Graph ( II)

1. Graph (II) Shortest path, Minimum spanning tree GGuy 19-3-2011

2. Review DFS (Depth-First-Search) Graph traverse Topological sort BFS (Breadth-First-Search) Finding shortest path

3. Shortest Path Problem In a weighted graph G We want to find a path from s to t, such that the sum of edge weight is minimum

4. Shortest Path S 3 2 1 3 T 3 3 1

5. Shortest Path S 3 2 1 3 T 3 3 1

6. Algorithms Single source? All pairs? Negative weight edge? Negative cycles?

7. Dijkstra’s algorithm for-each v, d[v] ← ∞ Q.Insert(s,0) while not Q.Empty() do (u, w) = Q.ExtractMin() if (visited[u]) continue; d[u] ← w for-each v where (u, v) in E Q.Insert(v, d[u] + weightuv)

8. Dijkstra’s algorithm S 3 2 1 3 T 3 3 1

9. Dijkstra’s algorithm 0 3 2 1 3 T 3 3 1

10. Dijkstra’s algorithm 0 3 2 1 3 T 3 2 3 1

11. Dijkstra’s algorithm 0 3 2 1 3 T 3 2 3 1

12. Dijkstra’s algorithm 0 3 3 2 1 3 T 3 2 3 1

13. Dijkstra’s algorithm 0 3 3 2 1 3 T 3 2 3 1

14. Dijkstra’s algorithm 0 3 3 2 1 3 T 3 2 3 1 3

15. Dijkstra’s algorithm 0 3 3 2 1 3 T 3 2 3 1 3

16. Dijkstra’s algorithm 0 3 3 2 1 3 T 3 2 3 1 3

17. Dijkstra’s algorithm 0 3 3 2 1 3 4 3 2 3 1 3

18. Dijkstra’s algorithm 0 3 3 2 1 3 4 3 2 3 1 3

19. Dijkstra’s algorithm 0 3 3 2 1 3 4 3 2 3 1 3

20. Dijkstra’s algorithm Implement Q using binary heap (priority queue) At most E edges in the heap Time complexity: O(E log (E)) = O(E log (V)) Space complexity: O(V+E)

21. Bellman Ford for-each v, d[v] ← ∞ d[s] ← 0 Do V-1 times for each edge (u,v) if (d[u] + weightuv< d[v]) d[v] ← d[u] + weightuv

22. Bellman Ford’s algorithm S 3 2 1 4 T 3 -2 1

23. Bellman Ford’s algorithm S 3 3 2 1 4 T 3 2 -2 1 4

24. Bellman Ford’s algorithm S 3 3 2 1 4 2 3 2 -2 1 3

25. Bellman Ford’s algorithm S 3 3 2 1 4 1 3 2 -2 1 3

26. Bellman Ford’s algorithm Assume the shortest path from s->t is P0 P1 P2 … Pm-1 where m < |V| and s is fixed In the 1st iteration, d[P1] is shortest In the 2nd iteration, d[P2] is shortest … In the m-1 iteration, d[Pm-1] is shortest

27. Bellman Ford’s algorithm After |V|-1 iterations, If there exists edge (u,v) where d[u] + weightuv< d[v] Negative cycle!

28. Bellman Ford’s algorithm Time complexity: O(VE) Space complexity: O(V)

29. Floyd Warshall’s algorithm for all pairs(i,j), d[i][j] ← ∞ for all edges(u,v), d[u][v] ← weightuv for k=1 to V for i=1 to V for j=1 to V d[i][j] = min(d[i][j], d[i][k]+d[k][j])

30. Floyd Warshall’s algorithm Assume the shortest path from s->t is P0 P1 P2 … Pm-1 for any s,t For example, 5->3->1->2->4->6 i=1: 5->1->6 i=2: 5->1->2->6 i=3: 5->3->1->2->6 i=4: 5->3->1->2->4->6 i=5: 5->3->1->2->4->6 i=6: 5->3->1->2->4->6 We won’t miss any shortest path!

31. Floyd Warshall’s algorithm Time complexity: O(V3) Space complexity: O(V2)

32. Summary

33. Tree What is a tree? G is connected and acyclic G is connected and |E| = |V| - 1 G is acyclic and |E| = |V| - 1 For any pairs in G, there is a unique path

34. Spanning Tree 3 2 1 3 3 3 1

35. Minimum Spanning Tree 3 2 1 3 3 3 1

36. Kruskal’s algorithm T← empty set of edges Sort the edges in increasing order For each edge e (in increasing order) if T + e does not contain a cycle add e to T

37. Kruskal’s algorithm How to detect a cycle? Disjoint Set

38. Kruskal’s algorithm If we choose an edge (u,v) Union(u, v)

39. Kruskal’s algorithm 3 2 1 2 2 2 1

40. Kruskal’s algorithm 3 2 1 2 2 2 1

41. Kruskal’s algorithm 3 2 1 2 2 2 1

42. Kruskal’s algorithm 3 2 1 2 2 2 1

43. Kruskal’s algorithm 3 2 1 2 2 2 1

44. Kruskal’s algorithm 3 2 1 2 2 2 1

45. Kruskal’s algorithm 3 2 1 2 2 2 1

46. Kruskal’s algorithm 3 2 1 2 2 2 1

47. Krustal’s algorithm Sorting: O(E log V) Select edge: O(E) Check union: O(α(V)) Overall: O(E log V + E α(V))

48. The Red Rule The Red Rule states that for any cycle in G, the largest weight edge will NOT be contained in any Minimum Spanning Tree.

49. Prim’s algorithm T← node 1 while size of T < V choose a vertex u that is not in V and the cost adding it to V is minimum add u to V

50. Prim’s algorithm 3 2 1 2 2 2 1