Definition of an AVL tree, Insertion of an AVL tree,Height of an AVL tree,Rotations in AVL tree,Representation of AVL tree,Deletion of an AVL tree,AVL search Tree, R0 rotation,R1 rotation,R-1 rotation in AVL tree
Row space, column space, null space And Rank, Nullity and Rank-Nullity theore...Hemin Patel
If A is an m×n matrix, then the subspace of Rn spanned by the row vectors of A is
called the row space of A, and the subspace of Rm spanned by the column vectors
is called the column space of A. The solution space of the homogeneous system of
equation Ax=0, which is a subspace of Rn, is called the nullsapce of A.
This document discusses balanced binary search trees (BSTs), specifically AVL trees. It explains that AVL trees ensure insertions and deletions are O(log N) by keeping the height difference between left and right subtrees no more than 1. It covers the four types of rotations (LL, RR, LR, RL) used to rebalance the tree after insertions or deletions. Deletions can also cause imbalance and require L, R, L0, L1, R0, R1, L-1, R-1 rotations depending on the balancing factors. Examples are provided for each type of rotation.
The document discusses spatial transformations of twists and wrenches between coordinate frames. It defines transformations for velocities, forces, moments and inertias using skew-symmetric cross product matrices. Euler angles using fixed axes and moving axes are described to represent orientations as rotations about X, Y, Z axes. Joint coordinate systems and anatomical landmarks are defined for the shank. Momentum is defined as linear and angular components and momentum wrenches transform in the same way as force wrenches between frames.
This document analyzes the motion of a tower of cylinders rolling without slipping on boards below and above each cylinder. It:
1) Models each pair of cylinders as a single cylinder with double the mass.
2) Derives equations relating the linear and angular accelerations of adjacent cylinders.
3) Diagnoses the stability of the system by analyzing the eigenvalues of the equation matrix, finding one eigenvalue corresponds to an exponentially growing solution and the other a decaying solution.
4) Concludes the initial conditions must correspond only to the decaying eigenvector solution for the system to be stable.
An AVL tree is a self-balancing binary search tree where the heights of the two child subtrees of any node differ by at most one. AVL trees perform rotations to rebalance the tree after insertions or deletions in order to maintain this height balance property. There are four types of rotations - left-left, left-right, right-right, and right-left - that are used to rebalance the tree as needed.
Transformation matrices can be used to describe that at what angle the servos need to be to reach the desired position in space or may be an underwater autonomous vehicle needs to reach or align itself with several different obstacles inside the water.
This document contains instructions for reflecting geometric shapes across various lines. It discusses reflecting a pentagon across the x-axis, a triangle across the y-axis, and other shapes across the lines x=3 and y=-2. The final reflection is of an unknown shape across the y-axis.
Row space, column space, null space And Rank, Nullity and Rank-Nullity theore...Hemin Patel
If A is an m×n matrix, then the subspace of Rn spanned by the row vectors of A is
called the row space of A, and the subspace of Rm spanned by the column vectors
is called the column space of A. The solution space of the homogeneous system of
equation Ax=0, which is a subspace of Rn, is called the nullsapce of A.
This document discusses balanced binary search trees (BSTs), specifically AVL trees. It explains that AVL trees ensure insertions and deletions are O(log N) by keeping the height difference between left and right subtrees no more than 1. It covers the four types of rotations (LL, RR, LR, RL) used to rebalance the tree after insertions or deletions. Deletions can also cause imbalance and require L, R, L0, L1, R0, R1, L-1, R-1 rotations depending on the balancing factors. Examples are provided for each type of rotation.
The document discusses spatial transformations of twists and wrenches between coordinate frames. It defines transformations for velocities, forces, moments and inertias using skew-symmetric cross product matrices. Euler angles using fixed axes and moving axes are described to represent orientations as rotations about X, Y, Z axes. Joint coordinate systems and anatomical landmarks are defined for the shank. Momentum is defined as linear and angular components and momentum wrenches transform in the same way as force wrenches between frames.
This document analyzes the motion of a tower of cylinders rolling without slipping on boards below and above each cylinder. It:
1) Models each pair of cylinders as a single cylinder with double the mass.
2) Derives equations relating the linear and angular accelerations of adjacent cylinders.
3) Diagnoses the stability of the system by analyzing the eigenvalues of the equation matrix, finding one eigenvalue corresponds to an exponentially growing solution and the other a decaying solution.
4) Concludes the initial conditions must correspond only to the decaying eigenvector solution for the system to be stable.
An AVL tree is a self-balancing binary search tree where the heights of the two child subtrees of any node differ by at most one. AVL trees perform rotations to rebalance the tree after insertions or deletions in order to maintain this height balance property. There are four types of rotations - left-left, left-right, right-right, and right-left - that are used to rebalance the tree as needed.
Transformation matrices can be used to describe that at what angle the servos need to be to reach the desired position in space or may be an underwater autonomous vehicle needs to reach or align itself with several different obstacles inside the water.
This document contains instructions for reflecting geometric shapes across various lines. It discusses reflecting a pentagon across the x-axis, a triangle across the y-axis, and other shapes across the lines x=3 and y=-2. The final reflection is of an unknown shape across the y-axis.
This document discusses balanced binary search trees (BSTs), specifically AVL trees. It begins by explaining that regular BSTs can have heights of O(N) in the worst case, making insertion and deletion operations slow. Balanced BSTs like AVL trees aim to keep the height O(log N) through rebalancing rotations after insertions or deletions. The document then covers AVL tree properties and balancing, the four types of rotations (LL, RR, LR, RL) used to rebalance after insertions, examples of constructing an AVL tree by insertion, and the different rotation types (R0, R1, R-1, L0, L1, L-1) used to rebalance
- AVL trees are binary search trees where the balance factor of every node is between -1 and 1, ensuring the tree remains balanced during insertions and deletions.
- When a node becomes unbalanced with a balance factor of 2 or -1 after an insertion or deletion, rotations are performed to balance the tree. Single rotations are LL or RR, double rotations are LR or RL.
- Rotations may involve the parent node (LL, RR), the grandparent node (LR, RL), or both (LR is a RR followed by a LL rotation).
- Rebalancing after deletions uses the same classification (L, R0, R1, R-1) and rotation techniques as insertions to
- AVL trees are binary search trees where the balance factor of every node is between -1 and 1, ensuring the tree remains balanced during insertions and deletions.
- When a node's balance factor becomes 2 or -1 after an insertion or deletion, rotations are performed to balance the tree. Single rotations are LL or RR, double rotations are LR or RL.
- Rotations may be needed during insertion to balance the tree. Deletions may require rebalancing along the path from the deleted node to the root. At most one rotation is required per insertion, but deletions could require O(log n) rotations.
The document discusses dynamic dictionaries and AVL trees. It provides:
1) An overview of common dictionary operations like get, insert, and delete and their time complexities in different data structures.
2) Details on AVL tree properties including balance factors, height bounds, rotations needed during insertions and deletions to maintain the balance factor property.
3) Explanations and examples of different types of rotations (single vs double) needed to rebalance the tree after an insertion or deletion causes an imbalance.
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.
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 balanced binary search trees, specifically AVL trees. It defines AVL trees as binary search trees where the heights of any node's left and right subtrees differ by at most one. It describes how insertions and deletions can cause the tree to become unbalanced and require rotations to restore the balance property. Rotations are classified based on the position of inserted/deleted nodes and include left-left, left-right, right-right, and right-left types. The time complexity of insertions and deletions in AVL trees is O(logN) due to rebalancing rotations after each operation.
The document discusses balanced binary search trees, specifically AVL trees. It explains that AVL trees ensure the height of the tree remains O(log N) during insertions and deletions by enforcing that the heights of all nodes' left and right subtrees differ by at most 1. The document outlines the process for inserting and deleting nodes from an AVL tree, which may require rotations to restore balance. Rotations are classified based on the position of inserted/deleted nodes and include left-left, left-right, right-right, and right-left varieties.
This document discusses AVL trees, which are self-balancing binary search trees. It begins by explaining the problem with skewed binary search trees, where the worst case time complexity of a search can be O(n). It then introduces AVL trees, which guarantee O(log n) search time by ensuring that the heights of any node's left and right subtrees differ by at most 1. The document covers balance factors, rotations to rebalance the tree after insertions, and the four types of rotations - left-left, right-right, left-right, and right-left. It provides an example of generating an AVL tree for given values through insertions and rotations.
This document discusses the implementation of AVL trees, which are self-balancing binary search trees. It covers calculating the height of AVL trees, storing node heights, performing insertions which may require single or double rotations to rebalance the tree, and the pros and cons of using AVL trees. AVL trees guarantee logarithmic time searches, insertions and deletions by keeping the heights of left and right subtrees differing by at most one.
AVL trees are self-balancing binary search trees. They ensure that the heights of the two subtrees of any node differ by at most one. This balance property is maintained during insertions and deletions via rotations. There are two types of rotations needed - a single rotation to balance nodes where the imbalance is on one side, and a double rotation to balance nodes where the imbalance is on both sides. All operations on AVL trees, including rotations, take O(log n) time, making them efficient for search, insertion, and deletion.
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
1. The document discusses the AVL tree, a self-balancing binary search tree. It was the first such tree invented by G.M. Adelson-Velsky and E.M. Landis in 1962.
2. In an AVL tree, the heights of the two child subtrees of any node can differ by at most one. Rebalancing operations are performed when this balance property is violated upon insertion or deletion.
3. There are four types of rebalancing rotations used - single left, single right, double left-right, and double right-left rotations. These restore the balance property and result in a height-balanced tree.
The document discusses AVL trees, which are self-balancing binary search 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. Rotations are used to rebalance the tree after insertions or deletions to maintain this height balance property. There are four types of rotations - left, right, left-right, and right-left - to handle different cases of imbalance. The document provides examples of inserting nodes into AVL trees and the resulting rotations required to maintain balance.
The document discusses crystal lattices and crystallography concepts including:
- The 14 Bravais lattices that describe the geometric arrangements of points or atoms in crystal structures.
- Miller indices for describing planes in crystal structures.
- Reciprocal lattices and how they relate to direct crystal lattices.
- Symmetry operations and elements that are present in different crystal systems.
- Stereographic projections for representing crystallographic planes and directions.
An AVL tree is a self-balancing binary search tree. It ensures that the heights of the two child subtrees of any node differ by at most one. It performs basic operations like search, insertion, and deletion in O(log n) time. When an insertion causes a height imbalance, rotations are performed to restore the balance. There are four types of rotations - left, right, left-right, and right-left depending on the location of the inserted node.
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.
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
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.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
This document discusses balanced binary search trees (BSTs), specifically AVL trees. It begins by explaining that regular BSTs can have heights of O(N) in the worst case, making insertion and deletion operations slow. Balanced BSTs like AVL trees aim to keep the height O(log N) through rebalancing rotations after insertions or deletions. The document then covers AVL tree properties and balancing, the four types of rotations (LL, RR, LR, RL) used to rebalance after insertions, examples of constructing an AVL tree by insertion, and the different rotation types (R0, R1, R-1, L0, L1, L-1) used to rebalance
- AVL trees are binary search trees where the balance factor of every node is between -1 and 1, ensuring the tree remains balanced during insertions and deletions.
- When a node becomes unbalanced with a balance factor of 2 or -1 after an insertion or deletion, rotations are performed to balance the tree. Single rotations are LL or RR, double rotations are LR or RL.
- Rotations may involve the parent node (LL, RR), the grandparent node (LR, RL), or both (LR is a RR followed by a LL rotation).
- Rebalancing after deletions uses the same classification (L, R0, R1, R-1) and rotation techniques as insertions to
- AVL trees are binary search trees where the balance factor of every node is between -1 and 1, ensuring the tree remains balanced during insertions and deletions.
- When a node's balance factor becomes 2 or -1 after an insertion or deletion, rotations are performed to balance the tree. Single rotations are LL or RR, double rotations are LR or RL.
- Rotations may be needed during insertion to balance the tree. Deletions may require rebalancing along the path from the deleted node to the root. At most one rotation is required per insertion, but deletions could require O(log n) rotations.
The document discusses dynamic dictionaries and AVL trees. It provides:
1) An overview of common dictionary operations like get, insert, and delete and their time complexities in different data structures.
2) Details on AVL tree properties including balance factors, height bounds, rotations needed during insertions and deletions to maintain the balance factor property.
3) Explanations and examples of different types of rotations (single vs double) needed to rebalance the tree after an insertion or deletion causes an imbalance.
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.
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 balanced binary search trees, specifically AVL trees. It defines AVL trees as binary search trees where the heights of any node's left and right subtrees differ by at most one. It describes how insertions and deletions can cause the tree to become unbalanced and require rotations to restore the balance property. Rotations are classified based on the position of inserted/deleted nodes and include left-left, left-right, right-right, and right-left types. The time complexity of insertions and deletions in AVL trees is O(logN) due to rebalancing rotations after each operation.
The document discusses balanced binary search trees, specifically AVL trees. It explains that AVL trees ensure the height of the tree remains O(log N) during insertions and deletions by enforcing that the heights of all nodes' left and right subtrees differ by at most 1. The document outlines the process for inserting and deleting nodes from an AVL tree, which may require rotations to restore balance. Rotations are classified based on the position of inserted/deleted nodes and include left-left, left-right, right-right, and right-left varieties.
This document discusses AVL trees, which are self-balancing binary search trees. It begins by explaining the problem with skewed binary search trees, where the worst case time complexity of a search can be O(n). It then introduces AVL trees, which guarantee O(log n) search time by ensuring that the heights of any node's left and right subtrees differ by at most 1. The document covers balance factors, rotations to rebalance the tree after insertions, and the four types of rotations - left-left, right-right, left-right, and right-left. It provides an example of generating an AVL tree for given values through insertions and rotations.
This document discusses the implementation of AVL trees, which are self-balancing binary search trees. It covers calculating the height of AVL trees, storing node heights, performing insertions which may require single or double rotations to rebalance the tree, and the pros and cons of using AVL trees. AVL trees guarantee logarithmic time searches, insertions and deletions by keeping the heights of left and right subtrees differing by at most one.
AVL trees are self-balancing binary search trees. They ensure that the heights of the two subtrees of any node differ by at most one. This balance property is maintained during insertions and deletions via rotations. There are two types of rotations needed - a single rotation to balance nodes where the imbalance is on one side, and a double rotation to balance nodes where the imbalance is on both sides. All operations on AVL trees, including rotations, take O(log n) time, making them efficient for search, insertion, and deletion.
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
1. The document discusses the AVL tree, a self-balancing binary search tree. It was the first such tree invented by G.M. Adelson-Velsky and E.M. Landis in 1962.
2. In an AVL tree, the heights of the two child subtrees of any node can differ by at most one. Rebalancing operations are performed when this balance property is violated upon insertion or deletion.
3. There are four types of rebalancing rotations used - single left, single right, double left-right, and double right-left rotations. These restore the balance property and result in a height-balanced tree.
The document discusses AVL trees, which are self-balancing binary search 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. Rotations are used to rebalance the tree after insertions or deletions to maintain this height balance property. There are four types of rotations - left, right, left-right, and right-left - to handle different cases of imbalance. The document provides examples of inserting nodes into AVL trees and the resulting rotations required to maintain balance.
The document discusses crystal lattices and crystallography concepts including:
- The 14 Bravais lattices that describe the geometric arrangements of points or atoms in crystal structures.
- Miller indices for describing planes in crystal structures.
- Reciprocal lattices and how they relate to direct crystal lattices.
- Symmetry operations and elements that are present in different crystal systems.
- Stereographic projections for representing crystallographic planes and directions.
An AVL tree is a self-balancing binary search tree. It ensures that the heights of the two child subtrees of any node differ by at most one. It performs basic operations like search, insertion, and deletion in O(log n) time. When an insertion causes a height imbalance, rotations are performed to restore the balance. There are four types of rotations - left, right, left-right, and right-left depending on the location of the inserted node.
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.
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
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.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
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আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
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3. Definition:
One of the more popular balanced tree known as
an AVL tree.
It was introduced in 1962 by Adelson - Velskii
and Landis.
An empty binary tree is an AVL tree. If T is a
nonempty binary tree with TL and TR as its left
and right subtrees, then T is an AVL tree iff,
(i) TL and TR are AVL trees
(ii) |hL - hR|<=1 where hL and hR are the
heights of TL and TR, respectively.
4. Let Nh be the minimum number of nodes in an
AVL tree of height h.
In the worst case the height of one of the subtrees
is h-1, and the height of the other is h-2.
Both subtrees are also AVL trees.
Nh=Nh-1 + Nh-2 + 1, N0=0, and N1=1
The similarity between this definition for Nh and
the definition of the Fibonacci numbers,
Fn=Fn-1 +Fn-2, F0=0 and F1=1
5. AVL Trees are normally represented by using
the linked representation scheme for binary
trees. However, to facilitate insertion and
deletion, a balance factor bf is associated with
each node.
The balance factor bf(x) of a node x is defined
as
height of left subtree of x – height of
right subtree of x
From the definition of an AVL tree, it follows
that the permissible balance factors are -1, 0
and 1.
7. The tree contains nodes with balance factors
other than -1,0 and 1, it is not an AVL tree.
When an insertion into an AVL tree using
algorithm then the results in a search tree that
has one or more nodes with balance factors
other than -1,0 and 1, the resulting tree is
unbalanced.
We can restore balance(i.e., make all balance
factors -1, 0 and 1) by shifting some of the
subtrees of the unbalanced tree
9. Before examining the subtree movement
needed to restore balance, let us make some
observations about the unbalanced tree that
results from an insertion.
I1 denotes insertion observation 1.
I1: In the unbalanced tree the balance factors
are limited to -2,-1,0,1 and 2.
I2: A node with balance factor 2 had a balance
factor 1 before the insertion.
Similarly, a node with balance factor -2 had a
balance factor -1 before the insertion.
10. I3: The balance factors of only those nodes on the
path from the root to the newly inserted node can
change as a result of the insertion.
I4: Let A denote the nearest ancestor of the newly
inserted node whose balance factor is either -2 or 2.
The balance factor of all nodes on the path from
A to the newly inserted node was 0 prior to the
insertion.
11. 1 0 2
X X X
XL XR
h-1 h-2
XL X’R
h-1 h-1
X’L XR
h h-2
(a) Before insertion (b) After inserting into XR (c) After inserting into XL
Balance factor of X is inside the node
Subtree heights are below subtree names
For the balance factor to become 0, the insertion must be
made in XR resulting in an X’R of height h-1.
The height of X’R must increase to h-1 as all balance
factors on the path from X to the newly inserted node were
0 prior to the insertion.
The height of X remains h, and the balance factors of the
ancestors of X are the same before and after the insertion,
so the tree is balanced.
12. A
0
1
A
A 2
AR
B 1 AR
h
0B
B’L
BRBL B’L BR
h+1
0
A
BR AR
h h h+1 h h h
(a) Before insertion (b) After inserting into BL (c) After LL rotation
h
Balance factors are inside nodes
Subtree heights are below subtree names
13. h
A
0B
2A
B
Cb
-1
0C
B AAR
h
BL BR
hh
BL
h
CL CR
BL CL CR AR
h h
AR
1
(a) Before insertion (b) After inserting into BR (c) After LR rotation
b=0=>bf(B)=bf(A)=0 after rotation
b=1=>bf(B)=0 and bf(A)=-1 after rotation
b=-1=>bf(B)=1 and bf(A)=0 after rotation
14. The transformations done to remedy LL and RR
imbalances are often called single rotations, while
those done for LR and RL imbalances are called
double rotations.
The transformation for an LR imbalance can be viewed
as an RR rotation followed by an LL rotation, while
that for an RL imbalance can be viewed as an LL
rotation followed by an RR rotation.
A single rotation(LL, LR, RR, or RL) is sufficient to
restore balance if the insertion causes imbalance.
15. Let q be the parent of the node that was physically deleted.
The node containing this element is deleted and the right-
child pointer from the root diverted to the only child of the
element node.
The parent of the deleted node is the root, so q is the root.
The nodes on the path from the root q have changed as a
result of the deletion, this path backward from q toward
the root.
If the deletion took place from the left subtree of q, bf(q)
decreases by 1, and if it took place from the right subtree
bf(q) increases by 1.
16. D1 : If the new balance factor of q is 0, its height
has decreased by 1.
D2 : If the new balance factor of q is either -1 or
1, its height is the same as before the deletion and the
balance factors of its ancestors are unchanged.
D3 : If the new balance factor of q is either -2 or
2, the tree is unbalanced at q.
17. 2
A’R
BL BR
h h
B
A
h-1
(b) After deletion from AR
-1
1 A
B
BL
h
BR A’R
h h-1
(c) After R0 rotation
1
0
BL BR
h h
AR
h
A
B
(a) Before deletion
An R0 rotation(single rotation)
0
18. An R0 imbalanced at A is rectified by performing
the rotation. Notice that the height of the shown
subtree was h + 2 before the deletion and is h + 2
after.
So the balance factors of the remaining nodes on the
path to the root are unchanged.
How to handle an R1 imbalance. While the pointer
changes are the same as for an R0 imbalance,
The new balance factors for A and B are different
and the height of the subtree the notation is now h + 1,
which is 1 less than before the deletion.
20. R1 rotation, we must continue to examine nodes
on the path to the root.
Unlike the case of an insertion, one rotation
may not suffice to restore balance following a
deletion.
The number of rotation needed is 0(log n).
The transformation needed when the imbalance
is of type R- 1.
The balance factors of A and B following the
rotation depend on the balance factor b of the
right child B.
21. 1
-1
b
2
-1
b
0A
B
BL
h - 1
CL CR
C
AR
h
A
B A’R
h - 1
CBL
h - 1
CL
CR
C
B A
BL
h - 1
CL CR A’R
h - 1
(a) Before deletion (b) After deletion from AR
(c) After R – 1 rotation
b =0=> bf(A) = bf(B) = 0 after rotation
b = 1 => bf(A) = -1 and bf(B) = 0 after rotation
b = -1 => bf(A) = 0 and bf(B) = 1 after rotation
An R – 1 rotation (double rotation)
22. This rotation leaves behind a
subtree of height h + 1, while the
subtree height prior to the deletion
was h + 2.
So we need to continue on the
path to the root.
LL and R1 rotation are identical.
LL and R0 rotation differ only in
the final balance factors of A and
B and LR and R-1 rotation are
identical.