Dynamic shortest paths
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Dynamic Shortest Paths. Camil Demetrescu University of Rome “La Sapienza” Giuseppe F. Italiano University of Rome “Tor Vergata”. update weight of edge (u,v) to w. Update (u,v,w) :. return distance from x to y. Query (x,y) :. (or shortest path from x to y).

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Dynamic Shortest Paths

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Dynamic shortest paths

Dynamic Shortest Paths

Camil Demetrescu

University of Rome “La Sapienza”

Giuseppe F. Italiano

University of Rome “Tor Vergata”


Dynamic all pairs shortest paths

update weight of edge (u,v) to w

Update(u,v,w):

return distance from x to y

Query(x,y):

(or shortest path from x to y)

Dynamic (All Pairs) Shortest Paths

Given a weighteddirectedgraph G=(V,E,w),perform any intermixed sequence of the following operations:


Previous work on dynamic apsp

Previous work on dynamic APSP

# papers

65-69

70-74

75-79

80-84

85-89

90-94

95-99

00-


Previous work on fully dynamic apsp

Graph

Weight

Update

Query

~

reals

O(n3)

Update recomputingfrom scratch

general

O(1)

~

[0,C]

O(n2.575 C0.681)

King 99

general

[0,C]

O(n2.5 (C log n)0.5)

O(1)

Henzinger et al. 97

planar

[0,C]

O(n9/7 log(nC))

Fackcharoemphol,

Rao 01

planar

reals

O(n9/5 log13/5 n)

general

reals

? < o(n3)

O(1)

D, Italiano 01

general

S reals

O(n2.5 (S log3 n)0.5)

O(1)

Previous work on fully dynamic APSP


A new approach

New combinatorial properties of graphs:

Uniform paths

A new approach


A new fully dynamic algorithm

Graph

Weight

Update

Query

King 99

general

[0,C]

O(n2.5 (C log n)0.5)

O(1)

Henzinger et al. 97

planar

[0,C]

O(n9/7 log(nC))

Fackcharoemphol,

Rao 01

planar

reals

O(n9/5 log13/5 n)

D, Italiano 01

general

S reals

O(n2.5 (S log3 n)0.5)

O(1)

D, Italiano 02

general

reals

O(n2 log n)

O(1)

A new fully dynamic algorithm


N 2 changes per update

(n)

(n)

(n2) changes per update

+1

-1

+1


Uniform paths

A path is uniform if all of its

subpaths are shortest paths

x

y

πxy

Shortest path

Shortest path

Not a shortest path

Shortest path

x

y

πxy

Uniform paths

UNIFORM

NOT

UNIFORM


Properties of uniform paths

Uniform paths

Shortest paths

Properties of Uniform paths

Theorem I

Shortest paths  Uniform paths


Properties of uniform paths1

For the sake of simplicity, we assume that no two

paths in the graph have the same weight.

Theorem II

Uniform paths πxy are internally vertex-disjoint

π1

π2

x

y

π3

Properties of Uniform paths

(Ties can be broken by adding a tiny fraction to the

weight of each edge)


Properties of uniform paths2

x

y

Properties of Uniform paths

Theorem III

There are at most n-1 uniform paths connecting x,y

This is a consequence of vertex-disjointess…


Dynamic graphs

We call dynamic graph a sequence of graphs <G0,…,Gk> such that, for any t, Gt-1and Gt differ in the weight of exactly one edge

Dynamic graphs


Uniform paths in dynamic graphs

A uniform path in Gt is appearing if it is not uniform in Gt-1

Gt-1

x

y

Gt

x

y

Uniform paths in dynamic graphs


Uniform paths in dynamic graphs1

Gt

x

y

A uniform path in Gt is disappearing if it is not uniform in Gt+1

Gt+1

x

y

Uniform paths in dynamic graphs


Uniform paths in dynamic graphs2

Theorem IV

At most n uniform paths can appear after an increase

At most 1 uniform path can disappear after an increase

Corollary

The amortized number of appearing uniform paths

per update in an increase-only sequence is O(1)

Uniform paths in dynamic graphs


Uniform paths in dynamic graphs3

What about fully dynamic sequences?

10

10

20

20

x

x

y

y

100

30

30

The weights of uniform paths that disappear and

then reappear do not change…

40

40

Uniform paths in dynamic graphs


Uniform paths in dynamic graphs4

We conjecture that the bound is O(1)…

Uniform paths in dynamic graphs

Theorem V

For any pair (x,y), the amortized number of uniform

paths πxyappearingwith a new weight per update in

a fully dynamic sequence is O(log n)


Shortest paths and edge weight updates

The shortest path is the same, but has different weight:

EASY

EASY

x

y



The shortest path is different (update = decrease):

x

y

-

Shortest paths and edge weight updates

How does a shortest path change after an update?


Shortest paths and edge weight updates1

a

b

+

HARD

The shortest path is different (update = increase):

If we look closer, we realize that the new shortest path

from a to b was already uniform before the update!

x

y

Shortest paths and edge weight updates


A new approach to dynamic apsp

For each pair x,y, maintain in a data structure uniform pathsconnecting x to y

Main idea:

The combinatorial properties of uniform paths imply that only a small piece of information needs to be updated at each time…

Are we done?

A new approach to dynamic APSP

NO


How to pay only once

x

x

y

y

x

y

This path stays the same while flipping

between uniform and non-uniform:

We would like to have an update algorithm that pays only once for it

over the whole sequence...

How to pay only once?


Looking at the substructure

may

x

y

resurrect!

x

y

This path remains a shortest path

after the insertion

This path is no longer a shortest path

after the insertion…

Looking at the substructure

…but if we removed the edge it would get a shortest path again!


Zombies

Zombies

A path is azombieif it used to be a shortest path,

and its edges have not been updated since then


Potentially uniform paths

shortest path

x

Uniform path

πxy

y

shortest path

shortest path or zombie

x

Potentiallyuniform path

πxy

y

shortest path or zombie

Potentially uniform paths

Relaxed notion of uniformity:

Subpaths do not need to be shortest at the same time


Properties of potentially uniform paths

Theorem I

Uniform paths  Potentially uniform paths

Uniform paths

Shortest paths

Properties of potentially uniform paths

Potentiallyuniform paths


Properties of potentially uniform paths1

Properties of potentially uniform paths

Theorem II

O(zn2) zombies at any time

O(zn2) new potentially uniform paths per update


How many zombies can we have

A lot!

We can construct a dynamic graph with (n3) zombies at any time, amortized.

100

100

100

(n)

(n)

90

90

100

90

80

80

(n)

70

= (n3)

# zombies = (n2)

+(n2)

+(n2)

(n)

How many zombies can we have?


Reducing of zombies smoothing

100

90

100

90

80

100

100

100

100

70

100

100

(n)

(n)

90

90

90

90

100

90

90

80

80

80

80

(n)

70

70

(n2)

(n2)

(n2)

0 (in general, O(n2))

Reducing # of zombies: Smoothing

At each update we pick an edge with the maximum number of zombies passing through it, and we remove and reinsert it

# zombies =


A new approach to dynamic apsp ii

For each pair x,y, maintain in a data structure the potentially uniform paths connecting x to y

Main idea:

The combinatorial properties of potentially uniform paths imply that, if we do smoothing, we have only O(n2) new potentially uniform paths per update, amortized…

A new approach to dynamic APSP (II)

There exists and updatealgorithm that spendsO(log n) time per potentially uniform path


Handling the hard case

EASY

+

The shortest path is different (update = increase):

If we maintain potentially uniform paths using priority queues,

we can find this path in O(1) time!

x

y

Handling the hard case


Conclusions

Uniform paths are the heart of dynamic shortest paths

Combinatorially well-behaved in dynamic graphs

New approach based on maintaining

potentially uniform paths

Applications to Railway Optimization Problems?

Solves the problem in its full generality

Conclusions


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