The DSW Algorithm. Data Structures and Algorithms in Java 1
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1 The DSW Algorithm The building block for tree transformations in this algorithm is the rotation There are two types of rotation, left and right, which are symmetrical to one another rotateright (Gr, Par, Ch) if Par is not the root of the tree // i.e., if Gr is not null grandparent Gr of child Ch becomes Ch s parent; right subtree of Ch becomes left subtree of Ch s parent Par; node Ch acquires Par as its right child; Data Structures and Algorithms in Java 1
2 The DSW Algorithm (continued) Figure 6-37 Right rotation of child Ch about parent Par Data Structures and Algorithms in Java 2
3 The DSW Algorithm (continued) Figure 6-38 Transforming a binary search tree into a backbone Data Structures and Algorithms in Java 3
4 The DSW Algorithm (continued) Figure 6-39 Transforming a backbone into a perfectly balanced tree Data Structures and Algorithms in Java 4
5 AVL Trees An AVL tree is one in which the height of the left and right subtrees of every node differ by at most one Figure 6-40 Examples of AVL trees Data Structures and Algorithms in Java 5
6 AVL Trees (continued) Figure 6-41 Balancing a tree after insertion of a node in the right subtree of node Q Data Structures and Algorithms in Java 6
7 AVL Trees (continued) Figure 6-42 Balancing a tree after insertion of a node in the left subtree of node Q Data Structures and Algorithms in Java 7
8 AVL Trees (continued) Figure 6-43 An example of inserting a new node (b) in an AVL tree (a), which requires one rotation (c) to restore the height balance Data Structures and Algorithms in Java 8
9 AVL Trees (continued) Figure 6-44 In an AVL tree (a) a new node is inserted (b) requiring no height adjustments Data Structures and Algorithms in Java 9
10 AVL Trees (continued) Figure 6-45 Rebalancing an AVL tree after deleting a node Data Structures and Algorithms in Java 10
11 AVL Trees (continued) Figure 6-45 Rebalancing an AVL tree after deleting a node (continued) Data Structures and Algorithms in Java 11
12 AVL Trees (continued) Figure 6-45 Rebalancing an AVL tree after deleting a node (continued) Data Structures and Algorithms in Java 12
13 Self-Adjusting Trees The strategy in self-adjusting trees is to restructure trees by moving up the tree with only those elements that are used more often, and creating a priority tree Data Structures and Algorithms in Java 13
14 Self-Restructuring Trees Single rotation Rotate a child about its parent if an element in a child is accessed, unless it is the root Figure 6-46 Restructuring a tree by using (a) a single rotation or (b) moving to the root when accessing node R Data Structures and Algorithms in Java 14
15 Self-Restructuring Trees (continued) Moving to the root Repeat the child parent rotation until the element being accessed is in the root Figure 6-46 Restructuring a tree by using (a) a single rotation or (b) moving to the root when accessing node R (continued) Data Structures and Algorithms in Java 15
16 Self-Restructuring Trees (continued) Figure 6-47 (a e) Moving element T to the root and then (e i) moving element S to the root Data Structures and Algorithms in Java 16
17 Splaying A modification of the move-to-the-root strategy is called splaying Splaying applies single rotations in pairs in an order depending on the links between the child, parent, and grandparent Semisplaying requires only one rotation for a homogeneous splay and continues splaying with the parent of the accessed node, not with the node itself Data Structures and Algorithms in Java 17
18 Splaying (continued) Figure 6-48 Examples of splaying Data Structures and Algorithms in Java 18
19 Splaying (continued) Figure 6-48 Examples of splaying (continued) Data Structures and Algorithms in Java 19
20 Splaying (continued) Figure 6-49 Restructuring a tree with splaying (a c) after accessing T and (c d) then R Data Structures and Algorithms in Java 20
21 Splaying (continued) Figure 6-50 (a c) Accessing T and restructuring the tree with semisplaying; (c d) accessing T again Data Structures and Algorithms in Java 21
22 Heaps A particular kind of binary tree, called a heap, has two properties: The value of each node is greater than or equal to the values stored in each of its children The tree is perfectly balanced, and the leaves in the last level are all in the leftmost positions These two properties define a max heap If greater in the first property is replaced with less, then the definition specifies a min heap Data Structures and Algorithms in Java 22
23 Heaps (continued) Figure 6-51 Examples of (a) heaps and (b c) nonheaps Data Structures and Algorithms in Java 23
24 Heaps (continued) Figure 6-52 The array [ ] seen as a tree Data Structures and Algorithms in Java 24
25 Heaps (continued) Figure 6-53 Different heaps constructed with the same elements Data Structures and Algorithms in Java 25
26 Heaps as Priority Queues Figure 6-54 Enqueuing an element to a heap Data Structures and Algorithms in Java 26
27 Heaps as Priority Queues (continued) Figure 6-55 Dequeuing an element from a heap Data Structures and Algorithms in Java 27
28 Heaps as Priority Queues (continued) Figure 6-56 Implementation of an algorithm to move the root element down a tree Data Structures and Algorithms in Java 28
29 Organizing Arrays as Heaps Figure 6-57 Organizing an array as a heap with a top-down method Data Structures and Algorithms in Java 29
30 Organizing Arrays as Heaps Figure 6-57 Organizing an array as a heap with a top-down method (continued) Data Structures and Algorithms in Java 30
31 Organizing Arrays as Heaps Figure 6-57 Organizing an array as a heap with a top-down method (continued) Data Structures and Algorithms in Java 31
32 Organizing Arrays as Heaps (continued) Figure 6-58 Transforming the array [ ] into a heap with a bottom-up method Data Structures and Algorithms in Java 32
33 Organizing Arrays as Heaps (continued) Figure 6-58 Transforming the array [ ] into a heap with a bottom-up method (continued) Data Structures and Algorithms in Java 33
34 Organizing Arrays as Heaps (continued) Figure 6-58 Transforming the array [ ] into a heap with a bottom-up method (continued) Data Structures and Algorithms in Java 34
35 Polish Notation and Expression Trees Polish notation is a special notation for propositional logic that eliminates all parentheses from formulas The compiler rejects everything that is not essential to retrieve the proper meaning of formulas rejecting it as syntactic sugar Data Structures and Algorithms in Java 35
36 Polish Notation and Expression Trees (continued) Figure 6-59 Examples of three expression trees and results of their traversals Data Structures and Algorithms in Java 36
37 Operations on Expression Trees Figure 6-60 An expression tree Data Structures and Algorithms in Java 37
38 Operations on Expression Trees (continued) Figure 6-61 Tree transformations for differentiation of multiplication and division Data Structures and Algorithms in Java 38
39 Case Study: Computing Word Frequencies Figure 6-62 Semisplay tree used for computing word frequencies Data Structures and Algorithms in Java 39
40 Case Study: Computing Word Frequencies (continued) Figure 6-63 Implementation of word frequency computation Data Structures and Algorithms in Java 40
41 Case Study: Computing Word Frequencies (continued) Figure 6-63 Implementation of word frequency computation (continued) Data Structures and Algorithms in Java 41
42 Case Study: Computing Word Frequencies (continued) Figure 6-63 Implementation of word frequency computation (continued) Data Structures and Algorithms in Java 42
43 Case Study: Computing Word Frequencies (continued) Figure 6-63 Implementation of word frequency computation (continued) Data Structures and Algorithms in Java 43
44 Case Study: Computing Word Frequencies (continued) Figure 6-63 Implementation of word frequency computation (continued) Data Structures and Algorithms in Java 44
45 Case Study: Computing Word Frequencies (continued) Figure 6-63 Implementation of word frequency computation (continued) Data Structures and Algorithms in Java 45
46 Case Study: Computing Word Frequencies (continued) Figure 6-63 Implementation of word frequency computation (continued) Data Structures and Algorithms in Java 46
47 Case Study: Computing Word Frequencies (continued) Figure 6-63 Implementation of word frequency computation (continued) Data Structures and Algorithms in Java 47
48 Case Study: Computing Word Frequencies (continued) Figure 6-63 Implementation of word frequency computation (continued) Data Structures and Algorithms in Java 48
49 Case Study: Computing Word Frequencies (continued) Figure 6-63 Implementation of word frequency computation (continued) Data Structures and Algorithms in Java 49
50 Case Study: Computing Word Frequencies (continued) Figure 6-63 Implementation of word frequency computation (continued) Data Structures and Algorithms in Java 50
51 Case Study: Computing Word Frequencies (continued) Figure 6-63 Implementation of word frequency computation (continued) Data Structures and Algorithms in Java 51
52 Case Study: Computing Word Frequencies (continued) Figure 6-63 Implementation of word frequency computation (continued) Data Structures and Algorithms in Java 52
53 Case Study: Computing Word Frequencies (continued) Figure 6-63 Implementation of word frequency computation (continued) Data Structures and Algorithms in Java 53
54 Case Study: Computing Word Frequencies (continued) Figure 6-63 Implementation of word frequency computation (continued) Data Structures and Algorithms in Java 54
55 Case Study: Computing Word Frequencies (continued) Figure 6-63 Implementation of word frequency computation (continued) Data Structures and Algorithms in Java 55
56 Case Study: Computing Word Frequencies (continued) Figure 6-63 Implementation of word frequency computation (continued) Data Structures and Algorithms in Java 56
57 Summary A tree is a data type that consists of nodes and arcs. The root is a node that has no parent; it can have only child nodes. Each node has to be reachable from the root through a unique sequence of arcs, called a path. An orderly tree is where all elements are stored according to some predetermined criterion of ordering. Data Structures and Algorithms in Java 57
58 Summary (continued) A binary tree is a tree whose nodes have two children (possibly empty), and each child is designated as either a left child or a right child. A decision tree is a binary tree in which all nodes have either zero or two nonempty children. Tree traversal is the process of visiting each node in the tree exactly one time. Threads are references to the predecessor and successor of the node according to an inorder traversal. Data Structures and Algorithms in Java 58
59 Summary (continued) An AVL tree is one in which the height of the left and right subtrees of every node differ by at most one. A modification of the move-to-the-root strategy is called splaying. Polish notation is a special notation for propositional logic that eliminates all parentheses from formulas. Data Structures and Algorithms in Java 59
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