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Exact and Approximate Distances in Graphs – A Survey. Uri Zwick Tel Aviv University. v. u. Distances and Shortest Paths. Variations. undirected directed. unweighted non-negative integer weights integer weights non-negative real weights real weights. given pair(s) single source - PowerPoint PPT Presentation
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ExactExact and and ApproximateApproximate Distances in Graphs – Distances in Graphs –
A SurveyA Survey
Uri ZwickUri Zwick
Tel Aviv UniversityTel Aviv University
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Distances and Shortest Distances and Shortest PathsPaths
u
v
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VariationsVariations
unweightednon-negative integer weights
integer weightsnon-negative real weights
real weights
undirecteddirected
exact resultsadditive error
multiplicative error
given pair(s)single source
all pairs
SpannersDistance oracles
deterministicrandomized
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Models of ComputationModels of Computation
Integer weights: word RAM modelEach weight is contained in a w-bit word. Allowed to perform additions, subtractions, comparisons, shifts, ANDs, ORs, XORs, and other bit operations.
Real weights: addition-comparison modelThe only operations allowed on edge weights are additions (subtractions) and comparisons. We again assume random access capabilities.
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Single-Source Shortest Paths“Classical” results
Unweighted graphsm+nBFS
Nonnegative real edge weights
m+n log nDijkstra ’59 Fredman-Tarjan ’87
General real edge weightsm nBellman ‘58
Ford ‘62
Integer edge weightsmn1/2log N
Goldberg ’95(Gabow-Tarjan ’89)
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SSSP, Priority queues and Sorting
Time of Dijkstra = m*(decrease key) + n*(extract min).
Monotone priority queues are enough.
Dijkstra’s algorithm sorts the distances.
If n elements can be sorted in nf(n) time, then SSSP can be solved in mf(n) time. [Thorup ’96]
For undirected graphs, the sorting bottleneck can be avoided! [Thorup ’97]
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Single-Source Shortest Pathsdirected graphs, nonnegative integer edge weights, randomized algorithms
m loglog nThorup ’96
m+(n log n)/w1/2-
Thorup ’96
m+nw1/4+
Raman ’97, Cherkassky-
Goldberg-Silverstein ’97
m+n(log n)1/3+Raman ’97
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Single-Source Shortest Paths
undirected graphs, nonnegative integer edge weights, deterministic algorithm
m+nThorup ’97
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Single-Source Shortest Paths deterministic algorithms
undirected
m(m,n)+n
loglog RPettie- Ramachandra
n ’01 directedm+n log RR – ratio between largest and smallest edge
weights
positive real weights
nonnegative integer weights
directedm log wHagerup ’00
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Open Problems: Open Problems: SSSPSSSP1. Directed SSSP with real edge
weights in o(mn) time?
2. Directed SSSP with integer edge weights in o(mn1/2log N) time?
3. Directed SSSP with non-negative integer edge weights in linear time?
4. What is the complexity of the SSSP problem with non-negative weights in the addition-comparison model?
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All-Pairs Shortest PathsAll-Pairs Shortest PathsDirected?WeightsComplexityReference
Yesrealmn+n2 log nJohnson ‘77
Yesreal+m*n+n2 log n
Kar-Kol-Phi ’93
McGeoch ‘95
Yesintegermn+n2 loglog nHagerup ’00
NointegermnThorup ’97
Norealmn(m,n)Pettie-Rama.
’01
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Min-Plus (Distance) Product
125
703
48
528
5
731
571
252
1036
}{min kjikk
ij bac
BAC
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Algebraic Product
kkjikij bac
BAC
The algebraic product of two n by n matrices over a ring can be computed using n algebraic operations (additions, subtractions, multiplications), where <2.376.
Strassen ’69, … , Coppersmith-Winograd ’90
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APSP and DPIf D is an n by n matrix containing the edge weightsof a graph, then Dn is the distance matrix.
APSP(n) DP(n) log n
APSP(n) 6 ( DP(n/2) + 2 DP(n/4) + 4 DP(n/8) + …) + O(n2)
DP(n) APSP(3n)
Furman ’70, Munro ’71, Fischer-Meyer ’71
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All-Pairs Shortest Paths directed graphs, real weights
n3Floyd ’62 Warshall
’62
*** n5/2 ***Fredman ‘76
n3(loglog n/log n)1/2
Fredman ’76
Takaoka ’92
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All-Pairs Shortest Paths undirected graphs, weights from {1,2,
…,M}
Mn < Mn2.367
Galil-Margalit ’92Alon-Naor ’92
Seidel ’92Shoshan-Zwick
’99 – exponent of fast matrix multiplicationCoppersmith-Winograd ’90]
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Seidel’s Algorithm unweighted undirected graphs
running time: n log n
Algorithm Seidel(A)if A=J then
return J-Ielse
C Seidel(A2)X CA , deg Ae-1dij 2cij – [xij < cijdegj]return D
endif
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All-Pairs Shortest Paths directed graphs, weights from {-M,…,0,
…,M}
(Galil-Margalit ’91)Zwick ‘98
nMnM
nM2.5750.681
ω4
12
ω4
1
2αω4
1)α(ω1
2αω4
α1
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Rectangular Matrix Multiplication
n
n n
n
nn
n
- The largest constant such that these algebraic products can be computed using n2+o(1) operations.>0.294
Coppersmith ‘97
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Sampled Distance Products
F D for i 1 to log3/2n do{
s (3/2)i
B rand(V,(10n ln n)/s)F min{ F , F[V,B]*F[B,V] }
}
n
n
n
|B|
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All-Pairs Shortest Paths directed graphs, weights from
[1,W](1+)-approximate distances and
paths
(n/) log(W/)Zwick ’98
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Open Problems: Open Problems: APSPAPSP
5. Are n5/2 additions-comparisons needed?
6. An n3- time algorithm in the add-comp model, counting all operations?
7. An n3-logM time algorithm?
8. An n5/2 time algorithm for unweighted directed graphs?
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An estimated distance ’(u,v) is of stretch t iff
(u,v) ’(u,v) t (u,v)
An estimated distance ’(u,v) is of surplus t iff
(u,v) ’(u,v) (u,v) + t
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All-Pairs Almost Shortest Pathsunweighted, undirected graphs
SurplusTime*Reference
2n5/2Aingworth-Chekuri-Indyk-Motwani ’96
2n3/2m1/2 , n7/3Dor-Halperin-Zwick ’96
4n5/3m1/3 , n11/5“
2(k-1)kn2-1/km1/k
kn2+1/(3k-4)“
Ignoring polylogarithmic factors
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All-Pairs Almost Shortest Pathsweighted undirected graphs
Stretch
Time*Reference
2n3/2m1/2Cohen-Zwick ‘97
7/3n7/3“
3n2“
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Multiplicative/Additive Approximations
For every >0, there is b=b(), such that an estimated distance ’(u,v) satisfying
(u,v) ’(u,v) (1+)(u,v) + b() , for every uS and vV,
can be computed in
O(mn+|S|n1+) time.
(Elkin-Peleg ’01) , Elkin ‘01
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Open Problems: Open Problems: Approx.Approx.
APSPAPSP9. Improve the surplus/time tradeoff.
Finite surplus in n2+o(1) time?
10.Improve the stretch/time tradeoff. Stretch < 3 in n2+o(1) time?
11.Further explore multiplicate/additive approximations.
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Spanners
Let G be a weighted undirected graph.
A subgraph H of G is a t-spanner of G
iff u,vG,
H(u,v) t G(u,v) .
Awerbuch ’85Peleg-Schäffer ‘89
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Example
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Theorem
For every k>1, every weighted
undirected graph on n vertices
has a (2k-1)-spanner with at
most n1+1/k edges.
Tight for k=1,2,3,5. Conjectured to be
tight for any k – equivalent to a girth
conjecture of Erdös.
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Proof/Algorithm: Consider the edges in non-decreasing
order of weight. Add each edge to the
spanner if it does not close a cycle of size
at most 2k.
The resulting graph is a (2k-1)-spanner
and it does not contain a cycle of size at
most 2k. Hence the number of edges is at
most n1+1/k.
[Althöfer, Das, Dobkin, Joseph, Soares ‘93]
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If |cycle|2k, then red edge can be removed.
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(a,b)-Spanners
Let G be an unweighted undirected graph.
A subgraph H of G is an (a,b)-spanner of G
iff u,vG,
H(u,v) a G(u,v) + b .
(Dor-Halperin-Zwick ’96, a=1)
Peleg-Elkin ‘01
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(a,b)SizeReference
(1,2)n3/2Dor-Halperin-Zwick ’96
(1+,b())n1+Elkin-Peleg ‘01
(a,b)-Spanners
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Open Problems: Open Problems: SpannersSpanners
12.Are there b<, and >0, such that every unweighted undirected graph on n vertices has a (1,b)-spanner with n3/2- edges?
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All-Pairs Shortest Paths
APSPalgorithm
n by ndistancematrix
The output matrix may be much larger than the input graph !!!
Input graph
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(Approximate) Distance Oracles
Preprocessingalgorithm
Compactdata structure
(u,v) ’(u,v)Query answeringalgorithm
Input graph
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Approximate Distance Oracles
StretchQuery time
SpacePreproc
. time
Reference
64kkn1/k
kn1+1/
kkmn1/k
Awerbuch-Berger-
Cowen-Peleg ‘93
2k+kn1/kCohen ‘93
2k-1kThorup-
Zwick ‘01
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Open Problems: Open Problems: Distance OraclesDistance Oracles
13.Deterministic construction of(2k-1,n1+1/k,k)-distance oracles in o(mn) time?
14.Constructing a (3,n3/2,1)-distance oracle in n2+o(1) time?
15.Distance oracles withadditive errors?
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A Slightly updated version of the survey can be found at
http://www.cs.tau.ac.il/~zwick/papers/dist-survey-
esa.ps.gzPlease send
suggestions/corrections/comments to