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Presentation on Algorithms

Presentation on Algorithms. Single-Source Shortest Path by WWW.ASSIGNMENTPOINT.COM. Review: Prim’s Algorithm. MST-Prim(G, w, r) Q = V[G]; for each u  Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q);

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Presentation on Algorithms

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  1. Presentation on Algorithms Single-Source Shortest Path by WWW.ASSIGNMENTPOINT.COM www.assignmentpoint.com

  2. Review: Prim’s Algorithm MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v); www.assignmentpoint.com

  3. Review: Prim’s Algorithm MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v); 6 4 9 5 14 2 10 15 3 8 Run on example graph www.assignmentpoint.com

  4. Review: Prim’s Algorithm MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v);  6 4 9 5    14 2 10 15    3 8  Run on example graph www.assignmentpoint.com

  5. Review: Prim’s Algorithm MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v);  6 4 9 5    14 2 10 15 r 0   3 8  Pick a start vertex r www.assignmentpoint.com

  6. Review: Prim’s Algorithm MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v);  6 4 9 5    14 2 10 15 u 0   3 8  Red vertices have been removed from Q www.assignmentpoint.com

  7. Review: Prim’s Algorithm MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v);  6 4 9 5    14 2 10 15 u 0   3 8 3 Red arrows indicate parent pointers www.assignmentpoint.com

  8. Review: Prim’s Algorithm MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v);  6 4 9 5 14   14 2 10 15 u 0   3 8 3 www.assignmentpoint.com

  9. Review: Prim’s Algorithm MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v);  6 4 9 5 14   14 2 10 15 0   3 8 3 u www.assignmentpoint.com

  10. Review: Prim’s Algorithm MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v);  6 4 9 5 14   14 2 10 15 0 8  3 8 3 u www.assignmentpoint.com

  11. Review: Prim’s Algorithm MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v);  6 4 9 5 10   14 2 10 15 0 8  3 8 3 u www.assignmentpoint.com

  12. Review: Prim’s Algorithm MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v);  6 4 9 5 10   14 2 10 15 0 8  3 8 3 u www.assignmentpoint.com

  13. Review: Prim’s Algorithm MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v);  6 4 9 5 10 2  14 2 10 15 0 8  3 8 3 u www.assignmentpoint.com

  14. Review: Prim’s Algorithm MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v);  6 4 9 5 10 2  14 2 10 15 0 8 15 3 8 3 u www.assignmentpoint.com

  15. Prim’s Algorithm u MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v);  6 4 9 5 10 2  14 2 10 15 0 8 15 3 8 3 www.assignmentpoint.com

  16. Review: Prim’s Algorithm u MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v);  6 4 9 5 10 2 9 14 2 10 15 0 8 15 3 8 3 www.assignmentpoint.com

  17. Review: Prim’s Algorithm u MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v); 4 6 4 9 5 10 2 9 14 2 10 15 0 8 15 3 8 3 www.assignmentpoint.com

  18. Review: Prim’s Algorithm u MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v); 4 6 4 9 5 5 2 9 14 2 10 15 0 8 15 3 8 3 www.assignmentpoint.com

  19. Review: Prim’s Algorithm u MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v); 4 6 4 9 5 5 2 9 14 2 10 15 0 8 15 3 8 3 www.assignmentpoint.com

  20. Review: Prim’s Algorithm u MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v); 4 6 4 9 5 5 2 9 14 2 10 15 0 8 15 3 8 3 www.assignmentpoint.com

  21. Review: Prim’s Algorithm u MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v); 4 6 4 9 5 5 2 9 14 2 10 15 0 8 15 3 8 3 www.assignmentpoint.com

  22. Review: Prim’s Algorithm MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v); 4 6 4 9 5 5 2 9 u 14 2 10 15 0 8 15 3 8 3 www.assignmentpoint.com

  23. Review: Prim’s Algorithm MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v); What is the hidden cost in this code? www.assignmentpoint.com

  24. Review: Prim’s Algorithm MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; DecreaseKey(v, w(u,v)); www.assignmentpoint.com

  25. Review: Prim’s Algorithm MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; DecreaseKey(v, w(u,v)); How often is ExtractMin() called? How often is DecreaseKey() called? www.assignmentpoint.com

  26. Review: Prim’s Algorithm MST-Prim(G, w, r) Q = V[G]; for each u Q key[u] = ; key[r] = 0; p[r] = NULL; while (Q not empty) u = ExtractMin(Q); for each v Adj[u] if (v Q and w(u,v) < key[v]) p[v] = u; key[v] = w(u,v); What will be the running time?A: Depends on queue binary heap: O(E lg V) Fibonacci heap: O(V lg V + E) www.assignmentpoint.com

  27. Single-Source Shortest Path • Problem: given a weighted directed graph G, find the minimum-weight path from a given source vertex s to another vertex v • “Shortest-path” = minimum weight • Weight of path is sum of edges • E.g., a road map: what is the shortest path from Rupnagar to Farmgate? www.assignmentpoint.com

  28. Shortest Path Properties • Again, we have optimal substructure: the shortest path consists of shortest subpaths: • Proof: suppose some subpath is not a shortest path • There must then exist a shorter subpath • Could substitute the shorter subpath for a shorter path • But then overall path is not shortest path. Contradiction www.assignmentpoint.com

  29. Shortest Path Properties • Define (u,v) to be the weight of the shortest path from u to v • Shortest paths satisfy the triangle inequality: (u,v)  (u,x) + (x,v) • “Proof”: x u v This path is no longer than any other path www.assignmentpoint.com

  30. Shortest Path Properties • In graphs with negative weight cycles, some shortest paths will not exist (Why?): < 0 www.assignmentpoint.com

  31. 2 2 9 6 5 5 Relax Relax 2 2 7 6 5 5 Relaxation • A key technique in shortest path algorithms is relaxation • Idea: for all v, maintain upper bound v.d on (s,v) Relax(u,v,w) { if (v.d > u.d+w) then v.d=u.d+w; } www.assignmentpoint.com

  32. Initialize d, which will converge to shortest-path value  Relaxation: Make |V|-1 passes, relaxing each edge Test for solutionUnder what condition do we get a solution? Bellman-Ford Algorithm BellmanFord() for each v  V v.d = ; s.d = 0; for i=1 to |V|-1 for each edge (u,v)  E Relax(u,v, w(u,v)); for each edge (u,v)  E if (v.d > u.d + w(u,v)) return “no solution”; Relax(u,v,w): if (v.d > u.d+w) then v.d=u.d+w www.assignmentpoint.com

  33. Bellman-Ford Algorithm BellmanFord() for each v  V v.d = ; s.d = 0; for i=1 to |V|-1 for each edge (u,v)  E Relax(u,v, w(u,v)); for each edge (u,v)  E if (v.d > u.d + w(u,v)) return “no solution”; Relax(u,v,w): if (v.d > u.d+w) then v.d=u.d+w What will be the running time? www.assignmentpoint.com

  34. Bellman-Ford Algorithm BellmanFord() for each v  V v.d = ; s.d = 0; for i=1 to |V|-1 for each edge (u,v)  E Relax(u,v, w(u,v)); for each edge (u,v)  E if (v.d > u.d + w(u,v)) return “no solution”; Relax(u,v,w): if (v.d > u.d+w) then v.d=u.d+w What will be the running time? A: O(VE) www.assignmentpoint.com

  35. Bellman-Ford Algorithm BellmanFord() for each v  V v.d = ; s.d = 0; for i=1 to |V|-1 for each edge (u,v)  E Relax(u,v, w(u,v)); for each edge (u,v)  E if (v.d > u.d + w(u,v)) return “no solution”; Relax(u,v,w): if (v.d > u.d+w) then v.d=u.d+w B -1 s 2 A E 2 3 1 -3 4 C D 5 Ex: work on board www.assignmentpoint.com

  36. Bellman-Ford • Note that order in which edges are processed affects how quickly it converges • Correctness: show v.d = (s,v) after |V|-1 passes • Consider any vertex v that is reachable from s, and let p = {v0, v1, ….., vk}, where v0 = s and vk = v, be any shortest path from s to v. • Because shortest paths are simple, p has at most |V| – 1 edges, and so k <= V – 1. • Each of the |V| – 1 iteration of the for loop relaxes all |E| edges. • Among the edges relaxed in the ith iteration, for i = 1, 2, …, k is (vi– 1, vi). • By the path relaxation property, therefore, v.d = vk.d = (s,vk) = (s,v) www.assignmentpoint.com

  37. Bellman-Ford • Path relaxation property: if p = {v0, v1, ..,vk}, is a shortest path from s = v0 to vk, and we relax the edges of p in the order (v0, v1), (v1, v2), …, (vk-1,vk), then vk.d = (s,vk). • This property holds regardless of any other relaxation steps that occur, even if they are intermixed with relaxations of the edges of p. www.assignmentpoint.com

  38. DAG Shortest Paths • Problem: finding shortest paths in DAG • Bellman-Ford takes O(VE) time. • How can we do better? • Idea: use topological sort • If were lucky and processes vertices on each shortest path from left to right, would be done in one pass • Every path in a dag is subsequence of topologically sorted vertex order, so processing verts in that order, we will do each path in forward order (will never relax edges out of vert before doing all edges into vert). • Thus: just one pass. What will be the running time? www.assignmentpoint.com

  39. Dijkstra’s Algorithm • If no negative edge weights, we can beat BF • Similar to breadth-first search • Grow a tree gradually, advancing from vertices taken from a queue • Also similar to Prim’s algorithm for MST • Use a priority queue keyed on v.d www.assignmentpoint.com

  40. B 2 10 A 4 3 D 5 1 C Ex: run the algorithm RelaxationStep Note: thisis really a call to Q->DecreaseKey() Dijkstra’s Algorithm Dijkstra(G) for each v  V v.d = ; s.d = 0; S = ; Q = V; while (Q  ) u = ExtractMin(Q); S = S U {u}; for each v  u->Adj[] if (v.d > u.d+w(u,v)) v.d = u.d+w(u,v); www.assignmentpoint.com

  41. Dijkstra’s Algorithm Dijkstra(G) for each v  V v.d = ; s.d = 0; S = ; Q = V; while (Q  ) u = ExtractMin(Q); S = S U {u}; for each v  u->Adj[] if (v.d > u.d+w(u,v)) v.d = u.d+w(u,v); How many times is ExtractMin() called? How many times is DecraseKey() called? What will be the total running time? www.assignmentpoint.com

  42. Dijkstra’s Algorithm Dijkstra(G) for each v  V v.d = ; s.d = 0; S = ; Q = V; while (Q  ) u = ExtractMin(Q); S = S U {u}; for each v  u->Adj[] if (v.d > u.d+w(u,v)) v.d = u.d+w(u,v); How many times is ExtractMin() called? How many times is DecraseKey() called? A: O(E lg V) using binary heap for Q Can acheive O(V lg V + E) with Fibonacci heaps www.assignmentpoint.com

  43. Dijkstra’s Algorithm Dijkstra(G) for each v  V v.d = ; s.d = 0; S = ; Q = V; while (Q  ) u = ExtractMin(Q); S = S U{u}; for each v  u->Adj[] if (v.d > u.d+w(u,v)) v.d = u.d+w(u,v); Correctness: we must show that when u is removed from Q, it has already converged www.assignmentpoint.com

  44. Correctness Of Dijkstra's Algorithm • Note that v.d  (s,v) v • Let u be first vertex picked s.t.  shorter path than u.d u.d > (s,u) • Let y be first vertex V-S on actual shortest path from su  d[y] = (s,y) • Because d[x] is set correctly for y's predecessor x  S on the shortest path, and • When we put x into S, we relaxed (x,y), giving d[y] the correct value p2 u s y x p2 www.assignmentpoint.com

  45. Correctness Of Dijkstra's Algorithm • Note that v.d  (s,v) v • Let u be first vertex picked s.t.  shorter path than u.d u.d > (s,u) • Let y be first vertex V-S on actual shortest path from su  d[y] = (s,y) • u.d > (s,u) = (s,y) + (y,u) (Why?) = d[y] + (y,u)  d[y] But if u.d > d[y], wouldn't have chosen u. Contradiction. p2 u s y x p2 www.assignmentpoint.com

  46. Disjoint-Set Union Problem • Want a data structure to support disjoint sets • Collection of disjoint sets S = {Si}, Si∩ Sj =  • Need to support following operations: • MakeSet(x): S = SU {{x}} • Union(Si, Sj): S = S - {Si, Sj} U {SiU Sj} • FindSet(X): return Si S such that x  Si • Before discussing implementation details, we look at example application: MSTs www.assignmentpoint.com

  47. Kruskal’s Algorithm Kruskal() { T = ; for each v  V MakeSet(v); sort E by increasing edge weight w for each (u,v)  E (in sorted order) if FindSet(u)  FindSet(v) T = T U {{u,v}}; Union(FindSet(u), FindSet(v)); } www.assignmentpoint.com

  48. Kruskal’s Algorithm Run the algorithm: Kruskal() { T = ; for each v  V MakeSet(v); sort E by increasing edge weight w for each (u,v)  E (in sorted order) if FindSet(u)  FindSet(v) T = T U {{u,v}}; Union(FindSet(u), FindSet(v)); } 2 19 9 14 17 8 25 5 21 13 1 www.assignmentpoint.com

  49. Kruskal’s Algorithm Run the algorithm: Kruskal() { T = ; for each v  V MakeSet(v); sort E by increasing edge weight w for each (u,v)  E (in sorted order) if FindSet(u)  FindSet(v) T = T U {{u,v}}; Union(FindSet(u), FindSet(v)); } 2 19 9 14 17 8 25 5 21 13 1 www.assignmentpoint.com

  50. Kruskal’s Algorithm Run the algorithm: Kruskal() { T = ; for each v  V MakeSet(v); sort E by increasing edge weight w for each (u,v)  E (in sorted order) if FindSet(u)  FindSet(v) T = T U {{u,v}}; Union(FindSet(u), FindSet(v)); } 2 19 9 14 17 8 25 5 21 13 1 www.assignmentpoint.com

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