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# JointworkwithV.JonesandD.Shlyakhtenko. Algebra,GeometryandPhysicsBonn/Berlinseminar Alice Guionnet Topologicalexpansions,Randommatricesandoperatoralgebras

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Topological expansions, Random matrices and operator algebras

AliceGuionnet

CNRS & ENS Lyon

Algebra, Geometry and Physics Bonn/Berlin seminar

Joint work with V. Jones and D. Shlyakhtenko.

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## What is in common between

And

(3)

Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

And

And

(4)

## What is in common between

And

And

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Outline

Maps

Random Matrices and the enumeration of maps SD equations

Loop models Subfactors theory Transport

(6)

## Topological expansions, Random matrices and operator algebras

Maps

Random Matrices and the enumeration of maps SD equations

Loop models Subfactors theory Transport

(7)

Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## What is a map ?

A map is a connected graph which is properly embedded into a surface, that is embedded in such a way that its edges do not cross and the faces (obtained by cutting the surface along the edges of the graph) are homeomorphic to disks. The genus of a map is the genus of such a surface.

By Euler formula, 2−2g = #{vertices}

+#{faces} −#{edges}.

= 2 + 3−3

2 1 3

(8)

## What is a map ?

Maps are connected graphs which are properly embedded into a surface, that is embedded in such a way that its edges do not cross and the faces (obtained by cutting the surface along the edges of the graph) are homeomorphic to disks. The genus of a map is the genus of such a surface.

By Euler formula, 2−2g = #{vertices}

+#{faces} −#{edges}.

= 2 + 1−3

1

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Enumeration of maps

Being given vertices with given valence, how many maps with genusg can we build ?

Recipe :

•Draw labeled vertices with labeled half-edges on a surface of genusg,

•Match the end points of these half-edges,

•Check the resulting map is properly embedded and could not be properly embedded on a surface with genus smaller than g,

•Count such matchings (which are the same only if matched labelled half-edges are the same).

8 1

2

4 3 5 6

8 7 1

2

3 4 5 6

7

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## Enumeration of maps

Being given vertices with given valence, how many maps with genusg can we build ?

Recipe :

•Draw labeled vertices with labeled half-edges on a surface of genusg,

•Match the end points of these half-edges,

•Check the resulting map is properly embedded and could not be properly embedded on a surface with genus smaller than g,

•Count such matchings (which are the same only if matched labelled half-edges are the same).

8 1

2

4 3 5 6

8 7 1

2

3 4 5 6

7

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Enumeration of maps

Being given vertices with given valence, how many maps with genusg can we build ?

Recipe :

•Draw vertices with labeled half-edges on a surface of genus g,

•Match the end points of these half-edges,

•Check the resulting map is properly embedded and could not be properly embedded on a surface with smaller genus,

•Count such matchings (which are the same only if matched labelled half-edges are the same).

5 6

8 7

1 2 3 4 5

6 7

8 1

2 3 4

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## Topological expansions, Random matrices and operator algebras

Maps

Random Matrices and the enumeration of maps SD equations

Loop models Subfactors theory Transport

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## The law of the GUE and the enumeration of maps

LetXN be a matrix following theGaussian UnitaryEnsemble, that is aN×N Hermitian matrix with i.i.d centered complex Gaussian entries with covarianceN−1, that is

dP(XN) = 1

ZN exp{−N

2Tr((XN)2)}dXN

Theorem (Harer-Zagier 86) For all p∈N

Z 1

NTr((XN)2p)dP(XN) =X

g≥0

N−2gM(2p;g).

equalsPN n=1

N n

(2p−1)!!2n−1 p

n−1

.M(2p;g) denotes the number of maps with genus g build over a vertex of valence2p.

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## The law of the GUE and the enumeration of maps

LetXN be a matrix following theGaussian UnitaryEnsemble, that is aN×N Hermitian matrix with i.i.d centered complex Gaussian entries with covarianceN−1, that is

dP(XN) = 1

ZN exp{−N

2Tr((XN)2)}dXN

Theorem (Harer-Zagier 86) For all p∈N

Z 1

NTr((XN)2p)dP(XN) =X

g≥0

N−2gM(2p;g).

equalsPN n=1

N n

(2p−1)!!2n−1 p

n−1

.M(2p;g) denotes the number of maps with genus g build over a vertex of valence2p.

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Proof “ Feynman diagrams”

E[1

NTr((XN)p)] = 1 N

N

X

i(1),...,i(p)=1

E[Xi(1)iN (2)Xi(2)i(3)N · · ·Xi(p)i(1)N ] Wick formula : If (G1,· · ·,G2n) is a centered Gaussian vector,

E[G1G2· · ·G2n] = X

1≤s1<s2..<sn≤2n ri>si

n

Y

j=1

E[GsjGrj].

Example : IfGi =G follows the standard Gaussian distribution E[Gp] = #{pair partitions of p points}

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## Proof “ Feynman diagrams”

E[Tr(XN)p] =

N

X

i(1),...,i(p)=1

E[Xi(1)i(2)N XiN(2)i(3)· · ·XiN(p)i(1)]

E[XiN(1)i(2)· · ·XiN(p)i(1)] =

i(1) i(1) i(2)

i(2)

i(3)

i(3) i(4)

i(4) i(5) i(5) i(6)

i(6)

AsE[XijNXkN`] =N−11ij=`k, only matchings so that indices are constant along the boundary of the faces contribute.

E[Tr((XN)p)] = X graph 1 vertex

degree p

N#faces−p/2

= X

N−2g+1M((xp,1);g) by Euler formula

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Random matrices and the enumeration of maps

’t Hooft 74’ and Br´ezin-Itzykson-Parisi-Zuber 78’

Lett= (ti)1≤i≤n∈Rn and setVt=Pn

i=1tixi.Formally, 1

N2log Z

eNtr(Vt(XN))dP(XN)

= X

k1,..,knN

X

g≥0

N−2g

n

Y

j=1

(tj)kj

kj! M((ki)1≤i≤n;g) with

M((ki)1≤i≤n;g) =]{maps of genus g with ki vertices of degree i}

5 6

8 7

1 2 3 4 5

6 7

8 1

2 3 4

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## Enumeration of colored maps

Consider vertices with colored half-edges and enumerate maps build by matching half-edges of the same color.

Such vertices are in bijection with monomials:

toq(X1, . . . ,Xd) =Xi1Xi2· · ·Xip associate a “star of typeq” given by the vertex withp drawn on the plan so that the first half-edge has colori1, the second colori2 etc until the last which has colorip. M((qi,ki)1≤i≤m,g) denotes the number of maps with genusg build onki stars of typeqi,1≤i ≤m.

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Random matrices and the enumeration of maps

’t Hooft (1974) and Br´ezin-Itzykson-Parisi-Zuber (1978) Let (q1, . . . ,qn) be monomials. Let t= (ti)1≤i≤n∈Rn and set Vt(X1, . . . ,Xm) =Pn

i=1tiqi(X1, . . . ,Xm).Formally, FVNt = 1

N2 log Z

eNtr(Vt(A1,· · ·,Am))dPN(A1)· · ·dPN(Am)

= X

k1,..,knN

X

g≥0

N−2g

n

Y

j=1

(tj)kj

kj! M((qi,ki)1≤i≤n,g) with

M((qi,ki)1≤i≤n,g) =]{maps of genusg with ki vertices of type qi} where maps are constructing by matching half-edges of the same color.

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## Example : The Ising model on random graphs

Takeq1(X1,X2) =X1X2,q2(X1,X2) =X14,q3(X1,X2) =X24 represented by

Then, 1 N2log

Z

eNTr(P3i=1tiqi(X1N,X2N))dP(X1N)dP(X2N) is a generating function for the enumeration of the the Ising model on random

graphs. Solved by Mehta (1986).

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Random matrices, maps and tracial states

’t Hooft 74’ and Br´ezin-Itzykson-Parisi-Zuber 78’

Let (q1,· · · ,qn) be monomials,Vt=Pn

i=1tiqi and put

dPVt(X1N,· · ·,XmN) =e−N2FVNt+NTr(Vt(X1N,···,XmN))dP(X1N)· · ·dP(XmN) Formally, for any monomialP

τtN(P) :=

Z 1 NTr

P(X1N, . . . ,XmN)

dPVt(X1N, . . . ,XmN)

= ∂sFVN

t+sP/N2|s=0

= X

g≥0

N−2g X

k1,..,knN n

Y

j=1

(tj)kj

kj! M((P,1),(qi,ki)1≤i≤n;g)

τtN is a tracial state :

τtN(PP)≥0, τtN(1) = 1, τtN(PQ) =τtN(QP).

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## Random matrices, maps and tracial states

’t Hooft 74’ and Br´ezin-Itzykson-Parisi-Zuber 78’

Let (q1,· · · ,qn) be monomials,Vt=Pn

i=1tiqi and put

dPVt(X1N,· · ·,XmN) =e−N2FVNt+NTr(Vt(X1N,···,XmN))dP(X1N)· · ·dP(XmN) Formally, for any monomialP

τtN(P) :=

Z 1 NTr

P(X1N, . . . ,XmN)

dPVt(X1N, . . . ,XmN)

= ∂sFVN

t+sP/N2|s=0

= X

g≥0

N−2g X

k1,..,knN n

Y

j=1

(tj)kj

kj! M((P,1),(qi,ki)1≤i≤n;g) τtN is a tracial state :

τtN(PP)≥0, τtN(1) = 1, τtN(PQ) =τtN(QP).

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## What is a non-commutative law ?

What is a classical law onRd? It is anon-negative linear map

Q :f ∈ Cb(Rd,R)→Q(f) = Z

f(x)dQ(x)∈R, Q(1) = 1

Anon-commutative law τ ofn self-adjoint variables is alinear map τ :P ∈ChX1,· · ·,Xdi →τ(P)∈C

It should satisfy

τ(PP)≥0 for all P, (zXi1· · ·Xik) = ¯zXik· · ·Xi1.

τ(1) = 1

τ(PQ) =τ(QP) for all P,Q∈ChX1,· · · ,Xdi.

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## What is a non-commutative law ?

What is a classical law onRd? It is anon-negative linear map

Q :f ∈ Cb(Rd,R)→Q(f) = Z

f(x)dQ(x)∈R, Q(1) = 1

τ :P ∈ChX1,· · ·,Xdi →τ(P)∈C It should satisfy

τ(PP)≥0 for all P, (zXi1· · ·Xik) = ¯zXik· · ·Xi1.

τ(1) = 1

τ(PQ) =τ(QP) for all P,Q∈ChX1,· · · ,Xdi.

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## What is a non-commutative law ?

What is a classical law onRd? It is anon-negative linear map

Q :f ∈ Cb(Rd,R)→Q(f) = Z

f(x)dQ(x)∈R, Q(1) = 1

Anon-commutative lawτ ofn self-adjoint variables is alinear map τ :P ∈ChX1,· · ·,Xdi →τ(P)∈C

It should satisfy

τ(PP)≥0 for all P, (zXi1· · ·Xik) = ¯zXik· · ·Xi1.

τ(1) = 1

τ(PQ) =τ(QP) for allP,Q∈ChX1,· · · ,Xdi.

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## The law of free semicircle variables

TakeX1N,· · · ,XdN be independent GUE matrices, that is

P

dX1N,· · · ,dXdN

= 1

(ZN)d exp{−N 2Tr(

d

X

i=1

(XiN)2)}Y dXiN.

Theorem (Voiculescu(91))

For any polynomial P∈ChX1,· · · ,Xdi

N→∞lim E[1

NTr(P(X1N,· · ·,XdN))] =σ(P) σ is the law of d free semicircle variables.

If P = Xi1Xi2· · ·Xik, σ(P) is the number of planar maps build over a star of type P.

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## From formal to asymptotic topological expansions

Form∈N and (q1,· · · ,qn) monomials, Vt =Pn

i=1tiqi,M >2 dPMVt(X1N,· · ·,XmN) = 1kXN

i k≤M

ZVN,M eNTr(Vt(X1N,...,XmN))dP(X1N)· · ·dP(XmN) ForM >2, allK ∈N,ti small enough so thatVt=Vt,for any monomialP

τtN(P)= Z 1

NTr

P(X1N, . . . ,XmN)

dPMVt(X1N,· · · ,XmN)

=

K

X

g=0

N−2g X

k1,..,knN n

Y

j=1

(tj)kj

kj! M((P,1),(qi,ki)1≤i≤n;g)+o(N−2K)

-m= 1 : Ambj´orn et al. 95’, Albeverio, Pastur, Scherbina 01’, Ercolani-McLaughlin 03’

-m≥2 : G-Maurel-Segala 06’, G-Shlyakhtenko 09’, Dabrowski 18’ Jekel 19’

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## From formal to asymptotic topological expansions

Form∈N and (q1,· · · ,qn) monomials, Vt =Pn

i=1tiqi,M >2 dPMVt(X1N,· · ·,XmN) = 1kXN

i k≤M

ZVN,M eNTr(Vt(X1N,...,XmN))dP(X1N)· · ·dP(XmN) ForM >2, allK ∈N,ti small enough so thatVt=Vt,for any monomialP

τtN(P)= Z 1

NTr

P(X1N, . . . ,XmN)

dPMVt(X1N,· · · ,XmN)

=

K

X

g=0

N−2g X

k1,..,knN n

Y

j=1

(tj)kj

kj! M((P,1),(qi,ki)1≤i≤n;g)+o(N−2K) -m= 1 : Ambj´orn et al. 95’, Albeverio, Pastur, Scherbina 01’,

Ercolani-McLaughlin 03’

-m≥2 : G-Maurel-Segala 06’, G-Shlyakhtenko 09’, Dabrowski 18’

Jekel 19’

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Topological expansions, Random matrices and operator algebras

Maps

Random Matrices and the enumeration of maps SD equations

Loop models Subfactors theory Transport

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## Schwinger-Dyson equations

Both matrix integrals and map enumerations are related with a third mathematical objects : TheSchwinger-Dyson equations.

They describe relations between moments, obtained thanks to integration by parts, for matrix integrals,

They describe the induction relations for the enumeration of maps.

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## First loop equation

LetV be a polynomial and set

dPV(X1N, . . . ,XmN) = (ZVN)−1eNTr(V(X1N,...,XmN))dP(X1N)· · ·dP(XmN) Then, for any polynomialP, anyi ∈ {1, . . . ,m}

Z 1

NTr⊗ 1

NTr(∂iP(X1N, . . . ,XmN))dPV(X1N, . . . ,XmN)

= Z 1

NTr((Xi −DiV)P(X1N, . . . ,XmN))dPV(X1N, . . . ,XmN) where for any monomialq

iq = X

q=q1Xiq2

q1⊗q2 Diq= X

q=q1Xiq2

q2q1

Proof : Based onR

f0(x)e−V(x)dx =R

f(x)V0(x)e−V(x)dx.

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## First order asymptotics

LetV be a polynomial and set

dPV(X1N, . . . ,XmN) = (ZVN)−1eNTr(V(X1N,...,XmN))dP(X1N)· · ·dP(XmN) AssumeV small (and add a cutoff if needed). The limit points τV

of

τXN(P) := 1

NTr(P(X1N, . . . ,XdN)) satisfy

(A) τV(XiP) =τV ⊗τV(∂iP) +τV(DiVP) with∂iq =P

q=q1Xiq2q1⊗q2, Diq=P

q=q1Xiq2q2q1, (B) |τV(Xi1· · ·Xik)| ≤4k.

fork ≤ N. Hence (A) comes from the loop equation Z

τXN ⊗τXN(∂iP)dPV = Z

τXN((Xi −DiV)P)dPV

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## First order asymptotics

LetV be a polynomial and set

dPV(X1N, . . . ,XmN) = (ZVN)−1eNTr(V(X1N,...,XmN))dP(X1N)· · ·dP(XmN) AssumeV small (and add a cutoff if needed). The limit points τV

of

τXN(P) := 1

NTr(P(X1N, . . . ,XdN)) satisfy

(A) τV(XiP) =τV ⊗τV(∂iP) +τV(DiVP) with∂iq =P

q=q1Xiq2q1⊗q2, Diq=P

q=q1Xiq2q2q1, (B) |τV(Xi1· · ·Xik)| ≤4k.

Proof : asPV is log-concave,τXN self-averages and satisfies (B) fork ≤√

N. Hence (A) comes from the loop equation Z

τXN ⊗τXN(∂iP)dPV = Z

τXN((Xi −DiV)P)dPV

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## First order asymptotics

IfV is small enough,there exists a unique solutionto (A) τV(XiP) =τV ⊗τV(∂iP) +τV(DiVP)

⇔τV(Xiq) = X

q=q1Xiq2

τV(q1V(q2) +X

j

tj X

qj=qj1Xiq2j

τV(qj2q1jq) (B) |τV(Xi1· · ·Xik)| ≤4k,

It is the generating function of planar maps τV(P) =X Ytiki

ki!M((P,1),(qi,ki); 0).

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## First order asymptotics

IfV is small enough,there exists a unique solutionto (A) τV(XiP) =τV ⊗τV(∂iP) +τV(DiVP)

⇔τV(Xiq) = X

q=q1Xiq2

τV(q1V(q2) +X

j

tj X

qj=qj1Xiq2j

τV(qj2q1jq)

(B) |τV(Xi1· · ·Xik)| ≤4k, HenceτXN converges to this solution.

It is the generating function of planar maps τV(P) =X Ytiki

ki!M((P,1),(qi,ki); 0).

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## First order asymptotics

IfV is small enough,there exists a unique solutionto (A) τV(XiP) =τV ⊗τV(∂iP) +τV(DiVP)

⇔τV(Xiq) = X

q=q1Xiq2

τV(q1V(q2) +X

j

tj X

qj=qj1Xiq2j

τV(qj2q1jq)

(B) |τV(Xi1· · ·Xik)| ≤4k, HenceτXN converges to this solution.

It is the generating function of planar maps τV(P) =X Ytiki

ki!M((P,1),(qi,ki); 0).

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Induction relations and non-commutative derivatives

Tutte’s surgery =Induction relations on maps.

Let M(p,n) be the number of planar maps with p vertices of degree 3 and one of degreen.

M(p,n)

= 3pM(p−1,n+ 1) +

n−2

X

k=0 p

X

`=0

Cp`M(`,k)M(p−`,n−k−2) Mt(xn) =P

p≥0 tp

p!M(p,n) satisfies the loop equation withV =x3 (A) Mt(xn) =tMt(xn−13x2) +Mt⊗Mt(∂xp−1)

(B) |Mt(xn)| ≤4n.

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Induction relations and non-commutative derivatives

Tutte’s surgery =Induction relations on maps.

Let M(p,n) be the number of planar maps with p vertices of degree 3 and one of degreen.

M(p,n)

= 3pM(p−1,n+ 1) +

n−2

X

k=0 p

X

`=0

Cp`M(`,k)M(p−`,n−k−2)

p!

(A) Mt(xn) =tMt(xn−13x2) +Mt⊗Mt(∂xp−1) (B) |Mt(xn)| ≤4n.

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Induction relations and non-commutative derivatives

Tutte’s surgery =Induction relations on maps.

Let M(p,n) be the number of planar maps with p vertices of degree 3 and one of degreen.

M(p,n)

= 3pM(p−1,n+ 1) +

n−2

X

k=0 p

X

`=0

Cp`M(`,k)M(p−`,n−k−2) Mt(xn) =P

p≥0 tp

p!M(p,n) satisfies the loop equation withV =x3 (A) Mt(xn) =tMt(xn−13x2) +Mt⊗Mt(∂xp−1)

(B) |Mt(xn)| ≤4n.

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## Topological expansions, Random matrices and operator algebras

Maps

Random Matrices and the enumeration of maps SD equations

Loop models Subfactors theory Transport

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Loop models

The Temperley-Lieb elements (TLE) are boxes with boundary points connected by non-intersecting strings, a shading and a marked boundary point.

*

LetS1, . . . ,Sn be (TLE) andβ1,· · · , βn be small real numbers.

The loop model is given, for any Temperley-Lieb elementS,by Trβ,δ(S) =X

ni≥0

X Y

1≤i≤n

βini ni]loops

where we sum over all planar maps with ni ele- ments Si and one ele- mentS.

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Main results

Theorem (G-Jones-Shlyakhtenko-Zinn Justin 10’ )

Let S1, . . . ,Sn be Temperley-Lieb elements, β1, . . . , βn∈Rn and consider the loop model

Trβ,δ(S) =X

ni≥0

X Y

1≤i≤n

βini ni]loops

Then,for δ∈I :={2 cos(πn)}n≥3∪[2,∞[and βi small enough Trβ,δ is a limit of matrix models.

Theorem (G-Jones-Shlyakhtenko-Zinn Justin 10’ ) Forδ ∈I and a Temperley-Lieb element S of the form

there exists an explicit formula forTrβ,δ(S). Cf Bousquet-Melou–Bernardi, Borot, Duplantier, Eynard, Kostov, Staudacher ...

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Main results

Theorem (G-Jones-Shlyakhtenko-Zinn Justin 10’ )

Let S1, . . . ,Sn be Temperley-Lieb elements, β1, . . . , βn∈Rn and consider the loop model

Trβ,δ(S) =X

ni≥0

X Y

1≤i≤n

βini ni]loops

Then,for δ∈I :={2 cos(πn)}n≥3∪[2,∞[and βi small enough Trβ,δ is a limit of matrix models.

For the Potts model, i.eS1= ,S2 =

Theorem (G-Jones-Shlyakhtenko-Zinn Justin 10’ ) Forδ ∈I and a Temperley-Lieb element S of the form

there exists an explicit formula forTrβ,δ(S).

Cf Bousquet-Melou–Bernardi, Borot, Duplantier, Eynard, Kostov, Staudacher ...

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## Random matrices and loop enumeration ; β = 0

Letδ=m∈N. For a (TLE) B, we denotep ∼B ` if a string joins thepth boundary point with the`th boundary point in B, then we associate toB withk strings the polynomial

qB(X) = X

ij=ip if j∼pB

1≤i`≤m

Xi1· · ·Xi2k.

qB(X) =

n

X

i,j,k=1

XiXjXjXiXkXk ⇔ Theorem

IfνN denotes the law of m independent GUE matrices,

N→∞lim Z 1

Ntr(qB(X))νN(dX) =X

m]loops=Tr0(B)

where we sum over all planar maps that can be built on B.

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Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Proof

By Voiculescu’s theorem, ifB = ,

N→∞lim Z 1

Ntr (qB(X))νN(dX)

=

n

X

i,j,k=1 N→∞lim

Z 1

Ntr (XiXjXjXiXkXkN(dX)

=X

i,j,k

X

i j j k k

=X

n]loops

because the indices have to be constant along loops.

(46)

Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Non integer fugacities, β = 0

Based on theconstruction of the planar algebra of a bipartite graph, Jones 99’.Recallp ∼B j if a string joins thepth dot with the jth do in the TL element B

j

* p

.

qB(X) = X

ij=ip if j∼pB

Xi1· · ·Xi2k ⇒qBv(X) = X

ej=epo ifj∼pB

σB(w)Xe1· · ·Xe2k

adjacency matrix of Γ has eigenvalueδ with eigenvector (µv)v∈V

withµv ≥0 (∃ for anyδ∈ {2 cos(πn)}n≥3∪[2,∞[)

•The sum runs over loops w =e1· · ·e2k in Γ which starts atv. v∈V+ iff ∗is in a white region.

•σB(w) is a well chosen weight.

(47)

Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Non integer fugacities, β = 0

Based on theconstruction of the planar algebra of a bipartite graph, Jones 99’.Recallp ∼B j if a string joins thepth dot with the jth do in the TL element B

j

* p

.

qB(X) = X

ij=ip if j∼pB

Xi1· · ·Xi2k ⇒qBv(X) = X

ej=epo ifj∼pB

σB(w)Xe1· · ·Xe2k

•ei edges of a bipartite graph Γ = (V =V+∪V,E) so that the adjacency matrix of Γ has eigenvalueδ with eigenvector (µv)v∈V withµv ≥0 (∃ for anyδ∈ {2 cos(πn)}n≥3∪[2,∞[)

•The sum runs over loops w =e1· · ·e2k in Γ which starts atv. v∈V+ iff ∗is in a white region.

•σB(w) is a well chosen weight.

(48)

Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Non integer fugacities, β = 0

Based on theconstruction of the planar algebra of a bipartite graph, Jones 99’.Recallp ∼B j if a string joins thepth dot with the jth do in the TL element B

j

* p

.

qB(X) = X

ij=ip if j∼pB

Xi1· · ·Xi2k ⇒qBv(X) = X

ej=epo ifj∼pB

σB(w)Xe1· · ·Xe2k

•ei edges of a bipartite graph Γ = (V =V+∪V,E) so that the adjacency matrix of Γ has eigenvalueδ with eigenvector (µv)v∈V withµv ≥0 (∃ for anyδ∈ {2 cos(πn)}n≥3∪[2,∞[)

•The sum runs over loops w =e1· · ·e2k in Γ which starts atv. v∈V+ iff ∗is in a white region.

(49)

Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Non integer fugacities, β = 0

Based on theconstruction of the planar algebra of a bipartite graph, Jones 99’.Recallp ∼B j if a string joins thepth dot with the jth do in the TL element B

j

* p

.

qB(X) = X

ij=ip if j∼pB

Xi1· · ·Xi2k ⇒qBv(X) = X

ej=epo ifj∼pB

σB(w)Xe1· · ·Xe2k

•ei edges of a bipartite graph Γ = (V =V+∪V,E) so that the adjacency matrix of Γ has eigenvalueδ with eigenvector (µv)v∈V withµv ≥0 (∃ for anyδ∈ {2 cos(πn)}n≥3∪[2,∞[)

•The sum runs over loops w =e1· · ·e2k in Γ which starts atv. v∈V+ iff ∗is in a white region.

•σB(w) is a well chosen weight.

(50)

Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Non integer fugacities,the matrix model, β = 0

Fore ∈E,e = (s(e),t(e)),XeM are independent (except Xeo =Xe) [Mµs(e)]×[Mµt(e)] matrices with i.i.d centered Gaussian entries with variance 1/(M√

µs(e)µt(e)).

Recall qBv(XM) = X

w=e1···e2kLB s(e1)=v

σB(w)XeM1 · · ·XeM

2k

Theorem (G-Jones-Shlyakhtenko 07’)

LetΓbe a bipartite graph as before. Let B be Temperley-Lieb element. For all v ∈V

M→∞lim E[ 1

vtr(qvB(XM))] =Tr0,δ(B) =X δ]loops where the sum runs above all planar maps built on B.

(51)

Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Non integer fugacities,the matrix model, β = 0

Fore ∈E,e = (s(e),t(e)),XeM are independent (except Xeo =Xe) [Mµs(e)]×[Mµt(e)] matrices with i.i.d centered Gaussian entries with variance 1/(M√

µs(e)µt(e)).

Recall qBv(XM) = X

w=e1···e2kLB s(e1)=v

σB(w)XeM1 · · ·XeM

2k

Theorem (G-Jones-Shlyakhtenko 07’)

LetΓbe a bipartite graph as before. Let B be Temperley-Lieb element. For all v ∈V

M→∞lim E[ 1

vtr(qvB(XM))] =Tr0,δ(B) =X δ]loops

where the sum runs above all planar maps built on B.

Based onP

e∈E:s(e)=vµt(e)=δµv.

(52)

## Non integer fugacities, β 6= 0

LetBi be Temperley Lieb elements with ∗with colorσi ∈ {+,−}, 1≤i ≤p. Let Γ be a bipartite graph whose adjacency matrix has eigenvalueδ as before. Let νM be the law of the previous

independent rectangular Gaussian matrices and set dν(BM

i)i(Xe) = 1kXek≤L

ZBN eMtr(Pp i=1βi

P

v∈Vσi µvqBiv (X))

M(Xe).

Theorem (G-Jones-Shlyakhtenko-Zinn Justin 10’) For any L>2, for βi small enough real numbers, for any Temperley-Lieb element B with colorσ, any v ∈Vσ,

M→∞lim Z 1

vtr(qBv(X))dν(BN

i)i(X) = X

ni≥0

]loops

p

Y

i=1

βini ni!

where we sum over the planar maps build on ni TL elements Bi

and one B. This isTrβ,δ(B).

(53)

Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Topological expansions, Random matrices and operator algebras

Maps

Random Matrices and the enumeration of maps SD equations

Loop models Subfactors theory Transport

(54)

Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Application to subfactors theory

Temperley-Lieb elements are boxes containing non-intersecting strings. We can endow this set with the multiplication :

and the trace given by τ(S) = X

R∈TL

δ]loops in S.R

T T.S=

S

Theorem (G-Jones-Shlyakhtenko 07 ’ ,Popa 89’ and 93’ ) Takeδ ∈I :={2 cos(πn)}n≥4∪]2,∞[

-τ is a tracial state, as a limit of matrix (or free var.) models.

-A tower of factors with index δ2 can be built .

(55)

Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Application to subfactors theory

Temperley-Lieb elements are boxes containing non-intersecting strings. We can endow this set with the multiplication :

and the trace given by τ(S) = X

R∈TL

δ]loops in S.R

T T.S=

S

Theorem (G-Jones-Shlyakhtenko 07 ’ ,Popa 89’ and 93’ ) Takeδ ∈I :={2 cos(πn)}n≥4∪]2,∞[

-τ is a tracial state, as a limit of matrix (or free var.) models.

-The corresponding von Neumann algebra is a factor.

-A tower of factors with index δ2 can be built .

(56)

## Application to subfactors theory

Temperley-Lieb elements are boxes containing non-intersecting strings. We can endow this set with the multiplication :

and the trace given by τ(S) = X

R∈TL

δ]loops in S.R

T T.S=

S

Theorem (G-Jones-Shlyakhtenko 07 ’ ,Popa 89’ and 93’ ) Takeδ ∈I :={2 cos(πn)}n≥4∪]2,∞[

-τ is a tracial state, as a limit of matrix (or free var.) models.

-The corresponding von Neumann algebra is a factor.

-A tower of factors with index δ2 can be built .

(57)

Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Topological expansions, Random matrices and operator algebras

Maps

Random Matrices and the enumeration of maps SD equations

Loop models Subfactors theory Transport

(58)

Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Convergence of the empirical distribution of matrices

LetXN = (X1N, . . . ,XdN) be a sequence ofN×N (random) Hermitian matrices and let ˆµN be its empirical distribution

ˆ

µN(P) = 1

NTr(P(XN)) Assume that for any polynomialP

N→∞lim µˆN(P) = lim

N→∞

1

NTr(P(XN)) =τ(P).(∗) Thenτ is a tracial state :

τ(PP)≥0, τ(PQ) =τ(QP), τ(I) = 1.

sequence of matricesXN such that (*) holds ?

Z. Ji, A. Natarajan,T. Vidick, J. Wright and H. Yuen (2020) : Answer is no(MIP*=RE). But a mystake in the proof was found and a patch posted.

(59)

Maps Random Matrices and the enumeration of maps SD equations Loop models Subfactors theory Transport

## Convergence of the empirical distribution of matrices

LetXN = (X1N, . . . ,XdN) be a sequence ofN×N (random) Hermitian matrices and let ˆµN be its empirical distribution

ˆ

µN(P) = 1

NTr(P(XN)) Assume that for any polynomialP

N→∞lim µˆN(P) = lim

N→∞

1

NTr(P(XN)) =τ(P).(∗) Thenτ is a tracial state :

τ(PP)≥0, τ(PQ) =τ(QP), τ(I) = 1.

Connes Question:For any tracial stateτ can you find a sequence of matricesXN such that (*) holds ?

Z. Ji, A. Natarajan,T. Vidick, J. Wright and H. Yuen (2020) : Answer is no(MIP*=RE). But a mystake in the proof was found and a patch posted.

(60)

## Convergence of the empirical distribution of matrices

LetXN = (X1N, . . . ,XdN) be a sequence ofN×N (random) Hermitian matrices and let ˆµN be its empirical distribution

ˆ

µN(P) = 1

NTr(P(XN)) Assume that for any polynomialP

N→∞lim µˆN(P) = lim

N→∞

1

NTr(P(XN)) =τ(P).(∗) Thenτ is a tracial state :

τ(PP)≥0, τ(PQ) =τ(QP), τ(I) = 1.

Connes Question:For any tracial stateτ can you find a sequence of matricesXN such that (*) holds ?

Z. Ji, A. Natarajan,T. Vidick, J. Wright and H. Yuen (2020) : Answer is no(MIP*=RE). But a mystake in the proof was found and a patch posted.

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