Dijkstra - PowerPoint PPT Presentation

Title: Dijkstra


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Dijkstras Algorithm Fibonacci Heap
Implementation

• by Amber McKenzie
• and Laura Boccanfuso

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Dijkstras Algorithm

• Question How do you know that Dijkstras
  algorithm finds the shortest path and is optimal
  when implemented with the Fibonacci heap?

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Single-Source Shortest Path

• For a given vertex, determine the shortest path
  between that vertex and every other vertex, i.e.
  minimum spanning tree.

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Premise of Dijkstras Algorithm

• First, finds the shortest path from the vertex to
  the nearest vertex.
• Then, finds the shortest path to the next nearest
  vertex, and so on.
• These vertices, for which the shortest paths have
  been found, form a subtree.
• Thus, the next nearest vertex must be among the
  vertices that are adjacent to those in the
  subtree these next nearest vertices are called
  fringe vertices.

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Premise cont.

• The fringe vertices are maintained in a priority
  queue which is updated with new distances from
  the source vertex at every iteration.
• A vertex is removed from the priority queue when
  it is the vertex with the shortest distance from
  the source vertex of those fringe vertices that
  are left.

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Pseudocode

• for every vertex v in V do
• dv ? 8 pv ? null
• Insert(Q, v, dv) //initialize vertex priority
  in the priority queue
• ds ? 0 Decrease(Q, s, ds) //update priority of
  s with ds
• VT ? Ø
• for i ? 0 to V - 1 do
• u ? DeleteMin(Q) //delete the minimum priority
  element
• VT ? Vt U u
• for every vertex u in V VT that is adjacent
  to u do
• if du w(u, u) lt du
• du ? du w(u, u) pu ? u
• Decrease(Q, u, du)

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Dijkstras Algorithm
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Dijkstras Algorithm
2
b
a
5
c
8
f
d
e
Tree vertices Remaining vertices
a(-, 0)
b(a, 2) c(a, 5) d(a, 8) e(-, 8) f(-, 8)
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Dijkstras Algorithm
2
b
a
5
6
2
c
8
f
d
e
Tree vertices Remaining vertices
b(a, 2)
c(b, 22) d(a, 8) e(-, 8 ) f(b, 26)
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Dijkstras Algorithm
2
b
a
5
6
2
c
8
f
3
1
d
e
Tree vertices Remaining vertices
c(b, 4)
d(a, 8) e(c, 41) f(b, 8)
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Dijkstras Algorithm
2
b
a
5
6
2
c
8
f
3
1
d
4
7
e
Tree vertices Remaining vertices
e(c, 5)
d(a, 8) f(b, 8)
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Dijkstras Algorithm
2
b
a
5
6
2
c
8
f
3
1
d
4
7
e
Tree vertices Remaining vertices
d(a, 8)
f(b, 8)
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Dijkstras Algorithm
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Dijkstras Algorithm Priority Queue

• Tree vertices Remaining vertices
• a(-, 0) b(a, 2) c(a, 5) d(a, 8) e(-, 8) f(-,
  8)
• b(a, 2) c(b, 22) d(a, 8) e(-, 8 ) f(b, 26)
• c(b, 4) d(a, 8) e(c, 41) f(b, 8)
• e(c, 5) d(a, 8) f(b, 8)
• d(a, 8) f(b, 8)
• f(b, 8)

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Fibonacci Heap Implementation

• What makes the Fibonacci Heap optimally suited
• for implementing the Dijkstra algorithm?

• Manipulation of heap/queue
• Time complexity efficiency

http//www.mcs.surrey.ac.uk/Personal/R.Knott/Fibon
acci/fibnat.htmlRabbits
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Fibonacci Heap Implementation

• Manipulation of heap/queue
• Insert operation creates a new heap with one
  element then performs a merge

4
2

• Merge operation concatenate the lists of
• tree roots of the two heaps
• Decrease_key take the node, decrease the
• key and reorder nodes if necessary, mark node
• or cut (if smaller than parent)
• Delete_min take root of min element and
  remove decrease
• number of roots by linking together ones
  with same degree,
• check each remaining node to find minimum
  and delete

7
5
9
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Fibonacci Heap Implementation
Time Complexity Efficiency
Operation USDL List 2-3 tree Heap Binomial Fibonacci
make O(1) O(1) O(1) O(1) O(1)
empty O(1) O(1) O(1) O(1) O(1)
insert O(1) O(logn) O(logn) O(logn) O(1)
find_min O(n) O(logn) O(1) O(logn) O(1)
delete_min O(n) O(logn) O(logn) O(logn) O(logn)
delete O(1) O(logn) O(logn) O(logn) O(logn)
merge O(1) O(n) O(n) O(logn) O(1)
decrease_key O(1) O(logn) O(logn) O(logn) O(1)
USDL list Unsorted Doubly Linked list
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Worst-case complexity

• Formula to discover the worst-case complexity for
  Dijkstras algorithm
• W(n,m) O(n cost of insert
• n cost of delete_min
• m
  cost of decrease_key)
• (Where n maximum size of
  priority queue
• m number of times inner loop is performed)

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Worst-case complexity (cont.)

• Unsorted linked list
• W(n,m) O(n 1 n n m 1) O(n2)
• 2-3 Tree
• W(n,m) O(n logn n logn m
  logn) O(mlogn)
• Fibonacci Heap
• W(n,m) O(n 1 n logn m 1)
  O(nlogn m)

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Optimality of Dijkstras Algorithm

• Adversary argument
• In this case, it is the argument that there
  exists a path between the source vertex s and the
  target vertex t that is shorter than the path
  already determined by the algorithm.

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Adversary Argument

• Since we have already determined the shortest
  paths to all the previous vertices that are now
  in the tree, this must mean that the path from s
  to t goes through some other vertex v whose
  distance from s has yet to be determined (meaning
  it is still in the priority queue).

v
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Adversary Argument Cont.

• The catch is that if this other vertex v through
  which t passes is still in the priority queue,
  then its distance to s is longer than that of all
  other vertices already in the tree.
• Thus it cannot be a shorter distance than that
  which is already determined between s and t.

v
23
References

• Algorithms and Data Structures
• Design, Correctness and Analysis
• Jeffrey H. Kingston
• A Result on the Computational Complexity of
  
• Heuristic Estimates for the A
  Algorithm
• Marco Valtorta
• The Design Analysis of Algorithms
• Anany Levitin
• Animation
• http//www.cs.auckland.ac.nz/software/A
  lgAnim/dijkstra.htmldijkstra_anim