Transcript Document

Propagating beliefs in spinglass models
Yoshiyuki Kabashima
Dept. of Compt. Intel. & Syst. Sci.
Tokyo Institute of Technology
[email protected]
@ 15/7/2003
Tokyo Institute of Technology
1
Background and Motivation


Active research on belief propagation (BP) in
information sciences (IS)
Similarity to methods in physics


TMM & Bethe approx.
Difference in interest


Physics ⇒ obtained solutions
IS ⇒ dynamics of the algorithm
may cause unexpected developments in both
fields
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Purpose and Results


Purpose:analyze dynamics of BP when
employed in spin-glass models
Results:


Macro. dyn. of BP ⇒ RS solution
Micro. stability of BP ⇔ AT condition
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Outline





SG model in Bayesian framework
Belief propagation
Macro. dyn. and RS solution
Micro. stability and AT condition
Summary
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Spin-glass models

SG models on a random (Bethe) lattice
M
H S | J    J 
 1



 S
l
lL
K-body interaction
C-bonds/spin
Randomly constructed for other aspects





1 J0 / C J
1 J0 / C J
PJ   
 J  J / C 
 J  J / C
2
2
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
5
SM and Bayesian Statistics

Boltzmann dist. = Bayes formula
 PJ  | S 
M
exp  H S | J 

Z J 
 1
PJ  | S 


M
S



exp  J   Sl 
lL    

PJ  | S  
2 cosh J  
 PS | J 
1
Magnetization = Posterior average
ml 
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S
S
l
exp  H S | J 
Z J 
  Sl PS | J 
S
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Graph Expression

Expression by a bipartite graph
K 3
J1
J3
J2
J4
JM
…
PJ1 | S
…
…
S1
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S2
S3
C  4 PS2 | J 
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SN
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Belief Propagation

Iterative inference by passing beliefs
J1
J3
J2
J4
JM
…
…
t
ml
ˆ l
m
t
…
S1
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S2
S3
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SN
8
More Precisely
mˆ t l 1  tanh J    mt k

kL   \ l
 t

1
t
ml  tanh   tanh mˆ l

 M l \ 




 



t
ml :Posterior average when J  is left out.
 
ˆ t l
tanh 1 m
: Effective field when
J
comes in.
:Estimator


mlt  tanh   tanh 1 mˆ t l 
 M l 

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 
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Macro. Dyn. vs. RS Solution

Distribution (histogram) of beliefs
 x  m , ˆ xˆ   (1 / NC )   xˆ  mˆ t l 



l 1 M l 
Known result for finite C:
N
 x   (1 / NC)
t
l 1



N
t
M l
t
l
Tree approximation (resampling graph/update)
Density evolution ⇒ RS solution
K 1
K 1
 t 1


t
ˆ
ˆ
ˆ







x

dx

x

x

tanh

J
x




l
l
l



l 1
l 1

 J


C 1

 C 1

t
1
 t x  


dx ˆ xˆ   x  tanh   tanh xˆ   







1


1





Richardson & Urbanke (2001) Vicente, Saad, YK (2000)
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Novel Result for Infinite C

Central limit theorem for infinite C

Evolution of average and variance

 h  Et
exp  
t

C 1
C 1
2
F



 t h     dxˆ ˆ t xˆ    h   tanh 1 xˆ   
 1
 1
2F t




2




Natural iteration of RS SP eqs.
 
 
2
t 1
t K 1
t 1
t K 1

, F  J  Q
 E  J 0 M
 t
t
t
2
M

Dz
tanh
F
z

E
,
Q

Dz
tanh





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 F z  E
t
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Experimental Validation

SK model(N=1000,J=1,T=0.5)
J 0  1.5
(AT stable)
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J 0  0.5
1
t
D 
N
(AT unstable)
 m
N
l 1
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t
l

t 1 2
l
m
12
Microscopic Instability


Possible microscopic instability while BP
seems to macroscopically converge
Stability analysis of the fixed point
ht l 1 
  
 M l \
tanh  J 
 m
kL
k
\l


1   tanh  J   mk 
kL   \ l


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
2
 
jL
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\l
1  m2j
mj
 htj
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Evolution of Perturbation
Dist. of Perturb. f t  y   1 / NC   y  ht l 
l 1 M l 
Perturbation Evolution
N



f
t 1
f  y     y  is attractive
⇔ the fixed point(=RS solution) is stable
C 1, K 1
 y     dyl f t  yl 
 1,l 1
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K 1




tanh  J   xl
2
C

1
K

1


1  xk
l 1
 y 

 y k 
2
K 1
 1

 k 1 xk




1

tanh

J
x





l




l 1




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J  , xl
14
Pictorial Expression

What is performed?
(t  1) th update
tth update :
J1
J3
J2
S1
S2
t
h21
J4
S3
t
h23
JM
…
…
…S
J1
J1
J3
J2
S1
S2
J4
S3
J4
JM
…
…
…
SN
S1
N
t 1
h21
t
h24
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J3
J2
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S2
S3
JM
…
…
…S
N
t 1
t 1
h23
h24
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Meaning of P. Evolution


Link to known results for infinite C
Central limit theorem:

t

1
y

a
f t y 
exp  
t
t

2
b
2b



2

 :Gaussian dist.


P. Evolution → Update of average & variance
t 1
K 2
t



a

(
K

1
)

J
M
1

Q
a
0

 t 1
2
K 2
2


b

(
K

1
)

J
Q
Dz
1

tanh




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
2
  
Fz  E b O a
t
t 2
16
Meaning of P. Evolution

Critical conditions for growth of
fluctuation
K 2

1  Q   1 :Average
(
K

1
)

J
M
0


2
K 2
2


(
K

1
)

J
Q
Dz
1

tanh
FzE






  1 :Variance
2
For K=2 (SK model)


Average → Tf: Para-Ferro transition
Variance → TAT: AT condition
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Analysis for finite C


Is P. evolution equivalent to AT analysis even
for finite C?

AT analysis for finite C is complicated.

But, P. evolution is (numerically) possible.
K=2 (Wong-Sherrington model)

Paramagnetic solution
Known result
Average→T





C

1
tanh

J

1
f

J
•Klein et al (1979)

2

C  1 tanh J   1 Variance→TAT •Mezard & Parisi

J

(1987)
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Analysis for Finite C

Ferromagnetic solution
Numerical evaluation of Tpevol: New result!
N=2000, K=2, C=4,
20000MCS/Spin
D: Tpevol in Ferro phase
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Summary

Close relationship between BP and the replica
analysis




Macro. dyn. ⇒ RS solution
Micro. stability ⇔AT condition
This correspondence may be useful for AT
analysis for SG models of finite connectivity.
Application to CDMA multiuser detection
(Kabashima, to appear in J. Phys. A, 2003)
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