estimating the longest increasing sequence in polylogarithmic time
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Estimating the longest increasing sequence in polylogarithmic time. C. Seshadhri ( Sandia National Labs) Joint work with Michael Saks ( Rutgers University ). - PowerPoint PPT PresentationTRANSCRIPT
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Estimating the longest increasing sequence in polylogarithmic time
C. Seshadhri (Sandia National Labs)
Joint work with Michael Saks (Rutgers University)
Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000
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The problem
Given array f:[n] → N, find (length of)
Longest Increasing Subsequence (LIS) Rather self-explanatory
By now, textbook dynamic programming problem [CLRS 01] Chapter 15.4 (Longest Common Subsequence),
Starred Problem 15.4-6 [Schensted 61, Fredman 75] O(n log n) algorithm
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Too much to read
Array f is extremely large, so can’t read all of it What can we say about LIS length, if we see very little?
|LIS| = LIS length
Read only poly(log n) positions Obviously randomized
Algorithm
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|LIS| is in range [0.4n, 0.6n]
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Uniform sample says nothing
Choose uniform random sample of poly(log n) size |LIS| = n/2, but random sample always increasing
So not really that easy to learn about |LIS|…
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Our result
We want range to be small
1 n
|LIS|
Algorithm
|LIS| in this range
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Our result
We want range to be small [This work] For any (constant) δ > 0
Algorithm gives additive δn approximation to |LIS|
Running time is (1/δ)1/δ(log n)c
1 n
|LIS|
Algorithm
|LIS| in this range
δn
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Our result
We want range to be small [This work] For any (constant) δ > 0
Algorithm gives additive δn approximation to |LIS|
Running time is (1/δ)1/δ(log n)c
[Ailon Chazelle Liu S 03] [Parnas Ron Rubinfeld 03]
Previous best: δ = ½
1 nn/2
|LIS|
δn Ad alert!
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Our result
We want range to be small [This work] For any (constant) δ > 0
Algorithm gives additive δn approximation to |LIS|
Running time is (1/δ)1/δ(log n)c
We get (1+ δ)-approx to distance to monotonicity Previously best was factor 2
1 nn/2
|LIS|
δn Ad alert!
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Prelims: the array in space
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15
20
1 2 3
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Prelims: the array in space
Input is points in plane, given as array (LIS is longest chain in partial order)
Increasing sequenceViolation
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A hard example
The decision for a point depends on small scale properties of “far away” portions
k
k
k 3k
k
k
k3k
10 points in each
|LIS| = 4k |LIS| = 2k
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A hard example
Random samples in neighborhoods of points are identical! “Can we really estimate LIS in polylog time?” Is it time for some heavy work?
I mean, time for lbs (lower bounds).
k k
k
k 3k
k
k3k
Outline (or lack thereof)
Will I show proofs? No
Will I show the algorithm? Maybe
I will try to demonstrate the main insight By a series of thought experiments
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The dynamic program
Closest LIS point to left gives “splitter” Find LIS is each blue region. Piece together!
So we break up original problem into subproblems
n/2
Splitter
Closest LIS point to left
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The dynamic program
But we don’t know right splitter. So try all possible! Only n different choices
Choose the one that gives the largest sum of LIS’s MaxS (|LIS-below-S| + |LIS-above-S|)
n/2
S
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The dynamic program
If you LIS in all small boxes, you can build LIS for bigger boxes
Not the most efficient DP… So our sublinear algo will mimic this process
n/2
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The IP
n/2
Is this pointon LIS?
Where is the splitter?
It is there.
LIS is in blue region
Splitter
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The IP
n/2
Where is the splitter?
It is there.
LIS is in blue region
This pointNOT on LIS
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The IP
n/2
I wish we knew the splitter in that region
It is there.
3n/4
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The IP
n/2
I think I know what will happen
next
You’re lucky I’m
here
3n/45n/8
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The IP
n/2
I think I know what will happen
next
You’re lucky I’m
here
3n/45n/8
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The IP
n/2
I think I know what will happen
next
You’re lucky I’m
here
3n/45n/8
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The interactive protocol
If point stays in blue region till very end, then it is good (on LIS). Otherwise, bad.
This takes (log n) steps, with the help of the wizard
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The interactive protocol
If point stays in blue region till very end, then it is good (on LIS). Otherwise, bad.
This takes (log n) steps, with the help of the wizard If we could simulate the wizard…
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The interactive protocol
If point stays in blue region till very end, then it is good (on LIS). Otherwise, bad.
This takes (log n) steps, with the help of the wizard If we could simulate the wizard…
What?? If you could simulate the wizard, you know the LIS!
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Find a splitter
n/2
Finding splitter may be hard, so try for approximate versions…?
But how do we determine the number of LIS points?
If very few LIS pointsoutside blue, this is not a bad splitter
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Find a splitter
n/2
If μ < 1/(100 log n), being against health care conservative is good enough
Total no. of points outside blue< μn
Conservative splitter
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Easy to check
n/2
Count fraction of sample outside blue poly(log n) samples checks this accurately
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Getting a conservative splitter
n/2
We can sample (log n) different candidates and check which of them disbelieves evolution is conservative
What if no conservative splitter exists?
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A liberal paradise
n/2
So we know that |LIS| < (1-μ) n
Leads to the next idea. Boosting approximations! Given δ-approx to LIS, can we get improve to δ’?
Choose any lineNo. of points outsideat least μn
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Boosting approximations
n/2
Take sum of outputs as total LIS estimate |LIS| = |LIS1| + |LIS2|, Est = Est1 + Est2
|Est1 – LIS1| < δn1 |Est2 – LIS2| < δn2
So |Est – LIS| < δ(n1 + n2)
n1+n2 < (1-μ)n, so |Est – LIS| < δ(1-μ)n !
Real splitter
No. of points outsideat least μn
Run δn-approxon points in box
Run δn-approxon points in box
n1
n2
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Putting it together
n/2
Check if each is conservative splitter If it is, we’re found right subproblems
Otherwise…
Conservative splitter?
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Putting it together
n/2
One of these is “close enough” to real splitter Est(S) = Left-Est(S) + Right-Est(S)
Run δn-approxon points in box
Run δn-approxon points in box
S
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Putting it together
n/2
One of these is “close enough” to real splitter Est(S) = Left-Est(S) + Right-Est(S) Final Estimate = maxS Est(S) Looks like a great idea!
We go from δn to δ(1- μ)n. Recur to keep improving approximation
Run δn-approxon points in box
Run δn-approxon points in box
S
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It fails, miserably
As we go up each level, approx gets better by (1-μ). So to get δ0 = ¼, how many levels needed?
¼ = ½ (1-μ)t So t = 1/μ We have running time at least 21/μ.
So, μ needs to be > 1/log log n.
Alg
AlgAlg
AlgAlg AlgAlg
δ0 = δ1(1-μ)
δ1
δ2
AlgAlg AlgAlg ½
1/μ
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Find a splitter
n/2
If μ < 1/(100 log n), being against health care conservative is good enough
Total no. of points outside blue< μn
Conservative splitter
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The basic dichotomy
P
We find splitter
Continue IP
Cannot find splitter
The “Dynamic Programming”phase
The “Interactive Protocol”phase
For IP, we need μ < 1/log n μn is error in each “level” of IP
For DP, we need μ > 1/log log n (1-μ) is decrease in approximation
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The basic dichotomy
P
We find splitter
Continue IP
Cannot find splitter
The “Dynamic Programming”phase
The “Interactive Protocol”phase
For IP, we need μ < 1/log n μn is error in each “level” of IP
For DP, we need μ > 1/log log n (1-μ) is decrease in approximation
Weaken Strengthen
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Reducing to smaller DP!
Run δ-approx on all poly(log n) such boxes
n/(log n)
n/(log n) Run δ-approx to get LISestimate inside box
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Reducing to smaller DP!
Run δ-approx on all poly(log n) such boxes Use Dynamic Program to find chain with largest sum of
estimates Longest path in DAG Can solve in poly(log n) time
Chain
n/(log n)
n/(log n)
Dichotomy theorem
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Either it is easy to find theright subproblems
One can go from δ-approx to (δ-δ2)-approx by a (log n) sized DP
OR
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The algorithm, in one slide
Overall running time becomes (log n)1/δ
*&^#$% miracle that the math works out
P
We find splitter
Continue IP
Cannot find splitter
Make poly(log n) calls to δ-approx. Solve DP of poly(log n) size.
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The even better version
Don’t exactly solve this dynamic program! Use our sublinear algo to approximately solve in
(loglog n) time. Then do it recursively… It’s painful
It’s all Greek: α β γ δ ε ζ λ μ ξ We had ν, but got rid of it
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What next? Sublinear dynamic programming! We get (1/δ)1/δ (log n)c time. Can we get
(log n)/δ time? Would be extremely cool. Completely optimal
Applications for other dynamic programs? How does one find the “right” subproblems in sublinear
time? Generalize the dichotomy Longest common subsequence/edit distance…?
Ask and you shall know…
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