1. Data Structures & Algorithms Recursion and Trees

2. Fundamental concept in math and CS Recursive definition Defined in terms of itself a N = a*a N-1, a 0 = 1 Recursive function Calls itself int exp(int base, int pow) { return (pow == 0 ? 1 : base*exp(base, pow- 1)); } Recursion

3. Recursive definition (and function) must: 1. have a base case – termination condition 2. always call a case smaller than itself All practical computations can be couched in a recursive framework! (see theory of computation) Recursion

4. Recursively defined structures e.g., binary tree Base case: Empty tree has no nodes Recursion: None-empty tree has a root node with two children, each the root of a binary tree Recursion

5. Widely used in CS and with trees... Mathematical recurrences Recursive programs Divide and Conquer Dynamic Programming Tree traversal DFS Recursion

6. Recursive algorithm – solves problem by solving one or more smaller instances of same problem Recurrence relation – factorial – N! = N(N-1)!, for N > 0, with 0! = 1. – In C++, use recursive functions Int factorial(int N) { if (N == 0) return 1; return N*factorial(N-1); } Recursive Algorithms

7. BTW, can often also be expressed as iteration E.g., can also write N! computation as a loop: int factorial(int N) { for (int t = 1, i = 1; i <= N; ++i) t *= i; return t; } Recursive Algorithms

8. Euclid's Algorithm is one of the oldest known algorithms Recursive method for finding the GCD of two integers int gcd(int m, int n) {// expect m >= n if (n == 0) return m; return gcd(n, m % n); } Base case Euclid’s Algorithm Recursive call to smaller instance

9. Recursive scheme that divides input into two (or some fixed number) of (roughly) equal parts Then makes a recursive call on each part Widely used approach Many important algorithms Depending on expense of dividing and combining, can be very efficient Divide & Conquer

10. Example: find the maximum element in an array a[N] (Easy to do iteratively...) Base case: Only one element – return it Divide: Split array into upper and lower halves Recursion: Find maximum of each half Combine results: Return larger of two maxima Divide & Conquer

11. Property 5.1: A recursive function that divides a problem of size N into two independent (non-empty) parts that it solves, recursively calls itself less than N times. Prf: T(1) = 0 T(N) = T(k) + T(N-k) + 1 for recursive call on size N divided into one part of size k and the other of size N-k Induct! Divide & Conquer

12. 3 pegs N disks, all on one peg Disks arranged from largest on bottom to smallest on top Must move all disks to target peg Can only move one disk at a time Must place disk on another peg Can never place larger disk on a smaller one Legend has it that the world will end when a certain group of monks finishes the task in a temple with 40 golden disks on 3 diamond pegs Towers of Hanoi

13. Target peg Which peg should top disk go on first? Towers of Hanoi

14. How many moves does this take? Towers of Hanoi

15. Property 5.2: The recursive d&c algorithm for the Towers of Hanoi problem produces a solution that has 2N – 1 moves. Prf: T(1) = 1 T(N) = T(N-1) + 1 + T(N-1) = 2 T(N-1) + 1 = 2N – 1 by induction Towers of Hanoi

16. Two other important D&C algorithms: Binary search MergeSort Algorithm Metric RecurrenceApprox. Soln. Binary Search comparisons C(N) = C(N/2)+1lg N MergeSort recursive calls A(N) = 2 A(N/2) + 1N MergeSort comparisons C(N) = 2 C(N/2) + NN lg N Divide & Conquer

17. In Divide & Conquer, it is essential that the subproblems be independent (partition the input) When this is not the case, life gets complicated! Sometimes, we can essentially fill up a table with values we compute once, rather than recompute every time they are needed. This is Dynamic Programming Issue – table may be too big! Dynamic Programming

18. Fibonacci Numbers: F[0] = 0 F[1] = 1 F[N] = F[N-1] + F[N-2] Horribly inefficient implementation: int F(int N) { if (N < 1) return 0; if (N == 1) return 1; return F(N-1) + F(N-2); } Dynamic Programming

19. How bad is this code? How many calls does it make to itself? F(N) makes F(N+1) calls! Exponential!!!! 10 1 2 1 3 10 1 5 10 1 2 1 8 10 1 2 1 3 10 1 13 10 1 2 1 3 10 1 5 10 1 2 1 Dynamic Programming

20. Can we do better? How? Make a table – compute once (yellow shapes) Fill up table 1010 1 1 10 1 2 110 1 10 1 2 1 3 10 1 10 1 2 1 Dynamic Programming 01 01 12 12 34 35 56 813 78 1 2 1 3 1 5 2 8 3 8

21. Property 5.3: Dynamic Programming reduces the running time of a recursive function to be at most the time it takes to evaluate the functions for all arguments less than or equal to the given argument, treating the cost of a recursive call as a constant. Dynamic Programming

22. A mathematical abstraction Central to many algorithms Describe dynamic properties of algorithms Build and use explicit tree data structures Examples: Family tree of descendants Sports tournaments (Who's In?) Organization Charts (Army) Parse tree of natural language sentence File systems Trees

23. Rooted trees Ordered trees M-ary trees and binary trees Defn: A tree is a nonempty collection of vertices and edges such that there is exactly one path between each pair of vertices. Defn: A path is a list of distinct vertices such that successive vertices have an edge between them Defn: A graph in which there is at most one path between each pair of vertices is a forest. Types of Trees

24. Binary TreeTernary Tree internal node external node root leaf

25. Types of Trees Rooted TreeFree Tree root node child parent sibling

26. Tree Representation Binary TreeRepresentation

27. Tree Representation Ordered TreeRepresentation Use linked list for siblings at each level, Pointer to left child

28. A binary tree with N internal nodes has N+1 external nodes A binary tree with N internal nodes has 2N links: N-1 to internal nodes and N+1 to external nodes The level of a node is one higher than the level of its parent, with the root at level 0. The path length of a tree is the sum of the levels of all the tree’s nodes The internal path length is the sum of levels of internal nodes; external path length is sum of levels of external nodes. Properties of Trees

29. The external path length of any binary tree with N nodes is 2N greater than the internal path length The height of a binary tree with N internal nodes is at least lg N and at most N-1. The internal path length of a binary tree with N internal nodes is at least N lg(N/4) and at most N(N-1)/2. Properties of Trees

30. Given pointer to a tree, visit every node in the tree systematically Inorder: Visit the left subtree, visit the root, then visit the right subtree Preorder: Visit the root, visit the left subtree, visit the right subtree. Postorder: Visit the left subtree, visit the right subtree, visit the root. Tree Traversal

31. Generic recursive traversal code Preorder? Inorder? Postorder? Tree Traversal void traverse(link h, void visit(link)) { if (h == NULL) return; visit(h); traverse(h->left, visit); traverse(h->right, visit); }

32. Generic iterative traversal code Preorder? Inorder? Postorder? Tree Traversal void traverse(link h, void visit(link)) { STACK s(max); s.push(h); while (!s.empty()) { visit(h = s.pop()); if (h->right != 0) s.push(h->right); if (h->left != 0) s.push(h->left); }

33. Generic iterative traversal code Level order = top to bottom, left to right Tree Traversal void traverse(link h, void visit(link)) { QUEUE q(max); q.put(h); while (!q.empty()) { visit(h = q.get()); if (h->left != 0) q.put(h->left); if (h->right != 0) q.put(h->right); }

34. Count number of nodes Compute height Compute internal path length Display tree Basic Tree Algorithms int count(link h) { if (h == NULL) return 0; return 1 + count(h->left) + count(h->right); }

35. Count number of nodes Compute height Compute internal path length Display tree Basic Tree Algorithms int height(link h) { if (h == NULL) return -1; int u = height(h->left); int v = height(h->right); return 1 + (u > v ? u : v); }

36. Basic Tree Algorithms void printnode(Item x, int h) { for (int i=0; i<h; ++i) cout << “ “; cout << x << endl; } void show(link t, int h) { if (t == NULL) { printnode(‘*’,h); return; } show(h->left, h+1); printnode(t->item, h); show(h->right, h+1); } Display tree (order?)

37. Tournament construction Start with array (list of competitors) Develop into tree with matches Divide and conquer: Split in half Make tourney with left (first) half Make a tourney with right (last) half Make a new node with links to the two tourneys Single item tourney = leaf with that item Item in interior nodes? Winner of tourney! Tree Algorithms

38. Recursive Graph Traversal DFS = Depth-First Search Generalization of tree traversal methods Basis for many algorithms for processing graphs Code: Starting at any node v Visit v Recursively visit each unvisited neighbor of v If graph is connected, will visit every node Need to be able to mark nodes as visited Don’t need to do this for trees (why not?) Set of edges on which calls are made forms a spanning tree Graph Traversal

39. Recursive Graph Traversal: DFS Property 5.10: DFS requires time O(V+E) in a graph with V vertices and E edges using the adjacency lists representation Adjacency list representation: one list node corresponding to each edge in the graph, and one list head pointer corresponding to each vertex in the graph. Graph Traversal void traverse(int k, void visit(int)) { visit(k); visited[k] = TRUE; for (link t=adj[k]; k!=0; t = t->next) if (!visited[t->v]) traverse(t->v, visit); }

40. BFS in graph Graph Traversal void traverse(int k, void visit(int)) { QUEUE q(V*V); q.put(k); while (!q.empty()) { if (!visited[k = q.get()] ) { visit(k); visited[k] = 1; for (link t=adj[k]; t!=0; t=t->next) if (!visited[t->v]) q.put(t->v); }