andrea montanari and ruediger urbanke tifr tuesday ...montanar/other/talks/mumbai09_1.pdf · and...
TRANSCRIPT
Andrea Montanari and Ruediger UrbankeTIFR
Tuesday, January 6th, 2008
Phase Transitions in Coding, Communications, and Inference
Outline
Outline
1) Thresholds in coding, the large size limit (definition and density evolution characterization)
2) The inversion of limits (length to infty vs size to infty)
Outline
1) Thresholds in coding, the large size limit (definition and density evolution characterization)
2) The inversion of limits (length to infty vs size to infty)
3) Phase transitions in measurements (compressed sensing versus message passing, dense versus sparse matrices)
4) Phase transitions in collaborative filtering (the low-rank matrix model)
Model
Shannon ’48
Model
Shannon ’48
binary erasures channelcapacity: R≤1-ε
Model
Shannon ’48
binary symmetric channelcapacity: R≤1-h(ε)
Channel Coding
Channel Coding
codeC={000, 010, 101, 111}
Channel Coding
codeC={000, 010, 101, 111}
n ... blocklength
Channel Coding
codeC={000, 010, 101, 111}
n ... blocklength
Channel Coding
codeC={000, 010, 101, 111}
n ... blocklength
Channel Coding
code
decoding
C={000, 010, 101, 111}
n ... blocklength
xMAP(y)=argmaxX in C p(x | y)
xiMAP(y)=argmaxXi p(xi |y)
Factor Graph Representation of Linear Codes
Factor Graph Representation of Linear Codes
every linear codeparity-check matrix
Factor Graph Representation of Linear Codes
(7, 4) Hamming code
every linear codeparity-check matrix
Factor Graph Representation of Linear Codes
(7, 4) Hamming code
every linear code
Tanner, Wiberg, Koetter, Loeliger, Frey
parity-check matrix
Low-Density Parity Check Codes
Gallager ‘60
Low-Density Parity Check Codes
(3, 4)-regular codes
Gallager ‘60
Low-Density Parity Check Codes
(3, 4)-regular codes
Gallager ‘60
Low-Density Parity Check Codes
(3, 4)-regular codes
Gallager ‘60
Low-Density Parity Check Codes
(3, 4)-regular codes
Gallager ‘60
Low-Density Parity Check Codes
(3, 4)-regular codes
Gallager ‘60
number of edges is linear in n
Ensemble
Ensemble
Ensemble
Ensemble
Ensemble
Variations on the Theme
Variations on the Theme
degree distributions as well as structure
Variations on the Theme
irregular LDPC ensemble
(Luby, Mitzenmacher, Shokrollahi, Spielman, and Stehman)
Variations on the Theme
regular RA ensemble
Divsalar, Jin, and McEliece
Variations on the Theme
irregular RA ensemble
Jin, Khandekar, and McEliece
Variations on the Theme
irregular MN ensemble
Davey, MacKay
Variations on the Theme
ARA ensemble
Abbasfar, Divsalar, Kung
Variations on the Theme
irregular LDGM ensemble
Variations on the Theme
turbo code
Berrou and Glavieux
Variations on the Theme
protograph
Thorpe, Andrews, Dolinar
Message-Passing Decoding -- BEC
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Message-Passing Decoding -- BEC
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Message-Passing Decoding -- BEC
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Message-Passing Decoding -- BEC
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Message-Passing Decoding -- BEC
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Message-Passing Decoding -- BEC
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Message-Passing Decoding -- BEC
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Message-Passing Decoding -- BEC
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Message-Passing Decoding -- BEC
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Message-Passing Decoding -- BEC
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Message-Passing Decoding -- BEC
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Message-Passing Decoding -- BEC
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Message-Passing Decoding -- BEC
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decoded
Message-Passing Decoding -- BEC
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decoded
Message-Passing Decoding -- BEC
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decoded
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Message-Passing Decoding -- BEC
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decoded
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Message-Passing Decoding -- BEC
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decoded
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Message-Passing Decoding -- BEC
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Message-Passing Decoding -- BSCGallager Algorithm
Asymptotic Analysis: Computation Graph
Asymptotic Analysis: Computation Graph
Asymptotic Analysis: Computation Graph
Asymptotic Analysis: Computation Graph
Asymptotic Analysis: Computation Graph
Asymptotic Analysis: Computation Graph
Asymptotic Analysis: Computation Graph
Asymptotic Analysis: Computation Graph
Asymptotic Analysis: Computation Graph
probability that computation graphof fixed depth becomes tree
tends to 1 as n tends to infinity
Asymptotic Analysis: Computation Graph
Asymptotic Analysis: Computation Graph
Asymptotic Analysis: Density Evolution -- BEC
Luby,Mitzenmacher, Shokrollahi, Spielman,
and Steman ‘97
Asymptotic Analysis: Density Evolution -- BEC
x x x
Luby,Mitzenmacher, Shokrollahi, Spielman,
and Steman ‘97
Asymptotic Analysis: Density Evolution -- BEC
x
1-(1-x)r-1
x x
Luby,Mitzenmacher, Shokrollahi, Spielman,
and Steman ‘97
Asymptotic Analysis: Density Evolution -- BEC
x
1-(1-x)r-1
x x
ε (1-(1-x)r-1)l-1
Luby,Mitzenmacher, Shokrollahi, Spielman,
and Steman ‘97
Asymptotic Analysis: Density Evolution -- BEC
x
1-(1-x)r-1
x x
ε (1-(1-x)r-1)l-1
ε
Luby,Mitzenmacher, Shokrollahi, Spielman,
and Steman ‘97
Asymptotic Analysis: Density Evolution -- BEC
ε
Asymptotic Analysis: Density Evolution -- BEC
ε
phase transition: εBP so that xt → 0 for ε< εBP
xt → x∞>0 for ε> εBP
Asymptotic Analysis: Density Evolution -- BEC
ε
phase transition: εBP so that xt → 0 for ε< εBP
xt → x∞>0 for ε> εBP
Asymptotic Analysis: Density Evolution -- BSC, Gallager Algorithm
xt =ε (1-p+(xt-1))+(1-ε) p-(xt-1)
p+(x)=((1+(1-2x)r-1)/2) l-1 p-(x)=((1-(1-2x)r-1)/2) l-1
Asymptotic Analysis: Density Evolution -- BSC, Gallager Algorithm
phase transition: εBP so that xt → 0 for ε< εBP
xt → x∞>0 for ε> εBP
xt =ε (1-p+(xt-1))+(1-ε) p-(xt-1)
p+(x)=((1+(1-2x)r-1)/2) l-1 p-(x)=((1-(1-2x)r-1)/2) l-1
Asymptotic Analysis: Density Evolution -- BSC, Gallager Algorithm
xt =ε (1-p+(xt-1))+(1-ε) p-(xt-1)
p+(x)=((1+(1-2x)r-1)/2) l-1 p-(x)=((1-(1-2x)r-1)/2) l-1
Asymptotic Analysis: Density Evolution -- BP
Inversion of Limits
Inversion of Limits
size versus number of iterations
Density Evolution Limit
Density Evolution Limit
Density Evolution Limit
Density Evolution Limit
Density Evolution Limit
Density Evolution Limit
Density Evolution Limit
Density Evolution Limit
Density Evolution Limit
Density Evolution Limit
Density Evolution Limit
“Practical” Limit
“Practical” Limit
“Practical” Limit
“Practical” Limit
“Practical” Limit
“Practical” Limit
“Practical” Limit
The Two Limits
Easy: (Density Evolution Limit)
Hard(er): (“Practical Limit”)
Binary Erasure Channel
Binary Erasure Channel
DE Limit
Binary Erasure Channel
DE Limit
Binary Erasure Channel
DE Limit
Binary Erasure Channel
DE Limit
“Practical” Limit
implies
What about “General” Case
expansion
probabilistic methods
Korada and U.
Expansion
expansion ~ 1-1/l
Expansion
Miller and Burshtein: Random element of LDPC(l, r, n) ensemble is expander with expansion close to 1-1/l
with high probability
expansion ~ 1-1/l
Why is Expansion Useful?
Setting: Channel
Setting: Ensemble
Setting: Algorithm
Aim: Show for this setting that ...
DE Limit
“Practical” Limit
implies
Proof Outline
linearize the algorithm combine with density evolution correlation and interaction witness randomizing noise outside the witness sub-critical birth and death process
Linearized Decoding Algorithm
Proof Outline
linearize the algorithm combine with density evolution correlation and interaction witness randomizing noise outside the witness sub-critical birth and death process
Combine with Density Evolution
Combine with Density Evolution
Combine with Density Evolution
Combine with Density Evolution
Proof Outline
linearize the algorithm combine with density evolution correlation and interaction witness randomizing noise outside the witness sub-critical birth and death process
Correlation and Interaction
Correlation and Interaction
Correlation and Interaction
Correlation and Interaction
0
Correlation and Interaction
0 1
Correlation and Interaction
0 1
Correlation and Interaction
0 1
0
Correlation and Interaction
0 1
00
Correlation and Interaction
0 1
000
Correlation and Interaction
0 1
1 000
Correlation and Interaction
0 1
1 000
Correlation and Interaction
0 1
1 000Expected growth:
Correlation and Interaction
0 1
1 000Expected growth:
(r-1)(r-1)
Correlation and Interaction
0 1
1 000Expected growth:
(r-1) 2 ε 2 ε
Correlation and Interaction
0 1
1 000Expected growth:
(r-1) 2 ε ?< 1
Correlation and Interaction
0 1
1 000Expected growth:
(r-1) 2 ε ?< 1
Problem: interaction correlation
Correlation and Interaction
Correlation and Interaction
Correlation and Interaction
Correlation and Interaction
Proof Outline
linearize the algorithm combine with density evolution correlation and interaction witness randomizing noise outside the witness sub-critical birth and death process
Witness
Witness
Witness
Witness
Witness
Witness
Witness
Witness
Witness
Witness
Proof Outline
linearize the algorithm combine with density evolution correlation and interaction witness randomizing noise outside the witness sub-critical birth and death process
Monotonicity
Monotonicity
Monotonicity
Monotonicity
Monotonicity
Monotonicity
Monotonicity
Randomizing the Noise Outside
Randomizing the Noise Outside
Randomizing the Noise Outside
→
Randomizing the Noise Outside
→
←⁄
Randomizing the Noise Outside
randomizing noise outside the witness increases the probability of error
FKG≤
Proof Outline
linearize the algorithm combine with density evolution correlation and interaction witness randomizing noise outside the witness sub-critical birth and death process
Expansion
random graph has expansion close to expansion of a treewith high probability
⇒this limits interaction
Expansion
random graph has expansion close to expansion of a treewith high probability
⇒this limits interaction
Expansion
random graph has expansion close to expansion of a treewith high probability
⇒this limits interaction
Expansion
random graph has expansion close to expansion of a treewith high probability
⇒this limits interaction
Expansion
random graph has expansion close to expansion of a treewith high probability
⇒this limits interaction
Expansion
random graph has expansion close to expansion of a treewith high probability
⇒this limits interaction
0
Expansion
random graph has expansion close to expansion of a treewith high probability
⇒this limits interaction
0 1
Expansion
random graph has expansion close to expansion of a treewith high probability
⇒this limits interaction
0 1
Expansion
random graph has expansion close to expansion of a treewith high probability
⇒this limits interaction
0 1
0
Expansion
random graph has expansion close to expansion of a treewith high probability
⇒this limits interaction
0 1
00
Expansion
random graph has expansion close to expansion of a treewith high probability
⇒this limits interaction
0 1
000
Expansion
random graph has expansion close to expansion of a treewith high probability
⇒this limits interaction
0 1
1 000
Expansion
random graph has expansion close to expansion of a treewith high probability
⇒this limits interaction
0 1
1 000
Expansion
random graph has expansion close to expansion of a treewith high probability
⇒this limits interaction
0 1
1 000
References
For a list of references see:http://ipg.epfl.ch/doku.php?id=en:courses:2007-2208:mct
Results
Open Problems
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Pb
channel entropy
Open Problems
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Pb
channel entropy
Open Problems
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0.2 0.4 0.6 0.8
Pb
channel entropy
Open Problems
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0.2 0.4 0.6 0.8
Pb
channel entropy