MIT OpenCourseWare http://ocw.mit.edu6.006 Introduction to AlgorithmsSpring 2008For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.Lecture 4 Balanced Binary Search Trees 6.006 Spring 2008 Lecture 4: Balanced Binary Search Trees Lecture Overview • The importance of being balanced AVL trees • – Definition – Balance – Insert Other balanced trees • • Data structures in general Readings CLRS Chapter 13. 1 and 13. 2 (but different approach: red-black trees) Recall: Binary Search Trees (BSTs) • rooted binary tree each node has • – key – left pointer – right pointer – parent pointer See Fig. 1 654120115029263211φφφFigure 1: Heights of nodes in a BST 1Lecture 4 Balanced Binary Search Trees 6.006 Spring 2008 x≤x≥xFigure 2: BST property • BST property (see Fig. 2). • height of node = length (� edges) of longest downward path to a leaf (see CLRS B.5 for details). The Importance of Being Balanced: • BSTs support insert, min, delete, rank, etc. in O(h) time, where h = height of tree (= height of root). • h is between lg(n) and n: Fig. 3). vs.Perfectly Balanced PathFigure 3: Balancing BSTs balanced BST maintains h = O(lg n) all operations run in O(lg n) time. • ⇒ 2Lecture 4 Balanced Binary Search Trees 6.006 Spring 2008 AVL Trees: Definition AVL trees are self-balancing binary search trees. These trees are named after their two inventors G.M. Adel’son-Vel’skii and E.M. Landis 1 An AVL tree is one that requires heights of left and right children of every node to differ by at most ±1. This is illustrated in Fig. 4) Figure 4: AVL Tree Concept In order to implement an AVL tree, follow two critical steps: • Treat nil tree as height −1. • Each node stores its height. This is inherently a DATA STRUCTURE AUGMENTATION procedure, similar to augmenting subtree size. Alternatively, one can just store dif-ference in heights. A good animation applet for AVL trees is available at this link .To compare Binary Search Trees and AVL balancing of trees use code provided here .1Original Russian article: Adelson-Velskii, G.; E. M. Landis (1962). ”An algorithm for the organization of information”. Proceedings of the USSR Academy of Sciences 146: 263266. (English translation by Myron J. Ricci in Soviet Math. Doklady, 3:12591263, 1962.) kk-13Lecture 4 Balanced Binary Search Trees 6.006 Spring 2008 Balance: The balance is the worst when every node differs by 1. Let Nh = min (� nodes). ⇒ Nh = Nh−1 + Nh−2 + 1 > 2Nh−2 ⇒ Nh > 2h/2 1 = h < lg h⇒ 2 Alternatively: Nh > Fn (nth Fibonacci number) In fact,Nh = Fn+2 − 1 (simple induction) φh Fh = √5 (rounded to nearest integer) 1 + √5where φ =2 ≈ 1.618 (golden ratio) = ⇒ maxh ≈ logφ(n) ≈ 1.440 lg(n) AVL Insert: 1. insert as in simple BST 2. work your way up tree, restoring AVL property (and updating heights as you go). Each Step: • suppose x is lowest node violating AVL • assume x is right-heavy (left case symmetric) • if x’s right child is right-heavy or balanced: follow steps in Fig. 5 • else follow steps in Fig. 6 • then continue up to x’s grandparent, greatgrandparent . . . 4Lecture 4 Balanced Binary Search Trees 6.006 Spring 2008 xyABCk+1kk-1k-1xzABCk+1k-1Left-Rotate(x)kkyxCABk+1kkk-1yxCABkkk-1k-1Left-Rotate(x)Figure 5: AVL Insert Balancing xzADk+1k-1Left-Rotate(x)k-1yxABkk-1yBCkk-1 ork-2Right-Rotate(z)zCDkk-1k+1k-1 ork-2Figure 6: AVL Insert Balancing 5Lecture 4 Balanced Binary Search Trees 6.006 Spring 2008 Example: An example implementation of the AVL Insert process is illustrated in Fig. 7 654120115029263211φφφ6541201150292621φφφ123Insert(23)x = 29: left-left case654120115026233211φφφ65412011501φφDone Insert(55)29φ322623129φφ65412011502φφ22623129φφx=65: left-right case55155412011501φφ22623129φφ65Done3φFigure 7: Illustration of AVL Tree Insert Process Comment 1. In general, process may need several rotations before an Insert is completed. Comment 2. Delete(-min) harder but possible. 6Lecture 4 Balanced Binary Search Trees 6.006 Spring 2008 Balanced Search Trees: There are many balanced search trees. AVL Trees Adel’son-Velsii and Landis 1962 B-Trees/2-3-4 Trees Bayer and McCreight 1972 (see CLRS 18) BB[α] Trees Nievergelt and Reingold 1973 Red-black Trees CLRS Chapter 13 Splay-Trees Sleator and Tarjan 1985 Skip Lists Pugh 1989 Scapegoat Trees Galperin and Rivest 1993 Treaps Seidel and Aragon 1996 Note 1. Skip Lists and Treaps use random numbers to make decisions fast with high probability. Note 2. Splay Trees and Scapegoat Trees are “amortized”: adding up costs for several operations = fast on average. ⇒ 7Lecture 4 Balanced Binary Search Trees 6.006 Spring 2008 Splay Trees Upon access (search or insert), move node to root by sequence of rotations and/or double-rotations (just like AVL trees). Height can be linear but still O(lg n) per operation “on average” (amortized) Note: We will see more on amortization in a couple of lectures. Optimality • For BSTs, cannot do better than O(lg n) per search in worst case. • In some cases, can do better e.g. – in-order traversal takes Θ(n) time for n elements. – put more frequent items near root A Conjecture: Splay trees are O(best BST) for every access pattern. • With fancier tricks, can achieve O(lg lg u) performance for integers 1 ··· u [Van Ernde Boas; see 6.854 or 6.851 (Advanced Data Structures)] Big Picture: Abstract Data Type(ADT): interface spec. e.g. Priority Queue: • Q = new-empty-queue() • Q.insert(x) • x = Q.deletemin() vs. Data Structure (DS): algorithm for each op. There are many possible DSs for one ADT. One example that we will discuss much later in the course is the “heap” priority queue.
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