# lec40a - Lecture 40 All Pairs Shortest Paths 20.2.3...

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Lecture 40 All Pairs Shortest Paths 20.2.3

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All-Pairs Shortest Paths Given an n vertex directed weighted graph, find a shortest path from vertex i to vertex j for each for the n 2 vertex pairs (i,j) . 1 2 3 4 5 6 7 5 7 1 7 9 1 9 4 4 5 16 4 2 8 1 2
Dijkstra’s Single Source Algorithm Use Dijkstra’s algorithm n times, once with each of the n vertices as the source vertex. 1 2 3 4 5 6 7 5 7 1 7 9 1 9 4 4 5 16 4 2 8 1 2

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Performance Time complexity is O(n 3 ) time. Works only when no edge has a cost < 0 .
Dynamic Programming Solution Time complexity is Θ (n 3 ) time. Works so long as there is no cycle whose length is < 0 . When there is a cycle whose length is < 0 , some shortest paths aren’t finite. If vertex 1 is on a cycle whose length is -2 , each time you go around this cycle once you get a 1 to 1 path that is 2 units shorter than the previous one. Simpler to code, smaller overheads. Known as Floyd’s shortest paths algorithm.

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Decision Sequence First decide the highest intermediate vertex (i.e., largest vertex number) on the shortest path from i to j . If the shortest path is i, 2, 6, 3, 8 , 5, 7, j the first decision is that vertex 8 is an intermediate vertex on the shortest path and no intermediate vertex is larger than 8 . Then decide the highest intermediate vertex on the path from i to 8 , and so on. i j
Problem State (i,j,k) denotes the problem of finding the shortest path from vertex i to vertex j that has no intermediate vertex larger than k . (i,j,n) denotes the problem of finding the shortest path from vertex i to vertex j (with no restrictions on intermediate vertices).

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lec40a - Lecture 40 All Pairs Shortest Paths 20.2.3...

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