PPT On Sorting And Searching Concepts In Data Structure | In Programming Language |
Download as pptx, pdf2 likes4,293 views
The document discusses various sorting and searching algorithms: - Bubble sort, selection sort, merge sort, quicksort are sorting algorithms that arrange data in a particular order like numerical or alphabetical. - Linear search and binary search are searching algorithms where linear search sequentially checks each item while binary search divides the data set in half with each comparison. - Examples are provided to illustrate how each algorithm works step-by-step on sample data sets.
1 of 29
Downloaded 144 times
Ad
Recommended
Merge sort and quick sort
Merge sort and quick sortShakila Mahjabin The document discusses different sorting algorithms including merge sort and quicksort. Merge sort has a divide and conquer approach where an array is divided into halves and the halves are merged back together in sorted order. This results in a runtime of O(n log n). Quicksort uses a partitioning approach, choosing a pivot element and partitioning the array into subarrays of elements less than or greater than the pivot. In the best case, this partitions the array in half at each step, resulting in a runtime of O(n log n). In the average case, the runtime is also O(n log n). In the worst case, the array is already sorted, resulting in unbalanced partitions and a quadratic runtime of O(n^2
Sorting Algorithms
Sorting AlgorithmsMohammed Hussein This document discusses different sorting algorithms including bubble sort, insertion sort, and selection sort. It provides details on each algorithm, including time complexity, code examples, and graphical examples. Bubble sort is an O(n2) algorithm that works by repeatedly comparing and swapping adjacent elements. Insertion sort also has O(n2) time complexity but is more efficient than bubble sort for small or partially sorted lists. Selection sort finds the minimum value and swaps it into place at each step.
Bubble sort, Selection sort SORTING .pptx
Bubble sort, Selection sort SORTING .pptxKalpana Mohan This document discusses different types of sorting algorithms. It describes internal sorting, which sorts data within main memory, and external sorting, which sorts data that exceeds main memory size. It then explains three sorting algorithms - bubble sort, selection sort, and insertion sort. For each algorithm, it provides the basic steps/approach and includes Java code examples.
Sorting algorithms
Sorting algorithmsCHANDAN KUMAR This document provides an overview of sorting algorithms. It defines sorting as arranging data in a particular order like ascending or descending. Common sorting algorithms discussed include bubble sort, selection sort, insertion sort, merge sort, and quick sort. For each algorithm, the working method, implementation in C, time and space complexity is explained. The document also covers sorting terminology like stable vs unstable sorting and adaptive vs non-adaptive algorithms. Overall, the document serves as a comprehensive introduction to sorting and different sorting techniques.
Bucket sort
Bucket sortHossain Md Shakhawat The document describes the bucket sorting algorithm. It works by dividing the input range into buckets and distributing elements into the buckets based on their values. Each bucket is then sorted individually, usually with insertion sort, and concatenated together in order. The time complexity is O(n) when the number of buckets k is Θ(n), as distributing elements takes O(n) time, sorting each bucket takes O(n log(n/k)) time, and concatenating takes O(k) time. Bucket sort assumes uniform distribution of input elements across the range.
Quick sort
Quick sortJehat Hassan The document provides an overview of the quick sort algorithm through diagrams and explanations. It begins by introducing quick sort and stating that it is one of the fastest sorting algorithms because it runs in O(n log n) time and uses less memory than other algorithms like merge sort. It then provides step-by-step examples to demonstrate how quick sort works by picking a pivot element, partitioning the array around the pivot, and recursively sorting the subarrays. The summary concludes by restating that quick sort is an efficient sorting algorithm due to its speed and memory usage.
Quick sort
Quick sortamar kakde Quicksort is a divide and conquer algorithm that works by partitioning an array around a pivot value and recursively sorting the sub-partitions. It first chooses a pivot element and partitions the array by placing all elements less than the pivot before it and all elements greater than it after it. It then recursively quicksorts the two partitions. This continues until the individual partitions only contain single elements at which point they are sorted. Quicksort has average case performance of O(n log n) time making it very efficient for large data sets.
Shell sorting
Shell sortingTUC Shell sort is a generalization of insertion sort that improves efficiency by allowing exchanges of elements far apart. It works by sorting arrays with increasingly smaller increments or gaps between elements, starting with the largest possible gap and reducing it until a gap of 1 is reached, at which point the list will be fully sorted. The algorithm avoids large shifts compared to insertion sort by first sorting sublists with far-apart elements to put items in nearly sorted order before switching to adjacent elements.
Insertion sort
Insertion sortalmaqboli Insertion sort works by iterating through an array, inserting each element into its sorted position by shifting other elements over. It finds the location where each element should be inserted into the sorted portion using a linear search, moving larger elements out of the way to make room. This sorting algorithm is most effective for small data sets and can be implemented recursively or iteratively through comparisons and shifts.
SEARCHING AND SORTING ALGORITHMS
SEARCHING AND SORTING ALGORITHMSGokul Hari The document discusses various searching and sorting algorithms. It describes linear search, binary search, and interpolation search for searching unsorted and sorted lists. It also explains different sorting algorithms like bubble sort, selection sort, insertion sort, quicksort, shellsort, heap sort, and merge sort. Linear search searches sequentially while binary search uses divide and conquer. Sorting algorithms like bubble sort, selection sort, and insertion sort are in-place and have quadratic time complexity in the worst case. Quicksort, mergesort, and heapsort generally have better performance.
Binary Search
Binary Searchkunj desai This document describes binary search and provides an example of how it works. It begins with an introduction to binary search, noting that it can only be used on sorted lists and involves comparing the search key to the middle element. It then provides pseudocode for the binary search algorithm. The document analyzes the time complexity of binary search as O(log n) in the average and worst cases. It notes the advantages of binary search are its efficiency, while the disadvantage is that the list must be sorted. Applications mentioned include database searching and solving equations.
Chapter 11 - Sorting and Searching
Chapter 11 - Sorting and SearchingEduardo Bergavera The document discusses various searching and sorting algorithms. It describes linear search, binary search, selection sort, bubble sort, and heapsort. For each algorithm, it provides pseudocode examples and analyzes their performance in terms of number of comparisons required in the worst case. Linear search requires N comparisons in the worst case, while binary search requires log N comparisons. Selection sort and bubble sort both require approximately N^2 comparisons, while heapsort requires 1.5NlogN comparisons.
Quick Sort
Quick SortShweta Sahu The document discusses sorting algorithms and randomized quicksort. It explains that quicksort is an efficient sorting algorithm that was developed by Tony Hoare in 1960. The quicksort algorithm works by picking a pivot element and reordering the array so that all smaller elements come before the pivot and larger elements come after. It then recursively applies this process to the subarrays. Randomized quicksort improves upon quicksort by choosing the pivot element randomly, making the expected performance of the algorithm good for any input.
Topological Sorting
Topological SortingShahDhruv21 The document provides an overview of topological sorting in directed acyclic graphs (DAGs), defining it as an ordering of vertices such that for every directed edge (u, v), vertex u comes before v. It includes examples of topological sorts and outlines algorithms for identifying node order based on their in-degrees. Additionally, the document mentions applications of topological sorting in build systems, software installation in Linux, task scheduling, and managing prerequisites in educational contexts.
Binary search
Binary searchAparnaKumari31 Binary search is an algorithm that finds the position of a target value within a sorted array. It works by recursively dividing the array range in half and searching only within the appropriate half. The time complexity is O(log n) in the average and worst cases and O(1) in the best case, making it very efficient for searching sorted data. However, it requires the list to be sorted for it to work.
Insertion sort
Insertion sort Monalisa Patel This document discusses insertion sort, including its mechanism, algorithm, runtime analysis, advantages, and disadvantages. Insertion sort works by iterating through an unsorted array and inserting each element into its sorted position by shifting other elements over. Its worst case runtime is O(n^2) when the array is reverse sorted, but it performs well on small, nearly sorted lists. While simple to implement, insertion sort is inefficient for large datasets compared to other algorithms.
Searching and Sorting Techniques in Data Structure
Searching and Sorting Techniques in Data StructureBalwant Gorad The document outlines a course on data structures taught by Mr. B.J. Gorad at the Sharad Institute of Technology, focusing on C programming, searching algorithms (linear and binary search), and sorting algorithms (bubble sort, insertion sort, selection sort, merge sort). It describes key course outcomes including understanding various data structures and selecting appropriate algorithms for applications. Additionally, the document compares linear and binary searching techniques, detailing their efficiencies and performances in terms of time complexity.
Binary Search - Design & Analysis of Algorithms
Binary Search - Design & Analysis of AlgorithmsDrishti Bhalla Binary search is an efficient algorithm for finding a target value within a sorted array. It works by repeatedly dividing the search range in half and checking the value at the midpoint. This eliminates about half of the remaining candidates in each step. The maximum number of comparisons needed is log n, where n is the number of elements. This makes binary search faster than linear search, which requires checking every element. The algorithm works by first finding the middle element, then checking if it matches the target. If not, it recursively searches either the lower or upper half depending on if the target is less than or greater than the middle element.
Linear search-and-binary-search
Linear search-and-binary-searchInternational Islamic University This document provides an overview of linear search and binary search algorithms.
It explains that linear search sequentially searches through an array one element at a time to find a target value. It is simple to implement but has poor efficiency as the time scales linearly with the size of the input.
Binary search is more efficient by cutting the search space in half at each step. It works on a sorted array by comparing the target to the middle element and determining which half to search next. The time complexity of binary search is logarithmic rather than linear.
Searching
SearchingAshim Lamichhane The document discusses search algorithms, distinguishing between linear (sequential) and binary search methods, alongside their respective terminologies and performance characteristics. It describes how linear search is applicable to both sorted and unsorted lists, while binary search requires a sorted list for efficient searching. Additionally, it introduces hashing as a method to expedite searching through the use of hash functions and addresses collision resolution techniques.
linear search and binary search
linear search and binary searchZia Ush Shamszaman The document outlines a lecture on search algorithms presented by Dr. Zia Ush Shamszaman, covering linear and binary search techniques. Linear search sequentially checks elements until a match is found, while binary search efficiently narrows down the search space in a sorted array by halving it at each step. The lecture also includes pseudocode for both algorithms along with their advantages and disadvantages.
Queues
QueuesAshim Lamichhane The document provides a comprehensive overview of queues in computer science, highlighting their definition as FIFO (first in, first out) structures where insertion occurs at the rear and deletion at the front. It details various operations associated with queues, such as enqueue and dequeue, and discusses both linear and circular queue implementations along with their advantages and disadvantages. Additionally, it outlines practical applications of queues in real-world scenarios like task scheduling and handling requests in call centers.
Stacks IN DATA STRUCTURES
Stacks IN DATA STRUCTURESSowmya Jyothi 1) Stacks are linear data structures that follow the LIFO (last-in, first-out) principle. Elements can only be inserted or removed from one end called the top of the stack.
2) The basic stack operations are push, which adds an element to the top of the stack, and pop, which removes an element from the top.
3) Stacks have many applications including evaluating arithmetic expressions by converting them to postfix notation and implementing the backtracking technique in recursive backtracking problems like tower of Hanoi.
single linked list
single linked listSathasivam Rangasamy This document discusses the implementation of a single linked list data structure. It describes the nodes that make up a linked list, which have an info field to store data and a next field pointing to the next node. The document outlines different ways to represent linked lists, including static arrays and dynamic pointers. It also provides algorithms for common linked list operations like traversing, inserting, and deleting nodes from the beginning, end, or a specified position within the list.
Array data structure
Array data structuremaamir farooq An array is a data structure that stores fixed number of items of the same type. It allows fast access of elements using indices. Basic array operations include traversing elements, inserting/deleting elements, searching for elements, and updating elements. Arrays are zero-indexed and elements are accessed via their index.
Searching techniques in Data Structure And Algorithm
Searching techniques in Data Structure And Algorithm03446940736 The document discusses searching techniques in data structures, focusing on linear, binary, and interpolation searches. Linear search sequentially checks each item, while binary search uses a divide and conquer approach requiring sorted data for faster lookup. Interpolation search is an optimized variant of binary search that probes positions based on value distribution, offering significant efficiency advantages over linear search.
sorting and its types
sorting and its typesSIVASHANKARIRAJAN This document discusses different types of sorting algorithms. It describes internal sorting and external sorting, with internal sorting handling all data in memory and external sorting requiring external memory. Bubble sort, selection sort, and insertion sort are briefly explained as examples of sorting methods. Bubble sort works by comparing adjacent elements and swapping if out of order, selection sort finds the minimum element and selection sort inserts elements into the sorted position. Pseudocode and examples are provided for each algorithm.
Circular link list.ppt
Circular link list.pptTirthika Bandi This document provides information on circular linked lists including:
- Circular linked lists have the last element point to the first element, allowing traversal of the list to repeat indefinitely.
- Both singly and doubly linked lists can be made circular. Circular lists are useful for applications that require repeated traversal.
- Types of circular lists include singly circular (one link between nodes) and doubly circular (two links between nodes).
- Operations like insertion, deletion, and display can be performed on circular lists similarly to linear lists with some adjustments for the circular nature.
Algorithm & data structures lec4&5
Algorithm & data structures lec4&5Abdul Khan The document summarizes different searching and sorting algorithms. It discusses linear search and binary search for searching algorithms. It explains that linear search has O(n) time complexity while binary search has O(log n) time complexity. For sorting algorithms, it describes bubble sort, selection sort, and insertion sort. It provides pseudocode to illustrate how each algorithm works to sort a list or array.
Searching and Sorting algorithms and working
Searching and Sorting algorithms and workingRitikaLohiya2 The document provides an overview of various searching and sorting algorithms in computer science, including linear and binary search for finding elements, as well as selection sort, bubble sort, and quick sort for organizing data. Each algorithm is explained with its methodology, complexities, and examples to illustrate the processes. Overall, it emphasizes the differences in efficiency and applicability of each algorithm, especially in relation to the size of the dataset.
More Related Content
What's hot (20)
Insertion sort
Insertion sortalmaqboli Insertion sort works by iterating through an array, inserting each element into its sorted position by shifting other elements over. It finds the location where each element should be inserted into the sorted portion using a linear search, moving larger elements out of the way to make room. This sorting algorithm is most effective for small data sets and can be implemented recursively or iteratively through comparisons and shifts.
SEARCHING AND SORTING ALGORITHMS
SEARCHING AND SORTING ALGORITHMSGokul Hari The document discusses various searching and sorting algorithms. It describes linear search, binary search, and interpolation search for searching unsorted and sorted lists. It also explains different sorting algorithms like bubble sort, selection sort, insertion sort, quicksort, shellsort, heap sort, and merge sort. Linear search searches sequentially while binary search uses divide and conquer. Sorting algorithms like bubble sort, selection sort, and insertion sort are in-place and have quadratic time complexity in the worst case. Quicksort, mergesort, and heapsort generally have better performance.
Binary Search
Binary Searchkunj desai This document describes binary search and provides an example of how it works. It begins with an introduction to binary search, noting that it can only be used on sorted lists and involves comparing the search key to the middle element. It then provides pseudocode for the binary search algorithm. The document analyzes the time complexity of binary search as O(log n) in the average and worst cases. It notes the advantages of binary search are its efficiency, while the disadvantage is that the list must be sorted. Applications mentioned include database searching and solving equations.
Chapter 11 - Sorting and Searching
Chapter 11 - Sorting and SearchingEduardo Bergavera The document discusses various searching and sorting algorithms. It describes linear search, binary search, selection sort, bubble sort, and heapsort. For each algorithm, it provides pseudocode examples and analyzes their performance in terms of number of comparisons required in the worst case. Linear search requires N comparisons in the worst case, while binary search requires log N comparisons. Selection sort and bubble sort both require approximately N^2 comparisons, while heapsort requires 1.5NlogN comparisons.
Quick Sort
Quick SortShweta Sahu The document discusses sorting algorithms and randomized quicksort. It explains that quicksort is an efficient sorting algorithm that was developed by Tony Hoare in 1960. The quicksort algorithm works by picking a pivot element and reordering the array so that all smaller elements come before the pivot and larger elements come after. It then recursively applies this process to the subarrays. Randomized quicksort improves upon quicksort by choosing the pivot element randomly, making the expected performance of the algorithm good for any input.
Topological Sorting
Topological SortingShahDhruv21 The document provides an overview of topological sorting in directed acyclic graphs (DAGs), defining it as an ordering of vertices such that for every directed edge (u, v), vertex u comes before v. It includes examples of topological sorts and outlines algorithms for identifying node order based on their in-degrees. Additionally, the document mentions applications of topological sorting in build systems, software installation in Linux, task scheduling, and managing prerequisites in educational contexts.
Binary search
Binary searchAparnaKumari31 Binary search is an algorithm that finds the position of a target value within a sorted array. It works by recursively dividing the array range in half and searching only within the appropriate half. The time complexity is O(log n) in the average and worst cases and O(1) in the best case, making it very efficient for searching sorted data. However, it requires the list to be sorted for it to work.
Insertion sort
Insertion sort Monalisa Patel This document discusses insertion sort, including its mechanism, algorithm, runtime analysis, advantages, and disadvantages. Insertion sort works by iterating through an unsorted array and inserting each element into its sorted position by shifting other elements over. Its worst case runtime is O(n^2) when the array is reverse sorted, but it performs well on small, nearly sorted lists. While simple to implement, insertion sort is inefficient for large datasets compared to other algorithms.
Searching and Sorting Techniques in Data Structure
Searching and Sorting Techniques in Data StructureBalwant Gorad The document outlines a course on data structures taught by Mr. B.J. Gorad at the Sharad Institute of Technology, focusing on C programming, searching algorithms (linear and binary search), and sorting algorithms (bubble sort, insertion sort, selection sort, merge sort). It describes key course outcomes including understanding various data structures and selecting appropriate algorithms for applications. Additionally, the document compares linear and binary searching techniques, detailing their efficiencies and performances in terms of time complexity.
Binary Search - Design & Analysis of Algorithms
Binary Search - Design & Analysis of AlgorithmsDrishti Bhalla Binary search is an efficient algorithm for finding a target value within a sorted array. It works by repeatedly dividing the search range in half and checking the value at the midpoint. This eliminates about half of the remaining candidates in each step. The maximum number of comparisons needed is log n, where n is the number of elements. This makes binary search faster than linear search, which requires checking every element. The algorithm works by first finding the middle element, then checking if it matches the target. If not, it recursively searches either the lower or upper half depending on if the target is less than or greater than the middle element.
Linear search-and-binary-search
Linear search-and-binary-searchInternational Islamic University This document provides an overview of linear search and binary search algorithms.
It explains that linear search sequentially searches through an array one element at a time to find a target value. It is simple to implement but has poor efficiency as the time scales linearly with the size of the input.
Binary search is more efficient by cutting the search space in half at each step. It works on a sorted array by comparing the target to the middle element and determining which half to search next. The time complexity of binary search is logarithmic rather than linear.
Searching
SearchingAshim Lamichhane The document discusses search algorithms, distinguishing between linear (sequential) and binary search methods, alongside their respective terminologies and performance characteristics. It describes how linear search is applicable to both sorted and unsorted lists, while binary search requires a sorted list for efficient searching. Additionally, it introduces hashing as a method to expedite searching through the use of hash functions and addresses collision resolution techniques.
linear search and binary search
linear search and binary searchZia Ush Shamszaman The document outlines a lecture on search algorithms presented by Dr. Zia Ush Shamszaman, covering linear and binary search techniques. Linear search sequentially checks elements until a match is found, while binary search efficiently narrows down the search space in a sorted array by halving it at each step. The lecture also includes pseudocode for both algorithms along with their advantages and disadvantages.
Queues
QueuesAshim Lamichhane The document provides a comprehensive overview of queues in computer science, highlighting their definition as FIFO (first in, first out) structures where insertion occurs at the rear and deletion at the front. It details various operations associated with queues, such as enqueue and dequeue, and discusses both linear and circular queue implementations along with their advantages and disadvantages. Additionally, it outlines practical applications of queues in real-world scenarios like task scheduling and handling requests in call centers.
Stacks IN DATA STRUCTURES
Stacks IN DATA STRUCTURESSowmya Jyothi 1) Stacks are linear data structures that follow the LIFO (last-in, first-out) principle. Elements can only be inserted or removed from one end called the top of the stack.
2) The basic stack operations are push, which adds an element to the top of the stack, and pop, which removes an element from the top.
3) Stacks have many applications including evaluating arithmetic expressions by converting them to postfix notation and implementing the backtracking technique in recursive backtracking problems like tower of Hanoi.
single linked list
single linked listSathasivam Rangasamy This document discusses the implementation of a single linked list data structure. It describes the nodes that make up a linked list, which have an info field to store data and a next field pointing to the next node. The document outlines different ways to represent linked lists, including static arrays and dynamic pointers. It also provides algorithms for common linked list operations like traversing, inserting, and deleting nodes from the beginning, end, or a specified position within the list.
Array data structure
Array data structuremaamir farooq An array is a data structure that stores fixed number of items of the same type. It allows fast access of elements using indices. Basic array operations include traversing elements, inserting/deleting elements, searching for elements, and updating elements. Arrays are zero-indexed and elements are accessed via their index.
Searching techniques in Data Structure And Algorithm
Searching techniques in Data Structure And Algorithm03446940736 The document discusses searching techniques in data structures, focusing on linear, binary, and interpolation searches. Linear search sequentially checks each item, while binary search uses a divide and conquer approach requiring sorted data for faster lookup. Interpolation search is an optimized variant of binary search that probes positions based on value distribution, offering significant efficiency advantages over linear search.
sorting and its types
sorting and its typesSIVASHANKARIRAJAN This document discusses different types of sorting algorithms. It describes internal sorting and external sorting, with internal sorting handling all data in memory and external sorting requiring external memory. Bubble sort, selection sort, and insertion sort are briefly explained as examples of sorting methods. Bubble sort works by comparing adjacent elements and swapping if out of order, selection sort finds the minimum element and selection sort inserts elements into the sorted position. Pseudocode and examples are provided for each algorithm.
Circular link list.ppt
Circular link list.pptTirthika Bandi This document provides information on circular linked lists including:
- Circular linked lists have the last element point to the first element, allowing traversal of the list to repeat indefinitely.
- Both singly and doubly linked lists can be made circular. Circular lists are useful for applications that require repeated traversal.
- Types of circular lists include singly circular (one link between nodes) and doubly circular (two links between nodes).
- Operations like insertion, deletion, and display can be performed on circular lists similarly to linear lists with some adjustments for the circular nature.
Similar to PPT On Sorting And Searching Concepts In Data Structure | In Programming Language | (20)
Algorithm & data structures lec4&5
Algorithm & data structures lec4&5Abdul Khan The document summarizes different searching and sorting algorithms. It discusses linear search and binary search for searching algorithms. It explains that linear search has O(n) time complexity while binary search has O(log n) time complexity. For sorting algorithms, it describes bubble sort, selection sort, and insertion sort. It provides pseudocode to illustrate how each algorithm works to sort a list or array.
Searching and Sorting algorithms and working
Searching and Sorting algorithms and workingRitikaLohiya2 The document provides an overview of various searching and sorting algorithms in computer science, including linear and binary search for finding elements, as well as selection sort, bubble sort, and quick sort for organizing data. Each algorithm is explained with its methodology, complexities, and examples to illustrate the processes. Overall, it emphasizes the differences in efficiency and applicability of each algorithm, especially in relation to the size of the dataset.
module2_dIVIDEncONQUER_2022.pdf
module2_dIVIDEncONQUER_2022.pdfShiwani Gupta The document describes various divide and conquer algorithms including binary search, merge sort, quicksort, and finding maximum and minimum elements. It begins by explaining the general divide and conquer approach of dividing a problem into smaller subproblems, solving the subproblems independently, and combining the solutions. Several examples are then provided with pseudocode and analysis of their divide and conquer implementations. Key algorithms covered in the document include binary search (log n time), merge sort (n log n time), and quicksort (n log n time on average).
Module 2_ Divide and Conquer Approach.pptx
Module 2_ Divide and Conquer Approach.pptxnikshaikh786 The document describes the divide and conquer approach and analyzes the complexity of several divide and conquer algorithms, including binary search, merge sort, quicksort, and finding minimum and maximum values. It explains the general divide and conquer method involves three steps: 1) divide the problem into subproblems, 2) solve the subproblems, and 3) combine the solutions to solve the original problem. It then provides detailed explanations and complexity analyses of specific divide and conquer algorithms.
Data structure using c module 3
Data structure using c module 3smruti sarangi The document provides an overview of data structures and algorithms in 'C', focusing on searching, sorting, and hashing techniques. It details searching methods like linear and binary search, their algorithms, and various sorting techniques such as selection sort, bubble sort, and merge sort, along with their corresponding algorithms. Additionally, it discusses hashing concepts such as hash tables, hash functions, and collision resolution strategies.
Data operatons & searching and sorting algorithms
Data operatons & searching and sorting algorithmsAnushdika Jeganathan This document discusses and compares various sorting and searching algorithms. It begins with an introduction to algorithm analysis and complexity measures like time and space complexity. It then defines sorting as arranging data in a particular order and searching as finding a particular element. Common sorting algorithms described include bubble sort, insertion sort, bucket sort, selection sort, heap sort, and merge sort. Searching algorithms covered are linear search and binary search. The document provides examples and pseudocode to explain how each algorithm works. It concludes that merge sort and bubble sort would be suitable for real-world scenarios based on their properties.
AJisthewewrtyuiojhghfdfsgvhjhklopi87ytrytfghjk
AJisthewewrtyuiojhghfdfsgvhjhklopi87ytrytfghjkPradipTadme The document discusses various searching and sorting algorithms in computer science, including linear and binary search, as well as multiple sorting algorithms such as selection sort, bubble sort, quick sort, and merge sort. It provides descriptions, algorithms, and examples for each method. Each algorithm is analyzed in terms of efficiency and complexity, highlighting their suitability for different data set sizes.
Weak 11-12 Sorting update.pptxbhjiiuuuuu
Weak 11-12 Sorting update.pptxbhjiiuuuuubaloch4551701 The document discusses several elementary sorting algorithms including selection sort, insertion sort, and bubble sort, detailing their mechanics and time complexities. It introduces more efficient algorithms like merge sort and quicksort, explaining their divide and conquer strategies along with their implementation specifics. The overall emphasis is on understanding the complexity and efficiency of various sorting methods.
Searching
SearchingMuhammad Farhan The document discusses two search algorithms: linear search and binary search. Linear search sequentially scans an array to find a value, while binary search divides the array in half at each step to quickly locate a value in a sorted array. It provides pseudocode for both algorithms and compares their benefits, with linear search being simpler but less efficient than binary search for large data sets.
Merge sort analysis and its real time applications
Merge sort analysis and its real time applicationsyazad dumasia The document provides an analysis of the merge sort algorithm and its applications in real-time. It begins with introducing sorting and different sorting techniques. Then it describes the merge sort algorithm, explaining the divide, conquer, and combine steps. It analyzes the time complexity of merge sort, showing that it has O(n log n) runtime. Finally, it discusses some real-time applications of merge sort, such as recommending similar products to users on e-commerce websites based on purchase history.
sorting and searching.pptx
sorting and searching.pptxParagAhir1 The document discusses various searching and sorting algorithms including linear/sequential search, binary search, selection sort, bubble sort, insertion sort, quick sort, and merge sort. It provides descriptions of each algorithm and examples to illustrate how they work on sample data sets. Key steps and properties of each algorithm are outlined such as complexity, how elements are compared and swapped during sorting, and dividing arrays during searching.
All Searching and Sorting Techniques in Data Structures
All Searching and Sorting Techniques in Data Structuressonalishinge2015 This ppt shows all the searching and sorting techniques in data structures.
SORT AND SEARCH ARRAY WITH WITH C++.pptx
SORT AND SEARCH ARRAY WITH WITH C++.pptxnarifmsit18seecs The document discusses various searching algorithms, including linear search, binary search, and interpolation search, explaining their algorithms, time complexities, and how they work on sorted and unsorted data. It also covers the merge sort algorithm, highlighting its divide-and-conquer approach, time complexities in different scenarios, and the benefits and drawbacks of using merge sort. Additionally, the document illustrates how these algorithms can be implemented using pseudocode and provides examples for better understanding.
DIVIDE AND CONQUERMETHOD IN DATASTRUCTURE.pptx
DIVIDE AND CONQUERMETHOD IN DATASTRUCTURE.pptxLakshmiSamivel The document discusses the divide and conquer algorithm and provides examples of algorithms that use this approach, including merge sort, quicksort, and binary search. It explains that divide and conquer works by dividing a problem into smaller subproblems, solving those subproblems recursively, and then combining the solutions to solve the original problem. Specific steps and pseudocode are provided for merge sort, quicksort, and binary search to illustrate how they apply the divide and conquer strategy.
Sorting
Sortinginvertis university The document provides an overview of sorting algorithms, emphasizing their classification based on criteria such as computational complexity, memory usage, and internal methods. It further details specific algorithms like quicksort and mergesort, explaining their mechanisms and processes. Efficient sorting is highlighted as a crucial step to optimize other algorithms and solve various problems.
Sorting Algorithms to arrange data in particular format
Sorting Algorithms to arrange data in particular formatitsusamazahid The document discusses various sorting algorithms, including their types, techniques, and efficiencies. It highlights in-place vs not-in-place sorting, adaptive vs non-adaptive sorting, and provides detailed explanations of algorithms like selection sort, bubble sort, insertion sort, merge sort, and quick sort. Each algorithm's method of operation, complexity, and implementation steps are outlined for better understanding.
advanced searching and sorting.pdf
advanced searching and sorting.pdfharamaya university The document discusses fundamental data structures in C, focusing on searching and sorting algorithms, namely linear and binary search. Linear search is simple and effective for unsorted arrays with a time complexity of O(n), while binary search is efficient for sorted arrays with a complexity of O(log n). It also covers various sorting techniques such as bubble sort, selection sort, and insertion sort, explaining their methodologies and complexities.
Bs,qs,divide and conquer 1
Bs,qs,divide and conquer 1subhashchandra197 The document discusses the divide-and-conquer algorithmic paradigm. It provides examples of binary search and quicksort, which both use the divide-and-conquer approach. It explains the three steps of divide-and-conquer as dividing the problem into subproblems, conquering the subproblems by solving them recursively, and combining the solutions to solve the original problem. Details are given on implementing binary search and quicksort, including pseudocode and analysis of their time complexities in best, worst and average cases.
Ad
More from Umesh Kumar (7)
Payroll System in india
Payroll System in india Umesh Kumar The document details the payroll process in India, highlighting its importance for employee satisfaction and legal compliance. It outlines the stages of payroll processing, including pre-payroll, actual payroll, and post-payroll activities, while emphasizing the need for accurate data collection and adherence to statutory regulations. Additionally, it compares various methods for managing payroll, such as using Excel, outsourcing, or employing specialized payroll software, underscoring the benefits of automation in ensuring efficient payroll management.
What Is Robotics & It's Simulation ?
What Is Robotics & It's Simulation ?Umesh Kumar Robotics is a branch of engineering that involves designing, building, and programming machines to perform tasks automatically. Robots are used in dangerous environments like bomb detection. The term "robotics" was first coined by science fiction author Isaac Asimov in the 1940s, derived from the word "robot" introduced by Karel Čapek in 1920, which comes from the Slavic word for labor. Fully autonomous robots emerged in the second half of the 20th century, beginning with the first digitally operated and programmable robot installed in 1961 to handle hot metal parts.
PPT On INTERNET
PPT On INTERNETUmesh Kumar The Internet is a global system of interconnected computer networks that use TCP/IP protocols to link devices worldwide. It consists of private, public, academic, business and government networks connected through various networking technologies. The Internet carries a wide range of information and services including the World Wide Web, email, telephony, and file sharing. It originated from the U.S. government's ARPANET and was later brought to the public on a larger scale in the 1990s. The most widely used part is the World Wide Web, which allows instant cross-referencing between hyperlinked documents through a system of web browsers.
PPT On MS-Access 2007 | Full Concepts |
PPT On MS-Access 2007 | Full Concepts |Umesh Kumar Microsoft Access is a database management system that allows information to be stored, organized, and manipulated in tables. Information is held in tables that can be linked, and specific information can be retrieved by running queries. Results of queries and organized data can be presented using reports and forms provide easy input of data and criteria for queries.
PPT On Ports | Full Concept | Ports In Depth
PPT On Ports | Full Concept | Ports In DepthUmesh Kumar The document discusses different types of ports that are used to connect external devices to computers. It describes ports for connecting modems, printers, mice and other peripherals. Common ports mentioned include serial ports, parallel ports, PS/2 ports, FireWire ports, Ethernet ports, USB ports, video graphics array (VGA) ports, power ports and audio ports. External devices connect to computers using cables and ports, which are slots on the motherboard.
PPT On Microsoft Excel 2007 Full Information.
PPT On Microsoft Excel 2007 Full Information.Umesh Kumar Microsoft Excel is a powerful tool used to create and format spreadsheets. Spreadsheets allow information to be organized in rows and columns and analyzed with automatic calculations. In Excel, workbooks contain worksheets which are made up of cells organized into rows and columns. Cells can contain numbers, text, formulas, and more. Formulas allow calculations to be performed on cell values and functions pre-written formulas that perform common calculations. Charts can be inserted to visually represent spreadsheet data.
PPT On Computer Generations
PPT On Computer GenerationsUmesh Kumar The document summarizes the five generations of computers based on the technologies used. The first generation used vacuum tubes and were very large, expensive machines that processed data in batches. The second generation used transistors which made computers smaller, faster, and more reliable. The third generation saw the introduction of integrated circuits which further miniaturized computers. The fourth generation began using microprocessors which made computers portable and introduced personal computers and the internet. The fifth generation is working on developing true artificial intelligence and natural language processing.
Ad
Recently uploaded (20)
UserCon Belgium: Honey, VMware increased my bill
UserCon Belgium: Honey, VMware increased my billstijn40 VMware’s pricing changes have forced organizations to rethink their datacenter cost management strategies. While FinOps is commonly associated with cloud environments, the FinOps Foundation has recently expanded its framework to include Scopes—and Datacenter is now officially part of the equation. In this session, we’ll map the FinOps Framework to a VMware-based datacenter, focusing on cost visibility, optimization, and automation. You’ll learn how to track costs more effectively, rightsize workloads, optimize licensing, and drive efficiency—all without migrating to the cloud. We’ll also explore how to align IT teams, finance, and leadership around cost-aware decision-making for on-prem environments. If your VMware bill keeps increasing and you need a new approach to cost management, this session is for you!
OpenACC and Open Hackathons Monthly Highlights June 2025
OpenACC and Open Hackathons Monthly Highlights June 2025OpenACC The OpenACC organization focuses on enhancing parallel computing skills and advancing interoperability in scientific applications through hackathons and training. The upcoming 2025 Open Accelerated Computing Summit (OACS) aims to explore the convergence of AI and HPC in scientific computing and foster knowledge sharing. This year's OACS welcomes talk submissions from a variety of topics, from Using Standard Language Parallelism to Computer Vision Applications. The document also highlights several open hackathons, a call to apply for NVIDIA Academic Grant Program and resources for optimizing scientific applications using OpenACC directives.
Lessons Learned from Developing Secure AI Workflows.pdf
Lessons Learned from Developing Secure AI Workflows.pdfPriyanka Aash Lessons Learned from Developing Secure AI Workflows
Connecting Data and Intelligence: The Role of FME in Machine Learning
Connecting Data and Intelligence: The Role of FME in Machine LearningSafe Software In this presentation, we want to explore powerful data integration and preparation for Machine Learning. FME is known for its ability to manipulate and transform geospatial data, connecting diverse data sources into efficient and automated workflows. By integrating FME with Machine Learning techniques, it is possible to transform raw data into valuable insights faster and more accurately, enabling intelligent analysis and data-driven decision making.
Using the SQLExecutor for Data Quality Management: aka One man's love for the...
Using the SQLExecutor for Data Quality Management: aka One man's love for the...Safe Software The SQLExecutor is one of FME’s most powerful and flexible transformers. Pivvot maintains a robust internal metadata hierarchy used to support ingestion and curation of thousands of external data sources that must be managed for quality before entering our platform. By using the SQLExecutor, Pivvot can efficiently detect problems and perform analysis before data is extracted from our staging environment, removing the need for rollbacks or cycles waisted on a failed job. This presentation will walk through three distinct examples of how Pivvot uses the SQLExecutor to engage its metadata hierarchy and integrate with its Data Quality Management workflows efficiently and within the source postgres database. Spatial Validation –Validating spatial prerequisites before entering a production environment. Reference Data Validation - Dynamically validate domain-ed columns across any table and multiple columns per table. Practical De-duplication - Removing identical or near-identical well point locations from two distinct source datasets in the same table.
Quantum AI Discoveries: Fractal Patterns Consciousness and Cyclical Universes
Quantum AI Discoveries: Fractal Patterns Consciousness and Cyclical UniversesSaikat Basu Embark on a cosmic journey exploring the intersection of quantum
computing, consciousness, and ancient wisdom. Together we'll uncover the
recursive patterns that bind our reality.
PyCon SG 25 - Firecracker Made Easy with Python.pdf
PyCon SG 25 - Firecracker Made Easy with Python.pdfMuhammad Yuga Nugraha Explore the ease of managing Firecracker microVM with the firecracker-python. In this session, I will introduce the basics of Firecracker microVM and demonstrate how this custom SDK facilitates microVM operations easily. We will delve into the design and development process behind the SDK, providing a behind-the-scenes look at its creation and features. While traditional Firecracker SDKs were primarily available in Go, this module brings a simplicity of Python to the table.
The Growing Value and Application of FME & GenAI
The Growing Value and Application of FME & GenAISafe Software With the cost of using Generative AI services dropping exponentially and the array of available models continually expanding, integrating AI into FME workflows has become inexpensive, accessible and effective. This presentation explores how GenAI within FME can cost-effectively transform data workflows by automating data extraction, validation, classification and augmentation tasks. We’ll discuss how FME’s no-code flexibility enables users to combine Generative AI and Computer Vision tools that create efficient workflows tailored to specific challenges. Using recent practical examples, we’ll demonstrate how these integrations can simplify complex tasks, save time and enhance data quality.
Cracking the Code - Unveiling Synergies Between Open Source Security and AI.pdf
Cracking the Code - Unveiling Synergies Between Open Source Security and AI.pdfPriyanka Aash Cracking the Code - Unveiling Synergies Between Open Source Security and AI
EIS-Webinar-Engineering-Retail-Infrastructure-06-16-2025.pdf
EIS-Webinar-Engineering-Retail-Infrastructure-06-16-2025.pdfEarley Information Science As AI reshapes expectations in retail and B2B commerce, organizations are recognizing a critical reality: meaningful AI outcomes depend on well-structured, adaptable infrastructure. In this session, Seth Earley is joined by Phil Ryan - AI strategist, search technologist, and founder of Glass Leopard Technologies - for a candid conversation on what it truly means to engineer systems for scale, agility, and intelligence.
Phil draws on more than two decades of experience leading search and AI initiatives for enterprise organizations. Together, he and Seth explore the challenges businesses face when legacy architectures limit personalization, agility, and real-time decisioning - and what needs to change to support agentic technologies and next-best-action capabilities.
Key themes from the webinar include:
Composability as a prerequisite for AI - Why modular, loosely coupled systems are essential for adapting to rapid innovation and evolving business needs
Search and relevance as foundational to AI - How techniques honed-in enterprise search have laid the groundwork for more responsive and intelligent customer experiences
From MDM and CDP to agentic systems - How data platforms are evolving to support richer customer context and dynamic orchestration
Engineering for business alignment - Why successful AI programs require architectural decisions grounded in measurable outcomes
The conversation is practical and forward-looking, connecting deep technical understanding with real-world business needs. Whether you’re modernizing your commerce stack or exploring how AI can enhance product discovery, personalization, or customer journeys, this session provides a clear-eyed view of the capabilities, constraints, and priorities that matter most.
Security Tips for Enterprise Azure Solutions
Security Tips for Enterprise Azure SolutionsMichele Leroux Bustamante Delivering solutions to Azure may involve a variety of architecture patterns involving your applications, APIs data and associated Azure resources that comprise the solution. This session will use reference architectures to illustrate the security considerations to protect your Azure resources and data, how to achieve Zero Trust, and why it matters. Topics covered will include specific security recommendations for types Azure resources and related network security practices. The goal is to give you a breadth of understanding as to typical security requirements to meet compliance and security controls in an enterprise solution.
Coordinated Disclosure for ML - What's Different and What's the Same.pdf
Coordinated Disclosure for ML - What's Different and What's the Same.pdfPriyanka Aash Coordinated Disclosure for ML - What's Different and What's the Same
You are not excused! How to avoid security blind spots on the way to production
You are not excused! How to avoid security blind spots on the way to productionMichele Leroux Bustamante We live in an ever evolving landscape for cyber threats creating security risk for your production systems. Mitigating these risks requires participation throughout all stages from development through production delivery - and by every role including architects, developers QA and DevOps engineers, product owners and leadership. No one is excused! This session will cover examples of common mistakes or missed opportunities that can lead to vulnerabilities in production - and ways to do better throughout the development lifecycle.
10 Key Challenges for AI within the EU Data Protection Framework.pdf
10 Key Challenges for AI within the EU Data Protection Framework.pdfPriyanka Aash 10 Key Challenges for AI within the EU Data Protection Framework
Tech-ASan: Two-stage check for Address Sanitizer - Yixuan Cao.pdf
Tech-ASan: Two-stage check for Address Sanitizer - Yixuan Cao.pdfcaoyixuan2019 A presentation at Internetware 2025.
Database Benchmarking for Performance Masterclass: Session 2 - Data Modeling ...
Database Benchmarking for Performance Masterclass: Session 2 - Data Modeling ...ScyllaDB Specific best practices and pitfalls for benchmarking popular high-performance databases.
You are not excused! How to avoid security blind spots on the way to production
You are not excused! How to avoid security blind spots on the way to productionMichele Leroux Bustamante
PPT On Sorting And Searching Concepts In Data Structure | In Programming Language |
- 2. An algorithm is a finite collection of well defined steps to solve A particular problem. An algorithm Is a finite sequence of instructions, Logic, step by step process for solving a problem.
- 5. Sorting technique to arranging data in a particular format. Sorting algorithm specifies the Way to arrange data in a particular order. Most common are numerical or lexicograpical order. Sorting is also used to represent data in more readable formats.
- 6. Telephone Directory – Telephone directory keeps telephone No. of people sorted on their Names. So that names can be searched. Dictionary – Dictionary keeps words in alphabetical order so that searching of any work becomes easy.
- 7. Bubble Sort is a simple sorting algorithm. This sorting algorithm is comparison Based algorithm in which each pair of adjacent elements is compared & elements Are swapped if they are not in order. This algorithm is not suitable for large data Sets as its average and worst case complexity.
- 8. We take an unsorted array for our example: 14 33 27 35 10 14 14 14 14 14 14 14 14 1433 33 33 27 33 27 27 27 35 35 35 35 10 10 10 10 27 27 27 27 27 33 33 33 33 10 35 35 35 10 35 10 10 10 35 33
- 10. This is a in-place comparison based sorting algorithm. Here, a sub-list is always sorted. For example, the lower part of an array is maintained to be sorted. The array is searched Sequentially and unsorted items are moved and inserted into sorted sub-list.
- 11. 14 33 27 3510 14 14 14 14 14 14 14 14 1433 33 33 33 27 27 27 27 10 10 10 10 35 35 35 35 27 27 27 27 27 33 33 33 10 10 10 10 10 33 35 35 35 35 35 33 We take an unsorted array for our example.
- 13. Selection sort is a simple sorting algorithm . This sorting algorithm is a in-place Comparison based algorithm in which the list is divided into two parts, sorted Part at left end and unsorted part at right end. intially end sorted part is empty And unsorted part is entire list.
- 14. We take the depicted array for our example : 14 33 27 10 35 14 10 10 10 10 10 10 10 1033 33 33 33 27 27 27 27 10 14 14 14 35 35 35 35 14 14 14 14 14 27 27 27 19 19 33 33 33 33 35 35 35 35 35 33 19 19 19 19 27 27 19 19 19 19
- 16. Merge Sort is a sorting technique based on divide & conquer technique. With worst case time complexity being respected algorithms. Merge Sort first divides the array into equal halves & Then combines them in a sorted manner.
- 17. To understand merge sort, we take an unsorted array as depicted below. 14 33 27 10 35 19 42 44 35 19 42 4414 33 27 10 14 33 27 10 35 19 42 44 14 33 27 10 35 19 42 44 14 33 10 27 19 35 42 44 Combine Them into another list
- 18. 10 14 33 27 19 35 42 44 After final merging, the list should look like this : 10 14 19 27 33 35 42 44
- 19. Quick sort is a highly efficient sorting algorithm and is based on partitioning of array Of data into smaller arrays. The quick sort partitions an array and then calls itself recursively twice to sort the resulting two sub arrays. This algorithm is quite efficient for large size data sets its average & worst case complexity are of 0.
- 20. The below given image representation explains how to find pivot value in the way. 10 42 35 33 31 27 26 19 14 44 10 14 19 26 27 31 33 35 42 44
- 22. Linear search is a very simple search algorithm. In this type search, a sequential search is made over all items one by one. Every items is checked & if a match founds them Particular item is returned otherwise search continues till the end of the data collection.
- 23. Suppose that we find 33 in array element Linear Search 10 14 19 26 27 31 33 35 42 44 = 33 10 14 19 26 27 31 33 35 42 44
- 24. Binary search is a fast search algorithm with run-time complexity of 0 (log n) . This search algorithm works on the principle of divide & compare. For this algorithm to work properly the data collection should be in sorted form.
- 25. The below given is our sorted array and assume that we need to search location Of value 31 using binary search. 10 14 19 26 27 31 33 35 42 44 0 1 2 3 4 5 6 7 8 9 First, we shall determine the half of the array by using this formula- Mid = low + (high – low) / 2 Here it is, 0+(9-0) / 2 = 4(integer value of 4.5). So 4 is the mid of array. 10 14 19 26 27 31 33 35 42 44 0 1 2 3 4 5 6 7 8 9
- 26. We change our low to mid + 1 and final the new value again. Low = mid + 1 Mid = low + (high – low) / 2 Our new mid 7 now. We compare the value stored at location 7 with our target value 31. 10 14 19 26 27 31 33 35 42 44 0 1 2 3 4 5 6 7 8 9 The value stored at location 7 is not match, rather it is less that what we are looking for. So the value must be in lower part from this location. 10 14 19 26 27 31 33 35 42 44 0 1 2 3 4 5 6 7 8 9
- 27. So we calculate the mid again. This time is 5. 10 14 19 26 27 31 33 35 42 44 0 1 2 3 4 5 6 7 8 9 We compare the value stored ad location 5 with our target value. We find that is a match. 10 14 19 26 27 31 33 35 42 44 0 1 2 3 4 5 6 7 8 9 We conclude that the target value 31 is stored at location 5.
- 29. End Of The Moment