Red-Black Trees 4/16/2017 8:38 AM Splay Trees 6 3 8 4 v z Splay Trees.

Slides:



Advertisements
Similar presentations
Splay Tree Algorithm Mingda Zhao CSC 252 Algorithms Smith College Fall, 2000.
Advertisements

Splay Trees CSE 331 Section 2 James Daly. Reminder Homework 2 is out Due Thursday in class Project 2 is out Covers tree sets Due next Friday at midnight.
AVL Trees1 Part-F2 AVL Trees v z. AVL Trees2 AVL Tree Definition (§ 9.2) AVL trees are balanced. An AVL Tree is a binary search tree such that.
Heaps1 Part-D2 Heaps Heaps2 Recall Priority Queue ADT (§ 7.1.3) A priority queue stores a collection of entries Each entry is a pair (key, value)
1 AVL Trees (10.2) CSE 2011 Winter April 2015.
1 prepared from lecture material © 2004 Goodrich & Tamassia COMMONWEALTH OF AUSTRALIA Copyright Regulations 1969 WARNING This material.
Binary Search Tree AVL Trees and Splay Trees
Splay Trees CSIT 402 Data Structures II. Motivation Problems with other balanced trees – AVL: extra storage/complexity for height fields Periulous delete.
CSE332: Data Abstractions Lecture 7: AVL Trees Dan Grossman Spring 2010.
Data Structures Lecture 11 Fang Yu Department of Management Information Systems National Chengchi University Fall 2010.
CSE332: Data Abstractions Lecture 7: AVL Trees Tyler Robison Summer
CSC401 – Analysis of Algorithms Lecture Notes 7 Multi-way Search Trees and Skip Lists Objectives: Introduce multi-way search trees especially (2,4) trees,
6/14/2015 6:48 AM(2,4) Trees /14/2015 6:48 AM(2,4) Trees2 Outline and Reading Multi-way search tree (§3.3.1) Definition Search (2,4)
© 2004 Goodrich, Tamassia, Dickerson Splay Trees v z.
Goodrich, Tamassia Search Trees1 Binary Search Trees   
CSC311: Data Structures 1 Chapter 10: Search Trees Objectives: Binary Search Trees: Search, update, and implementation AVL Trees: Properties and maintenance.
CSC 213 Lecture 7: Binary, AVL, and Splay Trees. Binary Search Trees (§ 9.1) Binary search tree (BST) is a binary tree storing key- value pairs (entries):
CS 202, Spring 2003 Fundamental Structures of Computer Science II Bilkent University1 Splay trees CS 202 – Fundamental Structures of Computer Science II.
1 /26 Red-black tree properties Every node in a red-black tree is either black or red Every null leaf is black No path from a leaf to a root can have two.
1 Red-Black Trees. 2 Black-Height of the tree = 4.
CSC 212 Lecture 19: Splay Trees, (2,4) Trees, and Red-Black Trees.
Balanced Trees. Binary Search tree with a balance condition Why? For every node in the tree, the height of its left and right subtrees must differ by.
AVL Trees v z. 2 AVL Tree Definition AVL trees are balanced. An AVL Tree is a binary search tree such that for every internal node v of T, the.
Trees1 Trees and Codes a bc de abcde.
Splay Trees Splay trees are binary search trees (BSTs) that:
David Kaplan Dept of Computer Science & Engineering Autumn 2001
Splay Trees and B-Trees
CSE373: Data Structures & Algorithms Lecture 7: AVL Trees Nicki Dell Spring 2014 CSE373: Data Structures & Algorithms1.
Advanced Data Structures and Algorithms COSC-600 Lecture presentation-6.
CMSC 341 Splay Trees. 8/3/2007 UMBC CMSC 341 SplayTrees 2 Problems with BSTs Because the shape of a BST is determined by the order that data is inserted,
CSC Analysis of Algorithms 3-1 CSC401 – Analysis of Algorithms Chapter 3 Search Trees and Skip Lists Objectives: Review binary search trees and present.
COMP20010: Algorithms and Imperative Programming Lecture 4 Ordered Dictionaries and Binary Search Trees AVL Trees.
Lecture 2 Red-Black Trees. 8/3/2007 UMBC CSMC 341 Red-Black-Trees-1 2 Red-Black Trees Definition: A red-black tree is a binary search tree in which: 
Search Trees Last Update: Nov 5, 2014 EECS2011: Search Trees1 “Grey Tree”, Piet Mondrian, 1912.
Search Trees. Binary Search Tree (§10.1) A binary search tree is a binary tree storing keys (or key-element pairs) at its internal nodes and satisfying.
Search Trees Chapter   . Outline  Binary Search Trees  AVL Trees  Splay Trees.
Binary Search Trees   . 2 Binary Search Tree Properties A binary search tree is a binary tree storing keys (or key-element pairs) at its.
Chapter 10: Search Trees Nancy Amato Parasol Lab, Dept. CSE, Texas A&M University Acknowledgement: These slides are adapted from slides provided with Data.
1 Searching the dictionary ADT binary search binary search trees.
CS 253: Algorithms Chapter 13 Balanced Binary Search Trees (Balanced BST) AVL Trees.
Chapter 2: Basic Data Structures. Spring 2003CS 3152 Basic Data Structures Stacks Queues Vectors, Linked Lists Trees (Including Balanced Trees) Priority.
Oct 26, 2001CSE 373, Autumn A Forest of Trees Binary search trees: simple. –good on average: O(log n) –bad in the worst case: O(n) AVL trees: more.
CS 61B Data Structures and Programming Methodology Aug 7, 2008 David Sun.
CSC 213 – Large Scale Programming Lecture 18: Zen & the Art of O (log n ) Search.
3.1. Binary Search Trees   . Ordered Dictionaries Keys are assumed to come from a total order. Old operations: insert, delete, find, …
Tree Rotations & Splay Trees. BST Structure BST's only perform well when balanced But common cases lead to unbalanced trees.
Jim Anderson Comp 750, Fall 2009 Splay Trees - 1 Splay Trees In balanced tree schemes, explicit rules are followed to ensure balance. In splay trees, there.
CSE373: Data Structures & Algorithms Lecture 7: AVL Trees Linda Shapiro Winter 2015.
AVL Trees 1. Balancing a BST Goal – Keep the height small – For any node, left and right sub-tree have approximately the same height Ensures fast (O(lgn))
CS 5243: Algorithms Balanced Trees AVL : Adelson-Velskii and Landis(1962)
1 COMP9024: Data Structures and Algorithms Week Six: Search Trees Hui Wu Session 1, 2014
Balancing Binary Search Trees. Balanced Binary Search Trees A BST is perfectly balanced if, for every node, the difference between the number of nodes.
CSE373: Data Structures & Algorithms Lecture 8: AVL Trees and Priority Queues Linda Shapiro Spring 2016.
Splay trees Go&Ta How do you organize your world?
SPLAY TREE The basic idea of the splay tree is that every time a node is accessed, it is pushed to the root by a series of tree rotations. This series.
Mingda Zhao CSC 252 Algorithms Smith College Fall, 2000
AA Trees.
COMP9024: Data Structures and Algorithms
Binary Search Trees < > =
Search Trees.
Chapter 10 Search Trees 10.1 Binary Search Trees Search Trees
Splay Trees.
Binary Search Tree Chapter 10.
Section 8.1 Trees.
11 Search Trees Hongfei Yan May 18, 2016.
Red-Black Trees.
Binary Search Trees < > =
Red-Black Trees 1/16/2019 1:56 PM Splay Trees v z Splay Trees.
Red-black tree properties
Splay Trees Binary search trees.
Presentation transcript:

Red-Black Trees 4/16/2017 8:38 AM Splay Trees 6 3 8 4 v z Splay Trees

Splay Trees are Binary Search Trees all the keys in the yellow region are  20 all the keys in the blue region are  20 (20,Z) note that two keys of equal value may be well-separated (10,A) (35,R) BST Rules: items stored only at internal nodes keys stored at nodes in the left subtree of v are less than or equal to the key stored at v keys stored at nodes in the right subtree of v are greater than or equal to the key stored at v An inorder traversal will return the keys in order (14,J) (7,T) (21,O) (37,P) (1,Q) (8,N) (36,L) (40,X) (1,C) (5,H) (7,P) (10,U) (2,R) (5,G) (5,I) (6,Y) Splay Trees

Searching in a Splay Tree: Starts the Same as in a BST (20,Z) (37,P) (21,O) (14,J) (7,T) (35,R) (10,A) (1,C) (1,Q) (5,G) (2,R) (5,H) (6,Y) (5,I) (8,N) (7,P) (36,L) (10,U) (40,X) Search proceeds down the tree to found item or an external node. Example: Search for time with key 11. Splay Trees

Example Searching in a BST, continued (20,Z) (37,P) (21,O) (14,J) (7,T) (35,R) (10,A) (1,C) (1,Q) (5,G) (2,R) (5,H) (6,Y) (5,I) (8,N) (7,P) (36,L) (10,U) (40,X) search for key 8, ends at an internal node. Splay Trees

Splay Trees do Rotations after Every Operation (Even Search) new operation: splay splaying moves a node to the root using rotations right rotation makes the left child x of a node y into y’s parent; y becomes the right child of x left rotation makes the right child y of a node x into x’s parent; x becomes the left child of y y x a right rotation about y a left rotation about x x T3 T1 y x y T1 T2 T2 T3 T1 y x T3 T2 T3 T1 T2 (structure of tree above y is not modified) (structure of tree above x is not modified) Splay Trees

Splaying: zig-zig zig-zig zig-zag zig zig zig-zag “x is a left-left grandchild” means x is a left child of its parent, which is itself a left child of its parent p is x’s parent; g is p’s parent start with node x is x a left-left grandchild? is x the root? zig-zig yes stop right-rotate about g, right-rotate about p yes no is x a right-right grandchild? is x a child of the root? zig-zig no left-rotate about g, left-rotate about p yes yes is x a right-left grandchild? is x the left child of the root? zig-zag no left-rotate about p, right-rotate about g yes is x a left-right grandchild? zig zig zig-zag yes right-rotate about the root left-rotate about the root right-rotate about p, left-rotate about g yes Splay Trees

Visualizing the Splaying Cases zig-zag x z z y z y T4 y T1 x T4 T1 T2 T3 T4 x T3 T2 T3 T1 T2 zig-zig y x zig T1 T4 x y x T2 w z w T3 y T1 T2 T3 T4 T3 T4 T1 T2 Splay Trees

Splaying Example 1. 2. 3. let x = (8,N) (20,Z) (37,P) (21,O) (14,J) (7,T) (35,R) (10,A) (1,C) (1,Q) (5,G) (2,R) (5,H) (6,Y) (5,I) (8,N) (7,P) (36,L) (10,U) (40,X) let x = (8,N) x is the right child of its parent, which is the left child of the grandparent left-rotate around p, then right-rotate around g g 1. (before rotating) p x (10,A) (20,Z) (37,P) (21,O) (35,R) (36,L) (40,X) (7,T) (1,C) (1,Q) (5,G) (2,R) (5,H) (6,Y) (5,I) (14,J) (8,N) (7,P) (10,U) x g p (10,A) (20,Z) (37,P) (21,O) (35,R) (36,L) (40,X) (7,T) (1,C) (1,Q) (5,G) (2,R) (5,H) (6,Y) (5,I) (14,J) (8,N) (7,P) (10,U) x g p 2. (after first rotation) 3. (after second rotation) x is not yet the root, so we splay again Splay Trees

Splaying Example, Continued now x is the left child of the root right-rotate around root (10,A) (20,Z) (37,P) (21,O) (35,R) (36,L) (40,X) (7,T) (1,C) (1,Q) (5,G) (2,R) (5,H) (6,Y) (5,I) (14,J) (8,N) (7,P) (10,U) x (10,A) (20,Z) (37,P) (21,O) (35,R) (36,L) (40,X) (7,T) (1,C) (1,Q) (5,G) (2,R) (5,H) (6,Y) (5,I) (14,J) (8,N) (7,P) (10,U) x 2. (after rotation) 1. (before applying rotation) x is the root, so stop Splay Trees

Example Result of Splaying (20,Z) (37,P) (21,O) (14,J) (7,T) (35,R) (10,A) (1,C) (1,Q) (5,G) (2,R) (5,H) (6,Y) (5,I) (8,N) (7,P) (36,L) (10,U) (40,X) Example Result of Splaying before tree might not be more balanced e.g. splay (40,X) before, the depth of the shallowest leaf is 3 and the deepest is 7 after, the depth of shallowest leaf is 1 and deepest is 8 (20,Z) (37,P) (21,O) (14,J) (7,T) (35,R) (10,A) (1,C) (1,Q) (5,G) (2,R) (5,H) (6,Y) (5,I) (8,N) (7,P) (36,L) (10,U) (40,X) (20,Z) (37,P) (21,O) (14,J) (7,T) (35,R) (10,A) (1,C) (1,Q) (5,G) (2,R) (5,H) (6,Y) (5,I) (8,N) (7,P) (36,L) (10,U) (40,X) after first splay after second splay Splay Trees

Splay Tree Definition a splay tree is a binary search tree where a node is splayed after it is accessed (for a search or update) deepest internal node accessed is splayed splaying costs O(h), where h is height of the tree – which is still O(n) worst-case O(h) rotations, each of which is O(1) Splay Trees

Splay Trees & Ordered Dictionaries which nodes are splayed after each operation? method splay node if key found, use that node if key not found, use parent of ending external node findElement insertElement use the new node containing the item inserted use the parent of the internal node that was actually removed from the tree (the parent of the node that the removed item was swapped with) removeElement Splay Trees

Amortized Analysis of Splay Trees Running time of each operation is proportional to time for splaying. Define rank(v) as the logarithm (base 2) of the number of nodes in subtree rooted at v. Costs: zig = $1, zig-zig = $2, zig-zag = $2. Thus, cost for playing a node at depth d = $d. Imagine that we store rank(v) cyber-dollars at each node v of the splay tree (just for the sake of analysis). Splay Trees

Cost per zig Doing a zig at x costs at most rank’(x) - rank(x): w T1 T2 T3 y T4 Doing a zig at x costs at most rank’(x) - rank(x): cost = rank’(x) + rank’(y) - rank(y) - rank(x) < rank’(x) - rank(x). Splay Trees

Cost per zig-zig and zig-zag y x T1 T2 T3 z T4 zig-zig Doing a zig-zig or zig-zag at x costs at most 3(rank’(x) - rank(x)) - 2. Proof: See Theorem 3.9, Page 192. zig-zag y x T2 T3 T4 z T1 Splay Trees

Cost of Splaying Cost of splaying a node x at depth d of a tree rooted at r: at most 3(rank(r) - rank(x)) - d + 2: Proof: Splaying x takes d/2 splaying substeps: Splay Trees

Performance of Splay Trees Recall: rank of a node is logarithm of its size. Thus, amortized cost of any splay operation is O(log n). In fact, the analysis goes through for any reasonable definition of rank(x). This implies that splay trees can actually adapt to perform searches on frequently-requested items much faster than O(log n) in some cases. (See Theorems 3.10 and 3.11.) Splay Trees