

THE END (?)Jairo Bochi Optimization of Lyapunov Exponents 43 / 48

Page 91: Optimization of Lyapunov Exponents

PROOF OF THE GEOMETRICAL LEMMA

Some important ideas in the proof come from the 2002 paperby Bousch and Mairesse (counter-examples to the finitenessconjecture).

Jairo Bochi Optimization of Lyapunov Exponents 44 / 48

Page 92: Optimization of Lyapunov Exponents

RecallLemma (Geometrical Lemma)K⊤, K⊥ = Mather sets of an 1-step dominated cocycle.

Then ξ, η ∈ K⊤ ⇒�

�[E1(ξ),E1(η);E2(ξ),E2(η)]�

� ≥ 1 ,

ξ, η ∈ K⊥ ⇒�

�[E1(ξ),E1(η);E2(ξ),E2(η)]�

� ≤ 1 .

In particular:

co-parallel configuration crossing configurationis forbidden en K⊤: is forbidden en K⊥:

E1(η)E2(η)

E2(ξ) E1(ξ)

E1(η)E2(ξ)

E1(η) E1(ξ)

Jairo Bochi Optimization of Lyapunov Exponents 45 / 48

Page 93: Optimization of Lyapunov Exponents

RecallLemma (Geometrical Lemma)K⊤, K⊥ = Mather sets of an 1-step dominated cocycle.

Then ξ, η ∈ K⊤ ⇒�

�[E1(ξ),E1(η);E2(ξ),E2(η)]�

� ≥ 1 ,

ξ, η ∈ K⊥ ⇒�

�[E1(ξ),E1(η);E2(ξ),E2(η)]�

� ≤ 1 .

In particular:co-parallel configuration crossing configuration

is forbidden en K⊤: is forbidden en K⊥:

E1(η)E2(η)

E2(ξ) E1(ξ)

E1(η)E2(ξ)

E1(η) E1(ξ)

Jairo Bochi Optimization of Lyapunov Exponents 45 / 48

Page 94: Optimization of Lyapunov Exponents

An important estimate

Lemma

Let ξ ∈ K⊤, u ∈ E1(ξ). Let v ∈−→M be such that u− v ∈ E2(ξ).

Then |||u||| ≤ |||v|||.

E1(ξ)

E2(ξ)

u

v ∈−→M

Jairo Bochi Optimization of Lyapunov Exponents 46 / 48

Page 95: Optimization of Lyapunov Exponents

Proof of the “important estimate” Lemma

E1(ξ)

E2(ξ)

u

v ∈−→M

Lemma: ξ ∈ K⊤ ⇒ |||u||| ≤ |||v|||

Proof: For all n ≥ 0,

un := A(n)(ξ)(u) ⇒ |||un||| = enλ⊤1 |||u|||

vn := A(n)(ξ)(v) ⇒ |||vn||| ≤ enλ⊤1 |||v|||

and so:|||v|||

|||u|||≥|||vn|||

|||un|||=: Qn

To prove the Lemma we show that Qn→ 0 as n→ +∞.

That’s easy, using that that ∠(un,vn)→ 0 exponentially fastand “Lipschitz property” of the Barabanov norms.

Jairo Bochi Optimization of Lyapunov Exponents 47 / 48

Page 96: Optimization of Lyapunov Exponents

Proof of the “important estimate” Lemma

E1(ξ)

E2(ξ)

u

v ∈−→M

Lemma: ξ ∈ K⊤ ⇒ |||u||| ≤ |||v|||

Proof: For all n ≥ 0,

un := A(n)(ξ)(u) ⇒ |||un||| = enλ⊤1 |||u|||

vn := A(n)(ξ)(v) ⇒ |||vn||| ≤ enλ⊤1 |||v|||

and so:|||v|||

|||u|||≥|||vn|||

|||un|||=: Qn

To prove the Lemma we show that Qn→ 0 as n→ +∞.

That’s easy, using that that ∠(un,vn)→ 0 exponentially fastand “Lipschitz property” of the Barabanov norms.

Jairo Bochi Optimization of Lyapunov Exponents 47 / 48

Page 97: Optimization of Lyapunov Exponents

Proof of the “important estimate” Lemma

E1(ξ)

E2(ξ)

u

v ∈−→M

Lemma: ξ ∈ K⊤ ⇒ |||u||| ≤ |||v|||

Proof: For all n ≥ 0,

un := A(n)(ξ)(u) ⇒ |||un||| = enλ⊤1 |||u|||

vn := A(n)(ξ)(v) ⇒ |||vn||| ≤ enλ⊤1 |||v|||

and so:|||v|||

|||u|||≥|||vn|||

|||un|||=: Qn

To prove the Lemma we show that Qn→ 0 as n→ +∞.

That’s easy, using that that ∠(un,vn)→ 0 exponentially fastand “Lipschitz property” of the Barabanov norms.

Jairo Bochi Optimization of Lyapunov Exponents 47 / 48

Page 98: Optimization of Lyapunov Exponents

Proof of the Geometrical LemmaAssume for a contradiction that ξ, η ∈ K⊤ are in co-parallel

configuration: .

Choose a nonzero vector u ∈ E1(ξ), andlet v, w be as in the figure.

E2(ξ)

E1(ξ)E1(η)E2(η)

uv

w

(Rem.: hidden cross-ratio)

Apply the “important estimate” twice:

|||u||| ≤ |||v||| ≤ |||w|||. Contradiction!

Jairo Bochi Optimization of Lyapunov Exponents 48 / 48

Page 99: Optimization of Lyapunov Exponents

Proof of the Geometrical LemmaAssume for a contradiction that ξ, η ∈ K⊤ are in co-parallel

configuration: . Choose a nonzero vector u ∈ E1(ξ), andlet v, w be as in the figure.

E2(ξ)

E1(ξ)E1(η)E2(η)

uv

w

(Rem.: hidden cross-ratio)

Apply the “important estimate” twice:

|||u||| ≤ |||v||| ≤ |||w|||. Contradiction!

Jairo Bochi Optimization of Lyapunov Exponents 48 / 48

Page 100: Optimization of Lyapunov Exponents

Proof of the Geometrical LemmaAssume for a contradiction that ξ, η ∈ K⊤ are in co-parallel

configuration: . Choose a nonzero vector u ∈ E1(ξ), andlet v, w be as in the figure.

E2(ξ)

E1(ξ)E1(η)E2(η)

uv

w

(Rem.: hidden cross-ratio)

Apply the “important estimate” twice:

|||u||| ≤ |||v||| ≤ |||w|||. Contradiction!

Jairo Bochi Optimization of Lyapunov Exponents 48 / 48