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6.006 Introduction to Algorithms
Spring 2008
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Lecture 18 Shortest Paths III: Dijkstra 6.006 Spring 2008
Lecture 18: Shortest Paths IV - Speeding up
Dijkstra
Lecture Overview
• Single-source single-target Dijkstra
Bidirectional search •
• Goal directed search - potentials and landmarks
Readings
Wagner, Dorothea, and Thomas Willhalm. "Speed-Up Techniques for Shortest-Path
Computations." In Lecture Notes in Computer Science: Proceedings of the 24th Annual
Symposium on Theoretical Aspects of Computer Science. Berlin
/Heidelberg, MA: Springer,
2007. ISBN: 978
3540709176. Read up to section 3.2.
DIJKSTRA single-source, single-target
Initialize()
Q V [G]←
while Q = φ
do u EXTRACT MIN(Q) (stop if u = t!)←
for each vertex v � Adj[u]
do RELAX(u, v, w)
Observation: If only shortest path from s to t is required, stop when t is removed from
Q, i.e., when u = t
DIJKSTRA Demo
B

C
A
D
E
5
19
11
7
15
4
13
A C E B D
7 12 18 22
D B E C A
4 13 15 22
E C A D B
5 12 13 16
Figure 1:
Dijkstra Demonstration with Balls and String
1
Lecture 18 Shortest Paths III: Dijkstra 6.006 Spring 2008
Bi-Directional Search
Note: Speedup techniques covered here do not change worst-case behavior, but reduce the
number of visited vertices in practice.
S
t
forward search
backward search
Figure 2:
Bi-directional Search

Bi-D Search
Alternate forward search from s
backward search from t
(follow edges backward)
d
f
(u) distances for forward search
d
b
(u) distances for backward search
Algorithm terminates when some vertex w has been processed, i.e., deleted from the queue
of both searches, Q
f
and Q
b
s
3
u
u’
t
3
3
5
5
w
Figure 3:
Bi-D Search
2
Lecture 18 Shortest Paths III: Dijkstra 6.006 Spring 2008
Subtlety: After search terminates, find node x with minimum value of d

f
(x) + d
b
(x). x may
not be the vertex w that caused termination as in example to the left!
Find shortest path from s to x using Π
f
and shortest path backwards from t to x using Π
b
.
Note: x will have been deleted from either Q
f
or Q
b
or both.
3
u
3
3
5
5
d
f
(s) = 0
d
f
(u) = 3
d
f
(w) = 5

Forward
3
3
3
5
5
d
b
(t) = 0
d
b
(u’) = 3
d
b
(w) = 5
Backward
3
3
3
5
5
d
f
(s) = 0
d
f
(u’) = 6
d
f
(t) = 10

Forward
3
3
3
5
5
d
b
(t) = 0
d
b
(u) = 6
d
b
(w) = 5
Backward
d
f
(u) = 3
d
f
(w) = 5
d
f
(s) = 0
d
b
(s) = 10
d
f

(w) = 5
d
f
(u) = 3
d
b
(u’) = 3
d
f
(u’) = 6
d
f
(t) = 10
u
u
u
u’
u’
u’
u’
s
s
s
s
w
w
w
w
t
t

t
t
3
3
3
5
5
d
f
(s) = 0
d
f
(u’) = 6
Forward
3
3
3
5
5
d
b
(t) = 0
d
b
(u) = 6
d
b
(w) = 5
Backward
d

f
(u) = 3
d
f
(w) = 5
d
b
(u’) = 3
u
u
u’
u’
s
s
w
w
t
t
deleted from both queues
so terminate!
Figure 4:
Forward and Backward
Minimum value for d
f
(x) + d
b
(x) over all vertices that have been processed in at least one
search
d
f

(u) + d
b
(u) = 3 + 6 = 9
3
Search
Lecture 18 Shortest Paths III: Dijkstra 6.006 Spring 2008
d
f
(u
�
) + d
b
(u
�
) = 6 + 3 = 9
d
f
(w) + d
b
(w) = 5 + 5 = 10
Goal-Directed Search or A
∗
Modify edge weights with potential function over vertices.
w (u, v) = w (u, v) − λ(u) + λ(v)
Search toward target:
v
v’
5
5
increase

go uphill
decrease
go downhill
Figure 5:
Targeted Search
Correctness
w(p) = w(p) − λ
t
(s) + λ
t
(t)
So shortest paths are maintained in modified graph with
w weights.
p
p’
s
t
Figure 6:
Modifying Edge Weights
To apply Dijkstra, we need w(u, v) ≥ 0 for all (u, v).
Choose potential function appropriately, to be feasible.
Landmarks
Small set of landmarks LCV . For all u�V, l�L, pre-compute δ(u, l).
δ(u, l) = δ(t, l) for each l.
CLAIM: λ
t
(l)
is feasible.
Potential λ
t

(l)
(u) =
4