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Randomized Algorithms CS648. Lecture 18 A pproximate Distance Oracles Algorithm for Min-cut : part 1. Approximate Distance oracles. All-Pairs Shortest Paths. Notations and Terminologies : A graph on vertices edges A path from to : a sequence , ,…, where ( , )

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randomized algorithms cs648

Randomized AlgorithmsCS648

Lecture 18

Approximate Distance Oracles

Algorithm for Min-cut : part 1

all pairs shortest paths
All-Pairs Shortest Paths

Notations and Terminologies :

A graph on

  • vertices
  • edges

A pathfrom to : a sequence , ,…,where (,)

Length of a path : sum of the weights on the edges of path .

Shortest path from to : the path of smallest length from to .

Distancefrom to : the length of the shortest path from to .

: Distance from to

all pairs shortest paths1
All-Pairs Shortest Paths

Problem Definition: Given a graph , build a compact data structure so that for any ,

  • can be reported in time
  • Shortest path from to can be reported in optimal time.

Results known:

  • size data structure

(Distance matrix and Witness matrix)

  • preprocessing time

(Dijkstra’s algorithm from each vertex)

Current-state-of-the-art RAM size: 8 GBs

Can’t handle graphs with even vertices (with RAM size) 

all pairs approximate shortest paths
All-Pairs Approximate Shortest Paths

Problem Definition: Given a graph , build a compact data structure so that for any , it reports in time satisfying

: stretch.

Aim: To achieve

  • Sub-quadratic space.
  • Sub-cubic preprocessing time.

With query time.

Many elegant results have been invented for undirected graphs 

a truly magical result
A truly magical result

Approximate Distance Oracles

MikkelThorupand Uri Zwick:

Approximate Distance Oracles for graphs,

Journal of ACM (4), 2005

the idea
The Idea

Given a graph ,

  • Compute a small set of Landmark vertices.
  • From each vertex , store distance to vertices present in its locality.
  • From each vertex , store distance to all vertices in the graph.

Questions:

  • What is the formal notion of locality?
  • How to retrieve distance from to a far away vertex ?
  • What is the guarantee of stretch ?
  • How to compute the desired set efficiently ?
3 approximate distance oracle
3-approximate distance oracle

Preprocessing-algorithm()

{ Compute set suitably;

For each store distance to all vertices;

For each

compute ;

Build a hash table storing distances from to ;

}

Query(,)

{ If return ;

else return ;

}

Global distance info.

Local distance info.

the real challenge left
The real challenge left

How to compute set such that

  • is small.
  • is small for each .

Fact1: It is difficult, if not impossible, to compute such a set deterministically.

Fact2: The structure of graph (the edges and weights) can be arbitrary and more complex than planar road/air networkk.

conquering the challenge
Conquering the challenge

Let be a fraction to be fixed later on.

Computing:

{ ∅;

For each

Add to independently with probability ;

return ;

}

Expected size of :

  • : random variable for ||
expected size of
Expected size of

=

… …

Increasing order of distance from

None of is present in

expected space of 3 approximate distance oracle
Expected space of 3-approximate distance oracle

Space for Global distance information:

=

=

=

Spacefor Local distance information:

=

=

To minimize the total space: (Balance the two terms)

Expected space:

Each vertex in keeps a Ball)

slide20

Theorem: An undirected weighted graph can be processed to build a data structure of expected size that can report 3-approximate distance between any pair of vertices in time.

Homework:

  • Convert to a Las Vegas algorithm with high probability bound on space.
  • Show that expected preprocessing time is
5 approximate distance oracle
Meant for only those (hopefully nonzero no. of) students whose aim is more than just a good grade in this course. 5-approximate distance oracle
min cut
Min-Cut

: undirected connected graph

Definition (cut):

A subset whose removal disconnects the graph.

Definition (min-cut): A cut of smallest size.

Problem Definition:Design algorithm to compute min-cut of a given graph.

min cut1
Min-Cut

Deterministic Algorithms:

  • time

- Designed in 1997,

- Quite complex to analyze and implement

Randomized Algorithms:

  • time Monte Carlo [1993]
  • time Monte Carlo [1996]

- Both are much simpler and easier to implement.

min cut2
Min-Cut

Question: How many cuts ?

Answer:

Question : what is relation between degree() and size of min-cut ?

Answer: size of min-cut degree()

Question : If size of min-cutis , what can be minimum value of ?

Answer:

contract
Contract()

Contract(

contract1
Contract()

Contract()

{ Let ;

Merge the two vertices and into one vertex;

Preserve multi-edges;

Remove the edge ;

Let be the modified graph;

return ;

}

Time complexity of Contract():

contract2
Contract()

Let be the size of min-cut of .

Let be any min-cut of .

Let be the graph after Contract().

Observation: Every cut of is also a cut of .

Question: Under what circumstance is a cut of ?

Answer: if .

Question: If is selected randomly uniformly, what is the probability that is preserved in ?

Answer:

contract3
Contract()

Let be the size of min-cut of .

Let be any min-cut of .

Lemma: If edge to be contracted is selected randomly uniformly, is preserved with probability at least .

Let ;

Contract().

Let ;

Contract().

Question: What is probability that is preserved in ?

Answer:

algorithm for min cut
Algorithm for min-cut

Min-cut():

{ Repeat ?? times

{

Let ;

Contract().

}

return the edges of multi-graph ;

}

Running time:

algorithm for min cut1
Algorithm for min-cut

Question: What is probability that is preserved during the algorithm ?

Answer:

=

=

>

algorithm for min cut2
Algorithm for min-cut

Min-cut-high-probability():

{ Repeat Min-cut() algorithm log times

and report the smallest cut computed;

}

Running time:

Error Probability :

<

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