The document discusses AVL trees, which are self-balancing binary search trees. It begins with an introduction to AVL trees, explaining that they were invented in 1962 and solve the worst-case scenarios of binary search trees by keeping the height balanced using balance factors between -1 and 1. The document then covers rotation techniques used in AVL trees, including left, right, left-right and right-left rotations performed during insertion and deletion. It also discusses the insertion, search and deletion algorithms for AVL trees and analyzes their time and space complexities. Finally, the advantages, disadvantages and applications of AVL trees are presented.
This document discusses AVL trees, which are height-balanced binary search trees. It defines AVL trees, explains why they are useful by comparing insertion performance to regular binary search trees, and covers balance factors, rotations, and the insertion algorithm. Key points made include that AVL trees have logarithmic time complexity for operations through self-balancing, and maintain an extra balance factor field for each node. Various example questions related to building AVL trees from data are also provided.
B+ trees are balanced search trees where all data records are stored in the leaf nodes and internal nodes store only keys. Records are accessed faster through linked leaf nodes. Nodes must be at least half full, and internal nodes have between n/2 and n child nodes. Search, insertion, and deletion operations may cause nodes to split or combine to maintain balance. B+ trees are commonly used to index large files to enable efficient retrieval of records.
This document describes AVL trees, a self-balancing binary search tree. AVL trees guarantee searching, insertion, and deletion operations will take O(log n) time by ensuring the heights of the two subtrees of every node differ by at most one. It discusses how to check if a tree is balanced, the different types of rotations performed during insertion to rebalance the tree if needed, and provides examples of left, right, left-left, right-right, left-right, and right-left rotations.
presentation on binary search trees for the subject analysis and design of algorithms, helpful to especially GTU students and computer and IT engineers
Traversal is a process to visit all the nodes of a tree and may print their values too. Because, all nodes are connected via edges (links) we always start from the root (head) node. That is, we cannot randomly access a node in a tree.
This document introduces doubly linked lists. A doubly linked list allows navigation in both directions by including a prev pointer in each node in addition to the next pointer found in singly linked lists. Each node contains data, a next pointer, and a prev pointer. The list also contains pointers to the first and last nodes. Basic operations on doubly linked lists include insertion and deletion at the beginning or end of the list as well as insertion or deletion after a specified node.
The document provides information about B+ trees and height balancing trees. It begins with an introduction to B+ trees, describing their properties, representation, advantages over B-trees, and algorithms for insertion and deletion. It then covers key points about B+ trees, provides examples of height balanced trees like AVL trees and 2-3-4 trees, and gives pseudocode for operations on these trees like calculating balancing factors. The document concludes with solved problems on B+ trees.
This document discusses AVL trees, which are height-balanced binary search trees. It defines AVL trees, explains why they are useful by comparing insertion performance to regular binary search trees, and covers balance factors, rotations, and the insertion algorithm. Key points made include that AVL trees have logarithmic time complexity for operations through self-balancing, and maintain an extra balance factor field for each node. Various example questions related to building AVL trees from data are also provided.
B+ trees are balanced search trees where all data records are stored in the leaf nodes and internal nodes store only keys. Records are accessed faster through linked leaf nodes. Nodes must be at least half full, and internal nodes have between n/2 and n child nodes. Search, insertion, and deletion operations may cause nodes to split or combine to maintain balance. B+ trees are commonly used to index large files to enable efficient retrieval of records.
This document describes AVL trees, a self-balancing binary search tree. AVL trees guarantee searching, insertion, and deletion operations will take O(log n) time by ensuring the heights of the two subtrees of every node differ by at most one. It discusses how to check if a tree is balanced, the different types of rotations performed during insertion to rebalance the tree if needed, and provides examples of left, right, left-left, right-right, left-right, and right-left rotations.
presentation on binary search trees for the subject analysis and design of algorithms, helpful to especially GTU students and computer and IT engineers
Traversal is a process to visit all the nodes of a tree and may print their values too. Because, all nodes are connected via edges (links) we always start from the root (head) node. That is, we cannot randomly access a node in a tree.
This document introduces doubly linked lists. A doubly linked list allows navigation in both directions by including a prev pointer in each node in addition to the next pointer found in singly linked lists. Each node contains data, a next pointer, and a prev pointer. The list also contains pointers to the first and last nodes. Basic operations on doubly linked lists include insertion and deletion at the beginning or end of the list as well as insertion or deletion after a specified node.
The document provides information about B+ trees and height balancing trees. It begins with an introduction to B+ trees, describing their properties, representation, advantages over B-trees, and algorithms for insertion and deletion. It then covers key points about B+ trees, provides examples of height balanced trees like AVL trees and 2-3-4 trees, and gives pseudocode for operations on these trees like calculating balancing factors. The document concludes with solved problems on B+ trees.
Here are the answers to the assessment questions:
1. 2
2. 1
3. 4
4. 4
The key aspects of a splay tree are that accessing a node causes it to be rotated to the root, decreasing its future access time. This self-organizing behavior is what gives splay trees their efficiency.
Data Structures & Recursion-Introduction.pdfMaryJacob24
This document provides an introduction to data structures and recursion. It defines data structures as organized collections of data and discusses common data structures like arrays, linked lists, stacks, and queues. Data structures are classified as primitive (like integers and characters) or non-primitive (like arrays and linked lists). Non-primitive structures are further divided into linear (arrays, linked lists) and non-linear (trees, graphs). Memory allocation techniques like static and dynamic allocation are also covered. The document concludes with an overview of recursion, including direct and indirect recursion, and examples of recursive functions like factorial and Fibonacci.
Binary trees are a non-linear data structure where each node has zero, one, or two child nodes. They are commonly used to represent hierarchical relationships. A binary tree has a root node at the top with child nodes below it. Binary trees can be empty or consist of a root node and left and right subtrees, which are also binary trees. They allow for efficient search, insert, and delete operations and can be represented using arrays or linked lists.
SQL Server supports two main types of indexes - clustered and nonclustered. A clustered index physically orders the data on disk based on the index key. Only one clustered index is allowed per table. A nonclustered index contains key values and row locators but does not determine the physical order of data. SQL Server supports up to 999 nonclustered indexes per table. The choice of index depends on the query patterns against the table and the desired performance characteristics.
1. The AVL tree is a self-balancing binary search tree proposed by Adelson-Velsky and Landis in 1962.
2. It ensures that the heights of the two child subtrees of any node differ by at most one.
3. Rebalancing is done by performing single or double rotations after an insertion causes a height imbalance of more than one.
The document discusses insertion sort, a simple sorting algorithm that builds a sorted output list from an input one element at a time. It is less efficient on large lists than more advanced algorithms. Insertion sort iterates through the input, at each step removing an element and inserting it into the correct position in the sorted output list. The best case for insertion sort is an already sorted array, while the worst is a reverse sorted array.
Data Warehouse:
A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format.
Reconciled data: detailed, current data intended to be the single, authoritative source for all decision support.
Extraction:
The Extract step covers the data extraction from the source system and makes it accessible for further processing. The main objective of the extract step is to retrieve all the required data from the source system with as little resources as possible.
Data Transformation:
Data transformation is the component of data reconcilation that converts data from the format of the source operational systems to the format of enterprise data warehouse.
Data Loading:
During the load step, it is necessary to ensure that the load is performed correctly and with as little resources as possible. The target of the Load process is often a database. In order to make the load process efficient, it is helpful to disable any constraints and indexes before the load and enable them back only after the load completes. The referential integrity needs to be maintained by ETL tool to ensure consistency.
A forest is a set of disjoint trees. Removing the root of a tree produces a forest. A forest can be transformed into a single binary tree by linking the binary tree representations of each tree in the forest through the rightChild field. Preorder, inorder, and postorder traversals of this binary tree correspond to forest preorder, inorder, and postorder traversals, which involve traversing the subtrees and trees of the forest in a specified order and visiting the roots.
A red-black tree is a self-balancing binary search tree where each node is colored red or black. It provides logarithmic time bounds for search, insert, and delete operations. It achieves self-balancing by restricting the number of red nodes along any path, ensuring that no path is more than twice as long as any other path. Rotations are used during insertion and deletion operations to rebalance the tree and maintain these properties.
The document discusses different types of trees used in data structures and algorithms. It defines trees and their key terminology like root, edge, parent, child, leaf nodes, etc. It then describes different types of trees including general trees, binary trees, binary search trees, and their properties. It also covers tree traversal methods like preorder, inorder and postorder traversal and representations of binary trees using arrays and linked lists.
This document presents information about AVL trees through a slideshow presentation. It begins by defining AVL trees as binary search trees where the heights of the subtrees of any node differ by at most 1. It then discusses that AVL trees ensure searching, insertion, and deletion take O(log N) time by keeping the tree balanced through rotations. The presentation notes that AVL trees remain balanced through single and double rotations during insertions and deletions, and that rebalancing can be done in O(log N) time. It concludes by emphasizing that AVL trees guarantee O(log N) search time in dynamic environments through maintaining balance.
Binary search trees are binary trees where all left descendants of a node are less than the node's value and all right descendants are greater. This structure allows for efficient search, insertion, and deletion operations. The document provides definitions and examples of binary search tree properties and operations like creation, traversal, searching, insertion, deletion, and finding minimum and maximum values. Applications include dynamically maintaining a sorted dataset to enable efficient search, insertion, and deletion.
Binary trees are a data structure where each node has at most two children. A binary tree node contains data and pointers to its left and right child nodes. Binary search trees are a type of binary tree where nodes are organized in a manner that allows for efficient searches, insertions, and deletions of nodes. The key operations on binary search trees are searching for a node, inserting a new node, and deleting an existing node through various algorithms that traverse the tree. Common traversals of binary trees include preorder, inorder, and postorder traversals.
This is a presentation on Linked Lists, one of the most important topics on Data Structures and algorithms. Anyone who is new to DSA or wants to have a theoretical understanding of the same can refer to it :D
A threaded binary tree is a variant of a binary tree that stores in-order predecessor and successor pointers instead of null pointers to facilitate faster traversal without recursion or an auxiliary stack. There are two types: single-threaded stores successor pointers only, and double-threaded stores both predecessor and successor pointers. This decreases wasted space from null pointers but makes insertion and deletion more complex. Advantages include optimal memory usage and faster traversal, while disadvantages include more complex insertion/deletion and slightly more memory usage for thread indicators.
This document discusses binary trees and various tree traversal algorithms. It defines what a binary tree is, including nodes, roots, leaves, and siblings. It explains different types of binary tree traversals including preorder, inorder, postorder, and level order. Pseudocode is provided for algorithms to perform inorder, preorder, and postorder traversals on a binary tree. Advantages of using trees are also listed.
The ETL process in data warehousing involves extraction, transformation, and loading of data. Data is extracted from operational databases, transformed to match the data warehouse schema, and loaded into the data warehouse database. As source data and business needs change, the ETL process must also evolve to maintain the data warehouse's value as a business decision making tool. The ETL process consists of extracting data from sources, transforming it to resolve conflicts and quality issues, and loading it into the target data warehouse structures.
Binary trees are a non-linear data structure where each node has at most two children, used to represent hierarchical relationships, with nodes connected through parent-child links and traversed through preorder, inorder, and postorder methods; they can be represented through arrays or linked lists and support common operations like search, insert, and delete through comparing node values and restructuring child pointers.
Binary search tree.
Balancedand unbalanced BST.
Approaches to balancing trees.
Balancing binary search trees.
Perfect balance.
Avl trees 1962.
Avl good but both perfect balance.
Height of an AVL tree
Nood
The document discusses AVL trees, which are self-balancing binary search trees. It provides information on AVL tree operations like insertion and deletion of nodes. Insertion may cause imbalance, requiring rotation operations like single, double, left, or right rotations to rebalance the tree. Deletion is similar but can require propagating rotations upward to restore balance. AVL trees provide O(log n) time for operations by staying balanced through rebalancing rotations after inserts and deletes.
Here are the answers to the assessment questions:
1. 2
2. 1
3. 4
4. 4
The key aspects of a splay tree are that accessing a node causes it to be rotated to the root, decreasing its future access time. This self-organizing behavior is what gives splay trees their efficiency.
Data Structures & Recursion-Introduction.pdfMaryJacob24
This document provides an introduction to data structures and recursion. It defines data structures as organized collections of data and discusses common data structures like arrays, linked lists, stacks, and queues. Data structures are classified as primitive (like integers and characters) or non-primitive (like arrays and linked lists). Non-primitive structures are further divided into linear (arrays, linked lists) and non-linear (trees, graphs). Memory allocation techniques like static and dynamic allocation are also covered. The document concludes with an overview of recursion, including direct and indirect recursion, and examples of recursive functions like factorial and Fibonacci.
Binary trees are a non-linear data structure where each node has zero, one, or two child nodes. They are commonly used to represent hierarchical relationships. A binary tree has a root node at the top with child nodes below it. Binary trees can be empty or consist of a root node and left and right subtrees, which are also binary trees. They allow for efficient search, insert, and delete operations and can be represented using arrays or linked lists.
SQL Server supports two main types of indexes - clustered and nonclustered. A clustered index physically orders the data on disk based on the index key. Only one clustered index is allowed per table. A nonclustered index contains key values and row locators but does not determine the physical order of data. SQL Server supports up to 999 nonclustered indexes per table. The choice of index depends on the query patterns against the table and the desired performance characteristics.
1. The AVL tree is a self-balancing binary search tree proposed by Adelson-Velsky and Landis in 1962.
2. It ensures that the heights of the two child subtrees of any node differ by at most one.
3. Rebalancing is done by performing single or double rotations after an insertion causes a height imbalance of more than one.
The document discusses insertion sort, a simple sorting algorithm that builds a sorted output list from an input one element at a time. It is less efficient on large lists than more advanced algorithms. Insertion sort iterates through the input, at each step removing an element and inserting it into the correct position in the sorted output list. The best case for insertion sort is an already sorted array, while the worst is a reverse sorted array.
Data Warehouse:
A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format.
Reconciled data: detailed, current data intended to be the single, authoritative source for all decision support.
Extraction:
The Extract step covers the data extraction from the source system and makes it accessible for further processing. The main objective of the extract step is to retrieve all the required data from the source system with as little resources as possible.
Data Transformation:
Data transformation is the component of data reconcilation that converts data from the format of the source operational systems to the format of enterprise data warehouse.
Data Loading:
During the load step, it is necessary to ensure that the load is performed correctly and with as little resources as possible. The target of the Load process is often a database. In order to make the load process efficient, it is helpful to disable any constraints and indexes before the load and enable them back only after the load completes. The referential integrity needs to be maintained by ETL tool to ensure consistency.
A forest is a set of disjoint trees. Removing the root of a tree produces a forest. A forest can be transformed into a single binary tree by linking the binary tree representations of each tree in the forest through the rightChild field. Preorder, inorder, and postorder traversals of this binary tree correspond to forest preorder, inorder, and postorder traversals, which involve traversing the subtrees and trees of the forest in a specified order and visiting the roots.
A red-black tree is a self-balancing binary search tree where each node is colored red or black. It provides logarithmic time bounds for search, insert, and delete operations. It achieves self-balancing by restricting the number of red nodes along any path, ensuring that no path is more than twice as long as any other path. Rotations are used during insertion and deletion operations to rebalance the tree and maintain these properties.
The document discusses different types of trees used in data structures and algorithms. It defines trees and their key terminology like root, edge, parent, child, leaf nodes, etc. It then describes different types of trees including general trees, binary trees, binary search trees, and their properties. It also covers tree traversal methods like preorder, inorder and postorder traversal and representations of binary trees using arrays and linked lists.
This document presents information about AVL trees through a slideshow presentation. It begins by defining AVL trees as binary search trees where the heights of the subtrees of any node differ by at most 1. It then discusses that AVL trees ensure searching, insertion, and deletion take O(log N) time by keeping the tree balanced through rotations. The presentation notes that AVL trees remain balanced through single and double rotations during insertions and deletions, and that rebalancing can be done in O(log N) time. It concludes by emphasizing that AVL trees guarantee O(log N) search time in dynamic environments through maintaining balance.
Binary search trees are binary trees where all left descendants of a node are less than the node's value and all right descendants are greater. This structure allows for efficient search, insertion, and deletion operations. The document provides definitions and examples of binary search tree properties and operations like creation, traversal, searching, insertion, deletion, and finding minimum and maximum values. Applications include dynamically maintaining a sorted dataset to enable efficient search, insertion, and deletion.
Binary trees are a data structure where each node has at most two children. A binary tree node contains data and pointers to its left and right child nodes. Binary search trees are a type of binary tree where nodes are organized in a manner that allows for efficient searches, insertions, and deletions of nodes. The key operations on binary search trees are searching for a node, inserting a new node, and deleting an existing node through various algorithms that traverse the tree. Common traversals of binary trees include preorder, inorder, and postorder traversals.
This is a presentation on Linked Lists, one of the most important topics on Data Structures and algorithms. Anyone who is new to DSA or wants to have a theoretical understanding of the same can refer to it :D
A threaded binary tree is a variant of a binary tree that stores in-order predecessor and successor pointers instead of null pointers to facilitate faster traversal without recursion or an auxiliary stack. There are two types: single-threaded stores successor pointers only, and double-threaded stores both predecessor and successor pointers. This decreases wasted space from null pointers but makes insertion and deletion more complex. Advantages include optimal memory usage and faster traversal, while disadvantages include more complex insertion/deletion and slightly more memory usage for thread indicators.
This document discusses binary trees and various tree traversal algorithms. It defines what a binary tree is, including nodes, roots, leaves, and siblings. It explains different types of binary tree traversals including preorder, inorder, postorder, and level order. Pseudocode is provided for algorithms to perform inorder, preorder, and postorder traversals on a binary tree. Advantages of using trees are also listed.
The ETL process in data warehousing involves extraction, transformation, and loading of data. Data is extracted from operational databases, transformed to match the data warehouse schema, and loaded into the data warehouse database. As source data and business needs change, the ETL process must also evolve to maintain the data warehouse's value as a business decision making tool. The ETL process consists of extracting data from sources, transforming it to resolve conflicts and quality issues, and loading it into the target data warehouse structures.
Binary trees are a non-linear data structure where each node has at most two children, used to represent hierarchical relationships, with nodes connected through parent-child links and traversed through preorder, inorder, and postorder methods; they can be represented through arrays or linked lists and support common operations like search, insert, and delete through comparing node values and restructuring child pointers.
Binary search tree.
Balancedand unbalanced BST.
Approaches to balancing trees.
Balancing binary search trees.
Perfect balance.
Avl trees 1962.
Avl good but both perfect balance.
Height of an AVL tree
Nood
The document discusses AVL trees, which are self-balancing binary search trees. It provides information on AVL tree operations like insertion and deletion of nodes. Insertion may cause imbalance, requiring rotation operations like single, double, left, or right rotations to rebalance the tree. Deletion is similar but can require propagating rotations upward to restore balance. AVL trees provide O(log n) time for operations by staying balanced through rebalancing rotations after inserts and deletes.
AVL Tree in Data Structures- It is height balanced tree with balance factor 1, -1 or 0. The different type of rotations used in this tree are: RR, LL, RL, LR
The document discusses AVL trees, which are self-balancing binary search trees. It explains that AVL trees ensure that the heights of the left and right subtrees of every node differ by at most one. This balancing property is maintained during insertions and deletions by performing single or double rotations if the balancing factor becomes too large. The document provides examples of insertions that require single or double rotations to rebalance the tree. It also notes some pros and cons of using AVL trees compared to other search tree structures.
An AVL tree is a self-balancing binary search tree that guarantees search, insertion, and deletion operations will take O(log n) time on average. It achieves this by ensuring the heights of the left and right subtrees of every node differ by at most one. When an insertion or deletion causes a height imbalance of two, rotations are performed to rebalance the tree.
AVL tree is the first dynamic tree in data structure which minimizes its height during insertion and deletion operations. This is because searching time is directly proportional to the height of binary search tree (BST). When insertion operation is performed it may result into increasing the height of the tree and when deletion is performed it may result into decreasing the height. To make the BST a height balance tree (AVL tree) creators of the AVL tree proposed various rotations. This paper shows BST and its operation and AVL.
The document discusses algorithms for balancing binary search trees, specifically AVL trees. It defines AVL trees as binary search trees where the heights of the left and right subtrees of every node differ by at most one. It explains that AVL trees maintain logarithmic time for operations through rotations to rebalance the tree after insertions or deletions. The key operations of inserting and removing nodes from an AVL tree are described, including cases requiring single or double rotations to restore the balance property.
This document discusses AVL trees, which are self-balancing binary search trees where the heights of the left and right subtrees of every node differ by at most one. It covers the definition of AVL trees, problems with regular binary search trees, properties of AVL trees including balance factors, and algorithms for insertion and deletion that rebalance the tree through rotations. The time complexity of operations on an AVL tree is O(log n). Four cases for rebalancing after an insertion are described - left-left, right-right, left-right, and right-left - along with the rotations needed to resolve each case. Deletion is also explained as removing the node and then rebalancing through rotations if needed.
Design data Analysis Avl Trees.pptx by piyush sir22001003058
AVL trees are self-balancing binary search trees that guarantee search, insertion, and deletion operations will take O(log n) time. They ensure the heights of the left and right subtrees of every node differ by at most one. When an insertion or deletion causes a height imbalance, rotations are performed to rebalance the tree. Rotations involve restructuring the tree by rotating nodes to restore the balanced property.
AVL tree ( Balanced Binary Search Tree)-Data StructureYaksh Jethva
An AVL tree is another balanced binary search tree.
DEFINITION: An AVL Tree is a height-balanced binary search tree.
DEFINITION: The balance factor of a binary tree is the difference in heights of its two subtrees (hR - hL).
The balance factor (bf) of a height balanced binary tree may take on one of the values -1, 0, +1.
The document discusses algorithms for balanced binary search trees, specifically AVL trees. It begins by explaining that AVL trees guarantee logarithmic time for operations by self-balancing after insertions and deletions to keep the height of the tree balanced. It then covers the properties of AVL trees, including that the difference between the heights of any node's left and right subtrees can be at most 1. The document also discusses the insertion, deletion, and rebalancing rotations performed on AVL trees to maintain their balanced structure.
The document discusses AVL trees, which are self-balancing binary search trees where the heights of the two subtrees of every node differ by at most one. This balancing property allows AVL trees to perform insertions and deletions in O(log n) time. The document describes how AVL trees store an additional balance factor variable with each node and must perform rotations to maintain balancing after insertions or deletions. It also provides details on the different types of rotations used during these operations.
AVL trees are self-balancing binary search trees. They ensure that the height of the left and right subtrees of every node differ by no more than one. This balancing property is achieved through rotations during insertions and deletions. There are four types of rotations - single left, single right, double left, and double right - that are used to balance the tree as needed and maintain the height difference of no more than one.
AVL Trees
Adelson-Velskii and Landis
Binary Search Tree - Best Time
All BST operations are O(d), where d is tree depth
minimum d is for a binary tree with N nodes
What is the best case tree?
What is the worst case tree?
So, best case running time of BST operations is O(log N)
Binary Search Tree - Worst Time
Worst case running time is O(N)
What happens when you Insert elements in ascending order?
Insert: 2, 4, 6, 8, 10, 12 into an empty BST
Problem: Lack of “balance”:
compare depths of left and right subtree
Unbalanced degenerate tree
Balanced and unbalanced BST
Approaches to balancing trees
Don't balance
May end up with some nodes very deep
Strict balance
The tree must always be balanced perfectly
Pretty good balance
Only allow a little out of balance
Adjust on access
Self-adjusting
Balancing Binary Search Trees
Many algorithms exist for keeping binary search trees balanced
Adelson-Velskii and Landis (AVL) trees (height-balanced trees)
Splay trees and other self-adjusting trees
B-trees and other multiway search trees
Perfect Balance
Want a complete tree after every operation
tree is full except possibly in the lower right
This is expensive
For example, insert 2 in the tree on the left and then rebuild as a complete tree
AVL - Good but not Perfect Balance
AVL trees are height-balanced binary search trees
Balance factor of a node
height(left subtree) - height(right subtree)
An AVL tree has balance factor calculated at every node
For every node, heights of left and right subtree can differ by no more than 1
Store current heights in each node
Height of an AVL Tree
N(h) = minimum number of nodes in an AVL tree of height h.
Basis
N(0) = 1, N(1) = 2
Induction
N(h) = N(h-1) + N(h-2) + 1
Solution (recall Fibonacci analysis)
N(h) > h ( 1.62)
Height of an AVL Tree
N(h) > h ( 1.62)
Suppose we have n nodes in an AVL tree of height h.
n > N(h) (because N(h) was the minimum)
n > h hence log n > h (relatively well balanced tree!!)
h < 1.44 log2n (i.e., Find takes O(logn))
Node Heights
Node Heights after Insert 7
Insert and Rotation in AVL Trees
Insert operation may cause balance factor to become 2 or –2 for some node
only nodes on the path from insertion point to root node have possibly changed in height
So after the Insert, go back up to the root node by node, updating heights
If a new balance factor is 2 or –2, adjust tree by rotation around the node
Single Rotation in an AVL Tree
Implementation
Single Rotation
Double Rotation
Implement Double Rotation in two lines.
Insertion in AVL Trees
Insert at the leaf (as for all BST)
only nodes on the path from insertion point to root node have possibly changed in height
So after the Insert, go back up to the root node by node, updating heights
If a new balance factor is 2 or –2, adjust tree by rotation around the node
Insert in BST
Insert in AVL trees
Example of Insertions in an A
An AVL tree is a self-balancing binary search tree where the difference between heights of left and right subtrees cannot be more than one for any node. It aims to perform efficient search, insertion, and deletion operations by keeping the tree balanced. Balancing is achieved through rotations when insertions or deletions cause height differences greater than one between subtrees. There are four types of rotations - left-left, right-right, left-right, and right-left - to rebalance the tree as needed after operations. This keeps search times efficient at O(log n) instead of the possible O(n) with an unbalanced binary search tree.
An AVL tree is a self-balancing binary search tree. It maintains the height of its left and right subtrees differing by at most one node at each level. This balance property ensures search, insertion, and deletion operations take O(log n) time. When inserting or deleting a node, rotations may be needed to rebalance the tree. Single and double rotations are used to fix height imbalances and restore the balance property.
Data structures trees and graphs - AVL tree.pptxMalligaarjunanN
The document discusses AVL trees, which are self-balancing binary search trees. It describes how AVL trees maintain a balance factor of -1, 0, or 1 through rotations. It covers insertion, deletion, and the different types of rotations performed to balance the tree. Examples are provided to illustrate insertion, deletion, and the resulting rotations. AVL trees provide logarithmic time performance for operations by keeping the tree height balanced.
Adelson velskii Landis rotations based onbanupriyar5
The document discusses AVL trees, which are self-balancing binary search trees. It describes how AVL trees maintain a balance factor of -1, 0, or 1 to ensure a height balance during insertion and deletion. It explains the different types of rotations (single, double) needed to balance the tree when the balance factor is outside this range after an operation. It also provides examples of insertion, deletion, and the resulting rotations to maintain a balanced AVL tree.
AVL trees are self-balancing binary search trees that ensure searches, insertions, and deletions take O(log n) time. They achieve this by enforcing that the heights of the left and right subtrees of every node differ by at most 1. If an insertion causes a height difference greater than 1, rotations are performed to rebalance the tree. Single rotations are used for outside cases, while double rotations are needed for inside cases to restore balance. This allows AVL trees to guarantee a tree height of O(log n), making them efficient for operations that rely on the tree being balanced.
AVL trees are self-balancing binary search trees that ensure searches, insertions, and deletions take O(log n) time. They achieve this by enforcing that the heights of the left and right subtrees of every node differ by at most 1. If an insertion causes a height difference greater than 1, rotations are performed to rebalance the tree. Single rotations are used for outside cases, while double rotations are needed for inside cases to restore balance. This allows AVL trees to guarantee a tree height of O(log n), making them efficient for operations that rely on the tree being balanced.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
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2. TABLE OF
CONTENTS
Basic Concept of AVL Tree
01
03
02
04
06
08
Rotation in AVL Tree
Insertion in AVL Tree
Search in AVL
Tree
AVL Tree Deletion
Advantages, Disadvantages &
Applications
Code in AVL Tree
05
07 Complexities of AVL Tree
4. Introduction to
AVL
● Named after inventors Adelson-Velsky and Landis invented in
1962.
● Self Balanced Binary Search Tree(Height Balancing of BST)
● Solve BST’s worst case situation(Left or Right Skewed BST)
BF = (height of left subtree) - (height of right
subtree)
● Uses “Balancing Factor(BF)” for height balancing
● BF only allows three values those are - 0 , 1 , -1
7. ❏ The tree is defined as a balanced AVL tree when the balance factor of each node is
between -1 and 1. On the other hand, when the balance factor is less than -1 or greater
than 1, then the tree is known as an unbalanced tree and needs to be balanced to get
the perfect AVL tree.
Balance Factor-
In AVL tree,
● Balance factor is defined for every node.
● Balance factor of a node = Height of its left subtree – Height of its right
subtree
8. ● Rotation Operations in AVL Tree
Rotation is performed in AVL Tree to turn the unbalanced tree into a balanced
tree by performing various rotation operations.
In general, there are four types of Rotations in the AVL tree:
1. Left Rotation
2. Right Rotation
3. Left-Right Rotation
4. Right-Left Rotation
The first two rotations are known as single rotations, and the next two are known
as double rotations.
9. 1. Right Rotation (RR):
When a node is inserted into the left subtree or deleted from the left subtree, the
AVL tree becomes unbalanced, and we need to balance it using LL rotation. This
LL rotation is also known as clockwise rotation applied on edge, which has the
highest balance factor.
10. 2. Left Rotation(LR):
When a node gets inserted into the right subtree or deleted from the right subtree, the AVL tree
becomes unbalanced, and we need to balance it by rotating the node in the anti-clockwise
direction.
11. 3. Left-Right Rotation(LR):
Left-Right Rotation is the combination of RR rotation and LL rotation. At first, RR rotation
is performed on the subtree then, LL rotation is performed on the part of the full tree from
inserted node to the first node
4. Right-Left Rotation(RL):
Right-left Rotation is the combination of LL rotation and RR rotation. In this case, the first
LL rotation is performed on the subtree where the change has been made; then, the RR
rotation is performed on the part of the full tree from the inserted node to the top of the
tree, that is, the first node.
13. ● Insertion is performed in the same way as in a binary search tree.
● The new node is added into AVL tree as the leaf node. The tree can be
balanced by applying rotations.
● Rotation is required only if, the balance factor of any node is disturbed
upon inserting the new node, otherwise the rotation is not required.
Insertion
14. Steps to follow for insertion
● Let the newly inserted node be w
○ Perform standard BST insert for w.
○ Starting from w, travel up and find the first unbalanced node. Let z be the first
unbalanced node, y be the child of z that comes on the path from w to z and x be the
grandchild of z that comes on the path from w to z.
○ Re-balance the tree by performing appropriate rotations on the subtree rooted with z.
There can be 4 possible cases that needs to be handled as x, y and z can be arranged in 4
ways. Following are the possible 4 arrangements:
■ y is left child of z and x is left child of y (Left Left Case)
■ y is left child of z and x is right child of y (Left Right Case)
■ y is right child of z and x is right child of y (Right Right Case)
■ y is right child of z and x is left child of y (Right Left Case)
31. After all There has been included a Implementation of Insertion and deletion procedure in C
language.
The source code link is:
https://drive.google.com/file/d/1m7bzOiNRaZtda6jogbD56n1WS7MCp-p9/view?usp=sharing
Let’s describe the code from code blocks-
33. ● The search operation in an AVL tree makes it better than binary search tree.
● The searching time complexity of the AVL tree is only O(log N).
● Even in the worst-case, time complexity of searching operation in an AVL tree
is O(log(N)), where N is the number of nodes of the tree.
● Because the height of the AVL tree is always balanced with self-balancing
capabilities.
34. Steps to follow the search operation:
● Start from the root node.
● If the root node is NULL, return false.
● Check if the current node’s value is equal to the value of the node to be
searched. If yes, return true.
● If the current node’s value is less than searched key then recur to the right
subtree.
● If the current node’s value is greater than searched key then recur to the
left subtree.
● If the searched key is not found, then the key is not present in the tree.
36. Implementation of Function to find a key in the AVL tree:
bool AVL_search(struct AVL_withparent* root, int key)
{
if (root == NULL) // If root is NULL
return false;
else if (root->key == key) // If found, return true
return true;
// Recur to the left subtree if the current node's value is greater than key
else if (root->key > key) {
bool val = AVL_search(root->left, key);
return val;
}
else {
bool val = AVL_search(root->right, key); // Otherwise, recur to the right subtree
return val;
}}
37. Implementation of main to find a key in the AVL tree:
int main()
{
struct AVL_withparent* root;
root = NULL;
root = Insert(root, NULL, 10); // Function call to insert the nodes
root = Insert(root, NULL, 20);
root = Insert(root, NULL, 30);
root = Insert(root, NULL, 40);
bool found = AVL_search(root, 40); // Function call to search for a node
if (found)
cout << "value found";
else
cout << "value not found";
return 0;
}
39. Time Complexity for Insertion
➢ Inserting an element into an AVL tree requires rotations, calculating the
balance factor and updating the height after insertion.
➢ Time for rotation —> constant time
➢ Time for calculating the balance factor —> constant time
➢ Traversing the tree for updating height —> O(log n)
SO the time complexity of insertion is O(log n).
40. Time Complexity for Search
➢ For search operation only need to traversing the tree
➢ Traversing the tree —> O(log n)
SO the time complexity for search is O(log n).
41. Time Complexity for Deletion
➢ Deleting an element from an AVL tree also requires rotations, calculating
the balance factor and updating the height after insertion.
➢ Time for rotation —> constant time
➢ Time for calculating the balance factor —> constant time
➢ Traversing the tree for updating height —> O(log n)
SO the time complexity of deletion is O(log n).
43. OPERATION BEST CASE AVERAGE CASE WORST CASE
Insert O (log n) O (log n) O (log n)
Delete O (log n) O (log n) O (log n)
Search O (1) O (log n) O (log n)
Traversal O (log n) O (log n) O (log n)
BEST CASE AVERAGE CASE WORST CASE
O (n) O (n) O (n)
Space Complexity:
45. Advantages of AVL Tree
- The height of the AVL tree is always balanced.
- The height never grows beyond log N, where N is the
total number of nodes in the tree.
- It gives better search time complexity when compared to
simple Binary Search trees.
- AVL trees have self-balancing capabilities.
46. Disadvantages of AVL Tree
- AVL trees can be difficult to implement.
- AVL trees have high constant factors for some
operations.
- Most STL implementations of the ordered associative
containers (sets, multisets, maps, and multimaps) use
red-black trees instead of AVL trees. Unlike AVL trees,
red-black trees require only one restructuring for removal
or insertion.
47. Application of AVL Tree
- In-memory sorts of sets and dictionaries.
- Database applications in which insertions and deletions
are fewer but there are frequent lookups for data
required.
- The applications that require improved searching apart
from the database applications.
- AVL tree is a balanced binary search tree which
employees rotation to maintain balance.
48. Problem-01:
In this problem you are given two type of query
1. Insert an integer to the list.
2. Given an integer x, you're about to find an integer k which represent x's index if the list is sorted in ascending order. Note that in
this problem we will use 1-based indexing.
As the problem title suggest, this problem intended to be solved using Balanced Binary Search Tree, one of its example is AVL Tree.
Input
The first line contains an integer Q, which denotes how many queries that follows.
The next Q lines will be one of the type queries which follow this format:
1 x means insert x to the list
2 x means find x's index if the list is sorted in ascending order.
Output
For each query type 2, print a line containing an integer as the answer or print "Data tidak ada" no quotes if the requested number does
not exist in the current lis.
Link–> https://www.spoj.com/problems/SDITSAVL/
49. Problem-01: Solution
1. typedef struct node {
2. int key;
3. struct node *left;
4. struct node *right;
5. int left_child;
6. int right_child;
7. int height;
8. } node;
1. int solve (node *root, int key) {
2. if (root != NULL) {
3. if (key > root -> key) return root -> left_child + 1 + solve(root -> right, key);
4. else if (key < root -> key) return solve(root -> left, key);
5. else return root -> left_child;
6. } else {
7. flag = 1;
8. return -1;
9. };
10. }
Link–> https://ideone.com/wNBots