Loading in 2 Seconds...

Time-space tradeoff lower bounds for non-uniform computation

Loading in 2 Seconds...

- 368 Views
- Uploaded on

Download Presentation
## PowerPoint Slideshow about 'Time-space tradeoff lower bounds for non-uniform computation' - RoyLauris

**An Image/Link below is provided (as is) to download presentation**

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

### Time-space tradeoff lower bounds for non-uniform computation

Paul Beame

University of Washington

4 July 2000

Why study time-space tradeoffs?

- To understand relationships between the two most critical measures of computation
- unified comparison of algorithms with varying time and space requirements.
- non-trivial tradeoffs arise frequently in practice
- avoid storing intermediate results by re-computing them

e.g. Sorting n integers from [1,n2]

- Merge sort
- S = O(n log n), T = O(n log n)
- Radix sort
- S = O(n log n), T = O(n)
- Selection sort
- only need - smallest value output so far - index of current element
- S = O(log n) , T = O(n2)

Complexity theory

- Hard problems
- prove LP
- prove non-trivial time lower bounds for natural decision problems in P
- First step
- Prove a space lower bound, e.g. S=w (log n), given an upper bound on time T, e.g. T=O(n) for a natural problem in P

An annoyance

- Time hierarchy theorems imply
- unnatural problems in P not solvable in time O(n)
- Makes ‘first step’ vacuous for unnatural problems

Non-uniform computation

- Non-trivial time lower bounds still open for problems in P
- First step still very interesting even without the restriction to natural problems
- Can yield bounds with precise constants
- But proving lower bounds may be harder

Talk outline

- The right non-uniform model (for now)
- branching programs
- Early success
- multi-output functions, e.g. sorting
- Progress on problems in P
- Crawling
- restricted branching programs
- That breakthrough first step (and more)
- true time-space tradeoffs
- The path ahead

Branching programs

x1

1

x3

x2

0

x4

x5

x5

x=(0,0,1,0,...)

x3

x1

To compute

f:{0,1} n {0,1}

on input (x1,…,xn)

follow path from

source to sink

x2

x7

x7

x8

0

1

Branching program properties

- Length = length of longest path
- Size = # of nodes
- Simulate TM’s
- node = configuration with input bits erased
- time T= Length
- space S=log2Size =TM space +log2n (head) = space on an index TM
- polysize = non-uniform L

TM space complexity

read-only input

x1 x2 x3 x4 … xn

working storage

Space = # of bits

of working storage

output

Branching program properties

- Simulate random-access machines (RAMs)
- not just sequential access
- Generalizations
- Multi-way version for xi in arbitrary domain D
- good for modeling RAM input registers
- Outputs on the edges
- good for modeling output tape for multi-output functions such as sorting
- BPs can be leveled w.l.o.g.
- like adding a clock to a TM

Talk outline

- The right non-uniform model (for now)
- branching programs
- Early success
- multi-output functions, e.g. sorting
- Progress on problems in P
- Crawling
- restricted branching programs
- That breakthrough first step (and more)
- true time-space tradeoffs
- The path ahead

Success for multi-output problems

- Sorting
- T S = W (n2/log n) [Borodin-Cook 82]
- T S = W (n2) [Beame 89]
- Matrix-vector product
- T S = W (n3) [Abrahamson 89]
- Many others including
- Matrix multiplication
- Pattern matching

Proof ideas: layers and trees

v0

- m outputs on input x
- at least m/r outputs in some tree Tv
- Only2Strees Tv
- Typical Claim
- ifT/r = en,each treeTvoutputspcorrect answers on only a c-pfraction of inputs
- Correct for all x implies 2Sc-m/r is at least 1
- S=W(m/r)=W(mn/T)

v1

T/r

v

T/r

vr-1

T

vr

0

1

Limitation of the technique

- Never more than T S =W (nm) where m is number of outputs
- “It is unfortunately crucial to our proof that sorting requires many output bits, and it remains an interesting open question whether a similar lower bound can be made to apply to a set recognition problem, such as recognizing whether all n input numbers are distinct.” [Cook: Turing Award Lecture, 1983]

Talk outline

- The right non-uniform model (for now)
- branching programs
- Early success
- multi-output functions, e.g. sorting
- Problems in P
- Crawling
- restricted branching programs
- That breakthrough first step (and more)
- true time-space tradeoffs
- The path ahead

Restricted branching programs

- Constant-width - only a constant number of nodes per level
- [Chandra-Furst-Lipton 83]
- Read-once - every variable read at most once per path
- [Wegener 84], [Simon-Szegedy 89], etc.
- Oblivious - same variable queried per level
- [Babai-Pudlak-Rodl-Szemeredi 87], [Alon-Maass 87], [Babai-Nisan-Szegedy 89]
- BDD = Oblivious read-once

BDDs and best-partition communication complexity

x7

- Givenf:{0,1}8->{0,1}
- Two-player game
- Player A has {x1,x3,x6,x7}
- Player B has {x2,x4,x5,x8}
- Goal:communicate fewest bits possible to compute f
- Possible protocol: Player A sends the name of node.
- BDD space # of bits sent for best partition into A and B

x1

x6

A

x3

x2

x8

B

x4

x5

0

1

Communication complexity ideas

- Each conversation for f:{0,1}Ax{0,1}B{0,1} corresponds to arectangleYAxYBof inputs YA {0,1}AYB {0,1}B
- BDD lower bounds
- sizemin(A,B)# of rectangles in tiling of inputs by f-constant rectangles with partition (A,B)
- Read-once bounds
- same tiling as BDD bounds but each rectangle in tiling may have a different partition

Restricted branching programs

- Read-k - no variable queried >k times on
- any path - syntactic read-k
- [Borodin-Razborov-Smolensky 89], [Okol’nishnikova 89], etc.
- any consistent path - semantic read-k
- many years of no results
- nothing for general branching programs either

Uniform tradeoffs

- SAT is not solvable using O(n1-e) space if time is n1+o(1). [Fortnow 97]
- uses diagonalization
- works for co-nondeterministic TM’s
- Extensions for SAT
- S=logO(1) n impliesT= W (n1.4142..-e ) deterministic[Lipton-Viglas 99]
- with up to no(1)advice [Tourlakis 00]
- S= O(n1-e) implies T=W (n 1.618..-e). [Fortnow-van Melkebeek 00]

Non-uniform computation

- [Beame-Saks-Thathachar FOCS 98]
- Syntactic read-k branching programs exponentially weaker than semantic read-twice.
- f(x) = “xTMx=0 (mod q)” x GF(q)n
- e nloglog n time W(n log1-en) space for q~n
- f(x) = “xTMx=0 (mod 3)” x {0,1}n
- 1.017n time implies W (n) space
- first Boolean result above time n for general branching programs

Non-uniform computation

- [Ajtai STOC 99]
- 0.5lognHamming distance for x [1,n2]n
- kn time implies W(n logn) space
- follows from [Beame-Saks-Thathachar 98]
- improved to W(nlog n) time by [Pagter-00]
- element distinctness for x [1,n2]n
- kn time implies W(n) space
- requires significant extension of techniques

That breakthrough first step!

x {0,1}n

- [Ajtai FOCS 99]
- f(x,y) = “xTMyx (mod 2)”
- kn time implies W(n) space
- First result for non-uniform Boolean computation showing
- time O(n) spacew(log n)

y {0,1}2n-1

Superlinear lower bounds

- [Beame-Saks-Sun-Vee FOCS 00]
- Extension to e-errorrandomized non-uniform algorithms
- Better time-space tradeoffs
- Apply to both element distinctness and f(x,y) = “xTMyx (mod 2)”

(m,a)-rectangles

- An (m,a)-rectangleRDXis a subset defined by disjoint sets A,BX, s DAUB SA DA, SB DBsuch that
- R = { z | zAUB = s, zA SA, zB SB }
- |A|,|B| m
- |SA|/|DA|, |SB|/|DB|a

m

m

x1

xn

SA

SB

A

B

An (m,a)-rectangleSA

SB

DB

DA

SA and SB eachhave density at least a

In general A and Bmay be interleaved in [1,n]

Key lemma [BST 98]

- Let program P use
- time T = kn
- space S
- accept fraction d of its inputs in Dn
- then P accepts all inputs in some (m,a)-rectangle where
- m = bn
- ais at leastd 2-4(k+1)m - (S+1)r
- b-1 ~ 2k and r~ k2 2k

Improved key lemma [Ajtai 99 s]

- Let program P use
- time T = kn
- space S
- accept fraction d of its inputs in Dn
- then P accepts all inputs in some (m,a)-rectangle where
- m = bn
- ais at least
- b-1 and r are constants depending on k

Proving lower bounds using the key lemmas

- Show that the desired function f
- evaluates to 1 a large fraction of the time
- i.e., d is large
- evaluates to 0 on some input in any large(m,a)-rectangle
- where large is given by the lemma bounds
- or ... do the same for f

Our new key lemma

- Let program P use time T = kn space S and accept fraction d of its inputs in Dn
- Almost all inputsP accepts are in (m,a)-rectangles accepted by P where
- m = bn
- ais at least
- b-1 and r are
- no input is in more thanO(k)rectangles

Proving randomized lower bounds from our key lemma

- Show that the desired function f
- evaluates to 1 a large fraction of the time
- i.e,d is large
- evaluates to 0 on a g fraction of inputs in any large-enough (m,a)-rectangle
- or ... do the same for f
- Gives space lower bound forO(gd/k)-errorrandomized algorithms running in time kn

(v1,…,vr-1)

(v1,…,vr-1)

f

f

f

(v1,…,vr-1)

vi-1vi

vi-1vi

Proof ideas: layers and treesv0

f =

v1

kn/r

v2

# of (v1,…,vr-1) is 2S(r-1)

kn

r

kn/r

vr-1

=

i=1

vr

can be computed inkn/r height

0

1

f

(v1,…,vr-1)

(v1,…,vr-1)

(r,e)-decision forest- The conjunction of r decision trees (BP’s that are trees) of height en
- Each is a computed by a (r,k/r)-decision forest
- Only 2S(r-1) of them
- The various accept disjoint sets of inputs

T2

T3

T4

Tr

Decision forest- Assume wlog all variables read on every input
- Fix an input x accepted by the forest
- Each tree reads only a small fraction of the variables on input x
- Fix two disjoint subsets oftrees,FandG

kn/r

Core variables

kn/r

- Can split the set of variables into
- core(x,F)=variables read only inF (=not read outside F)
- core(x,G)=variables read only in G (=not read outside G)
- remaining variables
- stem(x,F,G)=assignment to remaining variables
- General idea: use core(x,F), core(x,G), and stem(x,F,G) to define (m,a)-rectangles

T1

T2

T3

T4

Tt

A partition of accepted inputs

- Fix F, G,xaccepted byP
- Rx,F,G={ y | core(y,F)=core(x,F),core(y,G)=core(x,G),stem(y,F,G)=stem(x,F,G), and P accepts y}
- For each F, G the Rx,F,G partition the accepted inputs into equivalence classes
- Claim: the Rx,F,G are (m,a)-rectangles

Classes are rectangles

- Let A=core(x,F),B=core(x,G), s=stem(x,F,G)
- SA={yA| y in Rx,F,G }, SB={zB| z in Rx,F,G }
- Letw=(s,yA,zB)
- wagrees with y in all trees outsideG
- core(w,G)=core(y,G)=core(x,G)
- wagrees with z in all trees outsideF
- core(w,F)=core(z,F)=core(x,F)
- stem(w,F,G)=s=stem(x,F,G)
- Paccepts w since it accepts yand z
- So... w is in Rx,F,G

Few partitions suffice

- Only 4k pairs F,G suffice to cover almost all inputs accepted by Pby large(m,a)-rectangles Rx,F,G
- Choose F,G uniformly at random of suitable size, depending on access pattern of input
- probability that F,G isn’t good is tiny
- one such pair will work for almost all inputs with the given access pattern
- Only 4ksizes needed.

Special case: oblivious BPs

- core(x,F), core(x,G) don’t depend on x
- Choose Tiin F with prob qG with prob qneither with prob 1-2q

Rectangles, rank, & rigidity

- largest rectangle on which xATMxB is constant has a2-rank(M)
- [Borodin-Razborov-Smolensky 89]
- Lemma [Ajtai 99] Can fix y s.t. every bnxbn minor MAB of My has rank(MAB) cbn/log2(1/b)
- improvement of bounds of [Beame-Saks-Thathachar 98] & [Borodin-Razborov-Smolensky 89] for Sylvester matrices

High rank implies balance

- For any rectangle SAxSB{0,1}Ax{0,1}B with m(SAxSB) |A||B|23-rank(M)Pr[xATMxB= 1 | xA SA, xB SB]1/32Pr[xATMxB= 0 | xA SA, xB SB]1/32
- derived from result for inner product in r dimensions
- So rigidity also implies balance for all large rectangles and so
- Also follows for element distinctness
- [Babai-Frankl-Simon 86]

Talk outline

- The right non-uniform model (for now)
- branching programs
- Early success
- multi-output functions, e.g. sorting
- Progress on problems in P
- Crawling
- restricted branching programs
- That breakthrough first step (and more)
- true time-space tradeoffs
- The path ahead

Improving the bounds

- What is the limit?
- T=W(nlog(n/S))?
- T=W(n2/S) ?
- Current bounds for general BPs are almost equal to best current bounds for oblivious BPs !
- T=W(nlog(n/S)) using 2-party CC [AM]
- T=W(nlog2(n/S)) using multi-party CC [BNS]

Improving the bounds

- (m,a)-rectangles a 2-party CC idea
- insight: generalizing to non-oblivious BPs
- yields same bound as [AM] for oblivious BPs
- Generalize to multi-party CC ideas to get better bounds for general BPs?
- similar framework yields same bound as [BNS] for oblivious BPs
- Improve oblivious BP lower bounds?
- ideas other than communication complexity?

Extension to other problems

- Problem should be hard for (best-partition) 2-party communication complexity (after most variables fixed).
- try oblivious BPs first
- Prime candidate: (directed) st-connectivity
- Many non-uniform lower bounds in structured JAG models [Cook-Rackoff], [BBRRT], [Edmonds], [Barnes-Edmonds], [Achlioptas-Edmonds-Poon]
- Best-partition communication complexity bounds known

Limitations of current method

- Need n>T/r = decision tree height
- else all functions trivial
- so r > T/n
- A decision forest works on a 2-Sr fraction of the accepted inputs
- only place space bound is used
- So need Sr<n else d.f. need only work on one input
- implies ST/n < n, i.e. T < n2/S

Download Presentation

Connecting to Server..