Lecture 9: Binary tree basics
•Download as PPTX, PDF•
2 likes•807 views
Binary trees are data structures where every node has at most two children, a left and right child. A binary tree has a root node at the top and leaf nodes at the bottom with no children. The depth of a node is the length of the path from the root to that node, while the height is the length of the longest path between the root and a leaf plus one. Binary trees can be full, complete, or fully complete depending on how nodes are distributed. Common operations on binary trees include searching, summing nodes, and traversing them in inorder, preorder, or postorder sequences.
Report
Share
Report
Share
Recommended
tree in Data Structures
A tree is a hierarchical structure consisting of nodes connected by edges. It contains a finite set of nodes where each node stores data and links to successor nodes. The key components are the node, edge, parent, child, sibling, root, and leaf. The root is the top node, leaves have no children, and edges connect nodes in paths from the root to other nodes. Additional properties like level, degree, depth, and height further define the structure and relationships between nodes in a tree.
Binary tree
Trees are non-linear data structures that represent hierarchical relationships. They have elements called nodes connected by edges or branches. The root node has no parent, leaf nodes have no children, and sibling nodes share the same parent. Trees can be traversed in preorder, inorder, or postorder sequences by recursively processing nodes and their subtrees. Binary trees restrict nodes to having at most two children.
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.
TERMINOLOGIES OF TREE, TYPES OF TREE.pptx
A tree is a hierarchical data structure where data is organized in a parent-child relationship. A tree has a root node, edges that connect nodes, and terminologies like parent, child, sibling, leaf and internal nodes. There are different types of trees like binary trees where each node has at most two children, and traversal techniques like preorder, inorder and postorder traversal for depth first search and breadth first traversal for level order search.
Tree Basic concepts of Tree in Data Structure
A tree is a hierarchical data structure consisting of nodes connected by edges. The topmost node is the root node. Nodes can have child nodes below them in the hierarchy. Leaf nodes have no children. Binary trees restrict nodes to have at most two children. Different types of binary trees include full, perfect, degenerate, and skewed binary trees based on how nodes are structured. Trees are useful data structures that can represent hierarchical relationships.
Unit III.ppt
This document discusses trees and binary trees. It covers tree terminology like root, child, parent, leaf nodes, internal nodes, height, level. It explains different tree traversals like preorder, inorder, postorder traversal and level order traversal. It also discusses binary tree operations and implementations using linked nodes.
Introduction to tree ds
1. Trees are non-linear data structures that represent hierarchical relationships. They consist of nodes connected by edges, with one node designated as the root.
2. Trees can be represented using arrays or linked lists. Array representation requires knowing the height of the tree in advance, while linked lists do not waste memory but have slower access times.
3. Common tree terminology includes root, leaf nodes, internal nodes, siblings, ancestors, descendants, depth, height, and subtrees. Binary trees restrict nodes to at most two children each.
Unit 3 trees
Trees are hierarchical data structures used to represent data with parent-child relationships. A tree has a root node with child nodes, and each non-root node has one parent. Trees allow fast search, insertion, and deletion similar to linked lists but can also maintain ordering like arrays. Binary trees restrict nodes to at most two children, enabling efficient search, storage of expressions, and routing algorithms. Binary search trees organize nodes to enable fast lookups based on key values.
Recommended
tree in Data Structures
A tree is a hierarchical structure consisting of nodes connected by edges. It contains a finite set of nodes where each node stores data and links to successor nodes. The key components are the node, edge, parent, child, sibling, root, and leaf. The root is the top node, leaves have no children, and edges connect nodes in paths from the root to other nodes. Additional properties like level, degree, depth, and height further define the structure and relationships between nodes in a tree.
Binary tree
Trees are non-linear data structures that represent hierarchical relationships. They have elements called nodes connected by edges or branches. The root node has no parent, leaf nodes have no children, and sibling nodes share the same parent. Trees can be traversed in preorder, inorder, or postorder sequences by recursively processing nodes and their subtrees. Binary trees restrict nodes to having at most two children.
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.
TERMINOLOGIES OF TREE, TYPES OF TREE.pptx
A tree is a hierarchical data structure where data is organized in a parent-child relationship. A tree has a root node, edges that connect nodes, and terminologies like parent, child, sibling, leaf and internal nodes. There are different types of trees like binary trees where each node has at most two children, and traversal techniques like preorder, inorder and postorder traversal for depth first search and breadth first traversal for level order search.
Tree Basic concepts of Tree in Data Structure
A tree is a hierarchical data structure consisting of nodes connected by edges. The topmost node is the root node. Nodes can have child nodes below them in the hierarchy. Leaf nodes have no children. Binary trees restrict nodes to have at most two children. Different types of binary trees include full, perfect, degenerate, and skewed binary trees based on how nodes are structured. Trees are useful data structures that can represent hierarchical relationships.
Unit III.ppt
This document discusses trees and binary trees. It covers tree terminology like root, child, parent, leaf nodes, internal nodes, height, level. It explains different tree traversals like preorder, inorder, postorder traversal and level order traversal. It also discusses binary tree operations and implementations using linked nodes.
Introduction to tree ds
1. Trees are non-linear data structures that represent hierarchical relationships. They consist of nodes connected by edges, with one node designated as the root.
2. Trees can be represented using arrays or linked lists. Array representation requires knowing the height of the tree in advance, while linked lists do not waste memory but have slower access times.
3. Common tree terminology includes root, leaf nodes, internal nodes, siblings, ancestors, descendants, depth, height, and subtrees. Binary trees restrict nodes to at most two children each.
Unit 3 trees
Trees are hierarchical data structures used to represent data with parent-child relationships. A tree has a root node with child nodes, and each non-root node has one parent. Trees allow fast search, insertion, and deletion similar to linked lists but can also maintain ordering like arrays. Binary trees restrict nodes to at most two children, enabling efficient search, storage of expressions, and routing algorithms. Binary search trees organize nodes to enable fast lookups based on key values.
TREES34.pptx
The document defines key terms related to tree data structures including binary trees. It describes different types of binary trees such as full, complete, perfect, balanced, and degenerate binary trees. It also explains three common traversal techniques for binary trees: inorder, preorder, and postorder traversals.
Tree
Trees are fundamental data structures that combine advantages of ordered arrays and linked lists. They consist of nodes connected by edges, where nodes often represent entities and edges represent relationships between nodes. Common tree operations include traversing to visit all nodes in order, searching for a node by its key, and inserting or deleting nodes. These operations have O(log N) time complexity where N is the number of nodes. Binary trees restrict each node to at most two children while Huffman trees are used to compress data by assigning variable-length binary codes to characters based on their frequency.
Data structures 3
This document discusses various non-linear data structures including binary trees, binary search trees, and AVL trees. It provides definitions and examples of key tree concepts like nodes, leaves, paths, levels, and traversals. It also describes routines for operations on binary search trees like insertion, finding minimum/maximum, and deletion. For AVL trees, it explains how they differ from regular BSTs by restricting the height difference between subtrees to 1, and how rotations are used to rebalance the tree during insertions that cause imbalances.
Data structure(Part 2)
This document discusses different types of data structures including trees, binary search trees, and multidimensional arrays. It defines key tree terminology like root, child, parent, leaf nodes, and describes properties of binary search trees where the left subtree contains nodes with keys less than the parent node and the right subtree contains nodes with greater keys. It also provides an example of declaring and accessing elements in a two-dimensional array with rows and columns.
tree-160731205832.pptx
This document provides an introduction to trees as a non-linear data structure. It discusses key terms like root, parent, child, leaf nodes. It also covers different types of trees like binary trees and binary search trees. Tree traversal algorithms like preorder, inorder and postorder are explained along with pseudocode. Finally, it briefly discusses applications of trees and the Huffman algorithm for data compression.
Tree.pptx
This document discusses tree data structures and binary search trees. It begins by defining linear and non-linear data structures, with trees being a non-linear structure. It then defines general tree terminology like root, child, parent, leaves, etc. It describes tree traversal methods like preorder, inorder and postorder. It also discusses binary search trees and basic BST operations like insertion, deletion and searching. Finally, it discusses how BSTs can be used for sorting by inserting elements into a BST and then performing an inorder traversal.
Module - 5_Trees.pdf
The document discusses trees and graphs. It begins by defining a tree as a set of nodes where one node is the root and all other nodes can be partitioned into non-empty sub-trees. It then provides terminology for trees such as root node, sub-trees, leaf nodes, paths, ancestors, descendants, levels, and degrees. It describes different types of trees including general trees, forests, binary trees, expression trees, and tournament trees. It also covers linked and sequential representations of binary trees and traversing algorithms for binary trees including pre-order, in-order, post-order, and level-order traversals. Finally, it discusses applications of trees and binary search trees.
358 33 powerpoint-slides_10-trees_chapter-10
This document discusses binary trees and their representations and uses. It defines key terms related to binary trees like nodes, leaves, siblings, and levels. It describes different types of binary trees like complete and extended binary trees. It explains how to represent binary trees in memory using linked and sequential representations. It provides examples of how binary trees can be used to represent expressions and model tournaments. It also discusses algorithms for traversing binary trees in preorder, inorder and postorder sequences.
Binary tree
Tree representations can be list-based or use left child and right sibling pointers. Binary search trees store values such that all left descendants are less than the node value and all right descendants are greater. Common operations on binary search trees are search, insertion, and deletion through comparing node values, replacing nodes, or restructuring subtrees.
Tree
data structure notes for bca. only use for education purpose. it help u to understand data structure and its methods.
DSA-Unit-2.pptx
This document discusses data structures and algorithms related to trees and graphs. It begins by defining trees and describing their key properties like root, parent, child, leaf nodes, subtrees, levels, and tree traversal methods. It then discusses binary trees and their representations. The document also covers graph representations and key graph concepts like vertices, edges, paths, cycles, degrees. It describes traversal methods for graphs like depth first search and breadth first search. Finally, it discusses applications of trees and graphs in areas like routing, compression, and 3D graphics.
Unit 5 Tree.pptx
A tree data structure is a hierarchical structure where elements are arranged in multiple levels linked together through parent-child relationships. Each node contains data and links to other nodes as children. The topmost node is the root node, and nodes without children are leaf nodes. Tree structures are used to store hierarchical data efficiently and for operations like searching and sorting. Common tree types include binary trees, binary search trees, and AVL trees.
Tree 11.ppt
- A tree is a nonlinear hierarchical data structure that stores elements with parent-child relationships. Common examples include family trees, book tables of contents, and file system directories.
- Trees have nodes connected by edges, with one node designated as the root. Nodes have properties like parents, children, siblings, ancestors, descendants. A tree can be empty or have a root node with zero or more subtrees.
- Tree traversal algorithms like preorder, inorder, and postorder recursively visit each node by following different traversal orders. These are used to search, display, or evaluate information stored in the tree.
Tree
In computer science, a tree is a widely used abstract data type (ADT)—or data structure implementing this ADT—that simulates a hierarchical tree structure, with a root value and subtrees of children with a parent node, represented as a set of linked nodes.
Search tree,Tree and binary tree and heap tree
This document contains definitions and explanations of search trees and heaps. It defines search trees as data structures used for locating specific keys within a set where each node's key must be greater than keys in its left subtree and less than keys in its right subtree. It provides examples of constructing and performing operations on binary search trees like insertion, searching, and deletion. It defines heaps as binary trees that follow the shape and order property, with max heaps having each node greater than its children and min heaps having each node less than its children. It explains representing heaps in arrays and operations like deleting and inserting elements through reheapifying.
Final tree.ppt tells about tree presentation
This document defines and explains various tree data structures, including binary trees and binary search trees. It begins by defining a tree as a non-linear hierarchical data structure and lists key tree terminology such as root, parent, child, leaf nodes, and levels. It then explains different types of binary trees like complete, full, skewed, and expression trees. Various tree traversal methods including preorder, inorder and postorder are also defined. Finally, it provides the definition of a binary search tree as a binary tree where all left descendants of a node are less than or equal to the node and all right descendants are greater than or equal.
Trees
A tree is a hierarchical data structure where each node can have many children. It consists of a root node, edges that link parents to children, and leaf nodes without children. A binary tree restricts each node to having at most two children. Binary trees can be full, complete, or strict depending on how nodes are structured. Common operations on binary trees include inserting, deleting, searching, and traversing nodes. Binary trees have applications in compilers, data compression, search trees, and priority queues.
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 in data structure
Tree and graph data structures are used to represent hierarchical data. A tree has nodes connected by edges in a parent-child relationship where each node can have zero or more children but only one parent, forming a rooted tree. Common tree types include binary trees where each node has at most two children, binary search trees where the left child is less than the parent which is less than the right child, and B-trees where nodes can have multiple children. Trees are traversed using in-order, pre-order and post-order algorithms.
Tree
Trees are useful for representing hierarchically ordered data like directory structures and family trees. A tree is a collection of nodes connected by branches, with each node having at most one parent. It is defined recursively as a root node with zero or more subtrees. Binary trees restrict nodes to having at most two children. Trees can be traversed in preorder, inorder, or postorder. Binary search trees organize nodes to keep all left descendants less than the parent and all right descendants greater than the parent. Graphs generalize trees by allowing many-to-many relationships between nodes.
Lecture 11.2 : sorting
This document provides an overview of sorting algorithms including selection sort, bubble sort, insertion sort, merge sort, and heapsort. It discusses the time and space complexity of each algorithm, with merge sort having the best time complexity of O(n log n). Code examples and exercises are provided to help understand how each algorithm works. The goal is to help students learn common sorting techniques needed for coding interviews and problems.
Lecture 11.1 : heaps
This document discusses heaps and their implementation and applications. It begins by defining heaps as binary trees where the value of each parent node is less than its children, forming a min heap. It then discusses the key properties of heaps, including finding the minimum element in O(1) time. The document goes on to describe implementing a heap using an array and the basic operations of insertion, deletion, and heapsort in O(logn) time. It concludes by discussing the advantages of heaps for fast insertion and removal and their applications in priority queues and sorting.
More Related Content
Similar to Lecture 9: Binary tree basics
TREES34.pptx
The document defines key terms related to tree data structures including binary trees. It describes different types of binary trees such as full, complete, perfect, balanced, and degenerate binary trees. It also explains three common traversal techniques for binary trees: inorder, preorder, and postorder traversals.
Tree
Trees are fundamental data structures that combine advantages of ordered arrays and linked lists. They consist of nodes connected by edges, where nodes often represent entities and edges represent relationships between nodes. Common tree operations include traversing to visit all nodes in order, searching for a node by its key, and inserting or deleting nodes. These operations have O(log N) time complexity where N is the number of nodes. Binary trees restrict each node to at most two children while Huffman trees are used to compress data by assigning variable-length binary codes to characters based on their frequency.
Data structures 3
This document discusses various non-linear data structures including binary trees, binary search trees, and AVL trees. It provides definitions and examples of key tree concepts like nodes, leaves, paths, levels, and traversals. It also describes routines for operations on binary search trees like insertion, finding minimum/maximum, and deletion. For AVL trees, it explains how they differ from regular BSTs by restricting the height difference between subtrees to 1, and how rotations are used to rebalance the tree during insertions that cause imbalances.
Data structure(Part 2)
This document discusses different types of data structures including trees, binary search trees, and multidimensional arrays. It defines key tree terminology like root, child, parent, leaf nodes, and describes properties of binary search trees where the left subtree contains nodes with keys less than the parent node and the right subtree contains nodes with greater keys. It also provides an example of declaring and accessing elements in a two-dimensional array with rows and columns.
tree-160731205832.pptx
This document provides an introduction to trees as a non-linear data structure. It discusses key terms like root, parent, child, leaf nodes. It also covers different types of trees like binary trees and binary search trees. Tree traversal algorithms like preorder, inorder and postorder are explained along with pseudocode. Finally, it briefly discusses applications of trees and the Huffman algorithm for data compression.
Tree.pptx
This document discusses tree data structures and binary search trees. It begins by defining linear and non-linear data structures, with trees being a non-linear structure. It then defines general tree terminology like root, child, parent, leaves, etc. It describes tree traversal methods like preorder, inorder and postorder. It also discusses binary search trees and basic BST operations like insertion, deletion and searching. Finally, it discusses how BSTs can be used for sorting by inserting elements into a BST and then performing an inorder traversal.
Module - 5_Trees.pdf
The document discusses trees and graphs. It begins by defining a tree as a set of nodes where one node is the root and all other nodes can be partitioned into non-empty sub-trees. It then provides terminology for trees such as root node, sub-trees, leaf nodes, paths, ancestors, descendants, levels, and degrees. It describes different types of trees including general trees, forests, binary trees, expression trees, and tournament trees. It also covers linked and sequential representations of binary trees and traversing algorithms for binary trees including pre-order, in-order, post-order, and level-order traversals. Finally, it discusses applications of trees and binary search trees.
358 33 powerpoint-slides_10-trees_chapter-10
This document discusses binary trees and their representations and uses. It defines key terms related to binary trees like nodes, leaves, siblings, and levels. It describes different types of binary trees like complete and extended binary trees. It explains how to represent binary trees in memory using linked and sequential representations. It provides examples of how binary trees can be used to represent expressions and model tournaments. It also discusses algorithms for traversing binary trees in preorder, inorder and postorder sequences.
Binary tree
Tree representations can be list-based or use left child and right sibling pointers. Binary search trees store values such that all left descendants are less than the node value and all right descendants are greater. Common operations on binary search trees are search, insertion, and deletion through comparing node values, replacing nodes, or restructuring subtrees.
Tree
data structure notes for bca. only use for education purpose. it help u to understand data structure and its methods.
DSA-Unit-2.pptx
This document discusses data structures and algorithms related to trees and graphs. It begins by defining trees and describing their key properties like root, parent, child, leaf nodes, subtrees, levels, and tree traversal methods. It then discusses binary trees and their representations. The document also covers graph representations and key graph concepts like vertices, edges, paths, cycles, degrees. It describes traversal methods for graphs like depth first search and breadth first search. Finally, it discusses applications of trees and graphs in areas like routing, compression, and 3D graphics.
Unit 5 Tree.pptx
A tree data structure is a hierarchical structure where elements are arranged in multiple levels linked together through parent-child relationships. Each node contains data and links to other nodes as children. The topmost node is the root node, and nodes without children are leaf nodes. Tree structures are used to store hierarchical data efficiently and for operations like searching and sorting. Common tree types include binary trees, binary search trees, and AVL trees.
Tree 11.ppt
- A tree is a nonlinear hierarchical data structure that stores elements with parent-child relationships. Common examples include family trees, book tables of contents, and file system directories.
- Trees have nodes connected by edges, with one node designated as the root. Nodes have properties like parents, children, siblings, ancestors, descendants. A tree can be empty or have a root node with zero or more subtrees.
- Tree traversal algorithms like preorder, inorder, and postorder recursively visit each node by following different traversal orders. These are used to search, display, or evaluate information stored in the tree.
Tree
In computer science, a tree is a widely used abstract data type (ADT)—or data structure implementing this ADT—that simulates a hierarchical tree structure, with a root value and subtrees of children with a parent node, represented as a set of linked nodes.
Search tree,Tree and binary tree and heap tree
This document contains definitions and explanations of search trees and heaps. It defines search trees as data structures used for locating specific keys within a set where each node's key must be greater than keys in its left subtree and less than keys in its right subtree. It provides examples of constructing and performing operations on binary search trees like insertion, searching, and deletion. It defines heaps as binary trees that follow the shape and order property, with max heaps having each node greater than its children and min heaps having each node less than its children. It explains representing heaps in arrays and operations like deleting and inserting elements through reheapifying.
Final tree.ppt tells about tree presentation
This document defines and explains various tree data structures, including binary trees and binary search trees. It begins by defining a tree as a non-linear hierarchical data structure and lists key tree terminology such as root, parent, child, leaf nodes, and levels. It then explains different types of binary trees like complete, full, skewed, and expression trees. Various tree traversal methods including preorder, inorder and postorder are also defined. Finally, it provides the definition of a binary search tree as a binary tree where all left descendants of a node are less than or equal to the node and all right descendants are greater than or equal.
Trees
A tree is a hierarchical data structure where each node can have many children. It consists of a root node, edges that link parents to children, and leaf nodes without children. A binary tree restricts each node to having at most two children. Binary trees can be full, complete, or strict depending on how nodes are structured. Common operations on binary trees include inserting, deleting, searching, and traversing nodes. Binary trees have applications in compilers, data compression, search trees, and priority queues.
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 in data structure
Tree and graph data structures are used to represent hierarchical data. A tree has nodes connected by edges in a parent-child relationship where each node can have zero or more children but only one parent, forming a rooted tree. Common tree types include binary trees where each node has at most two children, binary search trees where the left child is less than the parent which is less than the right child, and B-trees where nodes can have multiple children. Trees are traversed using in-order, pre-order and post-order algorithms.
Tree
Trees are useful for representing hierarchically ordered data like directory structures and family trees. A tree is a collection of nodes connected by branches, with each node having at most one parent. It is defined recursively as a root node with zero or more subtrees. Binary trees restrict nodes to having at most two children. Trees can be traversed in preorder, inorder, or postorder. Binary search trees organize nodes to keep all left descendants less than the parent and all right descendants greater than the parent. Graphs generalize trees by allowing many-to-many relationships between nodes.
Similar to Lecture 9: Binary tree basics (20)
More from Vivek Bhargav
Lecture 11.2 : sorting
This document provides an overview of sorting algorithms including selection sort, bubble sort, insertion sort, merge sort, and heapsort. It discusses the time and space complexity of each algorithm, with merge sort having the best time complexity of O(n log n). Code examples and exercises are provided to help understand how each algorithm works. The goal is to help students learn common sorting techniques needed for coding interviews and problems.
Lecture 11.1 : heaps
This document discusses heaps and their implementation and applications. It begins by defining heaps as binary trees where the value of each parent node is less than its children, forming a min heap. It then discusses the key properties of heaps, including finding the minimum element in O(1) time. The document goes on to describe implementing a heap using an array and the basic operations of insertion, deletion, and heapsort in O(logn) time. It concludes by discussing the advantages of heaps for fast insertion and removal and their applications in priority queues and sorting.
Lecture 10 : trees - 2
This document provides examples of problems that can be solved using binary trees, including finding the maximum sum of a root-to-leaf path, finding the maximum leaf-to-leaf path, and performing a level order traversal. It also provides a link to a visualization of binary search trees.
Lecture 7 & 8: Stack & queue
The document discusses stacks and queues as data structures. It provides motivation for why data structure choice affects algorithm time complexity. It then covers implementation of stacks and queues using arrays and linked lists, including defining necessary functions like isEmpty, push, pop, and peek. Examples and steps are provided for implementing each with arrays and linked lists. The document also discusses using stacks and queues from the C++ STL and provides a problem to check for palindromes in a string using one pass and a stack or queue.
Lecture 6: linked list
This document provides an overview of linked lists in C++. It discusses how to represent a linked list node using a struct with a data field and pointer to the next node. It demonstrates how to traverse a linked list by accessing the data and next fields of each node. Several examples are provided for linked list operations like inserting and deleting nodes at different positions within the list.
Lecture 5: Asymptotic analysis of algorithms
The document discusses asymptotic analysis of algorithms to analyze efficiency in terms of time and space complexity. It explains that the number of basic operations like arithmetic, data movement, and control operations determines time complexity, while space complexity depends on the number of basic data types used. Different algorithms for counting numbers with factorial divisible by 5 are analyzed to find their time complexity. The time complexity of an algorithm can be expressed using Θ notation, which describes the dominant term as the input size increases.
Lecture 4: Functions
This document discusses functions in C/C++. It covers advantages of functions like modularity and reusability. It explains how to write functions, including return types, passing arguments by value or reference, and function prototypes. Examples demonstrate writing functions to find the maximum of two numbers, square a number, and convert characters to uppercase. The differences between call by value and call by reference are explained using examples. The document also discusses passing structures to functions and returning multiple values or values of different types from functions.
Lecture 3: Strings and Dynamic Memory Allocation
C++ strings are different from C strings and have additional functionality. Some key differences are that C++ strings can be initialized without needing strcpy, concatenated using +, and their length checked using length() or size(). Memory for C++ strings is automatically allocated and handled, unlike in C where memory must be manually allocated and managed.
Lecture 2: arrays and pointers
This document provides an introduction to arrays and pointers for beginner programmers. It explains what arrays and pointers are, how to declare and initialize them, and includes examples of common operations like accessing element values, taking addresses, and dereferencing pointers. Demo code is provided to illustrate summing array elements, finding maximum values, reversing an array, and printing two-dimensional arrays and pointers. The document aims to explain basic array and pointer concepts without advanced mathematics to help beginners learn programming fundamentals.
Lecture 1: basic syntax
This document provides an introduction to C++ programming for beginners. It covers installing C++, writing a "Hello World" program, using variables to store and manipulate data, if/else conditional statements, loops, and examples of programs to write that demonstrate various C++ concepts. The target audience is those with no prior coding experience who want to learn basic C++ skills quickly to help with job placement.
More from Vivek Bhargav (10)
Recently uploaded
Data Driven Maintenance | UReason Webinar
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Mechanical Engineering on AAI Summer Training Report-003.pdf
Mechanical Engineering PROJECT REPORT ON SUMMER VOCATIONAL TRAINING
AT MBB AIRPORT
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building
官方认证美国密歇根州立大学毕业证学位证书原版一模一样
原版一模一样【微信：741003700 】【美国密歇根州立大学毕业证学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Rainfall intensity duration frequency curve statistical analysis and modeling...
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
An improved modulation technique suitable for a three level flying capacitor ...
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
Unit-III-ELECTROCHEMICAL STORAGE DEVICES.ppt
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Advanced control scheme of doubly fed induction generator for wind turbine us...
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Software Engineering and Project Management - Introduction, Modeling Concepts...
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Recently uploaded (20)
Mechanical Engineering on AAI Summer Training Report-003.pdf
Mechanical Engineering on AAI Summer Training Report-003.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
Rainfall intensity duration frequency curve statistical analysis and modeling...
Rainfall intensity duration frequency curve statistical analysis and modeling...
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
An improved modulation technique suitable for a three level flying capacitor ...
An improved modulation technique suitable for a three level flying capacitor ...
Data Control Language.pptx Data Control Language.pptx
Data Control Language.pptx Data Control Language.pptx
Advanced control scheme of doubly fed induction generator for wind turbine us...
Advanced control scheme of doubly fed induction generator for wind turbine us...
Software Engineering and Project Management - Introduction, Modeling Concepts...
Software Engineering and Project Management - Introduction, Modeling Concepts...
Lecture 9: Binary tree basics
- 2. Binary Tree Definition • Every node has at most two children a left child and a right child. • Root - the top most node in a tree.
- 4. A Basic Tree Structure
- 5. Leaf • A node having no child is a leaf.
- 6. Depth of a node • The length of the path from the root to the node. • Depth(root)=0
- 7. Height of the tree • The height of a node is the length of the longest downward path between the node and a leaf + 1. • The height of a node is the length of the longest downward path between the root and a leaf +1.
- 9. Full Binary Tree • Every node has 2 children except the leaves.
- 10. Complete Binary Tree • A complete binary tree is a binary tree in which every level, except possibly the last, is completely filled, and all nodes are as far left as possible.
- 11. • A fully complete binary tree has n nodes what is the height of the tree?
- 12. • A fully complete binary tree has n nodes what are the number of leaves in the tree?
- 13. Implementation
- 14. Search an element in binary tree
- 15. Sum of all the nodes of the tree
- 16. Inorder traversal • Visit the left subtree first • Visit the node. • Visit the right subtree.
- 17. Code
- 18. Postorder traversal • Visit the left subtree first • Visit the right subtree • Visit the node.
- 19. Preorder traversal • Visit the node. • Visit the left subtree first • Visit the right subtree.
- 20. Vector vector<int> a; a.push_back(value); cout<<a[i]<<endl; Accessing the pushed back value similar to an array.(REST READ FROM NET)
- 21. Oj.leetcode.com https://oj.leetcode.com/problems/binary-tree-postorder- traversal/ https://oj.leetcode.com/problems/binary-tree-preorder- traversal/ https://oj.leetcode.com/problems/balanced-binary- tree/ https://oj.leetcode.com/problems/balanced-binary- tree/
- 22. • https://oj.leetcode.com/problems/binary-tree-level- order-traversal/ • https://oj.leetcode.com/problems/binary-tree-level- order-traversal/ • https://oj.leetcode.com/problems/binary-tree-inorder- traversal/ • https://oj.leetcode.com/problems/construct-binary- tree-from-inorder-and-postorder-traversal/ • https://oj.leetcode.com/problems/construct-binary- tree-from-preorder-and-inorder-traversal/