Title: Data-Powered%20Algorithms
1Data-Powered Algorithms
- Bernard Chazelle
- Princeton University
2 3 Linear Programming
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12N constraints and d variables
13N constraints and d variables
14Dimension Reduction
?25
?10000
Images (face recognition) Signals (voice
recognition) Text (NLP) . . . Nearest neighbor
searching Clustering . . .
15Dimension reduction
All pairwise distances nearly preserved
16Johnson-Lindenstrauss Transform (JLT)
d
v
Random Orthogonal Matrix
c log n ?2
d
17Friendly JLT
d
N(0,1)
N(0,1)
N(0,1)
N(0,1)
N(0,1)
N(0,1)
N(0,1)
N(0,1)
c log n ?2
N(0,1)
N(0,1)
N(0,1)
N(0,1)
N(0,1)
N(0,1)
N(0,1)
N(0,1)
18Friendlier JLT
d
c log n ?2
d log n ?2
?( )
19Sparse JLT ?
d
0
. . .
0
0
0
0
0
0
c log n ?2
1
d
0
0
0
0
0
. . .
o(1)-Fraction non-zeros
0
20Main Tool Uncertainty Principle
Heisenberg
Time
Frequency
21Fast Johnson-Lindenstrauss Transform (FJLT)
d
d
d
Discrete Fourier Transform
0 N(0,1)
d
. . .
Optimal ??
22 23theory
experimentation
24theory
experimentation
computation
25theory
experimentation
computation
26input
output
Most interesting problems are too hard !!
27input
output
So, we change the model
randomization
approximation
28input
output
PTAS for ETSP
randomization
approximation
29input
output
Impossible to approximate chromatic number
within a factor of
randomization
approximation
30input
output
Berkeley school (program checking
probabilistic proofs)
randomization
Property Testing RS96, GGR96
approximation
31 32Distance is 3
33Distance is 4
34no
bipartite
yes
35no
anything
bipartite
yes
GR97
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38Mixing case
18
17
7
62
polylog cycles
Birthday paradox
39Non-mixing case
Nonmixing implies small cuts
M89
40Dense graphs
Is graph k-colorable?
GGR98, AK99
41Main tool
Szemerédis Regularity Lemma
Far from k-colorable
Lots of witnesses
42Property Testing
http//www.cs.princeton.edu/chazelle/
- Graph algorithms
- connectivity
- acyclicity
- k-way cuts
- clique
- Distributions
- independence
- entropy
- monotonicity
- distances
- Geometry
- convexity
- disjointness
- delaunay
- plane EMST