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Given a digraph G = ( V, E ), and a cost function c ( e ) for e  E( G ). The length of a path P from source to destination is (also called weighted path length ). 2. v 1. v 2. 2. v 1. v 2. 4. 1. –10. 4. 1. 3. 10. 3. 2. 2. v 3. v 4. v 5. 2. 2.

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slide1

Given a digraph G = ( V, E ), and a cost function c( e ) for e E( G ). The length of a path P from source to destination is (also called weighted path length).

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§3 Shortest Path Algorithms

1. Single-Source Shortest-Path Problem

Given as input a weighted graph, G = ( V, E ), and a distinguished vertex, s, find the shortest weighted path from s to every other vertex in G.

Negative-cost cycle

Note: If there is no negative-cost cycle, the shortest path from s to s is defined to be zero.

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§3 Shortest Path Algorithms

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 Unweighted Shortest Paths

 Sketch of the idea

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Breadth-first search

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 Implementation

Table[ i ].Dist ::= distance from s to vi/* initialized to be  except for s */

Table[ i ].Known ::= 1 if vi is checked; or 0 if not

Table[ i ].Path ::= for tracking the path /* initialized to be 0 */

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slide3

§3 Shortest Path Algorithms

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void Unweighted( Table T )

{ int CurrDist;

Vertex V, W;

for ( CurrDist = 0; CurrDist < NumVertex; CurrDist ++ ) {

for ( each vertex V )

if ( !T[ V ].Known && T[ V ].Dist == CurrDist ) {

T[ V ].Known = true;

for ( each W adjacent to V )

if ( T[ W ].Dist == Infinity ) {

T[ W ].Dist = CurrDist + 1;

T[ W ].Path = V;

} /* end-if Dist == Infinity */

} /* end-if !Known && Dist == CurrDist */

} /* end-for CurrDist */

}

If V is unknown yet has Dist < Infinity, then Dist is either CurrDist or CurrDist+1.

 T = O( |V|2 )

The worst case:

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§3 Shortest Path Algorithms

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DistPath

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 Improvement

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void Unweighted( Table T )

{ /* T is initialized with the source vertex S given */

Queue Q;

Vertex V, W;

Q = CreateQueue (NumVertex ); MakeEmpty( Q );

Enqueue( S, Q ); /* Enqueue the source vertex */

while ( !IsEmpty( Q ) ) {

V = Dequeue( Q );

T[ V ].Known = true; /* not really necessary */

for ( each W adjacent to V )

if ( T[ W ].Dist == Infinity ) {

T[ W ].Dist = T[ V ].Dist + 1;

T[ W ].Path = V;

Enqueue( W, Q );

} /* end-if Dist == Infinity */

} /* end-while */

DisposeQueue( Q ); /* free memory */

}

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T = O( |V| + |E| )

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slide5

§3 Shortest Path Algorithms

Dijkstra’s Algorithm (for weighted shortest paths)

Let S = { s and vi’s whose shortest paths have been found }

For any u S, define distance [ u ] = minimal length of path { s ( vi S )  u }. If the paths are generated in non-decreasing order, then

 the shortest path must go through ONLYvi S ;

 u is chosen so that distance[ u ] = min{ wS | distance[ w ] } (If u is not unique, then we may select any of them) ; /* Greedy Method */

Why? If it is not true, then

there must be a vertex w on this path

that is not in S. Then ...

 if distance [ u1 ] < distance [ u2 ] and we add u1 into S, then distance [ u2 ] may change. If so, a shorter path from s to u2 must go through u1 and distance’ [ u2 ] = distance [ u1 ] + length(< u1, u2>).

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§3 Shortest Path Algorithms

Dist Path

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void Dijkstra( Table T )

{ /* T is initialized by Figure 9.30 on p.303 */

Vertex V, W;

for ( ; ; ) {

V = smallest unknown distance vertex;

if ( V == NotAVertex )

break;

T[ V ].Known = true;

for ( each W adjacent to V )

if ( !T[ W ].Known )

if ( T[ V ].Dist + Cvw < T[ W ].Dist ) {

Decrease( T[ W ].Dist to

T[ V ].Dist + Cvw );

T[ W ].Path = V;

} /* end-if update W */

} /* end-for( ; ; ) */

}

/* O( |V| ) */

/* not work for edge with negative cost */

Please read Figure 9.31 on p.304 for printing the path.

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slide7

§3 Shortest Path Algorithms

 Implementation 1

V = smallest unknown distance vertex;

/* simply scan the table – O( |V| ) */

Good if the graph is dense

T = O( |V|2 + |E| )

 Implementation 2

Home work:

p.339 9.5

Find the shortest paths

p.340 9.10

Modify Dijkstra’s algorithm

V = smallest unknown distance vertex;

/* keep distances in a priority queue and call DeleteMin – O( log|V| ) */

Decrease( T[ W ].Dist to T[ V ].Dist + Cvw );

Good if the graph is sparse

/* Method 1: DecreaseKey – O( log|V| ) */

T = O( |V| log|V| + |E| log|V| ) = O( |E| log|V| )

/* Method 2: insert W with updated Dist into the priority queue */

/* Must keep doing DeleteMin until an unknown vertex emerges */

T = O( |E| log|V| ) but requires |E| DeleteMin with |E| space

 Other improvements: Pairing heap (Ch.12) and Fibonacci heap (Ch. 11)

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slide8

§3 Shortest Path Algorithms

Too simple, and naïve…

Try this one out:

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– 2

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 Graphs with Negative Edge Costs

Hey I have a good idea:

why don’t we simply add a constant

 to each edge and thus remove

negative edges?

void WeightedNegative( Table T )

{ /* T is initialized by Figure 9.30 on p.303 */

Queue Q;

Vertex V, W;

Q = CreateQueue (NumVertex ); MakeEmpty( Q );

Enqueue( S, Q ); /* Enqueue the source vertex */

while ( !IsEmpty( Q ) ) {

V = Dequeue( Q );

for ( each W adjacent to V )

if ( T[ V ].Dist + Cvw < T[ W ].Dist ) {

T[ W ].Dist = T[ V ].Dist + Cvw;

T[ W ].Path = V;

if ( W is not already in Q )

Enqueue( W, Q );

} /* end-if update */

} /* end-while */

DisposeQueue( Q ); /* free memory */

}

T = O( |V|  |E| )

/* each vertex can dequeue at most |V| times */

/* no longer once per edge */

/* negative-cost cycle will cause indefinite loop */

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slide9

§3 Shortest Path Algorithms

Signals the completion ofai

ai ::= activity

vj

Index of vertex

EC Time

Lasting Time

Slack Time

LC Time

 Acyclic Graphs

If the graph is acyclic, vertices may be selected in topological order since when a vertex is selected, its distance can no longer be lowered without any incoming edges from unknown nodes.

T = O( |E| + |V| ) and no priority queue is needed.

Application: AOE ( Activity On Edge ) Networks

—— scheduling a project

 EC[ j ] \ LC[ j ] ::= the earliest \ latest completion time for node vj

 CPM ( Critical Path Method )

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slide10

§3 Shortest Path Algorithms

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a0=6

a3=1

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 Slack Time of <v,w> =

〖Example〗 AOE network of a hypothetical project

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Dummy activity

 Calculation of EC: Start from v0, for any ai = <v, w>, we have

 Calculation of LC: Start from the last vertex v8, for any ai = <v, w>, we have

 Critical Path ::= path consisting entirely of zero-slack edges.

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slide11

§3 Shortest Path Algorithms

2. All-Pairs Shortest Path Problem

For all pairs of vi and vj( i j ), find the shortest path between.

Method 1 Use single-source algorithmfor |V| times.

T = O( |V|3 ) – works fast on sparse graph.

Method 2 O( |V|3 ) algorithm given in Ch.10, works faster on dense graphs.

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slide12

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Determine the maximum amount of flow that can pass from s to t.

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§4 Network Flow Problems

〖Example〗 Consider the following network of pipes:

source

Note: Total coming in (v)

 Total going out (v)

where v  { s, t }

sink

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slide13

§4 Network Flow Problems

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Flow Gf

Residual Gr

1. A Simple Algorithm

Step 1: Find any path s t in Gr;

Step 4: If (there is a path s t in Gr )

Goto Step 1;

Else

End.

Step 2: Take the minimum edge on this path as the amount of flow and add to Gf;

augmenting path

Step 3: Update Grand remove the 0 flow edges;

13/17

slide14

§4 Network Flow Problems

s

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G

You are right!

What if I pick up the path

s a  d  t

first?

It is simple indeed.

But I bet that you will point out

some problems here…

Uh-oh…

Seems we cannot be

greedy at this point.

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slide15

§4 Network Flow Problems

For each edge ( v, w ) with flow fv, w in Gf, add an edge ( w, v ) with flow fv, w in Gr.

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Residual Gr

2. A Solution – allow the algorithm to undo its decisions

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〖Proposition〗If the edge capabilities are rational numbers, this algorithm always terminate with a maximum flow.

Note: The algorithm works for G with cycles as well.

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slide16

§4 Network Flow Problems

s

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1 000 000

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1 000 000

1 000 000

t

3. Analysis ( If the capacities are all integers )

 An augmenting path can be found by an unweighted shortest path algorithm.

T = O( ) where f is the maximum flow.

f · |E|

 Always choose the augmenting path that allows the largest increase in flow.

/* modify Dijkstra’s algorithm */

T = Taugmentation * Tfind a path

= O( |E| log capmax) * O( |E| log |V| )

= O( |E|2 log |V| ) if capmax is a small integer.

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slide17

§4 Network Flow Problems

 Always choose the augmenting path that has the least number of edges.

Home work:

p.341 9.11

A test case.

T = Taugmentation * Tfind a path

= O( |E| ) * O( |E| · |V| )

/* unweighted shortest path algorithm */

= O( |E|2 |V| )

  • Note:
  • If every v  { s, t } has either a single incoming edge of capacity 1 or a single outgoing edge of capacity 1, then time bound is reduced to O( |E| |V|1/2 ).
  • The min-cost flow problem is to find, among all maximum flows, the one flow of minimum cost provided that each edge has a cost per unit of flow.

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