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CS161  Minimum Spanning Trees
and Single Source Shortest Paths
David Kauchak
Single Source Shortest Paths
•
Given a graph
G
and two vertices
s,t
what is the shortest path from
s
to
t
?
For an unweighted graph,
BFS
gives us a solution to this problem.
For weighted graphs, as it turns out, we can calculate the shortest
distance from
s
to all vertices
t
∈
V
in worst case the same amount of
time for any particular
t
, so we’ll look at this problem, which is the
single source shortest paths.
•
Shortest path property
If the path
v
1
,v
2
,v
3
,...,v
k
where
v
i
∈
V
is the shortest path from
v
1
to
v
k
then for all 1
≤
i
≤
j
≤
k
,
v
i
,v
i
+1
,...,v
j
is the shortest path
from
v
i
to
v
j
Proof: Consider that a shorter path exists between
v
i
and
v
j
, then
we could use this path instead of the path
v
i
,v
i
+1
,...,v
j
in the path
from
v
1
to
v
k
, resulting in a shorter path from
v
1
to
v
k
, but this is a
contradiction.
•
General idea for all the algorithms
mark each vertex with an upper bound on the distance from the source
to that node. Decrease that value until it is correct.
•
Dijkstra’s algorithm
Assume that all of the weights are positive
1
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View Full Document Like BFS, exept our frontier that we expand is based on the weights
of the edges not the number of edges
Dijkstra
(
G,s
)
1
for
all
v
∈
V
2
dist
[
v
]
← ∞
3
prev
[
v
]
←
null
4
dist
[
s
]
←
0
5
Q
←
MakeHeap
(
V
)
6
while
!
Empty
(
Q
)
7
u
←
ExtractMin
(
Q
)
8
for
all edges (
u,v
)
∈
E
9
if
dist
[
v
]
> dist
[
u
] +
w
(
u,v
)
10
dist
[
v
]
←
dist
[
u
] +
w
(
u,v
)
11
DecreaseKey
(
Q,v,dist
[
v
])
12
prev
[
v
]
←
u
Example
Why doesn’t this hold with negative weights?
Consider the graph:
A
→
B
: 1
,C
: 10
B
→
D
: 1
C
→
D
:

10
D
→
E
: 5
What is the shortest path from
A
to
E
?
–
Is it correct?
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This document was uploaded on 05/25/2011.
 Summer '09
 Algorithms

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