Review session2
•Download as PPT, PDF•
1 like•685 views
The document discusses binary search trees and their properties. It explains that a binary search tree is a binary tree where every node's left subtree contains values less than the node's value and the right subtree contains greater values. Operations like search, insert, delete can be done in O(h) time where h is the height of the tree. The height is O(log n) for balanced trees but can be O(n) for unbalanced trees. The document also provides examples of using a binary search tree to sort a set of numbers in O(n log n) time by building the BST and doing an inorder traversal.
Report
Share
Report
Share
![Binary Search Trees ,[object Object],[object Object],[object Object],parent left child right child root 17 11 19](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Chapter 3 ds
This document provides an outline and overview of topics related to arrays and matrices. It begins with an introduction to one-dimensional arrays, how they are represented in memory, and common operations on arrays. It then discusses the limitations of linear arrays and introduces multidimensional arrays. Specific types of multidimensional arrays covered include two-dimensional arrays and special matrices such as diagonal, tridiagonal, lower triangular, and upper triangular matrices. Memory mapping techniques for multidimensional arrays like row-major and column-major ordering are also summarized.
Proof of O(log *n) time complexity of Union find (Presentation by Wei Li, Zeh...
The document discusses the union find algorithm and its time complexity. It defines the union find problem and three operations: MAKE-SET, FIND, and UNION. It describes optimizations like union by rank and path compression that achieve near-linear time complexity of O(m log* n) for m operations on n elements. It proves several lemmas about ranks and buckets to establish this time complexity through an analysis of the costs of find operations.
Chapter 9 ds
The document discusses binary search trees (BSTs), including their representation as linked data structures, common operations like search, insertion, deletion, finding minimum/maximum elements, and predecessors/successors. While BST operations take O(h) time where h is the tree height, h can be as large as n for an unbalanced tree. The document thus notes that better balanced search trees like AVL trees and red-black trees are preferable to ensure O(log n) performance of operations.
17. Trees and Tree Like Structures
This document provides an overview of trees and tree-like data structures. It discusses trees and related terminology like nodes, edges, and traversals. It also covers implementing trees recursively and with a Tree<T> class. Binary trees and different traversal orders are explained. Balanced search trees like AVL trees, red-black trees, and B-trees are introduced for maintaining search trees in a balanced way. Finally, graphs are defined as a set of nodes connected by edges.
Preparation Data Structures 06 arrays representation
Here are my slides for my preparation class for possible Master students in Electrical Engineering and Computer Science (Specialization in Computer Science)... for the entrance examination here at Cinvestav GDL.
Tree and binary tree
The document discusses tree data structures and binary search trees. It defines key tree terminology like root, internal and leaf nodes. It explains tree traversal algorithms like preorder, inorder and postorder traversal using recursion and iteration. It also describes operations on binary search trees like searching, inserting and deleting nodes. The complexity of these BST operations is provided. Algorithms to find the inorder predecessor and successor of a node in a BST are presented.
17. Java data structures trees representation and traversal
In this chapter we will discuss tree data structures, like trees and graphs. The abilities of these data structures are really important for the modern programming. Each of this data structures is used for building a model of real life problems, which are efficiently solved using this model.
هياكلبيانات
1) The document discusses various data structures and algorithms including arrays, stacks, queues, pointers, and linked lists.
2) It provides details on common data structure operations like insertion, deletion, sorting, and searching. Linear and non-linear data structures are described.
3) Examples of how each data structure works are given, such as the push and pop operations for stacks, and insertion and deletion for queues. Applications like arithmetic expressions and recursion are discussed.
Recommended
Chapter 3 ds
This document provides an outline and overview of topics related to arrays and matrices. It begins with an introduction to one-dimensional arrays, how they are represented in memory, and common operations on arrays. It then discusses the limitations of linear arrays and introduces multidimensional arrays. Specific types of multidimensional arrays covered include two-dimensional arrays and special matrices such as diagonal, tridiagonal, lower triangular, and upper triangular matrices. Memory mapping techniques for multidimensional arrays like row-major and column-major ordering are also summarized.
Proof of O(log *n) time complexity of Union find (Presentation by Wei Li, Zeh...
The document discusses the union find algorithm and its time complexity. It defines the union find problem and three operations: MAKE-SET, FIND, and UNION. It describes optimizations like union by rank and path compression that achieve near-linear time complexity of O(m log* n) for m operations on n elements. It proves several lemmas about ranks and buckets to establish this time complexity through an analysis of the costs of find operations.
Chapter 9 ds
The document discusses binary search trees (BSTs), including their representation as linked data structures, common operations like search, insertion, deletion, finding minimum/maximum elements, and predecessors/successors. While BST operations take O(h) time where h is the tree height, h can be as large as n for an unbalanced tree. The document thus notes that better balanced search trees like AVL trees and red-black trees are preferable to ensure O(log n) performance of operations.
17. Trees and Tree Like Structures
This document provides an overview of trees and tree-like data structures. It discusses trees and related terminology like nodes, edges, and traversals. It also covers implementing trees recursively and with a Tree<T> class. Binary trees and different traversal orders are explained. Balanced search trees like AVL trees, red-black trees, and B-trees are introduced for maintaining search trees in a balanced way. Finally, graphs are defined as a set of nodes connected by edges.
Preparation Data Structures 06 arrays representation
Here are my slides for my preparation class for possible Master students in Electrical Engineering and Computer Science (Specialization in Computer Science)... for the entrance examination here at Cinvestav GDL.
Tree and binary tree
The document discusses tree data structures and binary search trees. It defines key tree terminology like root, internal and leaf nodes. It explains tree traversal algorithms like preorder, inorder and postorder traversal using recursion and iteration. It also describes operations on binary search trees like searching, inserting and deleting nodes. The complexity of these BST operations is provided. Algorithms to find the inorder predecessor and successor of a node in a BST are presented.
17. Java data structures trees representation and traversal
In this chapter we will discuss tree data structures, like trees and graphs. The abilities of these data structures are really important for the modern programming. Each of this data structures is used for building a model of real life problems, which are efficiently solved using this model.
هياكلبيانات
1) The document discusses various data structures and algorithms including arrays, stacks, queues, pointers, and linked lists.
2) It provides details on common data structure operations like insertion, deletion, sorting, and searching. Linear and non-linear data structures are described.
3) Examples of how each data structure works are given, such as the push and pop operations for stacks, and insertion and deletion for queues. Applications like arithmetic expressions and recursion are discussed.
Arrays and structures
This document summarizes arrays and structures in C including:
1) Arrays are sets of index-value pairs that use consecutive memory locations. Structures group related data and can reference themselves.
2) C implements 1D arrays using consecutive memory locations accessed via indexes. Structures allow defining custom data types.
3) Operations on ordered lists include retrieving, inserting, deleting elements. Polynomials are represented as ordered pairs of exponents and coefficients. Addition involves comparing exponents and combining coefficients.
binary search tree
- A binary search tree (BST) is a binary tree where all left descendants of a node are less than or equal to the node's value, and all right descendants are greater than or equal.
- Searching, insertion, and deletion in a BST follow the structure of the tree - searching compares the target value to the current node to determine which subtree to search, insertion adds new nodes by comparing to parent nodes, and deletion removes nodes and restructures the tree as needed.
- Common operations like searching, insertion, and deletion are accomplished by traversing the tree from the root node down, making comparisons at each level to determine the path through left or right subtrees.
Chapter 5 ds
The document outlines the key concepts of linked lists including:
- Linked lists allow for dynamic resizing and efficient insertion/deletion unlike arrays.
- A linked list contains nodes that have a data field and a pointer to the next node.
- Common operations on linked lists include insertion, deletion, searching, and traversing the list.
- The time complexity of these operations depends on whether it's at the head, tail, or interior node.
- Linked lists can be implemented using classes for the node and linked list objects.
Chapter 4 ds
This document provides an overview of sorting algorithms including bubble sort, insertion sort, shellsort, and others. It discusses why sorting is important, provides pseudocode for common sorting algorithms, and gives examples of how each algorithm works on sample data. The runtime of sorting algorithms like insertion sort and shellsort are analyzed, with insertion sort having quadratic runtime in the worst case and shellsort having unknown but likely better than quadratic runtime.
Binary search trees
Binary search trees (BSTs) are binary trees where all keys in the left subtree of a node are less than or equal to the key of that node, and all keys in the right subtree are greater than or equal to the node's key. Common BST operations include search, insertion, deletion, finding the minimum/maximum keys, and tree traversals like inorder, preorder, and postorder walks. These operations have worst-case time complexity proportional to the height of the tree.
Set Operations - Union Find and Bloom Filters
Set Operations - make set, union, find and contains are standard operations that appear in many scenarios. Union Find is a marvelous data structure to solve problems involving union and find operations.
Different use arises when we merely want to answer queries on whether a set contains an element x without keeping the entire set in the memory. Bloom Filters play an interesting role there.
Preparation Data Structures 10 trees
This document provides an overview of tree data structures and binary trees. It begins by defining trees and their basic concepts such as subtrees, leaves, levels, and roots. It then defines binary trees and contrasts them with general trees. The document discusses calculating the height of full binary trees and using trees to represent arithmetic expressions. It also covers traversing trees and different ways of representing trees in memory.
DATASTRUCTURES UNIT-1
This document describes the array-based representation of linear lists as a data structure. It defines a linear list as a collection of elements in the form (e1, e2, ..., en) where ei is an individual element and n is the length of the list. An array is used to store the elements, where the ith element is stored at location i-1. It then provides code for a LinearList class that implements basic linear list operations like find, search, delete, insert using this array-based representation in O(1) or O(n) time.
Unit 1 introduction to data structure
This document provides an introduction to data structures. It discusses key concepts like abstract data types, different types of data structures including primitive and non-primitive, and common operations on data structures like traversing, searching, inserting, deleting, sorting and merging. It also covers algorithm analysis including time and space complexity and asymptotic notations. Specific data structures like arrays, linked lists, stacks, queues, trees and graphs are mentioned. The document concludes with discussions on pointers and structures in C/C++.
17. Trees and Graphs
Trees. Defining, Creating and Traversing Trees. Traversing the File System
Binary Search Trees. Balanced Trees
Graphs and Graphs Traversal Algorithms
Exercises: Working with Trees and Graphs
Tree
1) Tree
2) General Tree
3) Binary Tree
4) Full Binay Tree, Complete Binay Tree
5) Binary Tree Traversal (DFS & BFS)
6) Binary Search Tree
7) Reconstruction of Binay Tree
8) Expression Tree
9) Evaluation of postfix expression
10) Infix to Prefix using stack
11) Infix to Postfix using stack
12) Threaded Binary Tree
13) AVL-Tree
14) AVL-Tree Rotation
Tree traversal techniques
In computer science, tree traversal (also known as tree search) is a form of graph traversal and refers to the process of visiting (checking and/or updating) each node in a tree data structure, exactly once. Such traversals are classified by the order in which the nodes are visited. The following algorithms are described for a binary tree, but they may be generalized to other trees as well.
1.5 binary search tree
1) Tree data structures involve nodes that can have zero or more child nodes and at most one parent node. Binary trees restrict nodes to having zero, one, or two children.
2) Binary search trees have the property that all left descendants of a node are less than the node's value and all right descendants are greater. This allows efficient searching in O(log n) time.
3) Common tree operations include insertion, deletion, and traversal. Balanced binary search trees use rotations to maintain balance during these operations.
Advanced data structures slide 1 2
This document discusses data structures and algorithms. It begins by listing the objectives of understanding data types, abstract data types, data structures, algorithms, addressing methods, mathematical functions used to analyze algorithms, and measuring complexity using time complexity and Big-O notation. It then explains the problem solving process and defines key terms like data type, abstract data type, data structure, algorithm, and addressing methods. Finally, it discusses mathematical functions used in algorithm analysis and measuring an algorithm's complexity.
BINARY SEARCH TREE
A binary tree is composed of nodes where each node contains a value and pointers to its left and right children. A binary tree traversal involves systematically visiting each node by traversing either breadth-first or depth-first. Breadth-first traversal visits nodes by level while depth-first traversal can be pre-order, in-order, or post-order depending on when the node is visited. Threaded binary trees reduce the number of null pointers by using them to point to other nodes for more efficient traversals.
Trees (data structure)
This document discusses trees as a data structure. It defines trees as structures containing nodes where each node can have zero or more children and at most one parent. Binary trees are defined as trees where each node has at most two children. The document discusses tree terminology like root, leaf, and height. It also covers binary search trees and their properties for storing and searching data, as well as algorithms for inserting and deleting nodes. Finally, it briefly discusses other types of trees like balanced search trees and parse trees.
Array
This document provides an overview of arrays in C programming. It defines an array as a variable that can store multiple values of the same type. It describes how to declare and initialize single-dimensional and multi-dimensional arrays, access array elements, pass arrays to functions, and the relationship between arrays and pointers in C. Key points covered include declaring arrays with syntax like datatype arrayName[arraySize], initializing arrays, accessing elements by index, and that arrays decay to pointers when passed to functions.
Trees
This document discusses binary trees and tree traversal algorithms. It begins with definitions of trees and binary trees. It then covers tree traversal methods including preorder, inorder, postorder and level order traversal. Implementation of these traversals using recursion is shown. Applications to arithmetic expressions and propositional logic are discussed. Node structures for representing binary trees and evaluating expressions are also presented.
Folding Unfolded - Polyglot FP for Fun and Profit - Haskell and Scala
(download for perfect quality) - See how recursive functions and structural induction relate to recursive datatypes.
Follow along as the fold abstraction is introduced and explained.
Watch as folding is used to simplify the definition of recursive functions over recursive datatypes
Part 1 - through the work of Richard Bird and Graham Hutton.
Errata:
slide 7, 11 fib(0) is 0,rather than 1
slide 23: was supposed to be followed by 2-3 slides recapitulating definitions of factorial and fibonacci with and without foldr, plus translation to scala
slide 36: concat not invoked in concat example
slides 48 and 49: unwanted 'm' in definition of sum
throughout: a couple of typographical errors
throughout: several aesthetic imperfections (wrong font, wrong font colour)
algorithm unit 1
The document discusses algorithm analysis and design. It begins with an introduction to analyzing algorithms including average case analysis and solving recurrences. It then provides definitions of algorithms both informal and formal. Key aspects of algorithm study and specification methods like pseudocode are outlined. Selection sort and tower of Hanoi problems are presented as examples and analyzed for time and space complexity. Average case analysis is discussed assuming all inputs are equally likely.
Phd Slides
The document discusses tool support for designing, managing, and optimizing multi-device user interfaces. It introduces the Gummy design environment, which allows creating high-fidelity prototypes across devices. It also discusses techniques for managing multiple prototypes, such as automatic transformations and linked editing. Finally, it covers live UI design techniques like the Design Mirror for iteratively optimizing designs by intertwining testing and design.
Phd defense slides
Slides of my PhD presentation @ Eurecom, presenting our work on publishing and consuming geo-spatial data and government data using Semantic Web technologies.
More Related Content
What's hot
Arrays and structures
This document summarizes arrays and structures in C including:
1) Arrays are sets of index-value pairs that use consecutive memory locations. Structures group related data and can reference themselves.
2) C implements 1D arrays using consecutive memory locations accessed via indexes. Structures allow defining custom data types.
3) Operations on ordered lists include retrieving, inserting, deleting elements. Polynomials are represented as ordered pairs of exponents and coefficients. Addition involves comparing exponents and combining coefficients.
binary search tree
- A binary search tree (BST) is a binary tree where all left descendants of a node are less than or equal to the node's value, and all right descendants are greater than or equal.
- Searching, insertion, and deletion in a BST follow the structure of the tree - searching compares the target value to the current node to determine which subtree to search, insertion adds new nodes by comparing to parent nodes, and deletion removes nodes and restructures the tree as needed.
- Common operations like searching, insertion, and deletion are accomplished by traversing the tree from the root node down, making comparisons at each level to determine the path through left or right subtrees.
Chapter 5 ds
The document outlines the key concepts of linked lists including:
- Linked lists allow for dynamic resizing and efficient insertion/deletion unlike arrays.
- A linked list contains nodes that have a data field and a pointer to the next node.
- Common operations on linked lists include insertion, deletion, searching, and traversing the list.
- The time complexity of these operations depends on whether it's at the head, tail, or interior node.
- Linked lists can be implemented using classes for the node and linked list objects.
Chapter 4 ds
This document provides an overview of sorting algorithms including bubble sort, insertion sort, shellsort, and others. It discusses why sorting is important, provides pseudocode for common sorting algorithms, and gives examples of how each algorithm works on sample data. The runtime of sorting algorithms like insertion sort and shellsort are analyzed, with insertion sort having quadratic runtime in the worst case and shellsort having unknown but likely better than quadratic runtime.
Binary search trees
Binary search trees (BSTs) are binary trees where all keys in the left subtree of a node are less than or equal to the key of that node, and all keys in the right subtree are greater than or equal to the node's key. Common BST operations include search, insertion, deletion, finding the minimum/maximum keys, and tree traversals like inorder, preorder, and postorder walks. These operations have worst-case time complexity proportional to the height of the tree.
Set Operations - Union Find and Bloom Filters
Set Operations - make set, union, find and contains are standard operations that appear in many scenarios. Union Find is a marvelous data structure to solve problems involving union and find operations.
Different use arises when we merely want to answer queries on whether a set contains an element x without keeping the entire set in the memory. Bloom Filters play an interesting role there.
Preparation Data Structures 10 trees
This document provides an overview of tree data structures and binary trees. It begins by defining trees and their basic concepts such as subtrees, leaves, levels, and roots. It then defines binary trees and contrasts them with general trees. The document discusses calculating the height of full binary trees and using trees to represent arithmetic expressions. It also covers traversing trees and different ways of representing trees in memory.
DATASTRUCTURES UNIT-1
This document describes the array-based representation of linear lists as a data structure. It defines a linear list as a collection of elements in the form (e1, e2, ..., en) where ei is an individual element and n is the length of the list. An array is used to store the elements, where the ith element is stored at location i-1. It then provides code for a LinearList class that implements basic linear list operations like find, search, delete, insert using this array-based representation in O(1) or O(n) time.
Unit 1 introduction to data structure
This document provides an introduction to data structures. It discusses key concepts like abstract data types, different types of data structures including primitive and non-primitive, and common operations on data structures like traversing, searching, inserting, deleting, sorting and merging. It also covers algorithm analysis including time and space complexity and asymptotic notations. Specific data structures like arrays, linked lists, stacks, queues, trees and graphs are mentioned. The document concludes with discussions on pointers and structures in C/C++.
17. Trees and Graphs
Trees. Defining, Creating and Traversing Trees. Traversing the File System
Binary Search Trees. Balanced Trees
Graphs and Graphs Traversal Algorithms
Exercises: Working with Trees and Graphs
Tree
1) Tree
2) General Tree
3) Binary Tree
4) Full Binay Tree, Complete Binay Tree
5) Binary Tree Traversal (DFS & BFS)
6) Binary Search Tree
7) Reconstruction of Binay Tree
8) Expression Tree
9) Evaluation of postfix expression
10) Infix to Prefix using stack
11) Infix to Postfix using stack
12) Threaded Binary Tree
13) AVL-Tree
14) AVL-Tree Rotation
Tree traversal techniques
In computer science, tree traversal (also known as tree search) is a form of graph traversal and refers to the process of visiting (checking and/or updating) each node in a tree data structure, exactly once. Such traversals are classified by the order in which the nodes are visited. The following algorithms are described for a binary tree, but they may be generalized to other trees as well.
1.5 binary search tree
1) Tree data structures involve nodes that can have zero or more child nodes and at most one parent node. Binary trees restrict nodes to having zero, one, or two children.
2) Binary search trees have the property that all left descendants of a node are less than the node's value and all right descendants are greater. This allows efficient searching in O(log n) time.
3) Common tree operations include insertion, deletion, and traversal. Balanced binary search trees use rotations to maintain balance during these operations.
Advanced data structures slide 1 2
This document discusses data structures and algorithms. It begins by listing the objectives of understanding data types, abstract data types, data structures, algorithms, addressing methods, mathematical functions used to analyze algorithms, and measuring complexity using time complexity and Big-O notation. It then explains the problem solving process and defines key terms like data type, abstract data type, data structure, algorithm, and addressing methods. Finally, it discusses mathematical functions used in algorithm analysis and measuring an algorithm's complexity.
BINARY SEARCH TREE
A binary tree is composed of nodes where each node contains a value and pointers to its left and right children. A binary tree traversal involves systematically visiting each node by traversing either breadth-first or depth-first. Breadth-first traversal visits nodes by level while depth-first traversal can be pre-order, in-order, or post-order depending on when the node is visited. Threaded binary trees reduce the number of null pointers by using them to point to other nodes for more efficient traversals.
Trees (data structure)
This document discusses trees as a data structure. It defines trees as structures containing nodes where each node can have zero or more children and at most one parent. Binary trees are defined as trees where each node has at most two children. The document discusses tree terminology like root, leaf, and height. It also covers binary search trees and their properties for storing and searching data, as well as algorithms for inserting and deleting nodes. Finally, it briefly discusses other types of trees like balanced search trees and parse trees.
Array
This document provides an overview of arrays in C programming. It defines an array as a variable that can store multiple values of the same type. It describes how to declare and initialize single-dimensional and multi-dimensional arrays, access array elements, pass arrays to functions, and the relationship between arrays and pointers in C. Key points covered include declaring arrays with syntax like datatype arrayName[arraySize], initializing arrays, accessing elements by index, and that arrays decay to pointers when passed to functions.
Trees
This document discusses binary trees and tree traversal algorithms. It begins with definitions of trees and binary trees. It then covers tree traversal methods including preorder, inorder, postorder and level order traversal. Implementation of these traversals using recursion is shown. Applications to arithmetic expressions and propositional logic are discussed. Node structures for representing binary trees and evaluating expressions are also presented.
Folding Unfolded - Polyglot FP for Fun and Profit - Haskell and Scala
(download for perfect quality) - See how recursive functions and structural induction relate to recursive datatypes.
Follow along as the fold abstraction is introduced and explained.
Watch as folding is used to simplify the definition of recursive functions over recursive datatypes
Part 1 - through the work of Richard Bird and Graham Hutton.
Errata:
slide 7, 11 fib(0) is 0,rather than 1
slide 23: was supposed to be followed by 2-3 slides recapitulating definitions of factorial and fibonacci with and without foldr, plus translation to scala
slide 36: concat not invoked in concat example
slides 48 and 49: unwanted 'm' in definition of sum
throughout: a couple of typographical errors
throughout: several aesthetic imperfections (wrong font, wrong font colour)
algorithm unit 1
The document discusses algorithm analysis and design. It begins with an introduction to analyzing algorithms including average case analysis and solving recurrences. It then provides definitions of algorithms both informal and formal. Key aspects of algorithm study and specification methods like pseudocode are outlined. Selection sort and tower of Hanoi problems are presented as examples and analyzed for time and space complexity. Average case analysis is discussed assuming all inputs are equally likely.
What's hot (20)
Folding Unfolded - Polyglot FP for Fun and Profit - Haskell and Scala
Folding Unfolded - Polyglot FP for Fun and Profit - Haskell and Scala
Viewers also liked
Phd Slides
The document discusses tool support for designing, managing, and optimizing multi-device user interfaces. It introduces the Gummy design environment, which allows creating high-fidelity prototypes across devices. It also discusses techniques for managing multiple prototypes, such as automatic transformations and linked editing. Finally, it covers live UI design techniques like the Design Mirror for iteratively optimizing designs by intertwining testing and design.
Phd defense slides
Slides of my PhD presentation @ Eurecom, presenting our work on publishing and consuming geo-spatial data and government data using Semantic Web technologies.
PhD Slides
The document discusses adding checkpointing capabilities to SystemC simulations. It proposes two approaches: process save/restore, which saves the entire process state, and state variable save/restore, which only saves the values of state variables. It describes adding checkpointing support to the SystemC kernel to allow saving simulation state to disk and restoring later. Checkpoint data sizes are small, on the order of kilobytes, compared to gigabytes for other solutions. This allows portability and reverse execution with low overhead. Integrating SystemC with virtual platforms and the performance impacts are also discussed.
Identifying, annotating, and filtering arguments and opinions on the social w...
How do we support people in using/reusing arguments and opinions on the World Wide Web? WIMMICS, INRIA seminar based on my PhD viva slides.
PowerPoint Presentation Sample # 1
The document provides tips for living frugally as a college student. It recommends setting a budget for expenses beyond tuition and books, buying used textbooks when possible, taking advantage of happy hours and free or cheap on-campus activities, cooking meals instead of eating out, furnishing housing with used items, and applying for scholarships to reduce costs. Overall, the document emphasizes living within one's means as a student through careful budgeting and cost-saving strategies.
PhD Dissertation Presentation v 2.4
The document summarizes Robert Hickey's PhD dissertation defense. The dissertation investigated whether improving energy efficiency in Bulgaria through basic residential building envelope retrofits could reduce greenhouse gas emissions while providing economic benefits. It was hypothesized that existing financial resources for energy efficiency would be insufficient to meet emissions targets. The investigation included: forecasting Bulgaria's emissions from 2015-2020; calculating abatement costs using three models; determining retrofit costs; and comparing models and real cases. The results suggested retrofits could significantly reduce emissions at low or negative costs, and that current financing was inadequate for stated targets.
Home automation using android mobiles
This document discusses home automation through an Android mobile device. It describes a system where a Bluetooth module and relays are used to allow an Android phone to remotely control home appliances. The phone acts as the host controller, communicating with client modules attached to devices via Bluetooth. The system allows users to control lights, temperature and other electronics from their mobile device.
What REALLY Differentiates The Best Content Marketers From The Rest
The document discusses the results of a study on the impact of COVID-19 lockdowns on air pollution. Researchers analyzed data from dozens of countries and found that lockdowns led to an average decline of nearly 30% in nitrogen dioxide levels over cities. However, they also observed that this improvement was temporary and air pollution rebounded once lockdowns were lifted as vehicle traffic increased again. Overall, the study highlights how lockdowns can provide short-term benefits to air quality but sustained changes are needed to maintain those improvements.
Viewers also liked (8)
Identifying, annotating, and filtering arguments and opinions on the social w...
Identifying, annotating, and filtering arguments and opinions on the social w...
What REALLY Differentiates The Best Content Marketers From The Rest
What REALLY Differentiates The Best Content Marketers From The Rest
Similar to Review session2
Chap 5 Tree.ppt
The document discusses trees and graphs data structures. It begins with introducing different types of trees like binary trees, binary search trees, threaded binary trees, and their various traversal algorithms like inorder, preorder and postorder traversals. It then discusses tree operations like copying trees, testing for equality. It also covers the satisfiability problem and how binary trees can be used to represent logical expressions to solve this problem. Finally, it discusses threaded binary trees where null links in a binary tree are replaced with threads, and how this allows for efficient inorder traversal in linear time.
Binary trees1
The document discusses binary trees and binary search trees. It defines key terms like root, child, parent, leaves, height, depth. It explains tree traversal methods like preorder, inorder and postorder. It then describes binary search trees and how they store keys in a way that searching, insertion and deletion can be done efficiently in O(log n) time. It discusses implementation of operations like search, insert, delete on BSTs. It introduces balanced binary search trees like AVL trees that ensure height is O(log n) through rotations during insertions and deletions.
Q
1. Hash tables are good for random access of elements but not sequential access. When records need to be accessed sequentially, hashing can be problematic because elements are stored in random locations instead of consecutively.
2. To find the successor of a node in a binary search tree, we take the right child. This operation has a runtime complexity of O(1).
3. When comparing operations like insertion, deletion, and searching between different data structures, arrays generally have the best performance for insertion and searching, while linked lists have better performance for deletion and allow for easy insertion/deletion anywhere. Binary search trees fall between these two.
DSA (Data Structure and Algorithm) Questions
The document contains questions related to data structures and algorithms. Some questions ask to perform operations on data structures like stacks, queues, trees, graphs and hash tables. Other questions involve algorithms like topological sorting, shortest path algorithms, minimum spanning tree algorithms, and binary search tree operations. The document tests understanding of concepts taught in an algorithms course through examples and problem solving questions.
Trees in Data Structure
This document defines and provides examples of trees and binary trees. It begins by defining trees as hierarchical data structures with nodes and edges. It then provides definitions for terms like path, forest, ordered tree, height, and multiway tree. It specifically defines binary trees as having two children per node. The document gives examples and properties of binary trees, including full, complete, and binary search trees. It also explains linear and linked representations of binary trees and different traversal methods like preorder, postorder and inorder. Finally, it provides examples of insertion and deletion operations in binary search trees.
Sorting
Insertion sort and merge sort are discussed. Insertion sort works by inserting elements in the proper position one by one. Its worst case time is O(N^2). Merge sort uses a divide and conquer approach to sort elements. It divides the list into halves, recursively sorts the halves, and then merges the sorted halves. Merging two sorted lists takes linear time. The overall time for merge sort is O(N log N). Heaps are discussed as a way to implement priority queues. A heap has the heap property where a node is always less than its children. This allows finding the minimum element and deleting it to take O(log N) time.
Data Structure and Algorithms Binary Tree
This document discusses binary trees and their traversal. It defines binary trees, their properties such as levels and degrees of nodes. It describes different types of binary trees like complete, skewed, etc. It also explains different traversal techniques like preorder, inorder and postorder traversals and provides algorithms to implement these traversals recursively as well as using stacks. Memory representations of binary trees like sequential and linked are also summarized.
Algo-Exercises-2-hash-AVL-Tree.ppt
1. The document discusses algorithms for handling collisions in hashing, including open hashing which uses separate chaining with linked lists, and closed hashing which resolves collisions through techniques like linear probing.
2. It also discusses binary search trees and their operations, as well as balanced search trees like AVL and red-black trees which have logarithmic time performance.
3. Finally, it covers multiway search trees including 2-3 trees and how they allow for more than one key per node, providing improved time performance over standard binary search trees.
7.tree
The document discusses various tree data structures including binary trees and their terminology. It defines a tree as a set of nodes connected by links/branches where one node is designated as the root. Key terms discussed include child, parent, leaf, root, and level. The document also covers different ways to represent trees using arrays and linked lists and how to traverse trees using preorder, inorder, and postorder traversal algorithms.
Lecture notes data structures tree
The document discusses different types of tree data structures, including general trees, binary trees, binary search trees, and their traversal methods. General trees allow nodes to have any number of children, while binary trees restrict nodes to having 0, 1, or 2 children. Binary search trees organize nodes so that all left descendants are less than the parent and all right descendants are greater. Common traversal orders for trees include preorder, inorder, and postorder, which differ in whether they process the root node before or after visiting child nodes.
03-data-structures.pdf
This document provides an overview of several common data structures: stacks, queues, heaps, priority queues, union-find structures, binary search trees, Fenwick trees, and lowest common ancestor trees. For each data structure, it describes their key properties and operations and provides examples of how to implement them to achieve efficient runtimes, often in O(log n) time. It also notes the related standard library implementations available in C++ and Java.
Fundamentals of data structures
The document discusses different data structures and their implementations and applications. It covers arrays, linked lists, stacks, queues, binary trees, and binary search. The key points are:
- Arrays allow fast access but have fixed size; linked lists can grow dynamically but access is slower.
- Binary trees allow fast (O(log n)) search, insertion, and deletion operations due to their hierarchical structure.
- Stacks and queues are useful for modeling LIFO and FIFO data access with applications like function calls and job scheduling.
- Binary search runs in O(log n) time by recursively dividing the search space for sorted data.
2 depth first
The document describes depth-first search (DFS) of a graph. It discusses applications of DFS such as finding paths between nodes and determining if a graph is connected. It then explains the concepts of a depth-first tree, back edges, and cross edges. Finally, it provides pseudocode for a recursive and iterative DFS algorithm that uses data structures like a stack and arrays to track node labels, parents, and next nodes to visit.
ds 10-Binary Tree.ppt
The document discusses binary trees, including their memory representations (sequential and linked), tree traversals (pre-order, in-order, and post-order), and algorithms for traversing binary trees using stacks. Binary tree traversal algorithms include recursively visiting nodes in a certain order (pre-order visits root then left then right subtrees, etc.) or using a stack to simulate the recursion.
CS-102 BST_27_3_14v2.pdf
Binary search trees have the following key properties:
1. Each node contains a value.
2. The left subtree of a node contains only values smaller than the node's value.
3. The right subtree of a node contains only values larger than the node's value.
Binary search trees allow for efficient insertion and search operations in O(log n) time due to their structure. Deletion may require rebalancing the tree to maintain the binary search tree properties.
Admissions in india 2015
This document contains information about trees and binary trees. It begins with definitions of trees, tree terminology like root, child, parent and traversals like preorder, inorder and postorder. It then discusses properties of binary trees like complete and full binary trees. Various representations of trees like linked and sequential representations are described. Finally, it provides examples of using trees to represent expressions and evaluating them using traversals.
lecture 13
The document discusses red-black trees, which are binary search trees augmented with node colors to guarantee a height of O(log n). It describes the properties that red-black trees must satisfy, including that every node is red or black, leaves are black, and if a node is red its children are black. It then proves that these properties ensure the height is O(log n) by showing a subtree has at least 2^bh - 1 nodes, where bh is the black-height. Finally, it notes that common operations like search, insert and delete run in O(log n) time on red-black trees.
trees.ppt
The document discusses trees and their properties. It begins by defining trees as connected undirected graphs without simple circuits. It then discusses characteristics of trees such as being acyclic and having no multiple edges or loops. Examples of trees are given and criteria for determining if a graph is a tree. Properties of trees like the number of edges and relationship between internal/leaf nodes are covered. Terminology used in rooted trees like root, parent, child, ancestors and descendants are defined. Applications of trees like binary search trees and text encoding are also summarized.
Advanced data structure
A data structure is needed to represent a country with cities connected by bidirectional roads. The data structure will allow finding the longest road between any two cities in the country based on query inputs of two cities. A graph data structure using adjacency lists can model the country, with cities as nodes and roads as edges between nodes labeled with their costs. Queries can then find the longest cost path between two cities using graph traversal algorithms.
210 trees
This document discusses trees and binary trees. It covers tree definitions, properties, traversals, and common algorithms. Specifically for binary trees, it discusses their implementation, operations like insertion and deletion, and binary search trees. BSTs allow for efficient search, insertion, and deletion in O(h) time where h is the tree height. Balanced BSTs like red-black trees maintain h as O(log n) to guarantee efficient performance.
Similar to Review session2 (20)
Recently uploaded
A Survey of Techniques for Maximizing LLM Performance.pptx
A Survey of Techniques for Maximizing LLM Performance
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective UpskillingExcellence Foundation for South Sudan
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.How to Build a Module in Odoo 17 Using the Scaffold Method
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
The Diamonds of 2023-2024 in the IGRA collection
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
RPMS Template 2023-2024 by: Irene S. Rueco
PCOS corelations and management through Ayurveda.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Digital Artifact 1 - 10VCD Environments Unit
Digital Artifact 1 - 10VCD Environments Unit - NGV Pavilion Concept Design
Top five deadliest dog breeds in America
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...National Information Standards Organization (NISO)
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.Hindi varnamala | hindi alphabet PPT.pdf
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
How to Manage Your Lost Opportunities in Odoo 17 CRM
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama UniversityAkanksha trivedi rama nursing college kanpur.
Natural birth techniques are various type such as/ water birth , alexender method, hypnosis, bradley method, lamaze method etcRecently uploaded (20)
A Survey of Techniques for Maximizing LLM Performance.pptx
A Survey of Techniques for Maximizing LLM Performance.pptx
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective Upskilling
How to Build a Module in Odoo 17 Using the Scaffold Method
How to Build a Module in Odoo 17 Using the Scaffold Method
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Liberal Approach to the Study of Indian Politics.pdf
Liberal Approach to the Study of Indian Politics.pdf
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pride Month Slides 2024 David Douglas School District
Pride Month Slides 2024 David Douglas School District
How to Manage Your Lost Opportunities in Odoo 17 CRM
How to Manage Your Lost Opportunities in Odoo 17 CRM
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Review session2
- 1. Data Structures Review Session 2 Ramakrishna, PhD student. Grading Assistant for this course