RAIK 283: Data Structures & Algorithms

1. RAIK 283: Data Structures & Algorithms Transform and Conquer Dr. Ying Lu ylu@cse.unl.edu Design and Analysis of Algorithms – Chapter 6

2. RAIK 283: Data Structures & Algorithms • Giving credit where credit is due: • Most of the lecture notes are based on the slides from the Textbook’s companion website • http://www.aw-bc.com/info/levitin • Some examples and slides are based on lecture notes created by Dr. Ben Choi, Louisiana Technical University and Dr. Chuck Cusack, Hope College and Dr. Ellen Walker from Hiram College • I have modified many of their slides and added new slides. Design and Analysis of Algorithms – Chapter 6

3. An example problem • Checking element uniqueness in an array Algorithm UniqueElements(A[0..n-1]) for m = 0 to n-2 do for k = m+1 to n-1 do if A[m] = A[k] return false return true • Can we come up with a better algorithm? Design and Analysis of Algorithms – Chapter 6

4. Transform and Conquer Solve problem by transforming it into: • a more convenient instance of the same problem (instance simplification) • presorting • Gaussian elimination • a different representation of the same instance (representation change) • balanced search trees • heaps and heapsort • polynomial evaluation by Horner’s rule • a different problem altogether (problem reduction) • compute lcm(m, n) = m*n/gcd(m,n) • reductions to graph problems • linear programming Design and Analysis of Algorithms – Chapter 6

5. Binary Search Trees (BSTs) • Binary Search Tree property • A binary tree in which the key of an internal node is greater than the keys in its left subtree and less than or equal to the keys in its right subtree. Design and Analysis of Algorithms – Chapter 5

6. Analysis of BST Operations • All of the BST operations have time complexity (h), where h is the height of the tree • However, in the worst-case, the height may be (n) where n is the number of internal nodes • For example, a long chain tree structure • For a tree as balanced as possible, O(log2n) • The objective is to make the tree as balanced as possible • Technique: Binary Tree Rotations • Balanced search tree: AVL tree and 2-3 tree Design and Analysis of Algorithms – Chapter 5

7. Left- and Right-Rotations • The BST property still holds after a rotation. Design and Analysis of Algorithms – Chapter 6

8. Binary Tree Rotations • Left Rotation on (15, 25) Design and Analysis of Algorithms – Chapter 6

9. Balanced trees: AVL trees • For every node, difference in height between left and right subtrees is at most 1 • AVL property is maintained through rotations, each time the tree becomes unbalanced • lg n≤h ≤ 1.4404 lg (n + 2) - 1.3277 average: 1.01 lg n + 0.1 for large n • A similar idea: red-black trees (height of subtrees is allowed to differ by up to a factor of 2) Design and Analysis of Algorithms – Chapter 6

10. Balance factor • Algorithm maintains balance factor(difference in height between a node’s left and right subtrees) for each node. For example: Design and Analysis of Algorithms – Chapter 6

11. In-class exercise • Page 225: Exercise 6.3.1 Design and Analysis of Algorithms – Chapter 6

12. General case: single R-rotation Design and Analysis of Algorithms – Chapter 6

13. When single L-rotation? Design and Analysis of Algorithms – Chapter 6

14. Single L-Rotation Design and Analysis of Algorithms – Chapter 6

15. Double LR-rotation Design and Analysis of Algorithms – Chapter 6

16. When double RL-rotation? Design and Analysis of Algorithms – Chapter 6

17. Double RL-rotation Design and Analysis of Algorithms – Chapter 6

18. AVL tree rotations • Small examples: • 1, 2, 3 • 3, 2, 1 • 1, 3, 2 • 3, 1, 2 • Large example: 5, 6, 8, 3, 2, 4, 7 Design and Analysis of Algorithms – Chapter 6

19. In-class exercise • Construct an AVL tree for the list: 4, 5, 7, 2, 1, 3, 6 by successive insertions. • Page 226: Exercise 6.3.4 • For each of the following lists, construct an AVL tree by inserting their elements successively, starting with the empty tree. • A) 1, 2, 3, 4, 5, 6 • B) 6, 5, 4, 3, 2, 1 • C) 3, 6, 5, 1, 2, 4 Design and Analysis of Algorithms – Chapter 6

20. Balanced trees: AVL trees • For every node, difference in height between left and right subtrees is at most 1 • AVL property is maintained through rotations, each time the tree becomes unbalanced • lg n≤h ≤ 1.4404 lg (n + 2) - 1.3277 average: 1.01 lg n + 0.1 for large n • Disadvantage: needs extra storage for maintaining node balance Design and Analysis of Algorithms – Chapter 6

21. < x , y> <=x >x and <=y >y 2-3 Trees • Relax constraint that a node has 2 children • Allow 2-child nodes and 3-child nodes • With bigger nodes, tree is shorter & branchier • 2-node is just like before (one item, two children) • 3-node has two values and 3 children (left, middle, right) • All leaves are in the same level Design and Analysis of Algorithms – Chapter 6

22. Why 2-3 tree • Faster searching? • Actually, no. 2-3 tree is about as fast as an “equally balanced” binary tree, because you sometimes have to make 2 comparisons to get past a 3-node • Easier to keep balanced? • Yes, definitely. • Insertion can split 3-nodes into 2-nodes, or promote 2-nodes to 3-nodes to keep tree balanced! Design and Analysis of Algorithms – Chapter 6

23. Inserting into 2-3 Tree • As for binary tree, start by searching for the item • If you don’t find it, and you stop at a 2-node, upgrade the 2-node to a 3-node. • If you don’t find it, and you stop at a 3-node, you can’t just add another value. So, replace the 3-node by 2 2-nodes and push the middle value up to the parent node • Repeat recursively until you upgrade a 2-node or create a new root. Design and Analysis of Algorithms – Chapter 6

24. An example • Construction of a 2-3 tree for • the list 9, 5, 8, 3, 2, 4, 7 Design and Analysis of Algorithms – Chapter 6

25. 2-3 trees • When is a new root created? • What is the height of a 2-3 tree? Design and Analysis of Algorithms – Chapter 6

26. 2-3 trees • When is a new root created? • What is the height of a 2-3 tree? • (logn) (refer to Page 224-225) Design and Analysis of Algorithms – Chapter 6

27. Why is this better? • Intuitively, you unbalance a binary tree when you add height to one path significantly more than other possible paths. • With the 2-3 tree insertion algorithm, you can only add height to the tree when you create a new root, and this adds one unit of height to all paths simultaneously. Design and Analysis of Algorithms – Chapter 6

28. In-Class Exercise 31 Solution Design and Analysis of Algorithms – Chapter 6

29. After-class exercise • How to delete a node from a 2-3 Tree? Design and Analysis of Algorithms – Chapter 6

30. In-Class Exercises (II) • Design “transform and conquer” algorithms • 6.1.7 • 6.1.8 Design and Analysis of Algorithms – Chapter 6

31. Priority Queue Implementation? • A priority queue is the abstract data type (ADT) of an ordered set with the operations: • find element with highest priority • delete element with highest priority • insert element with assigned priority • What data structure should we use to implement a priority queue efficiently? Design and Analysis of Algorithms – Chapter 6