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CS 332: Algorithms

CS 332: Algorithms. Topological Sort Minimum Spanning Trees. Review: Breadth-First Search. BFS(G, s) { initialize vertices; Q = {s}; // Q is a queue (duh); initialize to s while (Q not empty) { u = RemoveTop(Q); for each v  u->adj {

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CS 332: Algorithms

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  1. CS 332: Algorithms Topological Sort Minimum Spanning Trees David Luebke 13/10/2014

  2. Review: Breadth-First Search BFS(G, s) { initialize vertices; Q = {s}; // Q is a queue (duh); initialize to s while (Q not empty) { u = RemoveTop(Q); for each v  u->adj { if (v->color == WHITE) v->color = GREY; v->d = u->d + 1; v->p = u; Enqueue(Q, v); } u->color = BLACK; } } v->d represents depth in tree v->p represents parent in tree David Luebke 23/10/2014

  3. DFS(G) { for each vertex u  G->V { u->color = WHITE; } time = 0; for each vertex u  G->V { if (u->color == WHITE) DFS_Visit(u); } } DFS_Visit(u) { u->color = YELLOW; time = time+1; u->d = time; for each v  u->Adj[] { if (v->color == WHITE) DFS_Visit(v); } u->color = BLACK; time = time+1; u->f = time; } Review: DFS Code David Luebke 33/10/2014

  4. Review: DFS Example sourcevertex David Luebke 43/10/2014

  5. Review: DFS Example sourcevertex d f 1 | | | | | | | | David Luebke 53/10/2014

  6. Review: DFS Example sourcevertex d f 1 | | | 2 | | | | | David Luebke 63/10/2014

  7. Review: DFS Example sourcevertex d f 1 | | | 2 | | 3 | | | David Luebke 73/10/2014

  8. Review: DFS Example sourcevertex d f 1 | | | 2 | | 3 | 4 | | David Luebke 83/10/2014

  9. Review: DFS Example sourcevertex d f 1 | | | 2 | | 3 | 4 5 | | David Luebke 93/10/2014

  10. Review: DFS Example sourcevertex d f 1 | | | 2 | | 3 | 4 5 | 6 | David Luebke 103/10/2014

  11. Review: DFS Example sourcevertex d f 1 | 8 | | 2 | 7 | 3 | 4 5 | 6 | David Luebke 113/10/2014

  12. Review: DFS Example sourcevertex d f 1 | 8 | | 2 | 7 | 3 | 4 5 | 6 | David Luebke 123/10/2014

  13. Review: DFS Example sourcevertex d f 1 | 8 | | 2 | 7 9 | 3 | 4 5 | 6 | David Luebke 133/10/2014

  14. Review: DFS Example sourcevertex d f 1 | 8 | | 2 | 7 9 |10 3 | 4 5 | 6 | David Luebke 143/10/2014

  15. Review: DFS Example sourcevertex d f 1 | 8 |11 | 2 | 7 9 |10 3 | 4 5 | 6 | David Luebke 153/10/2014

  16. Review: DFS Example sourcevertex d f 1 |12 8 |11 | 2 | 7 9 |10 3 | 4 5 | 6 | David Luebke 163/10/2014

  17. Review: DFS Example sourcevertex d f 1 |12 8 |11 13| 2 | 7 9 |10 3 | 4 5 | 6 | David Luebke 173/10/2014

  18. Review: DFS Example sourcevertex d f 1 |12 8 |11 13| 2 | 7 9 |10 3 | 4 5 | 6 14| David Luebke 183/10/2014

  19. Review: DFS Example sourcevertex d f 1 |12 8 |11 13| 2 | 7 9 |10 3 | 4 5 | 6 14|15 David Luebke 193/10/2014

  20. Review: DFS Example sourcevertex d f 1 |12 8 |11 13|16 2 | 7 9 |10 3 | 4 5 | 6 14|15 David Luebke 203/10/2014

  21. Review: Kinds Of Edges • Thm: If G is undirected, a DFS produces only tree and back edges • Thm: An undirected graph is acyclic iff a DFS yields no back edges • Thus, can run DFS to find cycles David Luebke 213/10/2014

  22. Review: Kinds of Edges sourcevertex d f 1 |12 8 |11 13|16 2 | 7 9 |10 3 | 4 5 | 6 14|15 Tree edges Back edges Forward edges Cross edges David Luebke 223/10/2014

  23. DFS And Cycles • Running time: O(V+E) • We can actually determine if cycles exist in O(V) time: • In an undirected acyclic forest, |E|  |V| - 1 • So count the edges: if ever see |V| distinct edges, must have seen a back edge along the way • Why not just test if |E| <|V| and answer the question in constant time? David Luebke 233/10/2014

  24. Directed Acyclic Graphs • A directed acyclic graph or DAG is a directed graph with no directed cycles: David Luebke 243/10/2014

  25. DFS and DAGs • Argue that a directed graph G is acyclic iff a DFS of G yields no back edges: • Forward: if G is acyclic, will be no back edges • Trivial: a back edge implies a cycle • Backward: if no back edges, G is acyclic • Argue contrapositive: G has a cycle   a back edge • Let v be the vertex on the cycle first discovered, and u be the predecessor of v on the cycle • When v discovered, whole cycle is white • Must visit everything reachable from v before returning from DFS-Visit() • So path from uv is yellowyellow, thus (u, v) is a back edge David Luebke 253/10/2014

  26. Topological Sort • Topological sort of a DAG: • Linear ordering of all vertices in graph G such that vertex u comes before vertex v if edge (u, v)  G • Real-world example: getting dressed David Luebke 263/10/2014

  27. Getting Dressed Underwear Socks Watch Pants Shoes Shirt Belt Tie Jacket David Luebke 273/10/2014

  28. Getting Dressed Underwear Socks Watch Pants Shoes Shirt Belt Tie Jacket Socks Underwear Pants Shoes Watch Shirt Belt Tie Jacket David Luebke 283/10/2014

  29. Topological Sort Algorithm Topological-Sort() { Run DFS When a vertex is finished, output it Vertices are output in reverse topological order } • Time: O(V+E) • Correctness: Want to prove that (u,v)  G  uf > vf David Luebke 293/10/2014

  30. Correctness of Topological Sort • Claim: (u,v)  G  uf > vf • When (u,v) is explored, u is yellow • v = yellow  (u,v) is back edge. Contradiction (Why?) • v = white  v becomes descendent of u  vf < uf (since must finish v before backtracking and finishing u) • v = black  v already finished  vf < uf David Luebke 303/10/2014

  31. Minimum Spanning Tree • Problem: given a connected, undirected, weighted graph: 6 4 5 9 14 2 10 15 3 8 David Luebke 313/10/2014

  32. Minimum Spanning Tree • Problem: given a connected, undirected, weighted graph, find a spanning tree using edges that minimize the total weight 6 4 5 9 14 2 10 15 3 8 David Luebke 323/10/2014

  33. Minimum Spanning Tree • Which edges form the minimum spanning tree (MST) of the below graph? A 6 4 5 9 H B C 14 2 10 15 G E D 3 8 F David Luebke 333/10/2014

  34. Minimum Spanning Tree • Answer: A 6 4 5 9 H B C 14 2 10 15 G E D 3 8 F David Luebke 343/10/2014

  35. Minimum Spanning Tree • MSTs satisfy the optimal substructure property: an optimal tree is composed of optimal subtrees • Let T be an MST of G with an edge (u,v) in the middle • Removing (u,v) partitions T into two trees T1 and T2 • Claim: T1 is an MST of G1 = (V1,E1), and T2 is an MST of G2 = (V2,E2) (Do V1 and V2 share vertices? Why?) • Proof: w(T) = w(u,v) + w(T1) + w(T2)(There can’t be a better tree than T1 or T2, or T would be suboptimal) David Luebke 353/10/2014

  36. Minimum Spanning Tree • Thm: • Let T be MST of G, and let A  T be subtree of T • Let (u,v) be min-weight edge connecting A to V-A • Then (u,v)  T David Luebke 363/10/2014

  37. Minimum Spanning Tree • Thm: • Let T be MST of G, and let A  T be subtree of T • Let (u,v) be min-weight edge connecting A to V-A • Then (u,v)  T • Proof: in book (see Thm 24.1) David Luebke 373/10/2014

  38. 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); David Luebke 383/10/2014

  39. 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 David Luebke 393/10/2014

  40. 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 David Luebke 403/10/2014

  41. 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 David Luebke 413/10/2014

  42. 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 David Luebke 423/10/2014

  43. 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 David Luebke 433/10/2014

  44. 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 David Luebke 443/10/2014

  45. 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 David Luebke 453/10/2014

  46. 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 David Luebke 463/10/2014

  47. 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 David Luebke 473/10/2014

  48. 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 David Luebke 483/10/2014

  49. 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 David Luebke 493/10/2014

  50. 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 David Luebke 503/10/2014

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