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Pseudorandom Generators for Combinatorial Shapes. Parikshit Gopalan , MSR SVC Raghu Meka, UT Austin Omer Reingold , MSR SVC David Zuckerman, UT Austin. PRGs for Small Space?. Is RL = L?. Modular Sums. Comb. Rectangles. Saks-Zhou: . - PowerPoint PPT Presentation
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Pseudorandom Generators for Combinatorial Shapes
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Parikshit Gopalan, MSR SVC
Raghu Meka, UT AustinOmer Reingold, MSR SVC
David Zuckerman, UT Austin
PRGs for Small Space?
Poly. width ROBPs. Nis-INW best.
Is RL = L?
2
Saks-Zhou: Nis 90, INW94: PRGs for polynomial width ROBP’s with seed .
Can do O(log n) for these!
Small-Bias
Comb. Rectangles
Modular Sums
0/1 Halfspaces
Combinatorial shapes: unifies and generalizes
all.
What are Combinatorial Shapes?
3
Fooling Linear Forms
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For
Question: Can we have this “pseudorandomly”?
Generate ,
Why Fool Linear Forms?
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Special case: small-bias spaces
Symmetric functions on subsets.Previous best: Nisan90, INW94.
Been difficult to beat Nisan-INW barrier for natural cases.
Question: Generate ,
Combinatorial Rectangles
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What about
Applications: Volume estimation, integration.
Combinatorial Shapes
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Combinatorial Shapes
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PRGs for Combinatorial Shapes
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Unifies and generalizesCombinatorial rectangles – sym. function h is
ANDSmall-bias spaces – m = 2, h is parity0-1 halfspaces – m = 2, h is shifted majority
Thm: PRG for (m,n)-Comb. shapes with
seed .
Previous Results
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Reference Function Class Seed LengthNis90, INW94 All ShapesLLSZ92 Comb. Rects, Hitting
setsEGL+92, ASWZ96, Lu02
Comb. RectanglesNN93, LRTV09, MZ09
Modular Sums
M., Zuckerman 10
Halfspaces
Our Results
Discrete Central Limit TheoremSum of ind. random variables ~ Gaussian
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Thm:
Discrete Central Limit TheoremClose in stat. distance to binomial
distribution
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Optimal error: .• Proof analytical - Stein’s method (Barbour-
Xia98).
Thm:
This Talk
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1. PRGs for Cshapes with m = 2.Illustrates main ideas for general case.
2. PRG for general Cshapes.
3. Proof of discrete central limit theorem.
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Question: Generate ,
Fooling Cshapes for m = 2 ~ Fooling 0/1 linear forms in TV.
Fooling Linear Forms in TV
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1. Fool linear forms with small test sizes.Bounded independence, hashing.
2. Fool 0-1 linear forms in cdf distance.PRG for halfspaces: M., Zuckerman
3. PRG on n/2 vars + PRG fooling in cdf PRG for linear forms, large test sets.
Thm MZ10: PRG for halfspaces with seed
3. Convolution Lem: close in cdf to close in TV.
Analysis of recursionElementary proof of discrete CLT.
Question: Generate ,
Recursion Step for 0-1 Linear Forms
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For intuition consider
X1 Xn/2+1 Xn… Xn/2 …
PRG -fool in TV PRG -fool in CDF
PRG -fool in TV
True randomness
PRG -fool in TV
Recursion Step: Convolution Lemma
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Lem:
Convolution Lemma
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Problem: Y could be even, Z odd.Define Y’:Approach:
Lem:
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Convexity of : Enough to study
Recursion for General Case
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Problem: Test set skewed to first half.
Solution: Do the partitioning randomly. Test set splits evenly to each half.Can’t use new bits for every step.
Analysis: Induction. Balance out test set. Final Touch: Use Nisan-INW across recursions.
Recursion for General Case
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X1
Xn
X2 … X
3
X1 Xi …
MZ on n/2 Vars
Xj …
MZ on n/4 Vars
…
Truly random
Geometric dec. blocks via Pairwise
Permutations
Fool 0-1 Linear forms in TV with seed
This Talk
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1. PRGs for Cshapes with m = 2.Illustrates main ideas for general case.
2. PRG for general Cshapes.
3. Proof of discrete central limit theorem.
From Shapes to Sums
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From m = 2 to General m
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Test set Large vs Small
For large: true ~ binomial
For small: k-wise
High or Low VarianceVar. high: shift-
invarianceFor small: k-wise
1. PRG fooling low variance CSums.Sandwiching poly., bounded independence.
2. PRG fooling high var. CSums in cdf.Same generator, similar analysis.
3. PRG on n/2 vars + PRG fooling in cdf PRG for high variance CSums
PRGs for CShapes
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3. Convolution Lemma. Work with shift invariance.Balance out variances (ala test set
sizes).
Low Variance Combinatorial Sums
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Need to look at the generator for halfspaces.
Some notation: Pairwise-indep. hash familyk-wise independent generatorWe use
INW on top to choose z’s.
Core Generator
x1
x2
x3 … x
nx5
x4
xk … x
1x3
xk
x5
x4
x2
1 2 t
… xn… x
5x4
x2
2 t
xnxn
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Randomness:
Low Variance Combinatorial Sums
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Why easy for m = 2? Low var. ~ small test setTest set well spread out: no bucket more than
O(1).O(1)-independence suffices.x
1x3
xk
1
… … x5
x4
x2
2 t
xn
x3
xk
x5
Low Variance Combinatorial Sums
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For general m: can have small biases.Each coordinate has non-zero but small bias.
x1
x3
xk
1
… … x5
x4
x2
2 t
xn
Low Variance Combinatorial Sums
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Total variance Variance in each bucket !Let’s exploit that.
x1
x3
xk
1
… … x5
x4
x2
2 t
xn
Low Variance Combinatorial Sums
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Use 22-wise independence in each bucket.
Union bound across buckets.Proof of lemma: sandwiching
polynomials.
Summary of PRG for CSums
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1. PRGs for low-var CSumsBounded independence, hashingSandwiching polynomials
2. PRGs for high-var CSums in cdfPRG for halfspaces
3. PRG on n/2 vars + PRG in cdf PRG for high-var CSums.
PRG for CSums
This Talk
34
1. PRGs for Cshapes with m = 2.Illustrates main ideas for general case.
2. PRG for general Cshapes.
3. Proof of discrete central limit theorem.
Discrete Central Limit TheoremClose in stat. distance to binomial
distribution
35
Thm:
Lem:
Convolution Lemma
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Same mean, variance
All four approx.same means,
variances
Discrete Central Limit Theorem
37
Discrete Central Limit Theorem
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By CLT: small.By unimodality: shift
invariant.
Hence proved!General integer valued case
similar.
All parts have similar means and variances
Open Problems
39
Optimal dependence on error rate?Non-explicit: Solve for halfspaces
More general/better notions of symmetry?Capture “order oblivious” small space.
Better PRGs for Small Space?
40
Thank You