CS 332: Algorithms

1. CS 332: Algorithms Graph Algorithms David Luebke 16/9/2014

2. 7 10 i = [7,10]; i low = 7; ihigh = 10 5 11 17 19 4 8 15 18 21 23 Interval Trees • The problem: maintain a set of intervals • E.g., time intervals for a scheduling program: David Luebke 26/9/2014

3. 7 10 i = [7,10]; i low = 7; ihigh = 10 5 11 17 19 4 8 15 18 21 23 Review: Interval Trees • The problem: maintain a set of intervals • E.g., time intervals for a scheduling program: • Query: find an interval in the set that overlaps a given query interval • [14,16]  [15,18] • [16,19]  [15,18] or [17,19] • [12,14]  NULL David Luebke 36/9/2014

4. Review: Interval Trees • Following the methodology: • Pick underlying data structure • Red-black trees will store intervals, keyed on ilow • Decide what additional information to store • Store the maximum endpoint in the subtree rooted at i • Figure out how to maintain the information • Update max as traverse down during insert • Recalculate max after delete with a traversal up the tree • Update during rotations • Develop the desired new operations David Luebke 46/9/2014

5. Review: Interval Trees intmax [17,19]23 [5,11]18 [21,23]23 [4,8]8 [15,18]18 [7,10]10 Note that: David Luebke 56/9/2014

6. Review: Searching Interval Trees IntervalSearch(T, i) { x = T->root; while (x != NULL && !overlap(i, x->interval)) if (x->left != NULL && x->left->max  i->low) x = x->left; else x = x->right; return x } • What will be the running time? David Luebke 66/9/2014

7. Review: Correctness of IntervalSearch() • Key idea: need to check only 1 of node’s 2 children • Case 1: search goes right • Show that  overlap in right subtree, or no overlap at all • Case 2: search goes left • Show that  overlap in left subtree, or no overlap at all David Luebke 76/9/2014

8. Correctness of IntervalSearch() • Case 1: if search goes right,  overlap in the right subtree or no overlap in either subtree • If  overlap in right subtree, we’re done • Otherwise: • xleft = NULL, or x  left  max < x  low (Why?) • Thus, no overlap in left subtree! while (x != NULL && !overlap(i, x->interval)) if (x->left != NULL && x->left->max  i->low) x = x->left; else x = x->right; return x; David Luebke 86/9/2014

9. Review: Correctness of IntervalSearch() • Case 2: if search goes left,  overlap in the left subtree or no overlap in either subtree • If  overlap in left subtree, we’re done • Otherwise: • i low  x left max, by branch condition • x left max = y high for some y in left subtree • Since i and y don’t overlap and i low  y high,i high < y low • Since tree is sorted by low’s, i high < any low in right subtree • Thus, no overlap in right subtree while (x != NULL && !overlap(i, x->interval)) if (x->left != NULL && x->left->max  i->low) x = x->left; else x = x->right; return x; David Luebke 96/9/2014

10. Next Up: Graph Algorithms • Going to skip some advanced data structures • B-Trees • Balanced search tree designed to minimize disk I/O • Fibonacci heaps • Heap structure that supports efficient “merge heap” op • Requires amortized analysis techniques • Will hopefully return to these • Meantime: graph algorithms • Should be largely review, easier for exam David Luebke 106/9/2014

11. Graphs • A graph G = (V, E) • V = set of vertices • E = set of edges = subset of V  V • Thus |E| = O(|V|2) David Luebke 116/9/2014

12. Graph Variations • Variations: • A connected graphhas a path from every vertex to every other • In an undirected graph: • Edge (u,v) = edge (v,u) • No self-loops • In a directed graph: • Edge (u,v) goes from vertex u to vertex v, notated uv David Luebke 126/9/2014

13. Graph Variations • More variations: • A weighted graph associates weights with either the edges or the vertices • E.g., a road map: edges might be weighted w/ distance • A multigraph allows multiple edges between the same vertices • E.g., the call graph in a program (a function can get called from multiple points in another function) David Luebke 136/9/2014

14. Graphs • We will typically express running times in terms of |E| and |V| (often dropping the |’s) • If |E|  |V|2 the graph is dense • If |E|  |V| the graph is sparse • If you know you are dealing with dense or sparse graphs, different data structures may make sense David Luebke 146/9/2014

15. 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 David Luebke 156/9/2014

16. Graphs: Adjacency Matrix • Example: 1 a d 2 4 b c 3 David Luebke 166/9/2014

17. Graphs: Adjacency Matrix • Example: 1 a d 2 4 b c 3 David Luebke 176/9/2014

18. Graphs: Adjacency Matrix • How much storage does the adjacency matrix require? • A: O(V2) • What is the minimum amount of storage needed by an adjacency matrix representation of an undirected graph with 4 vertices? • A: 6 bits • Undirected graph  matrix is symmetric • No self-loops don’t need diagonal David Luebke 186/9/2014

19. Graphs: Adjacency Matrix • The adjacency matrix is a dense representation • Usually too much storage for large graphs • But can be very efficient for small graphs • Most large interesting graphs are sparse • E.g., planar graphs, in which no edges cross, have |E| = O(|V|) by Euler’s formula • For this reason the adjacency list is often a more appropriate respresentation David Luebke 196/9/2014

20. Graphs: Adjacency List • Adjacency list: for each vertex v  V, store a list of vertices adjacent to v • Example: • Adj[1] = {2,3} • Adj[2] = {3} • Adj[3] = {} • Adj[4] = {3} • Variation: can also keep a list of edges coming into vertex 1 2 4 3 David Luebke 206/9/2014

21. Graphs: Adjacency List • How much storage is required? • The degree of a vertex v = # incident edges • Directed graphs have in-degree, out-degree • For directed graphs, # of items in adjacency lists is out-degree(v) = |E|takes (V + E) storage (Why?) • For undirected graphs, # items in adj lists is  degree(v) = 2 |E| (handshaking lemma)also (V + E) storage • So: Adjacency lists take O(V+E) storage David Luebke 216/9/2014

22. 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 226/9/2014

23. 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 236/9/2014

24. 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 246/9/2014

25. 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 256/9/2014

26. Breadth-First Search: Example r s t u         v w x y David Luebke 266/9/2014

27. Breadth-First Search: Example r s t u  0       v w x y Q: s David Luebke 276/9/2014

28. Breadth-First Search: Example r s t u 1 0    1   v w x y Q: w r David Luebke 286/9/2014

29. Breadth-First Search: Example r s t u 1 0 2   1 2  v w x y Q: r t x David Luebke 296/9/2014

30. Breadth-First Search: Example r s t u 1 0 2  2 1 2  v w x y Q: t x v David Luebke 306/9/2014

31. 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 316/9/2014

32. 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 326/9/2014

33. 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 336/9/2014

34. Breadth-First Search: Example r s t u 1 0 2 3 2 1 2 3 v w x y Q: y David Luebke 346/9/2014

35. Breadth-First Search: Example r s t u 1 0 2 3 2 1 2 3 v w x y Q: Ø David Luebke 356/9/2014

36. 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 366/9/2014

37. 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 tree? Total space used: O(max(degree(v))) = O(E) David Luebke 376/9/2014

38. 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 386/9/2014