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CS 332: Algorithms

CS 332: Algorithms. Graph Algorithms . Administrative. Test postponed to Friday Homework: Turned in last night by midnight: full credit Turned in tonight by midnight: 1 day late, 10% off Turned in tomorrow night: 2 days late, 30% off Extra credit lateness measured separately.

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CS 332: Algorithms

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  1. CS 332: Algorithms Graph Algorithms David Luebke 16/3/2014

  2. Administrative • Test postponed to Friday • Homework: • Turned in last night by midnight: full credit • Turned in tonight by midnight: 1 day late, 10% off • Turned in tomorrow night: 2 days late, 30% off • Extra credit lateness measured separately David Luebke 26/3/2014

  3. Review: Graphs • A graph G = (V, E) • V = set of vertices, E = set of edges • Densegraph: |E|  |V|2; Sparse graph: |E|  |V| • Undirected graph: • Edge (u,v) = edge (v,u) • No self-loops • Directed graph: • Edge (u,v) goes from vertex u to vertex v, notated uv • A weighted graph associates weights with either the edges or the vertices David Luebke 36/3/2014

  4. Review: Representing Graphs • Assume V = {1, 2, …, n} • An adjacency matrixrepresents the graph as a n x n matrix A: • A[i, j] = 1 if edge (i, j)  E (or weight of edge) = 0 if edge (i, j)  E • Storage requirements: O(V2) • A dense representation • But, can be very efficient for small graphs • Especially if store just one bit/edge • Undirected graph: only need one diagonal of matrix David Luebke 46/3/2014

  5. Review: Graph Searching • Given: a graph G = (V, E), directed or undirected • Goal: methodically explore every vertex and every edge • Ultimately: build a tree on the graph • Pick a vertex as the root • Choose certain edges to produce a tree • Note: might also build a forest if graph is not connected David Luebke 56/3/2014

  6. Review: Breadth-First Search • “Explore” a graph, turning it into a tree • One vertex at a time • Expand frontier of explored vertices across the breadth of the frontier • Builds a tree over the graph • Pick a source vertex to be the root • Find (“discover”) its children, then their children, etc. David Luebke 66/3/2014

  7. Review: Breadth-First Search • Again will associate vertex “colors” to guide the algorithm • White vertices have not been discovered • All vertices start out white • Grey vertices are discovered but not fully explored • They may be adjacent to white vertices • Black vertices are discovered and fully explored • They are adjacent only to black and gray vertices • Explore vertices by scanning adjacency list of grey vertices David Luebke 76/3/2014

  8. 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; } } What does v->d represent? What does v->p represent? David Luebke 86/3/2014

  9. Breadth-First Search: Example r s t u         v w x y David Luebke 96/3/2014

  10. Breadth-First Search: Example r s t u  0       v w x y Q: s David Luebke 106/3/2014

  11. Breadth-First Search: Example r s t u 1 0    1   v w x y Q: w r David Luebke 116/3/2014

  12. Breadth-First Search: Example r s t u 1 0 2   1 2  v w x y Q: r t x David Luebke 126/3/2014

  13. Breadth-First Search: Example r s t u 1 0 2  2 1 2  v w x y Q: t x v David Luebke 136/3/2014

  14. Breadth-First Search: Example r s t u 1 0 2 3 2 1 2  v w x y Q: x v u David Luebke 146/3/2014

  15. Breadth-First Search: Example r s t u 1 0 2 3 2 1 2 3 v w x y Q: v u y David Luebke 156/3/2014

  16. Breadth-First Search: Example r s t u 1 0 2 3 2 1 2 3 v w x y Q: u y David Luebke 166/3/2014

  17. Breadth-First Search: Example r s t u 1 0 2 3 2 1 2 3 v w x y Q: y David Luebke 176/3/2014

  18. Breadth-First Search: Example r s t u 1 0 2 3 2 1 2 3 v w x y Q: Ø David Luebke 186/3/2014

  19. Touch every vertex: O(V) u = every vertex, but only once (Why?) So v = every vertex that appears in some other vert’s adjacency list BFS: The Code Again BFS(G, s) { initialize vertices; Q = {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; } } What will be the running time? Total running time: O(V+E) David Luebke 196/3/2014

  20. BFS: The Code Again BFS(G, s) { initialize vertices; Q = {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; } } What will be the storage cost in addition to storing the graph? Total space used: O(max(degree(v))) = O(E) David Luebke 206/3/2014

  21. Breadth-First Search: Properties • BFS calculates the shortest-path distance to the source node • Shortest-path distance (s,v) = minimum number of edges from s to v, or  if v not reachable from s • Proof given in the book (p. 472-5) • BFS builds breadth-first tree, in which paths to root represent shortest paths in G • Thus can use BFS to calculate shortest path from one vertex to another in O(V+E) time David Luebke 216/3/2014

  22. Depth-First Search • Depth-first search is another strategy for exploring a graph • Explore “deeper” in the graph whenever possible • Edges are explored out of the most recently discovered vertex v that still has unexplored edges • When all of v’s edges have been explored, backtrack to the vertex from which v was discovered David Luebke 226/3/2014

  23. Depth-First Search • Vertices initially colored white • Then colored gray when discovered • Then black when finished David Luebke 236/3/2014

  24. 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 = GREY; 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; } Depth-First Search: The Code David Luebke 246/3/2014

  25. 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 = GREY; 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; } Depth-First Search: The Code What does u->d represent? David Luebke 256/3/2014

  26. 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 = GREY; 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; } Depth-First Search: The Code What does u->f represent? David Luebke 266/3/2014

  27. 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 = GREY; 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; } Depth-First Search: The Code Will all vertices eventually be colored black? David Luebke 276/3/2014

  28. 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 = GREY; 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; } Depth-First Search: The Code What will be the running time? David Luebke 286/3/2014

  29. 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 = GREY; 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; } Depth-First Search: The Code Running time: O(n2) because call DFS_Visit on each vertex, and the loop over Adj[] can run as many as |V| times David Luebke 296/3/2014

  30. 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 = GREY; 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; } Depth-First Search: The Code BUT, there is actually a tighter bound. How many times will DFS_Visit() actually be called? David Luebke 306/3/2014

  31. 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 = GREY; 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; } Depth-First Search: The Code So, running time of DFS = O(V+E) David Luebke 316/3/2014

  32. Depth-First Sort Analysis • This running time argument is an informal example of amortized analysis • “Charge” the exploration of edge to the edge: • Each loop in DFS_Visit can be attributed to an edge in the graph • Runs once/edge if directed graph, twice if undirected • Thus loop will run in O(E) time, algorithm O(V+E) • Considered linear for graph, b/c adj list requires O(V+E) storage • Important to be comfortable with this kind of reasoning and analysis David Luebke 326/3/2014

  33. DFS Example sourcevertex David Luebke 336/3/2014

  34. DFS Example sourcevertex d f 1 | | | | | | | | David Luebke 346/3/2014

  35. DFS Example sourcevertex d f 1 | | | 2 | | | | | David Luebke 356/3/2014

  36. DFS Example sourcevertex d f 1 | | | 2 | | 3 | | | David Luebke 366/3/2014

  37. DFS Example sourcevertex d f 1 | | | 2 | | 3 | 4 | | David Luebke 376/3/2014

  38. DFS Example sourcevertex d f 1 | | | 2 | | 3 | 4 5 | | David Luebke 386/3/2014

  39. DFS Example sourcevertex d f 1 | | | 2 | | 3 | 4 5 | 6 | David Luebke 396/3/2014

  40. DFS Example sourcevertex d f 1 | 8 | | 2 | 7 | 3 | 4 5 | 6 | David Luebke 406/3/2014

  41. DFS Example sourcevertex d f 1 | 8 | | 2 | 7 | 3 | 4 5 | 6 | David Luebke 416/3/2014

  42. DFS Example sourcevertex d f 1 | 8 | | 2 | 7 9 | 3 | 4 5 | 6 | What is the structure of the grey vertices? What do they represent? David Luebke 426/3/2014

  43. DFS Example sourcevertex d f 1 | 8 | | 2 | 7 9 |10 3 | 4 5 | 6 | David Luebke 436/3/2014

  44. DFS Example sourcevertex d f 1 | 8 |11 | 2 | 7 9 |10 3 | 4 5 | 6 | David Luebke 446/3/2014

  45. DFS Example sourcevertex d f 1 |12 8 |11 | 2 | 7 9 |10 3 | 4 5 | 6 | David Luebke 456/3/2014

  46. DFS Example sourcevertex d f 1 |12 8 |11 13| 2 | 7 9 |10 3 | 4 5 | 6 | David Luebke 466/3/2014

  47. DFS Example sourcevertex d f 1 |12 8 |11 13| 2 | 7 9 |10 3 | 4 5 | 6 14| David Luebke 476/3/2014

  48. DFS Example sourcevertex d f 1 |12 8 |11 13| 2 | 7 9 |10 3 | 4 5 | 6 14|15 David Luebke 486/3/2014

  49. DFS Example sourcevertex d f 1 |12 8 |11 13|16 2 | 7 9 |10 3 | 4 5 | 6 14|15 David Luebke 496/3/2014

  50. DFS: Kinds of edges • DFS introduces an important distinction among edges in the original graph: • Tree edge: encounter new (white) vertex • The tree edges form a spanning forest • Can tree edges form cycles? Why or why not? David Luebke 506/3/2014

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