Prof. Swarat Chaudhuri

1. COMP 482: Design and Analysis of Algorithms Prof. Swarat Chaudhuri Spring 2013 Lecture 4

2. Q3: Analyzing an algorithm • You have an array A with integer entries A[1],…, A[n] • Output a 2-D array B such that B[i,j] contains the sum A[i] + A[i+1] + … + A[j] • Here is an algorithm: • For i=1, 2, …, n • For j = i+1, 2, …, n { • Add up entries A[i] through A[j] • Store result in B[i,j] • } • } • Obtain an upper bound and a lower bound for the algorithm.

3. The lower bound is the more interesting one • Consider the times during the execution of the algorithm when i ≤ n/4 and j ≥ 3n/4. • In these cases, j − i + 1 ≥ 3n/4 − n/4 + 1 > n/2. Therefore, adding up the array entries A[i] through A[j] would require at least n/2 operations, since there are more then n/2 terms to add up. • How many times during the execution of the given algorithm do we encounter such cases? There are (n/4)2 pairs (i, j) with i ≤ n/4 and j ≥ 3n/4. the given algorithm enumerates over all of them, and as shown above, it must perform at least n/2 operations for each such pair. Therefore, the algorithm must perform at least n/2.(n/4)2 = n3/32 operations. This is Ω(n3), as desired.

4. Graphs3.1 Basic Definitions and Applications

5. Undirected Graphs • Undirected graph. G = (V, E) • V = nodes. • E = edges between pairs of nodes. • Captures pairwise relationship between objects. • Graph size parameters: n = |V|, m = |E|. V = { 1, 2, 3, 4, 5, 6, 7, 8 } E = { 1-2, 1-3, 2-3, 2-4, 2-5, 3-5, 3-7, 3-8, 4-5, 5-6 }n = 8 m = 11

6. Some Graph Applications Graph Nodes Edges transportation street intersections highways communication computers fiber optic cables World Wide Web web pages hyperlinks social people relationships food web species predator-prey software systems functions function calls scheduling tasks precedence constraints circuits gates wires

7. World Wide Web • Web graph. • Node: web page. • Edge: hyperlink from one page to another. cnn.com netscape.com novell.com cnnsi.com timewarner.com hbo.com sorpranos.com

8. 9-11 Terrorist Network • Social network graph. • Node: people. • Edge: relationship between two people. Reference: Valdis Krebs, http://www.firstmonday.org/issues/issue7_4/krebs

9. Ecological Food Web • Food web graph. • Node = species. • Edge = from prey to predator. Reference: http://www.twingroves.district96.k12.il.us/Wetlands/Salamander/SalGraphics/salfoodweb.giff

10. Graph Representation: Adjacency Matrix • Adjacency matrix. n-by-n matrix with Auv = 1 if (u, v) is an edge. • Two representations of each edge. • Space proportional to n2. • Checking if (u, v) is an edge takes (1) time. • Identifying all edges takes (n2) time. 1 2 3 4 5 6 7 8 1 0 1 1 0 0 0 0 0 2 1 0 1 1 1 0 0 0 3 1 1 0 0 1 0 1 1 4 0 1 0 1 1 0 0 0 5 0 1 1 1 0 1 0 0 6 0 0 0 0 1 0 0 0 7 0 0 1 0 0 0 0 1 8 0 0 1 0 0 0 1 0

11. Graph Representation: Adjacency List • Adjacency list. Node indexed array of lists. • Two representations of each edge. • Space proportional to m + n. • Checking if (u, v) is an edge takes O(deg(u)) time. • Identifying all edges takes (m + n) time. degree = number of neighbors of u 1 2 3 1 3 4 5 2 8 7 1 2 5 3 4 2 5 2 3 4 6 5 5 6 7 3 8 8 3 7

12. Paths and Connectivity • Def. A path in an undirected graph G = (V, E) is a sequence P of nodes v1, v2, …, vk-1, vk with the property that each consecutive pair vi, vi+1 is joined by an edge in E. • Def. A path is simple if all nodes are distinct. • Def. An undirected graph is connected if for every pair of nodes u and v, there is a path between u and v.

13. Cycles • Def. A cycle is a path v1, v2, …, vk-1, vk in which v1 = vk, k > 2, and the first k-1 nodes are all distinct. cycle C = 1-2-4-5-3-1

14. Trees • Def. An undirected graph is a tree if it is connected and does not contain a cycle. • Theorem. Let G be an undirected graph on n nodes. Any two of the following statements imply the third. • G is connected. • G does not contain a cycle. • G has n-1 edges.

15. Rooted Trees • Rooted tree. Given a tree T, choose a root node r and orient each edge away from r. • Importance. Models hierarchical structure. root r parent of v v child of v a tree the same tree, rooted at 1

16. Phylogeny Trees • Phylogeny trees. Describe evolutionary history of species.

17. GUI Containment Hierarchy • GUI containment hierarchy. Describe organization of GUI widgets. Reference: http://java.sun.com/docs/books/tutorial/uiswing/overview/anatomy.html

18. 3.2 Graph Traversal

19. Connectivity • s-t connectivity problem. Given two nodes s and t, is there a path between s and t? • s-t shortest path problem. Given two nodes s and t, what is the length of the shortest path between s and t? • Applications. • Facebook. • Maze traversal. • Erdos number. • Kevin Bacon number. • Fewest number of hops in a communication network.

20. L1 L2 s L n-1 Breadth First Search • BFS intuition. Explore outward from s in all possible directions, adding nodes one "layer" at a time. Effect: find “shallow” paths to nodes. • BFS algorithm. • L0 = { s }. • L1 = all neighbors of L0. • L2 = all nodes that do not belong to L0 or L1, and that have an edge to a node in L1. • Li+1 = all nodes that do not belong to an earlier layer, and that have an edge to a node in Li. • Theorem. For each i, Li consists of all nodes at distance exactly ifrom s. There is a path from s to t iff t appears in some layer.

21. Implementing BFS • Q: What’s a good way to implement the above algorithm? • A: Use a queue for the “frontier”

22. Breadth First Search • Property. Let T be a BFS tree of G = (V, E), and let (x, y) be an edge of G. Then the level of x and y differ by at most 1. L0 L1 L2 L3

23. Breadth First Search: Analysis • Theorem. The above implementation of BFS runs in O(m + n) time if the graph is given by its adjacency list representation. • Pf. • Easy to prove O(n2) running time: • at most n lists Li • each node occurs on at most one list; for loop runs  n times • when we consider node u, there are  n incident edges (u, v),and we spend O(1) processing each edge • Actually runs in O(m + n) time: • when we consider node u, there are deg(u) incident edges (u, v) • total time processing edges is uV deg(u) = 2m ▪ each edge (u, v) is counted exactly twicein sum: once in deg(u) and once in deg(v)

24. Connected Component • Connected component. Find all nodes reachable from s. • Connected component containing node 1 = { 1, 2, 3, 4, 5, 6, 7, 8 }.

25. Q1: Finding connected components • Give an algorithm to find the set of all connected components of an undirected graph.

26. Connected Component • Connected component. Find all nodes reachable from s. • Theorem. Upon termination, R is the connected component containing s. • BFS = explore in order of distance from s. R s u v it's safe to add v

27. Q2: Flood Fill • Flood fill. Given lime green pixel in an image, change color of entire blob of neighboring lime pixels to blue. recolor lime green blob to blue

28. Flood Fill • Flood fill. Given lime green pixel in an image, change color of entire blob of neighboring lime pixels to blue. recolor lime green blob to blue

29. Flood Fill • Flood fill. Given lime green pixel in an image, change color of entire blob of neighboring lime pixels to blue. • Node: pixel. • Edge: two neighboring lime pixels. • Blob: connected component of lime pixels. recolor lime green blob to blue

30. Depth-first search • Use recursion • DFS intuition. Explore outward from s along one path as far as possible, and backtrack when you cannot progress. Effect: find faraway nodes. • DFS(u): • Mark u as “Explored” and add u to R • For each edge (u,v) incident to u • If v is not marked “Explored” then • Recursively call DFS(v)

31. Depth-first search • Property. For a given recursive call DFS(u), all nodes marked “Explored” between the beginning and end of this recursive call are descendants of u in T. • Theorem. Let T be a depth-first search tree, let x and y be nodes in T, and let (x,y) be an edge of G that is not an edge of T. Then one of x or y is an ancestor of the other.

32. Q3: BFS and DFS trees • We have a connected graph G = (V, E) and a specific vertex u. Suppose we compute a DFS tree rooted at u, and obtain a tree T that includes all nodes of G. Suppose we then compute a BFS tree rooted at u, and obtain the same tree T. • Prove that G = T.

33. Answer • Suppose G has an edge e = {a, b} that does not belong to T. • As T is a DFS tree, one of the two ends must be an ancestor of the other—say a is an ancestor of b. • (*) Since T is a BFS tree, the distance of the two nodes from u in T can differ at most by one. • But if a is an ancestor of b, and (*) holds, then a must be the direct parent of b. This means that {a, b} is an edge in T. Contradiction.

34. Q4: Finding a cycle • Given a graph G, determine if it has a cycle. If so, the algorithm should output this cycle. • Answer: Assume that G is connected; otherwise work on the connected components. • Run BFS from an arbitrary node s, and obtain a BFS tree T. If every edge of G appears in the tree, then G = T and there is no cycle. • Otherwise, there is an edge e = (v, w) that is in G but not in T. Consider the least common ancestor u of v and w in T. We get a cycle from edge e and paths u-v and u-w in T.