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Top eigenvalue of a random matrix: Large deviations Satya N. Majumdar Laboratoire de Physique Th´ eorique et Mod` eles Statistiques,CNRS, Universit´ e Paris-Sud, France S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Page 1: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Top eigenvalue of a random matrix: Largedeviations

Satya N. Majumdar

Laboratoire de Physique Theorique et Modeles Statistiques,CNRS,Universite Paris-Sud, France

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 2: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

First Appearence of Random Matrices

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 3: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Covariance Matrix

11X

X21

X31

X22

X

phys. math

1

2

3

X =

in general

(MxN)

Xt

=X

11 X21

X31

X12 22 X

in general

(NxM)

W= XtX =

X11

+ X21+ X31

2 2 2X11 X12

+ X21

X

X22+ X

31X

X12

X12X11

+

X

X22X21+ X X31 X12

2 + X22

2+ X

2

(unnormalized)COVARIANCE MATRIX(NxN)

32

32

32

3232

Null model → random data: X → random (M × N) matrix

→ W = X tX → random N × N matrix (Wishart, 1928)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 4: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Covariance Matrix

11X

X21

X31

X22

X

phys. math

1

2

3

X =

in general

(MxN)

Xt

=X

11 X21

X31

X12 22 X

in general

(NxM)

W= XtX =

X11

+ X21+ X31

2 2 2X11 X12

+ X21

X

X22+ X

31X

X12

X12X11

+

X

X22X21+ X X31 X12

2 + X22

2+ X

2

(unnormalized)COVARIANCE MATRIX(NxN)

32

32

32

3232

Null model → random data: X → random (M × N) matrix

→ W = X tX → random N × N matrix (Wishart, 1928)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 5: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

RMT in Nuclear Physics: Eugene Wigner

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 6: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 7: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Random Matrices in Nuclear Physics

����

����

��������

��������

������������

��

����������

U238

Th232

spectra of heavy nuclei

E

E

WIGNER (’50)

DYSON, GAUDIN, MEHTA, .....

: replace complex H by random matrix

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 8: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Applications of Random Matrices

Physics: nuclear physics, quantum chaos, disorder and localization,mesoscopic transport, optics/lasers, quantum entanglement, neuralnetworks, gauge theory, QCD, matrix models, cosmology, string theory,statistical physics (growth models, interface, directed polymers...), ....

Mathematics: Riemann zeta function (number theory), free probabilitytheory, combinatorics and knot theory, determinantal points processes,integrable systems, ...

Statistics: multivariate statistics, principal component analysis (PCA),image processing, data compression, Bayesian model selection, ...

Information Theory: signal processing, wireless communications, ..

Biology: sequence matching, RNA folding, gene expression network ...

Economics and Finance: time series analysis,....

Recent Ref: The Oxford Handbook of Random Matrix Theory

ed. by G. Akemann, J. Baik and P. Di Francesco (2011)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 9: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Spectral Statistics in Random Matrix Theory (RMT)

Working model: real, symmetric N × N Gaussian random matrix

J =

J11 J12 . . . J1N

J12 J22 . . . J2N

. . . . . . . . . . . . . . .J1N J2N . . . JNN

Prob.[J] ∝ exp

−1

2

∑i,j

J2ij

= exp

[− 1

2 Tr(J2)]

−→ invariant under rotation

N real eigenvalues: λ1, λ2, . . . , λN −→ strongly correlated

Spectral statistics in RMT ⇒ statistics of {λ1, λ2, . . . , λN}

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 10: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Spectral Statistics in Random Matrix Theory (RMT)

Working model: real, symmetric N × N Gaussian random matrix

J =

J11 J12 . . . J1N

J12 J22 . . . J2N

. . . . . . . . . . . . . . .J1N J2N . . . JNN

Prob.[J] ∝ exp

−1

2

∑i,j

J2ij

= exp

[− 1

2 Tr(J2)]

−→ invariant under rotation

N real eigenvalues: λ1, λ2, . . . , λN −→ strongly correlated

Spectral statistics in RMT ⇒ statistics of {λ1, λ2, . . . , λN}

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 11: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Top Eigenvalue of a random matrix λmax

Recent excitements in statistical physics & mathematics on

λmax ⇒ the top eigenvalue of a random matrix

λ

large(left)

large(right)

max

maxPr( typical

TRACY−WIDOMTypical fluctuations (small)⇒ Tracy-Widom distribution→ ubiquitous

[directed polymer, random permutation,growth models, KPZ equation, sequencealignment, ....]

This talk ⇒ Atypical rare fluctuations ⇒ large deviation functions⇒ 3-rd order phase transition

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 12: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Top Eigenvalue of a random matrix λmax

Recent excitements in statistical physics & mathematics on

λmax ⇒ the top eigenvalue of a random matrix

λ

large(left)

large(right)

max

maxPr( typical

TRACY−WIDOMTypical fluctuations (small)⇒ Tracy-Widom distribution→ ubiquitous

[directed polymer, random permutation,growth models, KPZ equation, sequencealignment, ....]

This talk ⇒ Atypical rare fluctuations ⇒ large deviation functions⇒ 3-rd order phase transition

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 13: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Top Eigenvalue of a random matrix λmax

Recent excitements in statistical physics & mathematics on

λmax ⇒ the top eigenvalue of a random matrix

λ

large(left)

large(right)

max

maxPr( typical

TRACY−WIDOMTypical fluctuations (small)⇒ Tracy-Widom distribution→ ubiquitous

[directed polymer, random permutation,growth models, KPZ equation, sequencealignment, ....]

This talk ⇒ Atypical rare fluctuations ⇒ large deviation functions

⇒ 3-rd order phase transition

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 14: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Top Eigenvalue of a random matrix λmax

Recent excitements in statistical physics & mathematics on

λmax ⇒ the top eigenvalue of a random matrix

λ

large(left)

large(right)

max

maxPr( typical

TRACY−WIDOMTypical fluctuations (small)⇒ Tracy-Widom distribution→ ubiquitous

[directed polymer, random permutation,growth models, KPZ equation, sequencealignment, ....]

This talk ⇒ Atypical rare fluctuations ⇒ large deviation functions⇒ 3-rd order phase transition

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 15: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Plan:

• Top eigenvalue λmax of a Gaussian random matrix⇒ stability of a large complex system

• Prob. distr. of λmax ⇐⇒ Coulomb gas with a wall

Limiting distribution: Tracy-Widom

physics of large deviation tails: left tail (pushed Coulomb gas)right tail (unpushed Coulomb gas)

⇒ 3-rd order phase transition: Pushed ⇐⇒Unpushed(Unstable) ⇐⇒ (Stable)

• Similar 3-rd order phase transition in Yang-Mills gauge theory andother systems

⇒ ubiquitous

• Extension to Wishart matrices

⇒ Recent experiments in coupled fiber lasers system

• Summary and Generalizations

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 16: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Plan:

• Top eigenvalue λmax of a Gaussian random matrix⇒ stability of a large complex system

• Prob. distr. of λmax ⇐⇒ Coulomb gas with a wall

Limiting distribution: Tracy-Widom

physics of large deviation tails: left tail (pushed Coulomb gas)right tail (unpushed Coulomb gas)

⇒ 3-rd order phase transition: Pushed ⇐⇒Unpushed(Unstable) ⇐⇒ (Stable)

• Similar 3-rd order phase transition in Yang-Mills gauge theory andother systems

⇒ ubiquitous

• Extension to Wishart matrices

⇒ Recent experiments in coupled fiber lasers system

• Summary and Generalizations

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 17: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Plan:

• Top eigenvalue λmax of a Gaussian random matrix⇒ stability of a large complex system

• Prob. distr. of λmax ⇐⇒ Coulomb gas with a wall

Limiting distribution: Tracy-Widom

physics of large deviation tails: left tail (pushed Coulomb gas)right tail (unpushed Coulomb gas)

⇒ 3-rd order phase transition: Pushed ⇐⇒Unpushed(Unstable) ⇐⇒ (Stable)

• Similar 3-rd order phase transition in Yang-Mills gauge theory andother systems

⇒ ubiquitous

• Extension to Wishart matrices

⇒ Recent experiments in coupled fiber lasers system

• Summary and Generalizations

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 18: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Plan:

• Top eigenvalue λmax of a Gaussian random matrix⇒ stability of a large complex system

• Prob. distr. of λmax ⇐⇒ Coulomb gas with a wall

Limiting distribution: Tracy-Widom

physics of large deviation tails: left tail (pushed Coulomb gas)right tail (unpushed Coulomb gas)

⇒ 3-rd order phase transition: Pushed ⇐⇒Unpushed(Unstable) ⇐⇒ (Stable)

• Similar 3-rd order phase transition in Yang-Mills gauge theory andother systems

⇒ ubiquitous

• Extension to Wishart matrices

⇒ Recent experiments in coupled fiber lasers system

• Summary and Generalizations

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 19: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Plan:

• Top eigenvalue λmax of a Gaussian random matrix⇒ stability of a large complex system

• Prob. distr. of λmax ⇐⇒ Coulomb gas with a wall

Limiting distribution: Tracy-Widom

physics of large deviation tails: left tail (pushed Coulomb gas)right tail (unpushed Coulomb gas)

⇒ 3-rd order phase transition: Pushed ⇐⇒Unpushed(Unstable) ⇐⇒ (Stable)

• Similar 3-rd order phase transition in Yang-Mills gauge theory andother systems

⇒ ubiquitous

• Extension to Wishart matrices

⇒ Recent experiments in coupled fiber lasers system

• Summary and Generalizations

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 20: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Plan:

• Top eigenvalue λmax of a Gaussian random matrix⇒ stability of a large complex system

• Prob. distr. of λmax ⇐⇒ Coulomb gas with a wall

Limiting distribution: Tracy-Widom

physics of large deviation tails: left tail (pushed Coulomb gas)right tail (unpushed Coulomb gas)

⇒ 3-rd order phase transition: Pushed ⇐⇒Unpushed(Unstable) ⇐⇒ (Stable)

• Similar 3-rd order phase transition in Yang-Mills gauge theory andother systems

⇒ ubiquitous

• Extension to Wishart matrices

⇒ Recent experiments in coupled fiber lasers system

• Summary and Generalizations

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 21: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Plan:

• Top eigenvalue λmax of a Gaussian random matrix⇒ stability of a large complex system

• Prob. distr. of λmax ⇐⇒ Coulomb gas with a wall

Limiting distribution: Tracy-Widom

physics of large deviation tails: left tail (pushed Coulomb gas)right tail (unpushed Coulomb gas)

⇒ 3-rd order phase transition: Pushed ⇐⇒Unpushed(Unstable) ⇐⇒ (Stable)

• Similar 3-rd order phase transition in Yang-Mills gauge theory andother systems

⇒ ubiquitous

• Extension to Wishart matrices

⇒ Recent experiments in coupled fiber lasers system

• Summary and Generalizations

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 22: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

I. Whyλmax ?

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 23: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Stability of a Large Complex System

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 24: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Linear Stability of a Large Complex (RandomlyConnected) System

• Consider a stable non-interacting population of N species withequlibrium density ρ?i

Stable:

xi = ρi − ρ?i → small disturbed density

dxi/dt = −xi → relaxes back to 0

• Now switch on the interaction between species

dxi/dt = −xi + α∑N

j=1 Jij xj

Jij → (N × N) random interaction matrixα → interaction strength

• Question: What is the probabality that the system remains stable oncethe interaction is switched on?

(R.M. May, Nature, 238, 413, 1972)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 25: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Linear Stability of a Large Complex (RandomlyConnected) System

• Consider a stable non-interacting population of N species withequlibrium density ρ?i

Stable:

xi = ρi − ρ?i → small disturbed density

dxi/dt = −xi → relaxes back to 0

• Now switch on the interaction between species

dxi/dt = −xi + α∑N

j=1 Jij xj

Jij → (N × N) random interaction matrixα → interaction strength

• Question: What is the probabality that the system remains stable oncethe interaction is switched on?

(R.M. May, Nature, 238, 413, 1972)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 26: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Linear Stability of a Large Complex (RandomlyConnected) System

• Consider a stable non-interacting population of N species withequlibrium density ρ?i

Stable:

xi = ρi − ρ?i → small disturbed density

dxi/dt = −xi → relaxes back to 0

• Now switch on the interaction between species

dxi/dt = −xi + α∑N

j=1 Jij xj

Jij → (N × N) random interaction matrixα → interaction strength

• Question: What is the probabality that the system remains stable oncethe interaction is switched on?

(R.M. May, Nature, 238, 413, 1972)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 27: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Linear Stability of a Large Complex (RandomlyConnected) System

• Consider a stable non-interacting population of N species withequlibrium density ρ?i

Stable:

xi = ρi − ρ?i → small disturbed density

dxi/dt = −xi → relaxes back to 0

• Now switch on the interaction between species

dxi/dt = −xi + α∑N

j=1 Jij xj

Jij → (N × N) random interaction matrixα → interaction strength

• Question: What is the probabality that the system remains stable oncethe interaction is switched on?

(R.M. May, Nature, 238, 413, 1972)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 28: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Linear Stability of a Large Complex (RandomlyConnected) System

• Consider a stable non-interacting population of N species withequlibrium density ρ?i

Stable:

xi = ρi − ρ?i → small disturbed density

dxi/dt = −xi → relaxes back to 0

• Now switch on the interaction between species

dxi/dt = −xi + α∑N

j=1 Jij xj

Jij → (N × N) random interaction matrixα → interaction strength

• Question: What is the probabality that the system remains stable oncethe interaction is switched on?

(R.M. May, Nature, 238, 413, 1972)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 29: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Stability Criterion

• linear stability: ddt [x ] = [αJ − I ][x ] (J → random interaction matrix)

Let {λ1, λ2, · · · , λN} → eigenvalues of the matrix J

• Stable if αλi < 1 for all i = 1, 2, · · · ,N

⇒ λmax <1

α= w → stability criterion

w → inverse interaction strength

• Prob.(the system is stable)=Prob.[λmax < w ] =P(w ,N)

Cumulative distribution of the top eigenvalue

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 30: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Stability Criterion

• linear stability: ddt [x ] = [αJ − I ][x ] (J → random interaction matrix)

Let {λ1, λ2, · · · , λN} → eigenvalues of the matrix J

• Stable if αλi < 1 for all i = 1, 2, · · · ,N

⇒ λmax <1

α= w → stability criterion

w → inverse interaction strength

• Prob.(the system is stable)=Prob.[λmax < w ] =P(w ,N)

Cumulative distribution of the top eigenvalue

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 31: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Stability Criterion

• linear stability: ddt [x ] = [αJ − I ][x ] (J → random interaction matrix)

Let {λ1, λ2, · · · , λN} → eigenvalues of the matrix J

• Stable if αλi < 1 for all i = 1, 2, · · · ,N

⇒ λmax <1

α= w → stability criterion

w → inverse interaction strength

• Prob.(the system is stable)=Prob.[λmax < w ] =P(w ,N)

Cumulative distribution of the top eigenvalue

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 32: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Stability Criterion

• linear stability: ddt [x ] = [αJ − I ][x ] (J → random interaction matrix)

Let {λ1, λ2, · · · , λN} → eigenvalues of the matrix J

• Stable if αλi < 1 for all i = 1, 2, · · · ,N

⇒ λmax <1

α= w → stability criterion

w → inverse interaction strength

• Prob.(the system is stable)=Prob.[λmax < w ] =P(w ,N)

Cumulative distribution of the top eigenvalue

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 33: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Stable-Unstable Phase Transition as N →∞

• Assuming that the interaction matrix Jij → Real Symmetric Gaussian

Prob.[Jij ] ∝ exp[−N

2

∑i,j J

2ij

]∝ exp

[−N

2 Tr(J2)]

• May observed a sharp phase transition as N →∞:w = 1

α >√

2 ⇒ Stable (weakly interacting)

w = 1α <√

2 ⇒ Unstable (strongly interacting)

Prob.(the system is stable)=Prob.[λmax < w ] =P(w ,N)

P(

2

STABLE

)N,w

UNSTABLE = 1/αw0

1

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 34: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Stable-Unstable Phase Transition as N →∞

• Assuming that the interaction matrix Jij → Real Symmetric Gaussian

Prob.[Jij ] ∝ exp[−N

2

∑i,j J

2ij

]∝ exp

[−N

2 Tr(J2)]

• May observed a sharp phase transition as N →∞:w = 1

α >√

2 ⇒ Stable (weakly interacting)

w = 1α <√

2 ⇒ Unstable (strongly interacting)

Prob.(the system is stable)=Prob.[λmax < w ] =P(w ,N)

P(

2

STABLE

)N,w

UNSTABLE = 1/αw0

1

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 35: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Finite but Large N:

Prob.(the system is stable)=Prob.[λmax < w ] =P(w ,N)

What happens for finite but large N?

P(

2

STABLE

)N,w

UNSTABLE

finite but large N

= Prob.[ λmax

< w ]

0

1

w

• Is there any thermodynamic sense to this phase transition?

• What is the analogue of free energy?

• What is the order of this phase transition?

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 36: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Finite but Large N:

Prob.(the system is stable)=Prob.[λmax < w ] =P(w ,N)

What happens for finite but large N?

P(

2

STABLE

)N,w

UNSTABLE

finite but large N

= Prob.[ λmax

< w ]

0

1

w

• Is there any thermodynamic sense to this phase transition?

• What is the analogue of free energy?

• What is the order of this phase transition?S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 37: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

II. Summary of Results

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 38: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

For Large but Finite N: Summary of ResultsP(

2

1STABLE

)N,w

UNSTABLE

width of O (N−2/3)

finite but large N

= Prob.[ λmax

< w ]

w

P(w ,N) ∼ exp[−N2Φ−(w) + . . .

]for

√2− w ∼ O(1)

∼ F1

[√2N2/3

(w −

√2)]

for |w −√

2| ∼ O(N−2/3)

∼ 1− exp [−NΦ+(w) + . . .] for w −√

2 ∼ O(1)

Crossover function: F1(z) → Tracy-Widom (1994)

Exact tail functions: Φ∓(w) (Dean & S.M., 2006, S.M. & Vergassola, 2009)

Higher order corrections: (Borot, Eynard, S.M., & Nadal 2011, Nadal & S.M., 2011)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 39: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

For Large but Finite N: Summary of ResultsP(

2

1STABLE

)N,w

UNSTABLE

width of O (N−2/3)

finite but large N

= Prob.[ λmax

< w ]

w

P(w ,N) ∼ exp[−N2Φ−(w) + . . .

]for

√2− w ∼ O(1)

∼ F1

[√2N2/3

(w −

√2)]

for |w −√

2| ∼ O(N−2/3)

∼ 1− exp [−NΦ+(w) + . . .] for w −√

2 ∼ O(1)

Crossover function: F1(z) → Tracy-Widom (1994)

Exact tail functions: Φ∓(w) (Dean & S.M., 2006, S.M. & Vergassola, 2009)

Higher order corrections: (Borot, Eynard, S.M., & Nadal 2011, Nadal & S.M., 2011)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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For Large but Finite N: Summary of ResultsP(

2

1STABLE

)N,w

UNSTABLE

width of O (N−2/3)

finite but large N

= Prob.[ λmax

< w ]

w

P(w ,N) ∼ exp[−N2Φ−(w) + . . .

]for

√2− w ∼ O(1)

∼ F1

[√2N2/3

(w −

√2)]

for |w −√

2| ∼ O(N−2/3)

∼ 1− exp [−NΦ+(w) + . . .] for w −√

2 ∼ O(1)

Crossover function: F1(z) → Tracy-Widom (1994)

Exact tail functions: Φ∓(w) (Dean & S.M., 2006, S.M. & Vergassola, 2009)

Higher order corrections: (Borot, Eynard, S.M., & Nadal 2011, Nadal & S.M., 2011)S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Exact Left and Right Large Deviation Function

Using Coulomb gas + Saddle point method for large N:

• Left large deviation function:

Φ−(w) =1

108

[36w2 − w4 − (15w + w3)

√w2 + 6

+ 27(

ln(18)− 2 ln(w +√

6 + w2))]

where w <√

2

[D. S. Dean & S.M., PRL, 97, 160201 (2006)]

In particular, as w →√

2 (from left), Φ−(w)→ 16√

2(√

2− w)3

• Right large deviation function:

Φ+(w) =1

2w√w2 − 2 + ln

[w −

√w2 − 2√2

]where w >

√2

[S.M. & M. Vergassola, PRL, 102, 060601 (2009)]

As w →√

2 (from right), Φ+(w)→ 27/4

3 (w −√

2)3/2

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Exact Left and Right Large Deviation Function

Using Coulomb gas + Saddle point method for large N:

• Left large deviation function:

Φ−(w) =1

108

[36w2 − w4 − (15w + w3)

√w2 + 6

+ 27(

ln(18)− 2 ln(w +√

6 + w2))]

where w <√

2

[D. S. Dean & S.M., PRL, 97, 160201 (2006)]

In particular, as w →√

2 (from left), Φ−(w)→ 16√

2(√

2− w)3

• Right large deviation function:

Φ+(w) =1

2w√w2 − 2 + ln

[w −

√w2 − 2√2

]where w >

√2

[S.M. & M. Vergassola, PRL, 102, 060601 (2009)]

As w →√

2 (from right), Φ+(w)→ 27/4

3 (w −√

2)3/2

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Exact Left and Right Large Deviation Function

Using Coulomb gas + Saddle point method for large N:

• Left large deviation function:

Φ−(w) =1

108

[36w2 − w4 − (15w + w3)

√w2 + 6

+ 27(

ln(18)− 2 ln(w +√

6 + w2))]

where w <√

2

[D. S. Dean & S.M., PRL, 97, 160201 (2006)]

In particular, as w →√

2 (from left), Φ−(w)→ 16√

2(√

2− w)3

• Right large deviation function:

Φ+(w) =1

2w√w2 − 2 + ln

[w −

√w2 − 2√2

]where w >

√2

[S.M. & M. Vergassola, PRL, 102, 060601 (2009)]

As w →√

2 (from right), Φ+(w)→ 27/4

3 (w −√

2)3/2

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Exact Left and Right Large Deviation Function

Using Coulomb gas + Saddle point method for large N:

• Left large deviation function:

Φ−(w) =1

108

[36w2 − w4 − (15w + w3)

√w2 + 6

+ 27(

ln(18)− 2 ln(w +√

6 + w2))]

where w <√

2

[D. S. Dean & S.M., PRL, 97, 160201 (2006)]

In particular, as w →√

2 (from left), Φ−(w)→ 16√

2(√

2− w)3

• Right large deviation function:

Φ+(w) =1

2w√w2 − 2 + ln

[w −

√w2 − 2√2

]where w >

√2

[S.M. & M. Vergassola, PRL, 102, 060601 (2009)]

As w →√

2 (from right), Φ+(w)→ 27/4

3 (w −√

2)3/2

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 45: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Exact Left and Right Large Deviation Function

Using Coulomb gas + Saddle point method for large N:

• Left large deviation function:

Φ−(w) =1

108

[36w2 − w4 − (15w + w3)

√w2 + 6

+ 27(

ln(18)− 2 ln(w +√

6 + w2))]

where w <√

2

[D. S. Dean & S.M., PRL, 97, 160201 (2006)]

In particular, as w →√

2 (from left), Φ−(w)→ 16√

2(√

2− w)3

• Right large deviation function:

Φ+(w) =1

2w√w2 − 2 + ln

[w −

√w2 − 2√2

]where w >

√2

[S.M. & M. Vergassola, PRL, 102, 060601 (2009)]

As w →√

2 (from right), Φ+(w)→ 27/4

3 (w −√

2)3/2

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Large Deviation Functions

These large deviation functions Φ±(w) have been found useful in a largevariety of problems:

[Fyodorov 2004, Fyodorov & Williams 2007, Bray & Dean 2007, Auffinger, Ben Arous & Cerny

2010, Fydorov & Nadal 2012.... —— stationary points on random Gaussiansurfaces and spin glass landscapes]

[Cavagna, Garrahan, Giardina 2000,... —— Glassy systems]

[Susskind 2003, Douglas et. al. 2004, Aazami & Easther 2006, Marsh et. al. 2011, ...——String theory & Cosmology]

[Beltrani 2007, Dedieu & Malajovich, 2007, Houdre 2011...——Random Polynomials,Random Words (Young diagrams)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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3-rd Order Phase Transition

P(w ,N) ≈

exp{−N2Φ−(w) + . . .

}for w <

√2 (unstable)

1− exp {−NΦ+(w) + . . .} for w >√

2 (stable)

limN→∞

− 1

N2ln [P(w ,N)] =

Φ−(w) ∼ (

√2− w)3 as w →

√2−

0 as w →√

2+

−→ analogue of the free energy difference

2

limit

[−ln P]/N2

~ ( 2_w)

3N

w

finite N (

Tracy−Widom

large)

3-rd derivative → discontinuous

Crossover: N →∞, w →√

2 keeping

(w −√

2)N2/3 fixed

P(w ,N)→ F1

[√2N2/3

(w −

√2)]

→ Tracy-Widom

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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3-rd Order Phase Transition

P(w ,N) ≈

exp{−N2Φ−(w) + . . .

}for w <

√2 (unstable)

1− exp {−NΦ+(w) + . . .} for w >√

2 (stable)

limN→∞

− 1

N2ln [P(w ,N)] =

Φ−(w) ∼ (

√2− w)3 as w →

√2−

0 as w →√

2+

−→ analogue of the free energy difference

2

limit

[−ln P]/N2

~ ( 2_w)

3N

w

finite N (

Tracy−Widom

large)

3-rd derivative → discontinuous

Crossover: N →∞, w →√

2 keeping

(w −√

2)N2/3 fixed

P(w ,N)→ F1

[√2N2/3

(w −

√2)]

→ Tracy-Widom

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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3-rd Order Phase Transition

P(w ,N) ≈

exp{−N2Φ−(w) + . . .

}for w <

√2 (unstable)

1− exp {−NΦ+(w) + . . .} for w >√

2 (stable)

limN→∞

− 1

N2ln [P(w ,N)] =

Φ−(w) ∼ (

√2− w)3 as w →

√2−

0 as w →√

2+

−→ analogue of the free energy difference

2

limit

[−ln P]/N2

~ ( 2_w)

3N

w

finite N (

Tracy−Widom

large)

3-rd derivative → discontinuous

Crossover: N →∞, w →√

2 keeping

(w −√

2)N2/3 fixed

P(w ,N)→ F1

[√2N2/3

(w −

√2)]

→ Tracy-Widom

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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3-rd Order Phase Transition

P(w ,N) ≈

exp{−N2Φ−(w) + . . .

}for w <

√2 (unstable)

1− exp {−NΦ+(w) + . . .} for w >√

2 (stable)

limN→∞

− 1

N2ln [P(w ,N)] =

Φ−(w) ∼ (

√2− w)3 as w →

√2−

0 as w →√

2+

−→ analogue of the free energy difference

2

limit

[−ln P]/N2

~ ( 2_w)

3N

w

finite N (

Tracy−Widom

large)

3-rd derivative → discontinuous

Crossover: N →∞, w →√

2 keeping

(w −√

2)N2/3 fixed

P(w ,N)→ F1

[√2N2/3

(w −

√2)]

→ Tracy-Widom

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Large N Phase Transition: Phase Diagram

��������

1N

0 α = 1 w12

weakly interacting( )

STABLE

strongly interacting( )

UNSTABLE

crossover

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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A nice review of large-N gauge theory: M. Marino, arXiv:1206.6272

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Large N Phase Transition: Phase Diagram

��������

1N

0

crossover

U(N) 2−d lattice gauge theory in

coupling strength g

WEAK STRONG

GROSS−WITTEN−WADIA transition (1980)

gc

��������

1N

0 α = 1 w12

weakly interacting( )

STABLE

strongly interacting( )

UNSTABLE

crossover

Similar 3-rd order phase transition in U(N) lattice-gauge theory in 2-d

Unstable phase ≡ Strong coupling phase of Yang-Mills gauge theoryStable phase ≡ Weak coupling phase of Yang-Mills gauge theory

Tracy-Widom ⇒ crossover function in the double scaling regime(for finite but large N)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Large N Phase Transition: Phase Diagram

��������

1N

0

crossover

U(N) 2−d lattice gauge theory in

coupling strength g

WEAK STRONG

GROSS−WITTEN−WADIA transition (1980)

gc

��������

1N

0 α = 1 w12

weakly interacting( )

STABLE

strongly interacting( )

UNSTABLE

crossover

Similar 3-rd order phase transition in U(N) lattice-gauge theory in 2-d

Unstable phase ≡ Strong coupling phase of Yang-Mills gauge theoryStable phase ≡ Weak coupling phase of Yang-Mills gauge theory

Tracy-Widom ⇒ crossover function in the double scaling regime(for finite but large N)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Large N Phase Transition: Phase Diagram

��������

1N

0

crossover

U(N) 2−d lattice gauge theory in

coupling strength g

WEAK STRONG

GROSS−WITTEN−WADIA transition (1980)

gc

��������

1N

0 α = 1 w12

weakly interacting( )

STABLE

strongly interacting( )

UNSTABLE

crossover

Similar 3-rd order phase transition in U(N) lattice-gauge theory in 2-d

Unstable phase ≡ Strong coupling phase of Yang-Mills gauge theoryStable phase ≡ Weak coupling phase of Yang-Mills gauge theory

Tracy-Widom ⇒ crossover function in the double scaling regime(for finite but large N)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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III. Coulomb Gas

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Gaussian Random Matrices

• (N × N) Gaussian random matrix: J ≡ [Jij ]

• Ensembles: Orthogonal (GOE), Unitary (GUE) or Symplectic (GSE)

• Prob[Jij ] ∝ exp[−β2 N Tr

(J†J))]

• N real eigenvalues {λ1, λ2, . . . , λN} → correlated random variables

• Joint distribution of eigenvalues (Wigner, 1951)

P(λ1, λ2, . . . , λN) =1

ZNexp

[−β

2N

N∑i=1

λ2i

] ∏j<k

|λj − λk |β

where the Dyson index β = 1 (GOE), β = 2 (GUE) or β = 4 (GSE)

• ZN = Partition Function

=

∫ ∞−∞

. . .

∫ ∞−∞{∏i

dλi} exp

[−β

2N

N∑i=1

λ2i

] ∏j<k

|λj − λk |β

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Gaussian Random Matrices

• (N × N) Gaussian random matrix: J ≡ [Jij ]

• Ensembles: Orthogonal (GOE), Unitary (GUE) or Symplectic (GSE)

• Prob[Jij ] ∝ exp[−β2 N Tr

(J†J))]

• N real eigenvalues {λ1, λ2, . . . , λN} → correlated random variables

• Joint distribution of eigenvalues (Wigner, 1951)

P(λ1, λ2, . . . , λN) =1

ZNexp

[−β

2N

N∑i=1

λ2i

] ∏j<k

|λj − λk |β

where the Dyson index β = 1 (GOE), β = 2 (GUE) or β = 4 (GSE)

• ZN = Partition Function

=

∫ ∞−∞

. . .

∫ ∞−∞{∏i

dλi} exp

[−β

2N

N∑i=1

λ2i

] ∏j<k

|λj − λk |β

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Gaussian Random Matrices

• (N × N) Gaussian random matrix: J ≡ [Jij ]

• Ensembles: Orthogonal (GOE), Unitary (GUE) or Symplectic (GSE)

• Prob[Jij ] ∝ exp[−β2 N Tr

(J†J))]

• N real eigenvalues {λ1, λ2, . . . , λN} → correlated random variables

• Joint distribution of eigenvalues (Wigner, 1951)

P(λ1, λ2, . . . , λN) =1

ZNexp

[−β

2N

N∑i=1

λ2i

] ∏j<k

|λj − λk |β

where the Dyson index β = 1 (GOE), β = 2 (GUE) or β = 4 (GSE)

• ZN = Partition Function

=

∫ ∞−∞

. . .

∫ ∞−∞{∏i

dλi} exp

[−β

2N

N∑i=1

λ2i

] ∏j<k

|λj − λk |β

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Gaussian Random Matrices

• (N × N) Gaussian random matrix: J ≡ [Jij ]

• Ensembles: Orthogonal (GOE), Unitary (GUE) or Symplectic (GSE)

• Prob[Jij ] ∝ exp[−β2 N Tr

(J†J))]

• N real eigenvalues {λ1, λ2, . . . , λN} → correlated random variables

• Joint distribution of eigenvalues (Wigner, 1951)

P(λ1, λ2, . . . , λN) =1

ZNexp

[−β

2N

N∑i=1

λ2i

] ∏j<k

|λj − λk |β

where the Dyson index β = 1 (GOE), β = 2 (GUE) or β = 4 (GSE)

• ZN = Partition Function

=

∫ ∞−∞

. . .

∫ ∞−∞{∏i

dλi} exp

[−β

2N

N∑i=1

λ2i

] ∏j<k

|λj − λk |β

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Gaussian Random Matrices

• (N × N) Gaussian random matrix: J ≡ [Jij ]

• Ensembles: Orthogonal (GOE), Unitary (GUE) or Symplectic (GSE)

• Prob[Jij ] ∝ exp[−β2 N Tr

(J†J))]

• N real eigenvalues {λ1, λ2, . . . , λN} → correlated random variables

• Joint distribution of eigenvalues (Wigner, 1951)

P(λ1, λ2, . . . , λN) =1

ZNexp

[−β

2N

N∑i=1

λ2i

] ∏j<k

|λj − λk |β

where the Dyson index β = 1 (GOE), β = 2 (GUE) or β = 4 (GSE)

• ZN = Partition Function

=

∫ ∞−∞

. . .

∫ ∞−∞{∏i

dλi} exp

[−β

2N

N∑i=1

λ2i

] ∏j<k

|λj − λk |β

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Coulomb Gas Interpretation

• ZN=∫ ∞−∞

. . .

∫ ∞−∞{∏i

dλi} exp

−β2

N∑i=1

N λ2i −

∑j 6=k

log |λj − λk |

• 2-d Coulomb gas confined to a line (Dyson) with β → inverse temp.

�������� ����

���������������

���������������

����������������������������������������

λ1λ2 λ3 λ

Ν

λ

confining

parabolic

potential

0

• Balance of energy ⇒ N2 λ2 ∼ N2

• Typical eigenvalue: λtyp ∼ O(1) for large N

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Coulomb Gas Interpretation

• ZN=∫ ∞−∞

. . .

∫ ∞−∞{∏i

dλi} exp

−β2

N∑i=1

N λ2i −

∑j 6=k

log |λj − λk |

• 2-d Coulomb gas confined to a line (Dyson) with β → inverse temp.

�������� ����

���������������

���������������

����������������������������������������

λ1λ2 λ3 λ

Ν

λ

confining

parabolic

potential

0

• Balance of energy ⇒ N2 λ2 ∼ N2

• Typical eigenvalue: λtyp ∼ O(1) for large N

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Coulomb Gas Interpretation

• ZN=∫ ∞−∞

. . .

∫ ∞−∞{∏i

dλi} exp

−β2

N∑i=1

N λ2i −

∑j 6=k

log |λj − λk |

• 2-d Coulomb gas confined to a line (Dyson) with β → inverse temp.

�������� ����

���������������

���������������

����������������������������������������

λ1λ2 λ3 λ

Ν

λ

confining

parabolic

potential

0

• Balance of energy ⇒ N2 λ2 ∼ N2

• Typical eigenvalue: λtyp ∼ O(1) for large NS.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Spectral Density: Wigner’s Semicircle Law

• Av. density of states: ρ(λ,N) = 〈 1

N

N∑i=1

δ(λ− λi )〉

• Wigner’s Semi-circle: ρ(λ,N) −−−−→N→∞

ρ(λ) =1

π

√2− λ2

WIGNER SEMI−CIRCLE

0

SEA

ρ( λ)

− 2 2

λ

• 〈λmax〉 =√

2 for large N.

• λmax fluctuates from one sample to another. Prob[λmax,N] = ?

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Spectral Density: Wigner’s Semicircle Law

• Av. density of states: ρ(λ,N) = 〈 1

N

N∑i=1

δ(λ− λi )〉

• Wigner’s Semi-circle: ρ(λ,N) −−−−→N→∞

ρ(λ) =1

π

√2− λ2

WIGNER SEMI−CIRCLE

0

SEA

ρ( λ)

− 2 2

λ

• 〈λmax〉 =√

2 for large N.

• λmax fluctuates from one sample to another. Prob[λmax,N] = ?

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Spectral Density: Wigner’s Semicircle Law

• Av. density of states: ρ(λ,N) = 〈 1

N

N∑i=1

δ(λ− λi )〉

• Wigner’s Semi-circle: ρ(λ,N) −−−−→N→∞

ρ(λ) =1

π

√2− λ2

WIGNER SEMI−CIRCLE

0

SEA

ρ( λ)

− 2 2

λ

• 〈λmax〉 =√

2 for large N.

• λmax fluctuates from one sample to another. Prob[λmax,N] = ?S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Tracy-Widom distribution for λmax

λ0

TRACY−WIDOM

WIGNER SEMI−CIRCLE

SEA

− 2 2

N−2/3

ρ(λ ),ΝP(

2

1

0

)N,w

width of O (N−2/3)

finite but large N

= Prob.[ λmax

< w ]

cumulative distribution of λ max

Tracy−Widom

w

• 〈λmax〉 =√

2 ; typical fluctuation:∫∞λmax

ρ(λ) dλ ∼ 1/N

Using ρ(λ) ∼ (√

2− λ)1/2 ⇒ |λmax −√

2| ∼ N−2/3 → small

[Bowick & Brezin ’91, Forrester ’93]

• typical fluctuations are distributed via Tracy-Widom (’94):

• cumulative distribution: Prob[λmax ≤ w ,N]→ Fβ(√

2N2/3(w −

√2))

• Prob. density (pdf): fβ(x) = dFβ(x)/dx ; Fβ(x)→ Painleve-II

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Tracy-Widom distribution for λmax

λ0

TRACY−WIDOM

WIGNER SEMI−CIRCLE

SEA

− 2 2

N−2/3

ρ(λ ),ΝP(

2

1

0

)N,w

width of O (N−2/3)

finite but large N

= Prob.[ λmax

< w ]

cumulative distribution of λ max

Tracy−Widom

w

• 〈λmax〉 =√

2 ; typical fluctuation:∫∞λmax

ρ(λ) dλ ∼ 1/N

Using ρ(λ) ∼ (√

2− λ)1/2 ⇒ |λmax −√

2| ∼ N−2/3 → small

[Bowick & Brezin ’91, Forrester ’93]

• typical fluctuations are distributed via Tracy-Widom (’94):

• cumulative distribution: Prob[λmax ≤ w ,N]→ Fβ(√

2N2/3(w −

√2))

• Prob. density (pdf): fβ(x) = dFβ(x)/dx ; Fβ(x)→ Painleve-II

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Tracy-Widom distribution for λmax

λ0

TRACY−WIDOM

WIGNER SEMI−CIRCLE

SEA

− 2 2

N−2/3

ρ(λ ),ΝP(

2

1

0

)N,w

width of O (N−2/3)

finite but large N

= Prob.[ λmax

< w ]

cumulative distribution of λ max

Tracy−Widom

w

• 〈λmax〉 =√

2 ; typical fluctuation:∫∞λmax

ρ(λ) dλ ∼ 1/N

Using ρ(λ) ∼ (√

2− λ)1/2 ⇒ |λmax −√

2| ∼ N−2/3 → small

[Bowick & Brezin ’91, Forrester ’93]

• typical fluctuations are distributed via Tracy-Widom (’94):

• cumulative distribution: Prob[λmax ≤ w ,N]→ Fβ(√

2N2/3(w −

√2))

• Prob. density (pdf): fβ(x) = dFβ(x)/dx ; Fβ(x)→ Painleve-II

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Tracy-Widom distribution for λmax

λ0

TRACY−WIDOM

WIGNER SEMI−CIRCLE

SEA

− 2 2

N−2/3

ρ(λ ),ΝP(

2

1

0

)N,w

width of O (N−2/3)

finite but large N

= Prob.[ λmax

< w ]

cumulative distribution of λ max

Tracy−Widom

w

• 〈λmax〉 =√

2 ; typical fluctuation:∫∞λmax

ρ(λ) dλ ∼ 1/N

Using ρ(λ) ∼ (√

2− λ)1/2 ⇒ |λmax −√

2| ∼ N−2/3 → small

[Bowick & Brezin ’91, Forrester ’93]

• typical fluctuations are distributed via Tracy-Widom (’94):

• cumulative distribution: Prob[λmax ≤ w ,N]→ Fβ(√

2N2/3(w −

√2))

• Prob. density (pdf): fβ(x) = dFβ(x)/dx ; Fβ(x)→ Painleve-II

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Tracy-Widom Distribution for λmax

-4 -2 0 2x

0.1

0.2

0.3

0.4

0.5

Probability densities f(x)

β = 1

β = 2

β = 4

• Tracy-Widom density fβ(x) depends explicitly on β.

• Asymptotics: fβ(x) ∼ exp[− β

24 |x |3]

as x → −∞

∼ exp[− 2β

3 x3/2]

as x →∞

Applications: Growth models, Directed polymer, Sequence Matching .....(Baik, Borodin, Calabrese, Comtet, Corwin, Deift, Dotsenko, Dumitriu, Edelman, Ferrari,

Forrester, Johansson, Johnstone, Le doussal, Nadal, Nechaev, O’Connell, Peche, Prahofer,

Quastel, Rains, Rambeau, Rosso, Sano, Sasamoto, Schehr, Spohn, Takeuchi, Virag, ...)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Tracy-Widom Distribution for λmax

-4 -2 0 2x

0.1

0.2

0.3

0.4

0.5

Probability densities f(x)

β = 1

β = 2

β = 4

• Tracy-Widom density fβ(x) depends explicitly on β.

• Asymptotics: fβ(x) ∼ exp[− β

24 |x |3]

as x → −∞

∼ exp[− 2β

3 x3/2]

as x →∞

Applications: Growth models, Directed polymer, Sequence Matching .....(Baik, Borodin, Calabrese, Comtet, Corwin, Deift, Dotsenko, Dumitriu, Edelman, Ferrari,

Forrester, Johansson, Johnstone, Le doussal, Nadal, Nechaev, O’Connell, Peche, Prahofer,

Quastel, Rains, Rambeau, Rosso, Sano, Sasamoto, Schehr, Spohn, Takeuchi, Virag, ...)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Tracy-Widom Distribution for λmax

-4 -2 0 2x

0.1

0.2

0.3

0.4

0.5

Probability densities f(x)

β = 1

β = 2

β = 4

• Tracy-Widom density fβ(x) depends explicitly on β.

• Asymptotics: fβ(x) ∼ exp[− β

24 |x |3]

as x → −∞

∼ exp[− 2β

3 x3/2]

as x →∞

Applications: Growth models, Directed polymer, Sequence Matching .....(Baik, Borodin, Calabrese, Comtet, Corwin, Deift, Dotsenko, Dumitriu, Edelman, Ferrari,

Forrester, Johansson, Johnstone, Le doussal, Nadal, Nechaev, O’Connell, Peche, Prahofer,

Quastel, Rains, Rambeau, Rosso, Sano, Sasamoto, Schehr, Spohn, Takeuchi, Virag, ...)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Tracy-Widom Distribution for λmax

-4 -2 0 2x

0.1

0.2

0.3

0.4

0.5

Probability densities f(x)

β = 1

β = 2

β = 4

• Tracy-Widom density fβ(x) depends explicitly on β.

• Asymptotics: fβ(x) ∼ exp[− β

24 |x |3]

as x → −∞

∼ exp[− 2β

3 x3/2]

as x →∞

Applications: Growth models, Directed polymer, Sequence Matching .....(Baik, Borodin, Calabrese, Comtet, Corwin, Deift, Dotsenko, Dumitriu, Edelman, Ferrari,

Forrester, Johansson, Johnstone, Le doussal, Nadal, Nechaev, O’Connell, Peche, Prahofer,

Quastel, Rains, Rambeau, Rosso, Sano, Sasamoto, Schehr, Spohn, Takeuchi, Virag, ...)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Probability of Large Deviations of λmax:

ρ (λ, Ν)

λ0

TRACY−WIDOM

WIGNER SEMI−CIRCLE

N

LARGE DEVIATION

LEFT

RIGHT

−2/3

− 2 2

LARGE DEVIATION

• Tracy-Widom law Prob[λmax ≤ w ,N]→ Fβ(√

2N2/3 (w −√

2))

describes the prob. of typical (small) fluctuations of ∼ O(N−2/3)around the mean

√2, i.e., when |λmax −

√2| ∼ N−2/3

• Q: How to describe the prob. of large (atypical) fluctuations when

|λmax −√

2| ∼ O(1) → Large deviations from mean

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Probability of Large Deviations of λmax:

ρ (λ, Ν)

λ0

TRACY−WIDOM

WIGNER SEMI−CIRCLE

N

LARGE DEVIATION

LEFT

RIGHT

−2/3

− 2 2

LARGE DEVIATION

• Tracy-Widom law Prob[λmax ≤ w ,N]→ Fβ(√

2N2/3 (w −√

2))

describes the prob. of typical (small) fluctuations of ∼ O(N−2/3)around the mean

√2, i.e., when |λmax −

√2| ∼ N−2/3

• Q: How to describe the prob. of large (atypical) fluctuations when

|λmax −√

2| ∼ O(1) → Large deviations from mean

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Probability of Large Deviations of λmax:

ρ (λ, Ν)

λ0

TRACY−WIDOM

WIGNER SEMI−CIRCLE

N

LARGE DEVIATION

LEFT

RIGHT

−2/3

− 2 2

LARGE DEVIATION

• Tracy-Widom law Prob[λmax ≤ w ,N]→ Fβ(√

2N2/3 (w −√

2))

describes the prob. of typical (small) fluctuations of ∼ O(N−2/3)around the mean

√2, i.e., when |λmax −

√2| ∼ N−2/3

• Q: How to describe the prob. of large (atypical) fluctuations when

|λmax −√

2| ∼ O(1) → Large deviations from mean

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Large Deviation Tails of λmax

ρ (λ, Ν)

λ0

TRACY−WIDOM

WIGNER SEMI−CIRCLE

N

LARGE DEVIATION

LEFT

RIGHT

−2/3

− 2 2

LARGE DEVIATION

Prob. density of the top eigenvalue: Prob. [λmax = w ,N] behaves as:

∼ exp[−βN2Φ−(w)

]for

√2− w ∼ O(1)

∼ N2/3fβ[√

2N2/3(w −

√2)]

for |w −√

2| ∼ O(N−2/3)

∼ exp [−βNΦ+(w)] for w −√

2 ∼ O(1)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Large Deviation Tails of λmax

ρ (λ, Ν)

λ0

TRACY−WIDOM

WIGNER SEMI−CIRCLE

N

LARGE DEVIATION

LEFT

RIGHT

−2/3

− 2 2

LARGE DEVIATION

Prob. density of the top eigenvalue: Prob. [λmax = w ,N] behaves as:

∼ exp[−βN2Φ−(w)

]for

√2− w ∼ O(1)

∼ N2/3fβ[√

2N2/3(w −

√2)]

for |w −√

2| ∼ O(N−2/3)

∼ exp [−βNΦ+(w)] for w −√

2 ∼ O(1)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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IV. Saddle Point Method

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Distribution of λmax: Saddle Point Method

Prob[λmax ≤ w , N] = Prob[λ1 ≤ w , λ2 ≤ w , . . . , λN ≤ w ] =ZN(w)

ZN(∞)

ZN(w) =

∫ w

−∞. . .

∫ w

−∞{∏i

dλi} exp

−β2

NN∑i=1

λ2i −

∑j 6=k

log |λj − λk |

λ

denominator

WALL

numerator

λw

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Distribution of λmax: Saddle Point Method

Prob[λmax ≤ w , N] = Prob[λ1 ≤ w , λ2 ≤ w , . . . , λN ≤ w ] =ZN(w)

ZN(∞)

ZN(w) =

∫ w

−∞. . .

∫ w

−∞{∏i

dλi} exp

−β2

NN∑i=1

λ2i −

∑j 6=k

log |λj − λk |

λ

denominator

WALL

numerator

λw

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Distribution of λmax: Saddle Point Method

Prob[λmax ≤ w , N] = Prob[λ1 ≤ w , λ2 ≤ w , . . . , λN ≤ w ] =ZN(w)

ZN(∞)

ZN(w) =

∫ w

−∞. . .

∫ w

−∞{∏i

dλi} exp

−β2

NN∑i=1

λ2i −

∑j 6=k

log |λj − λk |

λ

denominator

WALL

numerator

λw

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Setting up the Saddle Point Method

• ZN(w) ∝∫ w

−∞

∏i

dλi exp[−βN2E ({λi})

]E ({λi}) =

1

2N

∑i

λ2i −

1

2N2

∑j 6=k

log |λj − λk |

• As N →∞ → discrete sum → continuous integral:

E [ρ(λ)] =1

2

[∫ w

−∞λ2 ρ(λ) dλ−

∫ w

−∞

∫ w

−∞ln |λ− λ′| ρ(λ) ρ(λ′) dλ dλ′

]where the charge density: ρ(λ) = 1

N

∑i δ(λ− λi )

ZN(w) ∝∫Dρ(λ) exp

[−β N2

{E [ρ(λ)] + C

(∫ρ(λ)dλ− 1

)}+ O(N)

]• for large N, minimize the action S [ρ(λ)] = E [ρ(λ)] + C [

∫ρ(λ)dλ− 1]

Saddle Point Method: δSδρ = 0⇒ ρw (λ)

⇒ ZN(w) ∼ exp[−βN2S [ρw (λ)]

]

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Setting up the Saddle Point Method

• ZN(w) ∝∫ w

−∞

∏i

dλi exp[−βN2E ({λi})

]E ({λi}) =

1

2N

∑i

λ2i −

1

2N2

∑j 6=k

log |λj − λk |

• As N →∞ → discrete sum → continuous integral:

E [ρ(λ)] =1

2

[∫ w

−∞λ2 ρ(λ) dλ−

∫ w

−∞

∫ w

−∞ln |λ− λ′| ρ(λ) ρ(λ′) dλ dλ′

]

where the charge density: ρ(λ) = 1N

∑i δ(λ− λi )

ZN(w) ∝∫Dρ(λ) exp

[−β N2

{E [ρ(λ)] + C

(∫ρ(λ)dλ− 1

)}+ O(N)

]• for large N, minimize the action S [ρ(λ)] = E [ρ(λ)] + C [

∫ρ(λ)dλ− 1]

Saddle Point Method: δSδρ = 0⇒ ρw (λ)

⇒ ZN(w) ∼ exp[−βN2S [ρw (λ)]

]

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Setting up the Saddle Point Method

• ZN(w) ∝∫ w

−∞

∏i

dλi exp[−βN2E ({λi})

]E ({λi}) =

1

2N

∑i

λ2i −

1

2N2

∑j 6=k

log |λj − λk |

• As N →∞ → discrete sum → continuous integral:

E [ρ(λ)] =1

2

[∫ w

−∞λ2 ρ(λ) dλ−

∫ w

−∞

∫ w

−∞ln |λ− λ′| ρ(λ) ρ(λ′) dλ dλ′

]where the charge density: ρ(λ) = 1

N

∑i δ(λ− λi )

ZN(w) ∝∫Dρ(λ) exp

[−β N2

{E [ρ(λ)] + C

(∫ρ(λ)dλ− 1

)}+ O(N)

]• for large N, minimize the action S [ρ(λ)] = E [ρ(λ)] + C [

∫ρ(λ)dλ− 1]

Saddle Point Method: δSδρ = 0⇒ ρw (λ)

⇒ ZN(w) ∼ exp[−βN2S [ρw (λ)]

]

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Setting up the Saddle Point Method

• ZN(w) ∝∫ w

−∞

∏i

dλi exp[−βN2E ({λi})

]E ({λi}) =

1

2N

∑i

λ2i −

1

2N2

∑j 6=k

log |λj − λk |

• As N →∞ → discrete sum → continuous integral:

E [ρ(λ)] =1

2

[∫ w

−∞λ2 ρ(λ) dλ−

∫ w

−∞

∫ w

−∞ln |λ− λ′| ρ(λ) ρ(λ′) dλ dλ′

]where the charge density: ρ(λ) = 1

N

∑i δ(λ− λi )

ZN(w) ∝∫Dρ(λ) exp

[−β N2

{E [ρ(λ)] + C

(∫ρ(λ)dλ− 1

)}+ O(N)

]

• for large N, minimize the action S [ρ(λ)] = E [ρ(λ)] + C [∫ρ(λ)dλ− 1]

Saddle Point Method: δSδρ = 0⇒ ρw (λ)

⇒ ZN(w) ∼ exp[−βN2S [ρw (λ)]

]

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Setting up the Saddle Point Method

• ZN(w) ∝∫ w

−∞

∏i

dλi exp[−βN2E ({λi})

]E ({λi}) =

1

2N

∑i

λ2i −

1

2N2

∑j 6=k

log |λj − λk |

• As N →∞ → discrete sum → continuous integral:

E [ρ(λ)] =1

2

[∫ w

−∞λ2 ρ(λ) dλ−

∫ w

−∞

∫ w

−∞ln |λ− λ′| ρ(λ) ρ(λ′) dλ dλ′

]where the charge density: ρ(λ) = 1

N

∑i δ(λ− λi )

ZN(w) ∝∫Dρ(λ) exp

[−β N2

{E [ρ(λ)] + C

(∫ρ(λ)dλ− 1

)}+ O(N)

]• for large N, minimize the action S [ρ(λ)] = E [ρ(λ)] + C [

∫ρ(λ)dλ− 1]

Saddle Point Method: δSδρ = 0⇒ ρw (λ)

⇒ ZN(w) ∼ exp[−βN2S [ρw (λ)]

]

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 90: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Setting up the Saddle Point Method

• ZN(w) ∝∫ w

−∞

∏i

dλi exp[−βN2E ({λi})

]E ({λi}) =

1

2N

∑i

λ2i −

1

2N2

∑j 6=k

log |λj − λk |

• As N →∞ → discrete sum → continuous integral:

E [ρ(λ)] =1

2

[∫ w

−∞λ2 ρ(λ) dλ−

∫ w

−∞

∫ w

−∞ln |λ− λ′| ρ(λ) ρ(λ′) dλ dλ′

]where the charge density: ρ(λ) = 1

N

∑i δ(λ− λi )

ZN(w) ∝∫Dρ(λ) exp

[−β N2

{E [ρ(λ)] + C

(∫ρ(λ)dλ− 1

)}+ O(N)

]• for large N, minimize the action S [ρ(λ)] = E [ρ(λ)] + C [

∫ρ(λ)dλ− 1]

Saddle Point Method: δSδρ = 0⇒ ρw (λ)

⇒ ZN(w) ∼ exp[−βN2S [ρw (λ)]

]S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Saddle Point Solution

• saddle point δSδf = 0⇒

λ2 − 2

∫ w

−∞ρw (λ′) ln |λ− λ′| dλ′ + C = 0

• Taking a derivative w.r.t. λ gives a singular integral Eq.

λ = P∫ w

−∞

ρw (λ′) dλ′

λ− λ′for λ ∈ [−∞,w ] → Semi-Hilbert transform

−→ force balance condition

• When w →∞: solution −→ Wigner semi-circle law

ρ∞(λ) = 1π

√2− λ2

Exact solution for all w :

[D. S. Dean & S.M., PRL, 97, 160201 (2006); PRE, 77, 041108 (2008)]

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Saddle Point Solution

• saddle point δSδf = 0⇒

λ2 − 2

∫ w

−∞ρw (λ′) ln |λ− λ′| dλ′ + C = 0

• Taking a derivative w.r.t. λ gives a singular integral Eq.

λ = P∫ w

−∞

ρw (λ′) dλ′

λ− λ′for λ ∈ [−∞,w ] → Semi-Hilbert transform

−→ force balance condition

• When w →∞: solution −→ Wigner semi-circle law

ρ∞(λ) = 1π

√2− λ2

Exact solution for all w :

[D. S. Dean & S.M., PRL, 97, 160201 (2006); PRE, 77, 041108 (2008)]

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Saddle Point Solution

• saddle point δSδf = 0⇒

λ2 − 2

∫ w

−∞ρw (λ′) ln |λ− λ′| dλ′ + C = 0

• Taking a derivative w.r.t. λ gives a singular integral Eq.

λ = P∫ w

−∞

ρw (λ′) dλ′

λ− λ′for λ ∈ [−∞,w ] → Semi-Hilbert transform

−→ force balance condition

• When w →∞: solution −→ Wigner semi-circle law

ρ∞(λ) = 1π

√2− λ2

Exact solution for all w :

[D. S. Dean & S.M., PRL, 97, 160201 (2006); PRE, 77, 041108 (2008)]

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Saddle Point Solution

• saddle point δSδf = 0⇒

λ2 − 2

∫ w

−∞ρw (λ′) ln |λ− λ′| dλ′ + C = 0

• Taking a derivative w.r.t. λ gives a singular integral Eq.

λ = P∫ w

−∞

ρw (λ′) dλ′

λ− λ′for λ ∈ [−∞,w ] → Semi-Hilbert transform

−→ force balance condition

• When w →∞: solution −→ Wigner semi-circle law

ρ∞(λ) = 1π

√2− λ2

Exact solution for all w :

[D. S. Dean & S.M., PRL, 97, 160201 (2006); PRE, 77, 041108 (2008)]

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Exact Saddle Point Solution

• Exact solution (D. Dean and S.M., 2006, 2008):

ρw (λ) =

√2− λ2 for w ≥

√2

√λ+L(w)

2π√w−λ [w + L(w)− 2λ] for w <

√2

where L(w) = [2√w2 + 6− w ]/3

����

��������

2 2−22−2

2 CRITICAL POINT

www

W=

W < 2 W= 2 W > 2

pushed critical unpushed(UNSTABLE) (STABLE)

ρw (λ) for different vs. Wλcharge density

L(w)− w

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Exact Saddle Point Solution

• Exact solution (D. Dean and S.M., 2006, 2008):

ρw (λ) =

√2− λ2 for w ≥

√2

√λ+L(w)

2π√w−λ [w + L(w)− 2λ] for w <

√2

where L(w) = [2√w2 + 6− w ]/3

����

��������

2 2−22−2

2 CRITICAL POINT

www

W=

W < 2 W= 2 W > 2

pushed critical unpushed(UNSTABLE) (STABLE)

ρw (λ) for different vs. Wλcharge density

L(w)− w

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Exact Saddle Point Solution

• Exact solution (D. Dean and S.M., 2006, 2008):

ρw (λ) =

√2− λ2 for w ≥

√2

√λ+L(w)

2π√w−λ [w + L(w)− 2λ] for w <

√2

where L(w) = [2√w2 + 6− w ]/3

����

��������

2 2−22−2

2 CRITICAL POINT

www

W=

W < 2 W= 2 W > 2

pushed critical unpushed(UNSTABLE) (STABLE)

ρw (λ) for different vs. Wλcharge density

L(w)− w

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Left Large Deviation Function

Prob[λmax ≤ w ,N] =ZN(w)

ZN(∞)∼ exp

[−βN2 {S [ρw (λ)]− S [ρ∞(λ)]}

]∼ exp

[−β N2 Φ−(w)

]

limN→∞

− 1

N2ln [P(w ,N)] = Φ−(w) → left large deviation function

physically Φ−(w) −→ energy cost in pushing the Coulomb gas

Φ−(w) =1

108

[36w2 − w4 − (15w + w3)

√w2 + 6

+ 27(

ln(18)− 2 ln(w +√

6 + w2))]

for w <√

2

(Dean & S.M., 2006,2008)

Note also that Φ−(w) ≈ 16√

2(√

2− w)3 as w →√

2 from below

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Left Large Deviation Function

Prob[λmax ≤ w ,N] =ZN(w)

ZN(∞)∼ exp

[−βN2 {S [ρw (λ)]− S [ρ∞(λ)]}

]∼ exp

[−β N2 Φ−(w)

]lim

N→∞− 1

N2ln [P(w ,N)] = Φ−(w) → left large deviation function

physically Φ−(w) −→ energy cost in pushing the Coulomb gas

Φ−(w) =1

108

[36w2 − w4 − (15w + w3)

√w2 + 6

+ 27(

ln(18)− 2 ln(w +√

6 + w2))]

for w <√

2

(Dean & S.M., 2006,2008)

Note also that Φ−(w) ≈ 16√

2(√

2− w)3 as w →√

2 from below

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Left Large Deviation Function

Prob[λmax ≤ w ,N] =ZN(w)

ZN(∞)∼ exp

[−βN2 {S [ρw (λ)]− S [ρ∞(λ)]}

]∼ exp

[−β N2 Φ−(w)

]lim

N→∞− 1

N2ln [P(w ,N)] = Φ−(w) → left large deviation function

physically Φ−(w) −→ energy cost in pushing the Coulomb gas

Φ−(w) =1

108

[36w2 − w4 − (15w + w3)

√w2 + 6

+ 27(

ln(18)− 2 ln(w +√

6 + w2))]

for w <√

2

(Dean & S.M., 2006,2008)

Note also that Φ−(w) ≈ 16√

2(√

2− w)3 as w →√

2 from below

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Left Large Deviation Function

Prob[λmax ≤ w ,N] =ZN(w)

ZN(∞)∼ exp

[−βN2 {S [ρw (λ)]− S [ρ∞(λ)]}

]∼ exp

[−β N2 Φ−(w)

]lim

N→∞− 1

N2ln [P(w ,N)] = Φ−(w) → left large deviation function

physically Φ−(w) −→ energy cost in pushing the Coulomb gas

Φ−(w) =1

108

[36w2 − w4 − (15w + w3)

√w2 + 6

+ 27(

ln(18)− 2 ln(w +√

6 + w2))]

for w <√

2

(Dean & S.M., 2006,2008)

Note also that Φ−(w) ≈ 16√

2(√

2− w)3 as w →√

2 from below

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Matching with the left tail of Tracy-Widom:

ρ (λ, Ν)

λ0

TRACY−WIDOM

WIGNER SEMI−CIRCLE

N

LARGE DEVIATION

LEFT

RIGHT

−2/3

− 2 2

LARGE DEVIATION

As w →√

2 from below, Φ−(w)→ (√

2−w)3

6√

2

→ matches with the left tail of the Tracy-Widom distribution

Prob.[λmax = w ,N] ∼ exp[−β N2 Φ−(w)

]∼ exp

[− β

24

∣∣√2N2/3 (w −√

2)∣∣3]

recovers the left tail of TW: fβ(x) ∼ exp[− β24 |x |

3] as x → −∞

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Matching with the left tail of Tracy-Widom:

ρ (λ, Ν)

λ0

TRACY−WIDOM

WIGNER SEMI−CIRCLE

N

LARGE DEVIATION

LEFT

RIGHT

−2/3

− 2 2

LARGE DEVIATION

As w →√

2 from below, Φ−(w)→ (√

2−w)3

6√

2

→ matches with the left tail of the Tracy-Widom distribution

Prob.[λmax = w ,N] ∼ exp[−β N2 Φ−(w)

]∼ exp

[− β

24

∣∣√2N2/3 (w −√

2)∣∣3]

recovers the left tail of TW: fβ(x) ∼ exp[− β24 |x |

3] as x → −∞

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Matching with the left tail of Tracy-Widom:

ρ (λ, Ν)

λ0

TRACY−WIDOM

WIGNER SEMI−CIRCLE

N

LARGE DEVIATION

LEFT

RIGHT

−2/3

− 2 2

LARGE DEVIATION

As w →√

2 from below, Φ−(w)→ (√

2−w)3

6√

2

→ matches with the left tail of the Tracy-Widom distribution

Prob.[λmax = w ,N] ∼ exp[−β N2 Φ−(w)

]∼ exp

[− β

24

∣∣√2N2/3 (w −√

2)∣∣3]

recovers the left tail of TW: fβ(x) ∼ exp[− β24 |x |

3] as x → −∞S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Right Large Deviation Function: w >√2

• For w ≥√

2, saddle point solution of the charge density ρw (λ) sticksto the semi-circle form: ρsc(λ) = 1

π

√2− λ2 for all w ≥

√2

⇒ Prob[λmax ≤ w ,N] = ZN (w)ZN (∞) ≈ 1 as N →∞

⇒ Need a different strategy

• Prob. density: p(w ,N) = ddW P(w ,N)

p(w ,N) ∝ e−βNw2/2∫ w

−∞ . . .∫ w

−∞ eβ∑N−1

j=1 ln |w−λj | PN−1 (λ1, λ2, . . . , λN−1)

−→ (N − 1)-fold integral

������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������

������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������

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����

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����

����

��������������������������������

������������������ λ max= w2 2−

WIGNER SEMI−CIRCLE

λ

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Right Large Deviation Function: w >√2

• For w ≥√

2, saddle point solution of the charge density ρw (λ) sticksto the semi-circle form: ρsc(λ) = 1

π

√2− λ2 for all w ≥

√2

⇒ Prob[λmax ≤ w ,N] = ZN (w)ZN (∞) ≈ 1 as N →∞

⇒ Need a different strategy

• Prob. density: p(w ,N) = ddW P(w ,N)

p(w ,N) ∝ e−βNw2/2∫ w

−∞ . . .∫ w

−∞ eβ∑N−1

j=1 ln |w−λj | PN−1 (λ1, λ2, . . . , λN−1)

−→ (N − 1)-fold integral

������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������

������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������

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��������������������������������

������������������ λ max= w2 2−

WIGNER SEMI−CIRCLE

λ

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Right Large Deviation Function: w >√2

• For w ≥√

2, saddle point solution of the charge density ρw (λ) sticksto the semi-circle form: ρsc(λ) = 1

π

√2− λ2 for all w ≥

√2

⇒ Prob[λmax ≤ w ,N] = ZN (w)ZN (∞) ≈ 1 as N →∞

⇒ Need a different strategy

• Prob. density: p(w ,N) = ddW P(w ,N)

p(w ,N) ∝ e−βNw2/2∫ w

−∞ . . .∫ w

−∞ eβ∑N−1

j=1 ln |w−λj | PN−1 (λ1, λ2, . . . , λN−1)

−→ (N − 1)-fold integral

������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������

������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������

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������������������ λ max= w2 2−

WIGNER SEMI−CIRCLE

λ

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Right Large Deviation Function: w >√2

• For w ≥√

2, saddle point solution of the charge density ρw (λ) sticksto the semi-circle form: ρsc(λ) = 1

π

√2− λ2 for all w ≥

√2

⇒ Prob[λmax ≤ w ,N] = ZN (w)ZN (∞) ≈ 1 as N →∞

⇒ Need a different strategy

• Prob. density: p(w ,N) = ddW P(w ,N)

p(w ,N) ∝ e−βNw2/2∫ w

−∞ . . .∫ w

−∞ eβ∑N−1

j=1 ln |w−λj | PN−1 (λ1, λ2, . . . , λN−1)

−→ (N − 1)-fold integral

������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������

������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������

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WIGNER SEMI−CIRCLE

λ

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Right Large Deviation Function: w >√2

• For w ≥√

2, saddle point solution of the charge density ρw (λ) sticksto the semi-circle form: ρsc(λ) = 1

π

√2− λ2 for all w ≥

√2

⇒ Prob[λmax ≤ w ,N] = ZN (w)ZN (∞) ≈ 1 as N →∞

⇒ Need a different strategy

• Prob. density: p(w ,N) = ddW P(w ,N)

p(w ,N) ∝ e−βNw2/2∫ w

−∞ . . .∫ w

−∞ eβ∑N−1

j=1 ln |w−λj | PN−1 (λ1, λ2, . . . , λN−1)

−→ (N − 1)-fold integral

������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������

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WIGNER SEMI−CIRCLE

λ

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Pulled Coulomb gas

p(w ,N) ∝ e−βNw2/2⟨eβ

∑j ln |w−λj |

Large N limit: p(w ,N) ∝ exp[−βN w2

2 + βN∫

ln(w − λ) ρsc(λ) dλ]

∼ exp[−β N Φ+(w)]

������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������

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WIGNER SEMI−CIRCLE

λ

N Φ+(w) = ∆E (w)

= w2

2 −∫√2

−√

2ln(w − λ) ρsc(λ) dλ

⇒ energy cost in pulling a charge out ofthe Wigner sea

⇒ Φ+(w) =1

2w√

w2 − 2 + ln

[w −

√w2 − 2√2

](w >

√2)

[S.M. & Vergassola, PRL, 102, 160201 (2009)]

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Pulled Coulomb gas

p(w ,N) ∝ e−βNw2/2⟨eβ

∑j ln |w−λj |

⟩Large N limit: p(w ,N) ∝ exp

[−βN w2

2 + βN∫

ln(w − λ) ρsc(λ) dλ]

∼ exp[−β N Φ+(w)]

������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������

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WIGNER SEMI−CIRCLE

λ

N Φ+(w) = ∆E (w)

= w2

2 −∫√2

−√

2ln(w − λ) ρsc(λ) dλ

⇒ energy cost in pulling a charge out ofthe Wigner sea

⇒ Φ+(w) =1

2w√

w2 − 2 + ln

[w −

√w2 − 2√2

](w >

√2)

[S.M. & Vergassola, PRL, 102, 160201 (2009)]

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Pulled Coulomb gas

p(w ,N) ∝ e−βNw2/2⟨eβ

∑j ln |w−λj |

⟩Large N limit: p(w ,N) ∝ exp

[−βN w2

2 + βN∫

ln(w − λ) ρsc(λ) dλ]

∼ exp[−β N Φ+(w)]

������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������

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WIGNER SEMI−CIRCLE

λ

N Φ+(w) = ∆E (w)

= w2

2 −∫√2

−√

2ln(w − λ) ρsc(λ) dλ

⇒ energy cost in pulling a charge out ofthe Wigner sea

⇒ Φ+(w) =1

2w√w2 − 2 + ln

[w −

√w2 − 2√2

](w >

√2)

[S.M. & Vergassola, PRL, 102, 160201 (2009)]

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Pulled Coulomb gas

p(w ,N) ∝ e−βNw2/2⟨eβ

∑j ln |w−λj |

⟩Large N limit: p(w ,N) ∝ exp

[−βN w2

2 + βN∫

ln(w − λ) ρsc(λ) dλ]

∼ exp[−β N Φ+(w)]

������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������

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������������������ λ max= w2 2−

WIGNER SEMI−CIRCLE

λ

N Φ+(w) = ∆E (w)

= w2

2 −∫√2

−√

2ln(w − λ) ρsc(λ) dλ

⇒ energy cost in pulling a charge out ofthe Wigner sea

⇒ Φ+(w) =1

2w√w2 − 2 + ln

[w −

√w2 − 2√2

](w >

√2)

[S.M. & Vergassola, PRL, 102, 160201 (2009)]S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Matching with the right tail of Tracy-Widom

ρ (λ, Ν)

λ0

TRACY−WIDOM

WIGNER SEMI−CIRCLE

N

LARGE DEVIATION

LEFT

RIGHT

−2/3

− 2 2

LARGE DEVIATION

As w →√

2 from above, Φ+(w)→ 27/4

3 (w −√

2)3/2

→ matches with the right tail of the Tracy-Widom distribution

Prob.[λmax = w ,N] ∼ exp [−β N Φ+(w)]

∼ exp[− 2β

3

∣∣√2N2/3 (w −√

2)∣∣3/2]

⇒ recovers the right tail of TW: fβ(x) ∼ exp[− 2β3 |x |

3/2] as x →∞

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Matching with the right tail of Tracy-Widom

ρ (λ, Ν)

λ0

TRACY−WIDOM

WIGNER SEMI−CIRCLE

N

LARGE DEVIATION

LEFT

RIGHT

−2/3

− 2 2

LARGE DEVIATION

As w →√

2 from above, Φ+(w)→ 27/4

3 (w −√

2)3/2

→ matches with the right tail of the Tracy-Widom distribution

Prob.[λmax = w ,N] ∼ exp [−β N Φ+(w)]

∼ exp[− 2β

3

∣∣√2N2/3 (w −√

2)∣∣3/2]

⇒ recovers the right tail of TW: fβ(x) ∼ exp[− 2β3 |x |

3/2] as x →∞

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Matching with the right tail of Tracy-Widom

ρ (λ, Ν)

λ0

TRACY−WIDOM

WIGNER SEMI−CIRCLE

N

LARGE DEVIATION

LEFT

RIGHT

−2/3

− 2 2

LARGE DEVIATION

As w →√

2 from above, Φ+(w)→ 27/4

3 (w −√

2)3/2

→ matches with the right tail of the Tracy-Widom distribution

Prob.[λmax = w ,N] ∼ exp [−β N Φ+(w)]

∼ exp[− 2β

3

∣∣√2N2/3 (w −√

2)∣∣3/2]

⇒ recovers the right tail of TW: fβ(x) ∼ exp[− 2β3 |x |

3/2] as x →∞S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Comparison with Simulations:

−2 −1 0 1 2 3

y=N−1/2

[t−(2N)1/2

]

0

20

40

60

80

−ln

(P(t

))

N × N real Gaussian matrix (β = 1): N = 10

squares → simulation points

red line → Tracy-Widom

blue line → left large deviation function (×N2)

green line → right large deviation function (×N).S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Summary and Generalizations

ρ (λ, Ν)

λ0

TRACY−WIDOM

WIGNER SEMI−CIRCLE

N

LARGE DEVIATION

LEFT

RIGHT

−2/3

− 2 2

LARGE DEVIATION

Prob. density of the top eigenvalue: Prob. [λmax = w ,N] behaves as:

∼ exp[−βN2Φ−(w)

]for

√2− w ∼ O(1)

∼ N2/3fβ[√

2N2/3(w −

√2)]

for |w −√

2| ∼ O(N−2/3)

∼ exp [−βNΦ+(w)] for w −√

2 ∼ O(1)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Summary and Generalizations

ρ (λ, Ν)

λ0

TRACY−WIDOM

WIGNER SEMI−CIRCLE

N

LARGE DEVIATION

LEFT

RIGHT

−2/3

− 2 2

LARGE DEVIATION

Prob. density of the top eigenvalue: Prob. [λmax = w ,N] behaves as:

∼ exp[−βN2Φ−(w)

]for

√2− w ∼ O(1)

∼ N2/3fβ[√

2N2/3(w −

√2)]

for |w −√

2| ∼ O(N−2/3)

∼ exp [−βNΦ+(w)] for w −√

2 ∼ O(1)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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3-rd Order Phase Transition

Cumulative prob. of λmax:

P (λmax ≤ w ,N) ≈

exp{−βN2Φ−(w) + . . .

}for w <

√2

1− A exp {−βNΦ+(w) + . . .} for w >√

2

limN→∞

− 1

βN2ln [P (λmax ≤ w)] =

Φ−(w) ∼

(√2− w

)3as w →

√2−

0 as w →√

2+

3-rd derivative → discontinuous

• Left −→ strong-coupling phase → perturbativehigher order corrections (1/N expansion) to free energy

[Borot, Eynard, S.M., & Nadal 2011]

• Right −→ weak-coupling phase → non-perturbativehigher order corrrections [Nadal & S.M. 2011, Borot & Nadal, 2012]

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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3-rd Order Phase Transition

Cumulative prob. of λmax:

P (λmax ≤ w ,N) ≈

exp{−βN2Φ−(w) + . . .

}for w <

√2

1− A exp {−βNΦ+(w) + . . .} for w >√

2

limN→∞

− 1

βN2ln [P (λmax ≤ w)] =

Φ−(w) ∼

(√2− w

)3as w →

√2−

0 as w →√

2+

3-rd derivative → discontinuous

• Left −→ strong-coupling phase → perturbativehigher order corrections (1/N expansion) to free energy

[Borot, Eynard, S.M., & Nadal 2011]

• Right −→ weak-coupling phase → non-perturbativehigher order corrrections [Nadal & S.M. 2011, Borot & Nadal, 2012]

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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3-rd Order Phase Transition

Cumulative prob. of λmax:

P (λmax ≤ w ,N) ≈

exp{−βN2Φ−(w) + . . .

}for w <

√2

1− A exp {−βNΦ+(w) + . . .} for w >√

2

limN→∞

− 1

βN2ln [P (λmax ≤ w)] =

Φ−(w) ∼

(√2− w

)3as w →

√2−

0 as w →√

2+

3-rd derivative → discontinuous

• Left −→ strong-coupling phase → perturbativehigher order corrections (1/N expansion) to free energy

[Borot, Eynard, S.M., & Nadal 2011]

• Right −→ weak-coupling phase → non-perturbativehigher order corrrections [Nadal & S.M. 2011, Borot & Nadal, 2012]

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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3-rd order transition → ubiquitous

• λmax for other matrix ensembles: Wishart: W = X †X → (N × N)→ covariance matrix

Typical: Tracy-Widom [Johansson 2000, Johnstone 2001]

Large deviations: Exact rate functions[Vivo, S.M., Bohigas 2007, S.M. & Vergassola 2009]

• large N gauge theory in 2-d [Gross, Witten, Wadia ’80, Douglas & Kazakov ’93]

• Distribution of MIMO capacity [Kazakopoulos et. al. 2010]

• Complexity in spin glass models [Fyodorov & Nadal 2013]

• Conductance and Shot Noise in Mesoscopic Cavities

• Entanglement entropy of a random pure state in a bipartite system

• Maximum displacement in Vicious walker problem

• Distribution of Wigner time-delay . . .

[ Bohigas, Comtet, Forrester, Nadal, Schehr, Texier, Vergassola, Vivo,..+S.M. (2008-2013) ]

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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3-rd order transition → ubiquitous

• λmax for other matrix ensembles: Wishart: W = X †X → (N × N)→ covariance matrix

Typical: Tracy-Widom [Johansson 2000, Johnstone 2001]

Large deviations: Exact rate functions[Vivo, S.M., Bohigas 2007, S.M. & Vergassola 2009]

• large N gauge theory in 2-d [Gross, Witten, Wadia ’80, Douglas & Kazakov ’93]

• Distribution of MIMO capacity [Kazakopoulos et. al. 2010]

• Complexity in spin glass models [Fyodorov & Nadal 2013]

• Conductance and Shot Noise in Mesoscopic Cavities

• Entanglement entropy of a random pure state in a bipartite system

• Maximum displacement in Vicious walker problem

• Distribution of Wigner time-delay . . .

[ Bohigas, Comtet, Forrester, Nadal, Schehr, Texier, Vergassola, Vivo,..+S.M. (2008-2013) ]

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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3-rd order transition → ubiquitous

• λmax for other matrix ensembles: Wishart: W = X †X → (N × N)→ covariance matrix

Typical: Tracy-Widom [Johansson 2000, Johnstone 2001]

Large deviations: Exact rate functions[Vivo, S.M., Bohigas 2007, S.M. & Vergassola 2009]

• large N gauge theory in 2-d [Gross, Witten, Wadia ’80, Douglas & Kazakov ’93]

• Distribution of MIMO capacity [Kazakopoulos et. al. 2010]

• Complexity in spin glass models [Fyodorov & Nadal 2013]

• Conductance and Shot Noise in Mesoscopic Cavities

• Entanglement entropy of a random pure state in a bipartite system

• Maximum displacement in Vicious walker problem

• Distribution of Wigner time-delay . . .

[ Bohigas, Comtet, Forrester, Nadal, Schehr, Texier, Vergassola, Vivo,..+S.M. (2008-2013) ]

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Basic mechanism for 3-rd order transition

www

gap

strong critical weak

Gap between the soft edge (square-root signularity) of the Coulombdroplet and the hard wall vanishes as a control parameter g goes througha critical value gc :

gap −→ 0 as g → gc

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Experimental Verification with Coupled Lasers

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Experimental Verification with Coupled Lasers

combined output power from fiber lasers ∝ λmax

λmax → top eigenvalue of the Wishart matrix W = X tX

where X → real symmetric Gaussian matrix (β = 1)S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Experimental Verification with Coupled Lasers

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Experimental Verification with coupled lasers

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Tracy-Widom density with β = 1

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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CollaboratorsStudents: C. Nadal (Oxford Univ., UK)

J. Randon-Furling (Univ. Paris-1, France)

R. Marino (LPTMS, Orsay)

Postdoc: D. Villamaina (ENS, Paris, France)

Collaborators:

• O. Bohigas, A. Comtet, G. Schehr, C. Texier, P. Vivo (LPTMS, Orsay, France)

• R. Allez (Paris-Dauphin, France)

• G. Borot (Geneva, Switzerland)

• J.-P. Bouchaud (CFM, Ecole Polytechnique, Paris, France)

• B. Eynard (Saclay, France)

• K. Damle, V. Tripathi (Tata Institute, Bombay, India)

• D.S. Dean (Bordeaux, France)

• P. J. Forrester (Melbourne, Australia)

• A. Lakshminarayan (IIT Madras, India)

• A. Scardichio (ICTP, Trieste, Italy)

• S. Tomsovic (Washington State Univ., USA)

• M. Vergassola (Institut Pasteur, Paris, France)S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Selected References;

• D.S. Dean, S.N. Majumdar, “ Large Deviations of Extreme Eigenvalues of Random Matrices”,Phys. Rev. Lett., 97, 160201 (2006); Phys. Rev. E., 77, 041108 (2008)

• S.N. Majumdar, M. Vergassola, “Large Deviations of the Maximum Eigenvalue for Wishart andGaussian Random Matrices”, Phys. Rev. Lett. 102, 060601 (2009)

• P. Vivo, S.N. Majumdar, O. Bohigas, “Distributions of Conductance and Shot Noise andassociated Phase Transitions”, Phys. Rev. Lett. 101, 216809 (2008), Phys. Rev. B 81, 104202(2010)

• C. Nadal, S.N. Majumdar, M. Vergassola “ Phase Transitions in the Distribution of BipartiteEntanglement of a Random Pure State”, Phys. Rev. Lett. 104, 110501 (2010); J. Stat. Phys.142, 403 (2011)

• S.N. Majumdar, C. Nadal, A. Scardichio, P. Vivo “ The Index Distribution of Gaussian RandomMatrices”, Phys. Rev. Lett. 103, 220603 (2009); Phys. Rev. E 83, 041105 (2011)

• G. Schehr, S.N. Majumdar, A. Comtet, J. Randon-Furling, “Exact distribution of the maximalheight of p vicious walkers”, Phys. Rev. Lett. 101, 150601 (2008)

• P.J. Forrester, S.N. Majumdar, G. Schehr, “Non-intersecting Brownian walkers and Yang-Millstheory on the sphere”, Nucl. Phys. B., 844, 500 (2011)

• K. Damle, S.N. Majumdar, V. Tripathi, P. Vivo, “Phase Transitions in the Distribution of theAndreev Conductance of Superconductor-Metal Junctions with Many Transverse Modes”, Phys.Rev. Lett. 107, 177206 (2011)

• C. Nadal and S.N. Majumdar, “A simple derivation of the Tracy-Widom distribution of themaximal eigenvalue of a Gaussian unitary random matrix”, J. Stat. Mech., P04001 (2011)

• G. Borot, B. Eynard, S.N. Majumdar and C. Nadal, “Large Deviations of the MaximalEigenvalue of Random Matrices”, J. Stat. Mech., P11024 (2011)

• C. Texier and S. N. Majumdar, “ Wigner time-delay distribution in chaotic cavities and freezingtransition”, Phys. Rev. Lett. 110, 250602 (2013).

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Recent review

Recent review: S.M. & G. Schehr, arXiv: 1311.0580

J. Stat. Mech. P01012 (2014)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Tracy-Widom distributions (1994)

The scaling function Fβ(x) has the expression:

• β = 1: F1(x) = exp[− 1

2

∫∞x

[(y − x)q2(y) + q(y)

]dy]

• β = 2: F2(x) = exp[−∫∞x

(y − x)q2(y) dy]

• β = 4: F4(x) = exp[− 1

2

∫∞x

(y − x)q2(y) dy]

cosh[

12

∫∞x

q(y) dy]

d2qdy2 = 2 q3(y) + y q(y) with q(y)→ Ai(y) as y →∞ → Painleve-II

• Probability density: fβ(x) = dFβ(x)/dx

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Tracy-Widom distributions (1994)

The scaling function Fβ(x) has the expression:

• β = 1: F1(x) = exp[− 1

2

∫∞x

[(y − x)q2(y) + q(y)

]dy]

• β = 2: F2(x) = exp[−∫∞x

(y − x)q2(y) dy]

• β = 4: F4(x) = exp[− 1

2

∫∞x

(y − x)q2(y) dy]

cosh[

12

∫∞x

q(y) dy]

d2qdy2 = 2 q3(y) + y q(y) with q(y)→ Ai(y) as y →∞ → Painleve-II

• Probability density: fβ(x) = dFβ(x)/dx

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Left Large Deviation: Beyond the Leading Order

• On the left side: λmax <√

2

Adapting ‘loop (Pastur) equations’ approach developed by Chekov,Eynard and collaborators:

−ln[Prob(λmax = w ,N)] = β Φ−(w)N2 + (β − 2)Φ1(w)N +

+ φβ lnN + Φ2(β,w) + O(1/N)

where explicit expressions for Φ1(w), φβ and Φ2(β,w) were obtainedrecently (Borot, Eynard, S.M., & Nadal, JSTAT, P11024 (2011))

• Setting w =√

2 + 2−1/2 N−2/3 x (with x < 0) gives the left tail(x → −∞) estimate of the TW density for all β

Prob.[λmax <

√2 + 2−1/2 N−2/3 x

]→ τβ |x |(β

2+4−6β)/2β exp[−β |x|

3

24 +√

2(β−2)6 |x |3/2

]where the constant τβ is →

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Left Large Deviation: Beyond the Leading Order

• On the left side: λmax <√

2

Adapting ‘loop (Pastur) equations’ approach developed by Chekov,Eynard and collaborators:

−ln[Prob(λmax = w ,N)] = β Φ−(w)N2 + (β − 2)Φ1(w)N +

+ φβ lnN + Φ2(β,w) + O(1/N)

where explicit expressions for Φ1(w), φβ and Φ2(β,w) were obtainedrecently (Borot, Eynard, S.M., & Nadal, JSTAT, P11024 (2011))

• Setting w =√

2 + 2−1/2 N−2/3 x (with x < 0) gives the left tail(x → −∞) estimate of the TW density for all β

Prob.[λmax <

√2 + 2−1/2 N−2/3 x

]→ τβ |x |(β

2+4−6β)/2β exp[−β |x|

3

24 +√

2(β−2)6 |x |3/2

]where the constant τβ is →

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Left Large Deviation: Beyond the Leading Order

• On the left side: λmax <√

2

Adapting ‘loop (Pastur) equations’ approach developed by Chekov,Eynard and collaborators:

−ln[Prob(λmax = w ,N)] = β Φ−(w)N2 + (β − 2)Φ1(w)N +

+ φβ lnN + Φ2(β,w) + O(1/N)

where explicit expressions for Φ1(w), φβ and Φ2(β,w) were obtainedrecently (Borot, Eynard, S.M., & Nadal, JSTAT, P11024 (2011))

• Setting w =√

2 + 2−1/2 N−2/3 x (with x < 0) gives the left tail(x → −∞) estimate of the TW density for all β

Prob.[λmax <

√2 + 2−1/2 N−2/3 x

]→ τβ |x |(β

2+4−6β)/2β exp[−β |x|

3

24 +√

2(β−2)6 |x |3/2

]where the constant τβ is →

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Left Large Deviation: Beyond the Leading Order

• On the left side: λmax <√

2

Adapting ‘loop (Pastur) equations’ approach developed by Chekov,Eynard and collaborators:

−ln[Prob(λmax = w ,N)] = β Φ−(w)N2 + (β − 2)Φ1(w)N +

+ φβ lnN + Φ2(β,w) + O(1/N)

where explicit expressions for Φ1(w), φβ and Φ2(β,w) were obtainedrecently (Borot, Eynard, S.M., & Nadal, JSTAT, P11024 (2011))

• Setting w =√

2 + 2−1/2 N−2/3 x (with x < 0) gives the left tail(x → −∞) estimate of the TW density for all β

Prob.[λmax <

√2 + 2−1/2 N−2/3 x

]→ τβ |x |(β

2+4−6β)/2β exp[−β |x|

3

24 +√

2(β−2)6 |x |3/2

]where the constant τβ is →

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Left Large Deviation: Beyond the Leading Order

• On the left side: λmax <√

2

Adapting ‘loop (Pastur) equations’ approach developed by Chekov,Eynard and collaborators:

−ln[Prob(λmax = w ,N)] = β Φ−(w)N2 + (β − 2)Φ1(w)N +

+ φβ lnN + Φ2(β,w) + O(1/N)

where explicit expressions for Φ1(w), φβ and Φ2(β,w) were obtainedrecently (Borot, Eynard, S.M., & Nadal, JSTAT, P11024 (2011))

• Setting w =√

2 + 2−1/2 N−2/3 x (with x < 0) gives the left tail(x → −∞) estimate of the TW density for all β

Prob.[λmax <

√2 + 2−1/2 N−2/3 x

]→ τβ |x |(β

2+4−6β)/2β exp[−β |x|

3

24 +√

2(β−2)6 |x |3/2

]

where the constant τβ is →

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Left Large Deviation: Beyond the Leading Order

• On the left side: λmax <√

2

Adapting ‘loop (Pastur) equations’ approach developed by Chekov,Eynard and collaborators:

−ln[Prob(λmax = w ,N)] = β Φ−(w)N2 + (β − 2)Φ1(w)N +

+ φβ lnN + Φ2(β,w) + O(1/N)

where explicit expressions for Φ1(w), φβ and Φ2(β,w) were obtainedrecently (Borot, Eynard, S.M., & Nadal, JSTAT, P11024 (2011))

• Setting w =√

2 + 2−1/2 N−2/3 x (with x < 0) gives the left tail(x → −∞) estimate of the TW density for all β

Prob.[λmax <

√2 + 2−1/2 N−2/3 x

]→ τβ |x |(β

2+4−6β)/2β exp[−β |x|

3

24 +√

2(β−2)6 |x |3/2

]where the constant τβ is →

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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The constant τβ

ln τβ =

(17

8− 25

24

(β2 + 4

))ln(2)− 1

4ln

(πβ2

2

)+

2

(1

12− ζ ′(−1)

)+γE6β

+

+

∫ ∞0

dx

[6 x coth(x/2)− 12− x2

12x2(eβ x/2 − 1)

](Borot, Eynard, S.M., & Nadal, 2011)

For β = 1, 2 and 4

→ agrees with Baik, Buckingham and DiFranco (2008)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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The constant τβ

ln τβ =

(17

8− 25

24

(β2 + 4

))ln(2)− 1

4ln

(πβ2

2

)+

2

(1

12− ζ ′(−1)

)+γE6β

+

+

∫ ∞0

dx

[6 x coth(x/2)− 12− x2

12x2(eβ x/2 − 1)

](Borot, Eynard, S.M., & Nadal, 2011)

For β = 1, 2 and 4

→ agrees with Baik, Buckingham and DiFranco (2008)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Right Large Deviation: Beyond the Leading Order

• On the right side: λmax >√

2

Using an ‘orthogonal polynomial’ (with an upper cut-off) method (forβ = 2) and adapting the techniques used by Gross and Matytsin, ’94 inthe context of two-dimensional Yang-Mills theory

Prob(λmax = w ,N) ≈ 1

2π√

2

e−2 N Φ+(w)

(w2 − 2)

where Φ+(w) = 12w√w2 − 2 + ln

[w−√w2−2√2

](C. Nadal and S.M., JSTAT, P04001, 2011)

Close to w →√

2+

, this gives

Prob.[λmax <

√2 + 2−1/2 N−2/3 x

]→ − 1

16π x3/2 e−(4/3) x3/2

→ precise asymptotics of the right tail of TW for β = 2 (Baik, 2006)

• For general β, precise right tail of TW → obtained recently(Dumaz and Virag, 2011)

• As a bonus, our method also provides a ‘simpler’ derivation of TWdistribution for β = 2 (Nadal and S.M., 2011)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Right Large Deviation: Beyond the Leading Order

• On the right side: λmax >√

2

Using an ‘orthogonal polynomial’ (with an upper cut-off) method (forβ = 2) and adapting the techniques used by Gross and Matytsin, ’94 inthe context of two-dimensional Yang-Mills theory

Prob(λmax = w ,N) ≈ 1

2π√

2

e−2 N Φ+(w)

(w2 − 2)

where Φ+(w) = 12w√w2 − 2 + ln

[w−√w2−2√2

](C. Nadal and S.M., JSTAT, P04001, 2011)

Close to w →√

2+

, this gives

Prob.[λmax <

√2 + 2−1/2 N−2/3 x

]→ − 1

16π x3/2 e−(4/3) x3/2

→ precise asymptotics of the right tail of TW for β = 2 (Baik, 2006)

• For general β, precise right tail of TW → obtained recently(Dumaz and Virag, 2011)

• As a bonus, our method also provides a ‘simpler’ derivation of TWdistribution for β = 2 (Nadal and S.M., 2011)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

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Right Large Deviation: Beyond the Leading Order

• On the right side: λmax >√

2

Using an ‘orthogonal polynomial’ (with an upper cut-off) method (forβ = 2) and adapting the techniques used by Gross and Matytsin, ’94 inthe context of two-dimensional Yang-Mills theory

Prob(λmax = w ,N) ≈ 1

2π√

2

e−2 N Φ+(w)

(w2 − 2)

where Φ+(w) = 12w√w2 − 2 + ln

[w−√w2−2√2

](C. Nadal and S.M., JSTAT, P04001, 2011)

Close to w →√

2+

, this gives

Prob.[λmax <

√2 + 2−1/2 N−2/3 x

]→ − 1

16π x3/2 e−(4/3) x3/2

→ precise asymptotics of the right tail of TW for β = 2 (Baik, 2006)

• For general β, precise right tail of TW → obtained recently(Dumaz and Virag, 2011)

• As a bonus, our method also provides a ‘simpler’ derivation of TWdistribution for β = 2 (Nadal and S.M., 2011)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 148: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Right Large Deviation: Beyond the Leading Order

• On the right side: λmax >√

2

Using an ‘orthogonal polynomial’ (with an upper cut-off) method (forβ = 2) and adapting the techniques used by Gross and Matytsin, ’94 inthe context of two-dimensional Yang-Mills theory

Prob(λmax = w ,N) ≈ 1

2π√

2

e−2 N Φ+(w)

(w2 − 2)

where Φ+(w) = 12w√w2 − 2 + ln

[w−√w2−2√2

](C. Nadal and S.M., JSTAT, P04001, 2011)

Close to w →√

2+

, this gives

Prob.[λmax <

√2 + 2−1/2 N−2/3 x

]→ − 1

16π x3/2 e−(4/3) x3/2

→ precise asymptotics of the right tail of TW for β = 2 (Baik, 2006)

• For general β, precise right tail of TW → obtained recently(Dumaz and Virag, 2011)

• As a bonus, our method also provides a ‘simpler’ derivation of TWdistribution for β = 2 (Nadal and S.M., 2011)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 149: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Right Large Deviation: Beyond the Leading Order

• On the right side: λmax >√

2

Using an ‘orthogonal polynomial’ (with an upper cut-off) method (forβ = 2) and adapting the techniques used by Gross and Matytsin, ’94 inthe context of two-dimensional Yang-Mills theory

Prob(λmax = w ,N) ≈ 1

2π√

2

e−2 N Φ+(w)

(w2 − 2)

where Φ+(w) = 12w√w2 − 2 + ln

[w−√w2−2√2

](C. Nadal and S.M., JSTAT, P04001, 2011)

Close to w →√

2+

, this gives

Prob.[λmax <

√2 + 2−1/2 N−2/3 x

]→ − 1

16π x3/2 e−(4/3) x3/2

→ precise asymptotics of the right tail of TW for β = 2 (Baik, 2006)

• For general β, precise right tail of TW → obtained recently(Dumaz and Virag, 2011)

• As a bonus, our method also provides a ‘simpler’ derivation of TWdistribution for β = 2 (Nadal and S.M., 2011)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 150: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Right Large Deviation: Beyond the Leading Order

• On the right side: λmax >√

2

Using an ‘orthogonal polynomial’ (with an upper cut-off) method (forβ = 2) and adapting the techniques used by Gross and Matytsin, ’94 inthe context of two-dimensional Yang-Mills theory

Prob(λmax = w ,N) ≈ 1

2π√

2

e−2 N Φ+(w)

(w2 − 2)

where Φ+(w) = 12w√w2 − 2 + ln

[w−√w2−2√2

](C. Nadal and S.M., JSTAT, P04001, 2011)

Close to w →√

2+

, this gives

Prob.[λmax <

√2 + 2−1/2 N−2/3 x

]→ − 1

16π x3/2 e−(4/3) x3/2

→ precise asymptotics of the right tail of TW for β = 2 (Baik, 2006)

• For general β, precise right tail of TW → obtained recently(Dumaz and Virag, 2011)

• As a bonus, our method also provides a ‘simpler’ derivation of TWdistribution for β = 2 (Nadal and S.M., 2011)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 151: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Right Large Deviation: Beyond the Leading Order

• On the right side: λmax >√

2

Using an ‘orthogonal polynomial’ (with an upper cut-off) method (forβ = 2) and adapting the techniques used by Gross and Matytsin, ’94 inthe context of two-dimensional Yang-Mills theory

Prob(λmax = w ,N) ≈ 1

2π√

2

e−2 N Φ+(w)

(w2 − 2)

where Φ+(w) = 12w√w2 − 2 + ln

[w−√w2−2√2

](C. Nadal and S.M., JSTAT, P04001, 2011)

Close to w →√

2+

, this gives

Prob.[λmax <

√2 + 2−1/2 N−2/3 x

]→ − 1

16π x3/2 e−(4/3) x3/2

→ precise asymptotics of the right tail of TW for β = 2 (Baik, 2006)

• For general β, precise right tail of TW → obtained recently(Dumaz and Virag, 2011)

• As a bonus, our method also provides a ‘simpler’ derivation of TWdistribution for β = 2 (Nadal and S.M., 2011)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 152: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

A simple example of large deviation tails

• Let M → no. of heads in N tosses of an unbiased coin

• Clearly P(M,N) =(NM

)2−N (M = 0, 1, . . . ,N) → binomial distribution

with mean= 〈M〉 = N2 and variance=σ2 = 〈

(M − N

2

)2〉 = N4

• typical fluctuations M − N2 ∼ O(

√N) are well described

by the Gaussian form: P(M,N) ∼ exp[− 2

N

(M − N

2

)2]

• Atypical large fluctuations M − N2 ∼ O(N) are not described by

Gaussian form

• Setting M/N = x and using Stirling’s formula N! ∼ NN+1/2e−N gives

P(M = Nx ,N) ∼ exp [−NΦ(x)] where

Φ(x) = x log(x) + (1− x) log(1− x) + log 2 → large deviation function

• Φ(x) → symmetric with a minimum at x = 1/2 andfor small arguments |x − 1/2| << 1, Φ(x) ≈ 2(x − 1/2)2

→ recovers the Gaussian form near the peak

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 153: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

A simple example of large deviation tails

• Let M → no. of heads in N tosses of an unbiased coin

• Clearly P(M,N) =(NM

)2−N (M = 0, 1, . . . ,N) → binomial distribution

with mean= 〈M〉 = N2 and variance=σ2 = 〈

(M − N

2

)2〉 = N4

• typical fluctuations M − N2 ∼ O(

√N) are well described

by the Gaussian form: P(M,N) ∼ exp[− 2

N

(M − N

2

)2]

• Atypical large fluctuations M − N2 ∼ O(N) are not described by

Gaussian form

• Setting M/N = x and using Stirling’s formula N! ∼ NN+1/2e−N gives

P(M = Nx ,N) ∼ exp [−NΦ(x)] where

Φ(x) = x log(x) + (1− x) log(1− x) + log 2 → large deviation function

• Φ(x) → symmetric with a minimum at x = 1/2 andfor small arguments |x − 1/2| << 1, Φ(x) ≈ 2(x − 1/2)2

→ recovers the Gaussian form near the peak

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 154: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

A simple example of large deviation tails

• Let M → no. of heads in N tosses of an unbiased coin

• Clearly P(M,N) =(NM

)2−N (M = 0, 1, . . . ,N) → binomial distribution

with mean= 〈M〉 = N2 and variance=σ2 = 〈

(M − N

2

)2〉 = N4

• typical fluctuations M − N2 ∼ O(

√N) are well described

by the Gaussian form: P(M,N) ∼ exp[− 2

N

(M − N

2

)2]

• Atypical large fluctuations M − N2 ∼ O(N) are not described by

Gaussian form

• Setting M/N = x and using Stirling’s formula N! ∼ NN+1/2e−N gives

P(M = Nx ,N) ∼ exp [−NΦ(x)] where

Φ(x) = x log(x) + (1− x) log(1− x) + log 2 → large deviation function

• Φ(x) → symmetric with a minimum at x = 1/2 andfor small arguments |x − 1/2| << 1, Φ(x) ≈ 2(x − 1/2)2

→ recovers the Gaussian form near the peak

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 155: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

A simple example of large deviation tails

• Let M → no. of heads in N tosses of an unbiased coin

• Clearly P(M,N) =(NM

)2−N (M = 0, 1, . . . ,N) → binomial distribution

with mean= 〈M〉 = N2 and variance=σ2 = 〈

(M − N

2

)2〉 = N4

• typical fluctuations M − N2 ∼ O(

√N) are well described

by the Gaussian form: P(M,N) ∼ exp[− 2

N

(M − N

2

)2]

• Atypical large fluctuations M − N2 ∼ O(N) are not described by

Gaussian form

• Setting M/N = x and using Stirling’s formula N! ∼ NN+1/2e−N gives

P(M = Nx ,N) ∼ exp [−NΦ(x)] where

Φ(x) = x log(x) + (1− x) log(1− x) + log 2 → large deviation function

• Φ(x) → symmetric with a minimum at x = 1/2 andfor small arguments |x − 1/2| << 1, Φ(x) ≈ 2(x − 1/2)2

→ recovers the Gaussian form near the peak

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 156: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

A simple example of large deviation tails

• Let M → no. of heads in N tosses of an unbiased coin

• Clearly P(M,N) =(NM

)2−N (M = 0, 1, . . . ,N) → binomial distribution

with mean= 〈M〉 = N2 and variance=σ2 = 〈

(M − N

2

)2〉 = N4

• typical fluctuations M − N2 ∼ O(

√N) are well described

by the Gaussian form: P(M,N) ∼ exp[− 2

N

(M − N

2

)2]

• Atypical large fluctuations M − N2 ∼ O(N) are not described by

Gaussian form

• Setting M/N = x and using Stirling’s formula N! ∼ NN+1/2e−N gives

P(M = Nx ,N) ∼ exp [−NΦ(x)] where

Φ(x) = x log(x) + (1− x) log(1− x) + log 2 → large deviation function

• Φ(x) → symmetric with a minimum at x = 1/2 andfor small arguments |x − 1/2| << 1, Φ(x) ≈ 2(x − 1/2)2

→ recovers the Gaussian form near the peak

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 157: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

A simple example of large deviation tails

• Let M → no. of heads in N tosses of an unbiased coin

• Clearly P(M,N) =(NM

)2−N (M = 0, 1, . . . ,N) → binomial distribution

with mean= 〈M〉 = N2 and variance=σ2 = 〈

(M − N

2

)2〉 = N4

• typical fluctuations M − N2 ∼ O(

√N) are well described

by the Gaussian form: P(M,N) ∼ exp[− 2

N

(M − N

2

)2]

• Atypical large fluctuations M − N2 ∼ O(N) are not described by

Gaussian form

• Setting M/N = x and using Stirling’s formula N! ∼ NN+1/2e−N gives

P(M = Nx ,N) ∼ exp [−NΦ(x)] where

Φ(x) = x log(x) + (1− x) log(1− x) + log 2 → large deviation function

• Φ(x) → symmetric with a minimum at x = 1/2 andfor small arguments |x − 1/2| << 1, Φ(x) ≈ 2(x − 1/2)2

→ recovers the Gaussian form near the peak

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 158: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

A simple example of large deviation tails

• Let M → no. of heads in N tosses of an unbiased coin

• Clearly P(M,N) =(NM

)2−N (M = 0, 1, . . . ,N) → binomial distribution

with mean= 〈M〉 = N2 and variance=σ2 = 〈

(M − N

2

)2〉 = N4

• typical fluctuations M − N2 ∼ O(

√N) are well described

by the Gaussian form: P(M,N) ∼ exp[− 2

N

(M − N

2

)2]

• Atypical large fluctuations M − N2 ∼ O(N) are not described by

Gaussian form

• Setting M/N = x and using Stirling’s formula N! ∼ NN+1/2e−N gives

P(M = Nx ,N) ∼ exp [−NΦ(x)] where

Φ(x) = x log(x) + (1− x) log(1− x) + log 2 → large deviation function

• Φ(x) → symmetric with a minimum at x = 1/2 andfor small arguments |x − 1/2| << 1, Φ(x) ≈ 2(x − 1/2)2

→ recovers the Gaussian form near the peak

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 159: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

A simple example of large deviation tails

• Let M → no. of heads in N tosses of an unbiased coin

• Clearly P(M,N) =(NM

)2−N (M = 0, 1, . . . ,N) → binomial distribution

with mean= 〈M〉 = N2 and variance=σ2 = 〈

(M − N

2

)2〉 = N4

• typical fluctuations M − N2 ∼ O(

√N) are well described

by the Gaussian form: P(M,N) ∼ exp[− 2

N

(M − N

2

)2]

• Atypical large fluctuations M − N2 ∼ O(N) are not described by

Gaussian form

• Setting M/N = x and using Stirling’s formula N! ∼ NN+1/2e−N gives

P(M = Nx ,N) ∼ exp [−NΦ(x)] where

Φ(x) = x log(x) + (1− x) log(1− x) + log 2 → large deviation function

• Φ(x) → symmetric with a minimum at x = 1/2 andfor small arguments |x − 1/2| << 1, Φ(x) ≈ 2(x − 1/2)2

→ recovers the Gaussian form near the peak

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 160: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Covariance Matrix

11X

X21

X31

X22

X

phys. math

1

2

3

X =

in general

(MxN)

Xt

=X

11 X21

X31

X12 22 X

in general

(NxM)

W= XtX =

X11

+ X21+ X31

2 2 2X11 X12

+ X21

X

X22+ X

31X

X12

X12X11

+

X

X22X21+ X X31 X12

2 + X22

2+ X

2

(unnormalized)COVARIANCE MATRIX(NxN)

32

32

32

3232

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 161: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Principal Component Analysis

Consider N students and M = 2 subjects (phys. and math.)

X → (N × 2) matrix and W = X tX → 2× 2 matrix

x

x

x

phys.

math

λ| 1>λ| 2>

If λ1>> λ2

strongly correlated

diagonalize W=X Xt [ λ1

, λ2 ]

x

x

x

xx

x

x

xx

x

x

x

phys.

math

λ| 1>λ| 2>

If λ1 λ2

diagonalize W=X Xt [ λ1

, λ2 ]

x

x

xx

x

x

x

x

xx

x

x

xx

~

(weak correlation)random

data compression via ‘Principal Component Analysis’ (PCA)

⇒ practical method for image compression in computer vision

Null model → random data: X → random (M × N) matrix

→ W = X tX → random N × N matrix (Wishart, 1928)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 162: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Principal Component Analysis

Consider N students and M = 2 subjects (phys. and math.)

X → (N × 2) matrix and W = X tX → 2× 2 matrix

x

x

x

phys.

math

λ| 1>λ| 2>

If λ1>> λ2

strongly correlated

diagonalize W=X Xt [ λ1

, λ2 ]

x

x

x

xx

x

x

xx

x

x

x

phys.

math

λ| 1>λ| 2>

If λ1 λ2

diagonalize W=X Xt [ λ1

, λ2 ]

x

x

xx

x

x

x

x

xx

x

x

xx

~

(weak correlation)random

data compression via ‘Principal Component Analysis’ (PCA)

⇒ practical method for image compression in computer vision

Null model → random data: X → random (M × N) matrix

→ W = X tX → random N × N matrix (Wishart, 1928)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 163: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Principal Component Analysis

Consider N students and M = 2 subjects (phys. and math.)

X → (N × 2) matrix and W = X tX → 2× 2 matrix

x

x

x

phys.

math

λ| 1>λ| 2>

If λ1>> λ2

strongly correlated

diagonalize W=X Xt [ λ1

, λ2 ]

x

x

x

xx

x

x

xx

x

x

x

phys.

math

λ| 1>λ| 2>

If λ1 λ2

diagonalize W=X Xt [ λ1

, λ2 ]

x

x

xx

x

x

x

x

xx

x

x

xx

~

(weak correlation)random

data compression via ‘Principal Component Analysis’ (PCA)

⇒ practical method for image compression in computer vision

Null model → random data: X → random (M × N) matrix

→ W = X tX → random N × N matrix (Wishart, 1928)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 164: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Principal Component Analysis

Consider N students and M = 2 subjects (phys. and math.)

X → (N × 2) matrix and W = X tX → 2× 2 matrix

x

x

x

phys.

math

λ| 1>λ| 2>

If λ1>> λ2

strongly correlated

diagonalize W=X Xt [ λ1

, λ2 ]

x

x

x

xx

x

x

xx

x

x

x

phys.

math

λ| 1>λ| 2>

If λ1 λ2

diagonalize W=X Xt [ λ1

, λ2 ]

x

x

xx

x

x

x

x

xx

x

x

xx

~

(weak correlation)random

data compression via ‘Principal Component Analysis’ (PCA)

⇒ practical method for image compression in computer vision

Null model → random data: X → random (M × N) matrix

→ W = X tX → random N × N matrix (Wishart, 1928)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 165: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Generalization to Wishart Matrices

• W = X †X → (N × N) square covariance matrix (Wishart, 1928)

• Entries of X Gaussian: Pr[X ] ∝ exp[−β2 N Tr(X †X )

]β = 1 → Real entries, β = 2 → Complex

• All eigenvalues of W = X †X are non-negative

• Average density of states for large N: Marcenko-Pastur (1967)

ρ(λ,N) = 〈 1

N

N∑i=1

δ(λ− λi )〉 −−−−→N→∞

ρ(λ) =1

√4− λλ

λ

ρ(λ) MARCENKO−PASTUR

0 4

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 166: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Generalization to Wishart Matrices

• W = X †X → (N × N) square covariance matrix (Wishart, 1928)

• Entries of X Gaussian: Pr[X ] ∝ exp[−β2 N Tr(X †X )

]β = 1 → Real entries, β = 2 → Complex

• All eigenvalues of W = X †X are non-negative

• Average density of states for large N: Marcenko-Pastur (1967)

ρ(λ,N) = 〈 1

N

N∑i=1

δ(λ− λi )〉 −−−−→N→∞

ρ(λ) =1

√4− λλ

λ

ρ(λ) MARCENKO−PASTUR

0 4

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 167: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Generalization to Wishart Matrices

• W = X †X → (N × N) square covariance matrix (Wishart, 1928)

• Entries of X Gaussian: Pr[X ] ∝ exp[−β2 N Tr(X †X )

]β = 1 → Real entries, β = 2 → Complex

• All eigenvalues of W = X †X are non-negative

• Average density of states for large N: Marcenko-Pastur (1967)

ρ(λ,N) = 〈 1

N

N∑i=1

δ(λ− λi )〉 −−−−→N→∞

ρ(λ) =1

√4− λλ

λ

ρ(λ) MARCENKO−PASTUR

0 4

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 168: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Generalization to Wishart Matrices

• W = X †X → (N × N) square covariance matrix (Wishart, 1928)

• Entries of X Gaussian: Pr[X ] ∝ exp[−β2 N Tr(X †X )

]β = 1 → Real entries, β = 2 → Complex

• All eigenvalues of W = X †X are non-negative

• Average density of states for large N: Marcenko-Pastur (1967)

ρ(λ,N) = 〈 1

N

N∑i=1

δ(λ− λi )〉 −−−−→N→∞

ρ(λ) =1

√4− λλ

λ

ρ(λ) MARCENKO−PASTUR

0 4

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 169: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Distribution of λmax

λ

ρ(λ)

0 4

N−2/3

TRACY−WIDOM

MARCENKO−PASTUR

LEFT

RIGHT

• 〈λmax〉 = 4 (as N →∞)

• typical fluctuations:λmax − 4 ∼ O(N2/3)

distributed via → Tracy-Widom(Johansson 2000, Johnstone 2001)

• For large deviations: λmax − 4 ∼ O(1)

P (λmax = w ,N) ≈

exp{−βN2Ψ−(w)

}for w < 4

exp {−βNΨ+(w)} for w > 4

• Ψ−(w) and Ψ+(w) → computed exactly

(Vivo, S.M. & Bohigas 2007, S.M. & Vergassola 2009)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 170: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Distribution of λmax

λ

ρ(λ)

0 4

N−2/3

TRACY−WIDOM

MARCENKO−PASTUR

LEFT

RIGHT

• 〈λmax〉 = 4 (as N →∞)

• typical fluctuations:λmax − 4 ∼ O(N2/3)

distributed via → Tracy-Widom(Johansson 2000, Johnstone 2001)

• For large deviations: λmax − 4 ∼ O(1)

P (λmax = w ,N) ≈

exp{−βN2Ψ−(w)

}for w < 4

exp {−βNΨ+(w)} for w > 4

• Ψ−(w) and Ψ+(w) → computed exactly

(Vivo, S.M. & Bohigas 2007, S.M. & Vergassola 2009)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 171: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Distribution of λmax

λ

ρ(λ)

0 4

N−2/3

TRACY−WIDOM

MARCENKO−PASTUR

LEFT

RIGHT

• 〈λmax〉 = 4 (as N →∞)

• typical fluctuations:λmax − 4 ∼ O(N2/3)

distributed via → Tracy-Widom(Johansson 2000, Johnstone 2001)

• For large deviations: λmax − 4 ∼ O(1)

P (λmax = w ,N) ≈

exp{−βN2Ψ−(w)

}for w < 4

exp {−βNΨ+(w)} for w > 4

• Ψ−(w) and Ψ+(w) → computed exactly

(Vivo, S.M. & Bohigas 2007, S.M. & Vergassola 2009)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 172: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Distribution of λmax

λ

ρ(λ)

0 4

N−2/3

TRACY−WIDOM

MARCENKO−PASTUR

LEFT

RIGHT

• 〈λmax〉 = 4 (as N →∞)

• typical fluctuations:λmax − 4 ∼ O(N2/3)

distributed via → Tracy-Widom(Johansson 2000, Johnstone 2001)

• For large deviations: λmax − 4 ∼ O(1)

P (λmax = w ,N) ≈

exp{−βN2Ψ−(w)

}for w < 4

exp {−βNΨ+(w)} for w > 4

• Ψ−(w) and Ψ+(w) → computed exactly

(Vivo, S.M. & Bohigas 2007, S.M. & Vergassola 2009)S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 173: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Exact Left and Right Large Deviation Functions

Using Coulomb gas + Saddle point method for large N:

• Left large deviation function:

Ψ−(w) = ln

[2

w

]− w − 4

8− (w − 4)2

64w ≤ 4

(Vivo, S.M. and Bohigas, 2007)

• Right large deviation function:

Ψ+(w) =

√w(w − 4)

4+ ln

[w − 2−

√w(w − 4)

2

]w ≥ 4

(S.M. and Vergassola, 2009)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 174: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Exact Left and Right Large Deviation Functions

Using Coulomb gas + Saddle point method for large N:

• Left large deviation function:

Ψ−(w) = ln

[2

w

]− w − 4

8− (w − 4)2

64w ≤ 4

(Vivo, S.M. and Bohigas, 2007)

• Right large deviation function:

Ψ+(w) =

√w(w − 4)

4+ ln

[w − 2−

√w(w − 4)

2

]w ≥ 4

(S.M. and Vergassola, 2009)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 175: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Other Problems with 3-rd Order Phase Transitions

• Bipartite Entanglement of a Random Pure State

Probability distribution of entanglement entropy

Nadal, S.M. & Vergassola, PRL, 110501 (2010); J. Stat. Phys. 142, 403 (2011)

• Conductance and Shot Noise in Mesoscopic Cavities

Random S-matrix: Distribution of Conductance and Shot Noise

Vivo, S.M. & Bohigas, PRL, 101, 216809 (2008), PRB, 81, 104202 (2010)

Damle, S.M., Tripathy, & Vivo, PRL, 107, 177206 (2011)

• Non-Intersecting Brownian Motions and Random Matrices

→ relation to 2-d Yang-Mills gauge theory

Schehr, S.M., Comtet, Randon-Furling, PRL, 101, 150601 (2008)

Forrester, S.M. & Schehr, Nucl. Phys. B 844, 500 (2011), J. Stat. Phys. (2013)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 176: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Other Problems with 3-rd Order Phase Transitions

• Bipartite Entanglement of a Random Pure State

Probability distribution of entanglement entropy

Nadal, S.M. & Vergassola, PRL, 110501 (2010); J. Stat. Phys. 142, 403 (2011)

• Conductance and Shot Noise in Mesoscopic Cavities

Random S-matrix: Distribution of Conductance and Shot Noise

Vivo, S.M. & Bohigas, PRL, 101, 216809 (2008), PRB, 81, 104202 (2010)

Damle, S.M., Tripathy, & Vivo, PRL, 107, 177206 (2011)

• Non-Intersecting Brownian Motions and Random Matrices

→ relation to 2-d Yang-Mills gauge theory

Schehr, S.M., Comtet, Randon-Furling, PRL, 101, 150601 (2008)

Forrester, S.M. & Schehr, Nucl. Phys. B 844, 500 (2011), J. Stat. Phys. (2013)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 177: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Other Problems with 3-rd Order Phase Transitions

• Bipartite Entanglement of a Random Pure State

Probability distribution of entanglement entropy

Nadal, S.M. & Vergassola, PRL, 110501 (2010); J. Stat. Phys. 142, 403 (2011)

• Conductance and Shot Noise in Mesoscopic Cavities

Random S-matrix: Distribution of Conductance and Shot Noise

Vivo, S.M. & Bohigas, PRL, 101, 216809 (2008), PRB, 81, 104202 (2010)

Damle, S.M., Tripathy, & Vivo, PRL, 107, 177206 (2011)

• Non-Intersecting Brownian Motions and Random Matrices

→ relation to 2-d Yang-Mills gauge theory

Schehr, S.M., Comtet, Randon-Furling, PRL, 101, 150601 (2008)

Forrester, S.M. & Schehr, Nucl. Phys. B 844, 500 (2011), J. Stat. Phys. (2013)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations

Page 178: laboratoriomatematicas.uniandes.edu.cocursillo... · 2014. 5. 30. · Applications of Random Matrices Physics:nuclear physics, quantum chaos, disorder and localization, mesoscopic

Other Problems with 3-rd Order Phase Transitions

• Bipartite Entanglement of a Random Pure State

Probability distribution of entanglement entropy

Nadal, S.M. & Vergassola, PRL, 110501 (2010); J. Stat. Phys. 142, 403 (2011)

• Conductance and Shot Noise in Mesoscopic Cavities

Random S-matrix: Distribution of Conductance and Shot Noise

Vivo, S.M. & Bohigas, PRL, 101, 216809 (2008), PRB, 81, 104202 (2010)

Damle, S.M., Tripathy, & Vivo, PRL, 107, 177206 (2011)

• Non-Intersecting Brownian Motions and Random Matrices

→ relation to 2-d Yang-Mills gauge theory

Schehr, S.M., Comtet, Randon-Furling, PRL, 101, 150601 (2008)

Forrester, S.M. & Schehr, Nucl. Phys. B 844, 500 (2011), J. Stat. Phys. (2013)

S.N. Majumdar Top eigenvalue of a random matrix: Large deviations