Array linear data_structure_2 (1)

Aug 5, 20115 likes4,200 views

An array is a linear data structure that stores a collection of elements of the same data type in adjacent memory locations. Arrays allow storing multiple elements and accessing them using an index. Common array operations include traversal, search, insertion, deletion, and sorting. Algorithms for performing these operations on arrays are presented, including pseudocode for array traversal, insertion, deletion, linear search, and sorting.

Technology Business

Array(ADT)
Linear Data Structure

www.eshikshak.co.in

What are Arrays ?
● An Aay is collection of elements stored in adjacent memory
locations.
● By ‘finite’ – specific number of elements in an Aay
● By ‘similar’ – all the elements in an Aay are of the same data type

www.eshikshak.co.in

What are Arrays ? (cont.)
● An Aay containing number of element is reference
using an index values 0, 1, …n-1
○ Lower bound–Lowest index value
○ Upper bound–Highest index value
● An Aay is set of pairs of an index and a value, for
each index there is value associated with it.
● Various categories of Aay
○ 1D, 2D and Multi-D

www.eshikshak.co.in

What are Arrays ? (Cont.)
● The number of elements in the Aay is called its
range.
● No matter how big an Aay is, its elements are
always stored in contiguous memory locations.

www.eshikshak.co.in

Array Operations
Operation Description
Traversal Processing each element in the Aay
Search Finding the location of an element with a given
value
Insertion Adding new element to an Aay
Deletion Removing an element from an Aay
Sorting Organizing the elements in some order
Merging Combining two Aays into a single Aay
Reversing Reversing the elements of an Aay

www.eshikshak.co.in

Row-Major and Column-Major
Arrangement
● All the elements of Aay are stored in adjacent memory.
● This leads to two possible Aangements of elements in memory
○ Row Major
○ ColumnMajor
● Base address , no. of rows ,& no. of columns helps to know any
element in an Aay

www.eshikshak.co.in

Algorithm for Array Traversal
● Let A be a linear Aay with Lower Bound LB and Upper Bound UB. The
following algorithm traverses A applying an operations PROCESS to each
element of A
Step 1. Initialize Counter
Set Counter = LB
Step 2. Repeat steps 3 and 4 while counter <= UB
Else GoTo Step 5
Step 3. Visit element
Apply PROCESS to A[counter]
Step 4. Increase Counter
Set counter = counter + 1
GoTo 2
Step 5. Exit

www.eshikshak.co.in

Algorithm for Insertion
Let A be a Linear Array, N is number of elements, k is the positive integer such
that k<=N, VAL to insert element at kth Position in an Array A
Step 1. Start
Step 2. Initialize Counter
Set J = N
Step 3. Repeat Steps 3 and 4 while J>=k otherwise GoTo Step
Step 4. Move Jth element downward
Set A[J+1] = A[J]
Step 5. Decrease Counter
Set J = J + 1
End of step 2 loop
Step 6. Insert element
Set A[k] = ITEM
Step 7. Reset N
Set N = N + 1
Step 8. Exit

www.eshikshak.co.in

Algorithm for Deletion
DELETE(A, N, K, VAL)
Let A be an linear Aay. N is the number of elements, k is the positive
integer such that k<=N. The algorithm deletes kth element from the
Aay.
Step 1. Start
Step 2. Set VAL = A[k]
Step 3. Repeat for J = k to N-1
[Move J+1 element Upward]
Set A[J] = A[J+1)
End of Loop
Step 4. Reset the number N of elements in A

Set N = N– 1
Step 5. Exit

www.eshikshak.co.in

Algorithm for Linear Search
Suppose A is linear Array with N elements, and VAL is the given item of
information. This algorithm finds the location LOC of item in A or sets LOC=0 if
search is unsuccessful
Step 1. Start
Step 2. [Insert VAL at the end of A]
Set A[N+1] = VAL
Step 3. [Initialize counter]
SET LOC = 1
Step 4. [Search for VAL]
Repeat while A[LOC] != VAL
Set LOC = LOC + 1
[End of loop]
Step 5. [Successful ?]
if LOC = N+1 then set LOC = 0
Step 6. Exit

www.eshikshak.co.in

Algorithm for sorting
Let A be an Aay of N elements. The following algorithm sorts
the elements of A.
Step 1. Start
Step 2. Repeat Steps 2 and 3 for k=1 to N-1
Step 3. Set PTR = 1 [Initialize pass pointer PTR]
Step 4. Repeat while PTR<=N-K [Execute Pass]
a. If A[PTR] > A[PTR+1], then
Interchange A[PTR] and A[PTR+1]
[End of if structure]
b. Set PTR = PTR + 1
[End of inner loop]
[End of step1 outer loop]
Step 5. Exit

www.eshikshak.co.in

This document discusses linear arrays and algorithms for common operations on arrays such as traversing, inserting, deleting, and sorting elements. It defines a linear array as a list of homogeneous data elements with a lower and upper bound. Algorithms provided include traversing from the lower to upper bound, inserting an element at a specified position by shifting other elements and incrementing the size, deleting an element by shifting elements and decrementing the size, and sorting an array using bubble sort by making multiple passes to swap adjacent elements in descending order.

daa-unit-3-greedy methodhodcsencet

The document discusses the greedy method algorithmic approach. It provides an overview of greedy algorithms including that they make locally optimal choices at each step to find a global optimal solution. The document also provides examples of problems that can be solved using greedy methods like job sequencing, the knapsack problem, finding minimum spanning trees, and single source shortest paths. It summarizes control flow and applications of greedy algorithms.

Array data structuremaamir farooq

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.

Asymptotic notationsNikhil Sharma

The document discusses asymptotic notations that are used to describe the time complexity of algorithms. It introduces big O notation, which describes asymptotic upper bounds, big Omega notation for lower bounds, and big Theta notation for tight bounds. Common time complexities are described such as O(1) for constant time, O(log N) for logarithmic time, and O(N^2) for quadratic time. The notations allow analyzing how efficiently algorithms use resources like time and space as the input size increases.

Analysis and Design of AlgorithmsBulbul Agrawal

The document is an introduction to the analysis and design of algorithms, detailing key terminologies, objectives, and strategies for efficient algorithm design. It covers topics like asymptotic notations, algorithm efficiency, and various design techniques including divide and conquer, greedy strategies, and dynamic programming. By the end of the course, students should be able to analyze algorithm complexity and implement fundamental algorithms in programming contexts.

Array Introduction One-dimensional array Multidimensional arrayimtiazalijoono

Deque and its applicationsJsaddam Hussain

This document discusses double-ended queues or deques. Deques allow elements to be added or removed from either end. There are two types: input restricted deques where elements can only be inserted at one end but removed from both ends, and output restricted deques where elements can only be removed from one end but inserted from both ends. Deques can function as stacks or queues depending on the insertion and removal ends. The document describes algorithms for common deque operations like insert_front, insert_back, remove_front, and remove_back. It also lists applications of deques like palindrome checking and task scheduling.

Divide and conquerDr Shashikant Athawale

The document discusses divide and conquer algorithms. It describes divide and conquer as a design strategy that involves dividing a problem into smaller subproblems, solving the subproblems recursively, and combining the solutions. It provides examples of divide and conquer algorithms like merge sort, quicksort, and binary search. Merge sort works by recursively sorting halves of an array until it is fully sorted. Quicksort selects a pivot element and partitions the array into subarrays of smaller and larger elements, recursively sorting the subarrays. Binary search recursively searches half-intervals of a sorted array to find a target value.

Breadth First Search & Depth First SearchKevin Jadiya

stack presentationShivalik college of engineering

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.

Array pptKaushal Mehta

This document discusses arrays in three sentences or less: Arrays allow storing and accessing multiple values under a single name, with each value stored in consecutive memory locations. Arrays come in one-dimensional, two-dimensional, and multi-dimensional forms and can be accessed using indexes. Common array operations include initialization, accessing elements, searching, sorting, and performing operations on all elements using loops.

Binary Search Treesagar yadav

Binary search trees are binary trees where all left descendants of a node are less than the node's value and all right descendants are greater. This structure allows for efficient search, insertion, and deletion operations. The document provides definitions and examples of binary search tree properties and operations like creation, traversal, searching, insertion, deletion, and finding minimum and maximum values. Applications include dynamically maintaining a sorted dataset to enable efficient search, insertion, and deletion.

Recursion - Algorithms and Data StructuresPriyanka Rana

The document discusses basic algorithms, particularly focusing on recursion as a method for solving problems like calculating factorials and summing elements in an array. It describes different types of recursion: linear, binary, and multiple, providing algorithms for each type and examples of their execution. Additionally, it introduces a combinatorial puzzle related to multiple recursion and encourages students to think about the drawbacks of recursive functions.

DAA AND DASBasavaraj Shetty

The document discusses the DAA and DAS instructions in microprocessors. DAA is used after addition instructions to adjust the result into valid BCD format. DAS performs similarly for subtraction instructions. Both affect flags and the implied AL register, adjusting digits if needed to account for decimal carries or borrows. Examples are provided demonstrating their use after addition and subtraction of packed BCD values.

Floating point arithmetic operations (1)cs19club

This document discusses floating point arithmetic operations including: - The components of a floating point number including the mantissa and exponent. - Normalization of floating point numbers to have a leading nonzero digit in the mantissa. - Common floating point operations like addition, subtraction, multiplication, and division and how they are performed. - The IEEE 754 standard for representing floating point numbers. - How floating point arithmetic is implemented in hardware including registers and adders used to process mantissas and exponents.

Complexity analysis in AlgorithmsDaffodil International University

1) The document discusses complexity analysis of algorithms, which involves determining the time efficiency of algorithms by counting the number of basic operations performed based on input size. 2) It covers motivations for complexity analysis, machine independence, and analyzing best, average, and worst case complexities. 3) Simple rules are provided for determining the complexity of code structures like loops, nested loops, if/else statements, and switch cases based on the number of iterations and branching.

Matrix chain multiplicationRespa Peter

Dynamic programming is an algorithm design technique that uses a tabular method and divide-and-conquer to solve problems with interdependent subproblems. The document focuses on matrix-chain multiplication, emphasizing the importance of parenthesization to minimize scalar multiplications required for matrix products. It details a structured approach consisting of characterizing optimal solutions, defining their recursive values, computing solutions bottom-up, and constructing the final optimal solution.

Data structureviswanathV8

Python programming : ArraysEmertxe Information Technologies Pvt Ltd

The document provides an extensive overview of single-dimensional arrays in Python, detailing their creation, indexing, slicing, methods for manipulation, and examples of importing modules like `array` and `numpy`. It further describes various operations that can be performed on these arrays including mathematical operations, comparisons, and exercises for practice. Additionally, the document touches on multi-dimensional arrays and various functions available in the `numpy` library for array management.

Algorithm analysissumitbardhan

This document provides an overview of algorithm analysis. It discusses how to analyze the time efficiency of algorithms by counting the number of operations and expressing efficiency using growth functions. Different common growth rates like constant, linear, quadratic, and exponential are introduced. Examples are provided to demonstrate how to determine the growth rate of different algorithms, including recursive algorithms, by deriving their time complexity functions. The key aspects covered are estimating algorithm runtime, comparing growth rates of algorithms, and using Big O notation to classify algorithms by their asymptotic behavior.

Input Output OrganizationKamal Acharya

Quick sort Algorithm Discussion And AnalysisSNJ Chaudhary

Quicksort is a divide-and-conquer algorithm that works by partitioning an array around a pivot element and recursively sorting the subarrays. In the average case, it has an efficiency of Θ(n log n) time as the partitioning typically divides the array into balanced subproblems. However, in the worst case of an already sorted array, it can be Θ(n^2) time due to highly unbalanced partitioning. Randomizing the choice of pivot helps avoid worst-case scenarios and achieve average-case efficiency in practice, making quicksort very efficient and commonly used.

Python ModulesNitin Reddy Katkam

Python modules allow code reuse and organization. A module is a Python file with a .py extension that contains functions and other objects. Modules can be imported and their contents accessed using dot notation. Modules have a __name__ variable that is set to the module name when imported but is set to "__main__" when the file is executed as a script. Packages are collections of modules organized into directories, with each directory being a package. The Python path defines locations where modules can be found during imports.

Data Structures- Part5 recursionAbdullah Al-hazmy

The document discusses recursion, including: 1) Recursion involves breaking a problem down into smaller subproblems until a base case is reached, then building up the solution to the overall problem from the solutions to the subproblems. 2) A recursive function is one that calls itself, with each call typically moving closer to a base case where the problem can be solved without recursion. 3) Recursion can be linear, involving one recursive call, or binary, involving two recursive calls to solve similar subproblems.

5.2 divide and conquerKrish_ver2

This document discusses the merge sort algorithm for sorting a sequence of numbers. It begins by introducing the divide and conquer approach, which merge sort uses. It then provides an example of how merge sort works, dividing the sequence into halves, sorting the halves recursively, and then merging the sorted halves together. The document proceeds to provide pseudocode for the merge sort and merge algorithms. It analyzes the running time of merge sort using recursion trees, determining that it runs in O(n log n) time. Finally, it covers techniques for solving recurrence relations that arise in algorithms like divide and conquer approaches.

header, circular and two way linked listsstudent

This document discusses different types of linked lists including header linked lists, circular header linked lists, grounded header linked lists, two-way linked lists, and provides algorithms for traversing, searching, inserting, and deleting nodes from circular header and two-way linked lists. Header linked lists contain a special header node and can be circular or grounded. Circular header linked lists have the last node point to the header node, while grounded lists have the last node point to null. Two-way lists contain forward and backward pointers to traverse in both directions. Algorithms for traversing, searching, and modifying nodes in circular header and two-way lists are presented.

Algo>Abstract data typeAin-ul-Moiz Khawaja

Abstract data types (ADTs) define a data type in terms of its possible values and operations, independent of implementation details. An ADT consists of a logical form defining the data items and operations, and a physical form comprising the data structures and algorithms that implement it. Simple data types like integers and characters are ADTs with basic storage structures like memory locations and operations implemented by hardware/software. More complex ADTs require user-defined data structures to organize data in memory or files along with subroutines implementing each operation.

DATA STRUCTURESbca2010

More Related Content

What's hot (20)

Divide and conquerDr Shashikant Athawale

Breadth First Search & Depth First SearchKevin Jadiya

stack presentationShivalik college of engineering

SEARCHING AND SORTING ALGORITHMSGokul Hari

Array pptKaushal Mehta

Binary Search Treesagar yadav

Recursion - Algorithms and Data StructuresPriyanka Rana

DAA AND DASBasavaraj Shetty

Floating point arithmetic operations (1)cs19club

Complexity analysis in AlgorithmsDaffodil International University

Matrix chain multiplicationRespa Peter

Data structureviswanathV8

Python programming : ArraysEmertxe Information Technologies Pvt Ltd

Algorithm analysissumitbardhan

Input Output OrganizationKamal Acharya

Quick sort Algorithm Discussion And AnalysisSNJ Chaudhary

Python ModulesNitin Reddy Katkam

Data Structures- Part5 recursionAbdullah Al-hazmy

5.2 divide and conquerKrish_ver2

header, circular and two way linked listsstudent

Divide and conquerDr Shashikant Athawale

Breadth First Search & Depth First SearchKevin Jadiya

stack presentationShivalik college of engineering

SEARCHING AND SORTING ALGORITHMSGokul Hari

Array pptKaushal Mehta

Binary Search Treesagar yadav

Recursion - Algorithms and Data StructuresPriyanka Rana

DAA AND DASBasavaraj Shetty

Floating point arithmetic operations (1)cs19club

Complexity analysis in AlgorithmsDaffodil International University

Matrix chain multiplicationRespa Peter

Data structureviswanathV8

Python programming : ArraysEmertxe Information Technologies Pvt Ltd

Algorithm analysissumitbardhan

Input Output OrganizationKamal Acharya

Quick sort Algorithm Discussion And AnalysisSNJ Chaudhary

Python ModulesNitin Reddy Katkam

Data Structures- Part5 recursionAbdullah Al-hazmy

5.2 divide and conquerKrish_ver2

header, circular and two way linked listsstudent

Viewers also liked (13)

Algo>Abstract data typeAin-ul-Moiz Khawaja

DATA STRUCTURESbca2010

Data structure lecture 3Kumar

The document discusses different data structures and sorting algorithms: 1. It describes arrays as a linear data structure where elements are stored in consecutive memory locations and can be accessed using an index. 2. It provides examples of traversing and manipulating arrays, such as inserting and deleting elements. Common sorting algorithms like bubble sort and selection sort are also summarized. 3. Bubble sort works by repeatedly stepping through the list and comparing adjacent elements, swapping them if they are in the wrong order until no swaps are needed, resulting in a sorted list in O(n^2) time. Selection sort finds the minimum element and swaps it into the front each iteration.

ArraysKomal Singh

Data structures and algorithmsHarry Potter

The document discusses abstract data types (ADTs), specifically queues. It defines a queue as a linear collection where elements are added to one end and removed from the other end, following a first-in, first-out (FIFO) approach. The key queue operations are enqueue, which adds an element, and dequeue, which removes the element that has been in the queue longest. Queues can be implemented using arrays or linked lists. Array implementations use head and tail pointers to track the start and end of the queue.

Infix-Postfix expression conversionRashmiranja625

The document discusses converting infix expressions to postfix expressions using a stack. It defines infix and postfix expressions, provides examples of each, and presents an algorithm that uses a stack to scan an infix expression from left to right and output an equivalent postfix expression. Key steps include pushing operators to the stack based on precedence and popping operators to the output when encountering operands and parentheses.

Abstract data typesLuis Goldster

This document discusses abstract data types (ADTs) and their implementation in various programming languages. It covers the key concepts of ADTs including data abstraction, encapsulation, information hiding, and defining the public interface separately from the private implementation. It provides examples of ADTs implemented using modules in Modula-2, packages in Ada, classes in C++, generics in Java and C#, and classes in Ruby. Parameterized and encapsulation constructs are also discussed as techniques for implementing and organizing ADTs.

Abstract data typesPoojith Chowdhary

This document discusses data abstraction and abstract data types (ADTs). It defines an ADT as a collection of data along with a set of operations on that data. An ADT specifies what operations can be performed but not how they are implemented. This allows data structures to be developed independently from solutions and hides implementation details behind the ADT's operations. The document provides examples of list ADTs and an array-based implementation of a list ADT in C++.

Abstract Data TypesReggie Niccolo Santos

Abstract data types (ADTs) specify operations on data without defining implementation. Common ADTs include sets, lists, stacks, queues, maps, trees and graphs. Sets contain unique elements; lists are ordered; stacks use last-in first-out access; queues use first-in first-out access; maps store key-value pairs; trees link parent nodes to children; and graphs link vertices with edges. Core operations for collections include adding, removing and accessing elements.

Sparse Matrix and PolynomialAroosa Rajput

The document discusses sparse matrices and polynomials in the context of a software engineering course. It describes the characteristics and representations of sparse matrices and provides C++ source code for performing addition and multiplication on them. Additionally, the document covers polynomials, their representation via arrays and linked lists, and includes code to add and multiply polynomial expressions.

Arraysarchikabhatia

The document discusses various concepts related to arrays in C/C++ including: - What an array is and its key properties like contiguous memory locations and common data type - Different types of arrays like single dimensional and multi dimensional - How to declare, initialize and access array elements - Passing arrays to functions - Searching and sorting arrays - Common array operations like insertion and deletion

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.

11 abstract data typesjigeno

The document discusses abstract data types and their implementation in various programming languages. It covers the concepts of abstraction, encapsulation, and information hiding. Some key points include: - Abstract data types define a user-created data type that encapsulates its representation and allows access only through specified operations. This hides implementation details and allows flexible changes. - Many languages support abstract data types through mechanisms like modules/packages that group related code and types, and specify public interfaces separately from private implementations. - Languages discussed include Simula 67, Ada, C++, and Java, and how each supports defining and using abstract data types through features like packages, classes, visibility modifiers, and generics.

Algo>Abstract data typeAin-ul-Moiz Khawaja

DATA STRUCTURESbca2010

Data structure lecture 3Kumar

ArraysKomal Singh

Data structures and algorithmsHarry Potter

Infix-Postfix expression conversionRashmiranja625

Abstract data typesLuis Goldster

Abstract data typesPoojith Chowdhary

Abstract Data TypesReggie Niccolo Santos

Sparse Matrix and PolynomialAroosa Rajput

Arraysarchikabhatia

Chapter 11 - Sorting and SearchingEduardo Bergavera

11 abstract data typesjigeno

Similar to Array linear data_structure_2 (1) (20)

data structure and algorithm Array.pptx btech 2nd yearpalhimanshi999

The document discusses arrays and algorithms. It begins by defining arrays as lists of homogeneous data elements referenced by indices. Linear arrays are one-dimensional while multi-dimensional arrays include matrices. Common array operations like traversal, insertion, deletion and searching algorithms like linear and binary search are described. For linear search, the best, average and worst case complexities are analyzed to be O(1), O(n/2) and O(n) respectively. Binary search is more efficient, having a complexity of O(log n) as it halves the search space on each comparison. Examples and pseudocode are provided to illustrate key concepts.

Updated Lab3.docxAleezaAnjum

This document outlines a lab manual for a Data Structures and Algorithms course. The lab covers basic operations on arrays in C++, including insertion, deletion, traversal, and searching algorithms. It provides pseudocode and C++ code examples for inserting and deleting elements from an array, traversing a linear array, and using linear and binary search algorithms. The objectives are to understand array operations, write algorithms, and implement them in code using the Dev C++ tool.

03 Linear Arrays Memory Representations .pdfKkSingh64

The document discusses linear arrays and their memory representation. It defines key concepts related to linear arrays like basic terminology, size/length of an array, indexes, upper and lower bounds. It describes how elements of a linear array are stored sequentially in contiguous memory locations. It also covers traversing arrays, insertion and deletion of elements, and searching techniques like linear search and binary search.

BCA DATA STRUCTURES LINEAR ARRAYS MRS.SOWMYA JYOTHISowmya Jyothi

1) The document discusses linear arrays and their representation in memory. A linear array stores elements in consecutive memory locations. 2) The number of elements in an array is given by the length formula: Length = Upper Bound - Lower Bound + 1. The address of each element can be calculated using the base address of the array. 3) Common array operations like traversing, inserting, and deleting elements are discussed along with algorithms to perform each operation. Representing polynomials and sparse matrices using arrays is also covered.

PPT Lecture 2.2.1 onn c++ data structuresmidtushar

This document provides information about an Elementary Data Structure Using C++ course. The course objectives are to enable students to understand C++ programming constructs, analyze and solve problems in C++, understand data structures concepts, and analyze algorithm efficiency. The course covers arrays, linear data structures, searching, sorting, and traversal algorithms. Key concepts discussed include linear arrays, multi-dimensional arrays, array implementation in memory, insertion and deletion in arrays, linear and binary search algorithms, and applications of arrays in sorting, matrix operations, and CPU scheduling.

arraysEnakshi Chanda

The document discusses different data structures and their properties. It explains that data structures can be linear or non-linear. Linear data structures like arrays have elements arranged in a sequence, while non-linear structures like trees and graphs do not. It then focuses on linear data structures and their representation in memory using arrays and linked lists. Key operations on linear structures like traversal, search, insertion and deletion are also summarized.

Data structures (Array 1 dimensional).pptxitzsomeone50

arrays1.ppt python programme arrays insertionbushraashraf639

The document discusses linear arrays, including definitions, notation, and algorithms for operations such as insertion, deletion, and searching. It explains how elements in an array are stored in consecutive memory locations and introduces multi-dimensional arrays and their representations. Additionally, it covers searching techniques like linear and binary search, highlighting the efficiency of binary search for sorted arrays.

Array data structureharsh112327

An array is a data structure that stores fixed number of items of the same type. It allows storing elements at numbered indexes and performing operations like traversing, inserting, deleting, searching and updating elements. Some key points about arrays include elements having indexes starting from 0, built-in support for basic operations, and default initialization of element values depending on data type.

ARRAY in python and c with examples .pptxabhishekmaurya102515

Data structure and algorithm All in Onejehan1987

The document provides information about data structures and algorithms. It defines key terms like data, information, data structure, algorithm and different types of algorithms. It discusses linear data structures like arrays, operations on arrays like traversing, searching, inserting and deleting elements. It also covers recursive functions, recursion concept, searching algorithms like sequential search and binary search along with their algorithms and examples.

DSA.pdfAkashSaha75835

This document contains information about arrays and algorithms for traversing, inserting, and deleting elements from a linear array. It defines an array as a list of homogeneous data elements referenced by consecutive integer indices and stored in successive memory locations. It provides pseudocode for algorithms to traverse a linear array by applying an operation to each element, insert a new element into a specified position by moving other elements, and delete an element from a specified position by moving elements into the freed space.

Data structure using c module 1smruti sarangi

The document provides a comprehensive overview of data structures, including definitions, types, and operations associated with both primitive and non-primitive structures. Key topics include arrays, stacks, queues, linked lists, and algorithms for operations such as insertion, deletion, and sorting. Additionally, it covers memory representation, asymptotic notation for algorithm analysis, and specific concepts like sparse matrices and their representations.

Unit 2 dsa LINEAR DATA STRUCTUREPUNE VIDYARTHI GRIHA'S COLLEGE OF ENGINEERING, NASHIK

The document discusses linear data structures using sequential organization. It begins by defining sequential access and linear data structures. Linear data structures traverse elements sequentially, with direct access to only one element. Examples given are arrays and linked lists. The document then focuses on arrays, providing definitions and discussing array representation in memory, basic array operations like traversal and searching, and multi-dimensional arrays. Two-dimensional and three-dimensional arrays are explained with examples.

358 33 powerpoint-slides_5-arrays_chapter-5sumitbardhan

The document discusses arrays and two-dimensional arrays in C programming. It defines arrays as collections of similar data elements stored in consecutive memory locations that can be accessed using an index. The key points are: 1) Arrays are declared with a type, name, and size and elements are accessed using indexes. 2) Two-dimensional arrays use two indexes to access elements, representing rows and columns. 3) Arrays can be initialized manually or by inputting values in a loop. 4) Pointers and addresses can be used to access and pass array elements between functions.

datastructureppt-190327174340 (1).pptxDEEPAK948083

This document provides an introduction to data structures presented by Prof. K. Adisesha. It defines data structures as representations of logical relationships between data elements that consider both the elements and their relationships. Data structures affect both structural and functional aspects of programs. They are classified as primitive or non-primitive, with primitive structures operated on directly by machine instructions and non-primitive structures derived from primitive ones. Linear data structures like stacks and queues have elements in sequence, while non-linear structures like trees and graphs have hierarchical or parent-child relationships. Common operations on data structures include traversal, insertion, selection, searching, sorting, merging, and deletion. Arrays are also discussed in detail as a fundamental data structure.

DSA - Array.pptx11STEM2PGROUP1

ArraysSARITHA REDDY

The document discusses arrays in data structures using C programming language. It defines what an array is and describes different types of arrays like one-dimensional, two-dimensional, and multi-dimensional arrays. It also explains array operations such as insertion, deletion, traversal, reversing, sorting, and searching. Additionally, it covers merging of arrays, arrays of pointers, and using arrays to represent polynomials.

2.DS ArrayChandan Singh

This document discusses data structures and linear arrays. It defines data structures as logical models of data organization and storage structures as how data structures are represented in computer memory. Linear arrays are introduced as lists of homogeneous elements referenced by consecutive integer indices. The elements of a linear array are stored in contiguous memory locations. Pointers and pointer arrays are also discussed, with pointers containing the addresses of array elements and pointer arrays having each element as a pointer. Two-dimensional arrays are covered, including how they are represented in memory either by columns or rows. Formulas are provided to calculate the memory locations of elements in linear and multi-dimensional arrays. Basic operations on linear arrays like traversal, insertion, and deletion are explained with pseudocode algorithms.

Array Operations.pptxdata structure array indsaar0454492

The document outlines data structures, focusing specifically on arrays, data types (built-in and derived), and their associated operations such as insertion, deletion, searching, and updating. It explains how arrays hold a fixed number of items of the same type and provides algorithms for basic array operations. Examples are provided to illustrate the insertion and deletion processes within an array context.

data structure and algorithm Array.pptx btech 2nd yearpalhimanshi999

Updated Lab3.docxAleezaAnjum

03 Linear Arrays Memory Representations .pdfKkSingh64

BCA DATA STRUCTURES LINEAR ARRAYS MRS.SOWMYA JYOTHISowmya Jyothi

PPT Lecture 2.2.1 onn c++ data structuresmidtushar

arraysEnakshi Chanda

Data structures (Array 1 dimensional).pptxitzsomeone50

arrays1.ppt python programme arrays insertionbushraashraf639

Array data structureharsh112327

ARRAY in python and c with examples .pptxabhishekmaurya102515

Data structure and algorithm All in Onejehan1987

DSA.pdfAkashSaha75835

Data structure using c module 1smruti sarangi

Unit 2 dsa LINEAR DATA STRUCTUREPUNE VIDYARTHI GRIHA'S COLLEGE OF ENGINEERING, NASHIK

358 33 powerpoint-slides_5-arrays_chapter-5sumitbardhan

datastructureppt-190327174340 (1).pptxDEEPAK948083

DSA - Array.pptx11STEM2PGROUP1

ArraysSARITHA REDDY

2.DS ArrayChandan Singh

Array Operations.pptxdata structure array indsaar0454492

More from eShikshak (20)

Modelling and evaluationeShikshak

The document discusses modelling and evaluation in machine learning. It defines what models are and how they are selected and trained for predictive and descriptive tasks. Specifically, it covers: 1) Models represent raw data in meaningful patterns and are selected based on the problem and data type, like regression for continuous numeric prediction. 2) Models are trained by assigning parameters to optimize an objective function and evaluate quality. Cross-validation is used to evaluate models. 3) Predictive models predict target values like classification to categorize data or regression for continuous targets. Descriptive models find patterns without targets for tasks like clustering. 4) Model performance can be affected by underfitting if too simple or overfitting if too complex,

Operators in pythoneShikshak

Operators in Python perform operations on operands. There are unary, binary, and ternary operators. The document discusses arithmetic, assignment, relational, logical, boolean, and bitwise operators in Python. Arithmetic operators include addition, subtraction, multiplication, division, modulus, exponent, and integer division. Assignment operators assign values to variables like +=, -=, *=, /=, %=, **=, and //= . Relational, logical, boolean, and bitwise operators are also discussed.

Datatypes in pythoneShikshak

The document discusses various Python datatypes. It explains that Python supports built-in and user-defined datatypes. The main built-in datatypes are None, numeric, sequence, set and mapping types. Numeric types include int, float and complex. Common sequence types are str, bytes, list, tuple and range. Sets can be created using set and frozenset datatypes. Mapping types represent a group of key-value pairs like dictionaries.

Introduction to pythoneShikshak

Python is a programming language created by Guido van Rossum in 1991 that combines features of C and Java. It is an open source, high-level, dynamically typed language that is both object-oriented and procedural. Python code is portable, platform independent, and can be integrated with other languages like C and Java. It has a large standard library and is an interpreted, extensible, and scalable scripting language.

Introduction to e commerceeShikshak

The document provides an overview of eCommerce, including definitions, key features, comparisons to traditional commerce, advantages for organizations, customers, and society. It discusses the eCommerce framework and anatomy of eCommerce applications. Specifically, it outlines how multimedia content serves as both "fuel and traffic" for eCommerce, and how robust storage servers are needed to handle large amounts of digital content for customers. The document provides a comprehensive introduction to the fundamental concepts of eCommerce.

Chapeter 2 introduction to cloud computingeShikshak

Cloud computing is a new paradigm for IT that delivers computing resources as a service. It allows users to access servers, storage, databases, and applications through the internet. Key characteristics include elasticity, scalability, multi-tenancy, and metered service. Cloud providers ensure reliability, security, and availability through techniques like virtualization, fault tolerance, and automated management of resources.

Unit 1.4 working of cloud computingeShikshak

Cloud computing provides IT resources and services over the Internet. There are three main service models - Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). SaaS provides applications to users while the provider manages the infrastructure. PaaS provides platforms for developers to create applications without worrying about infrastructure. IaaS provides basic computing and storage infrastructure for users to deploy and run software.

Unit 1.3 types of cloudeShikshak

The document discusses different types of cloud computing including public, private, hybrid, and community clouds. It provides details on each type, such as public clouds being available to the general public over the internet and shared between companies, while private clouds are dedicated to a single organization. Hybrid clouds combine public and private clouds. Community clouds are shared between organizations within a specific community. The document also covers cloud infrastructure and how businesses can implement cloud computing infrastructure.

Unit 1.2 move to cloud computingeShikshak

Cloud computing provides various advantages such as reduced costs, improved scalability, mobility and collaboration. However, migrating to the cloud also presents some challenges including security concerns, vendor lock-in, integration issues, and loss of control over IT resources. A successful cloud migration requires careful planning and execution of key stages - planning the project, executing the migration, and monitoring outcomes. It is also important to start small, trust cloud vendors to protect data, maintain user identity management, and plan for potential latency and outages.

Unit 1.1 introduction to cloud computingeShikshak

Cloud computing allows users to access applications and resources over the internet without needing to manage the underlying infrastructure. Key aspects of cloud computing include on-demand self-service, broad network access, resource pooling, rapid elasticity, and usage-based pricing. Early concepts of cloud computing date back to the 1960s, but it became more feasible with improved internet connectivity and virtualization technologies. Server crashes can occur during periods of peak usage and are usually addressed by adding more servers to balance the load.

Mesics lecture files in 'c'eShikshak

The document discusses files and streams in C language. It explains that a file is a collection of bytes stored on a secondary storage device and has different types like data, text, program etc. It also describes streams as a series of bytes flowing between a program and file. The key functions for working with files and streams in C like fopen(), fclose(), fread(), fwrite() are explained along with their usage.

Mesics lecture 8 arrays in 'c'eShikshak

1. An array is a data structure that stores elements of the same data type in a contiguous block of memory. It allows random access to elements using indices. 2. Arrays are declared with a type, name, and size and elements are accessed using the name and index. Indexing starts at 0. 3. Multidimensional arrays store elements in rows and columns and are declared with multiple sizes, allowing 2D or 3D arrays.

Mesics lecture 7 iteration and repetitive executionseShikshak

The document discusses loops in computer programming. It defines loops as blocks of code that are repeatedly executed. There are two main types of loops: counter-controlled loops, where the number of iterations is defined in advance, and sentinel-controlled loops, where the number depends on a condition. Common loop structures in C include the for, while, and do-while loops. The for loop allows predefined or open-ended repetition. The while loop executes until a condition is false. The do-while loop guarantees at least one iteration even if the condition is initially false.

Mesics lecture 5 input – output in ‘c’eShikshak

Mesics lecture 6 control statement = if -else if__elseeShikshak

Mesics lecture 4 c operators and experssionseShikshak

Operators in C allow operations to be performed on operands. Common operators include arithmetic, relational, logical, and bitwise operators. Operators have precedence and associativity that determine the order of operations. The conditional operator (?:) evaluates a condition and returns one of two expressions. Operators are used to perform calculations, comparisons, assignments, and logical tests in C programs.

Mesics lecture 5 input – output in ‘c’eShikshak

1. There are two main ways to handle input-output in C - formatted functions like printf() and scanf() which require format specifiers, and unformatted functions like getchar() and putchar() which work only with characters. 2. Formatted functions allow formatting of different data types like integers, floats, and strings. Unformatted functions only work with characters. 3. Common formatted functions include printf() for output and scanf() for input. printf() outputs data according to format specifiers, while scanf() reads input and stores it in variables based on specifiers.

Mesics lecture 3 c – constants and variableseShikshak

The document discusses various types of constants and variables in C programming. It describes primary constants like integer, real, and character constants and defines rules for constructing each type. Secondary constants include arrays, pointers, structures, and unions. Variables represent unknown values that can change during program execution. The document outlines naming conventions and rules for defining variables. It also covers C data types like integer, floating point, characters, and void and provides details on their sizes and ranges. Initialization of variables through assignment at declaration or dynamically at runtime is also summarized.

Lecture 7 relational_and_logical_operatorseShikshak

Lecture21 categoriesof userdefinedfunctions.ppteShikshak

The document discusses different categories of user defined functions in C programming. It describes functions with: 1) No arguments and no return values, which can be used to print lines or patterns. 2) Arguments but no return values, used to perform operations on passed arguments. 3) Arguments and a single return value, allowing two-way communication by passing and returning data. 4) No arguments but a return value, exemplified by the getchar() library function. 5) Returning multiple values using arrays or structures.

Modelling and evaluationeShikshak

Operators in pythoneShikshak

Datatypes in pythoneShikshak

Introduction to pythoneShikshak

Introduction to e commerceeShikshak

Chapeter 2 introduction to cloud computingeShikshak

Unit 1.4 working of cloud computingeShikshak

Unit 1.3 types of cloudeShikshak

Unit 1.2 move to cloud computingeShikshak

Unit 1.1 introduction to cloud computingeShikshak

Mesics lecture files in 'c'eShikshak

Mesics lecture 8 arrays in 'c'eShikshak

Mesics lecture 7 iteration and repetitive executionseShikshak

Mesics lecture 5 input – output in ‘c’eShikshak

Mesics lecture 6 control statement = if -else if__elseeShikshak

Mesics lecture 4 c operators and experssionseShikshak

Mesics lecture 5 input – output in ‘c’eShikshak

Mesics lecture 3 c – constants and variableseShikshak

Lecture 7 relational_and_logical_operatorseShikshak

Lecture21 categoriesof userdefinedfunctions.ppteShikshak

Recently uploaded (20)

AI vs Human Writing: Can You Tell the Difference?Shashi Sathyanarayana, Ph.D

OWASP Barcelona 2025 Threat Model LibraryPetraVukmirovic

9-1-1 Addressing: End-to-End Automation Using FMESafe Software

This session will cover a common use case for local and state/provincial governments who create and/or maintain their 9-1-1 addressing data, particularly address points and road centerlines. In this session, you'll learn how FME has helped Shelby County 9-1-1 (TN) automate the 9-1-1 addressing process; including automatically assigning attributes from disparate sources, on-the-fly QAQC of said data, and reporting. The FME logic that this presentation will cover includes: Table joins using attributes and geometry, Looping in custom transformers, Working with lists and Change detection.

Securing AI - There Is No Try, Only Do!.pdfPriyanka Aash

Techniques for Automatic Device Identification and Network Assignment.pdfPriyanka Aash

OpenPOWER Foundation & Open-Source Core InnovationsIBM

penPOWER offers a fully open, royalty-free CPU architecture for custom chip design. It enables both lightweight FPGA cores (like Microwatt) and high-performance processors (like POWER10). Developers have full access to source code, specs, and tools for end-to-end chip creation. It supports AI, HPC, cloud, and embedded workloads with proven performance. Backed by a global community, it fosters innovation, education, and collaboration.

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.

"Database isolation: how we deal with hundreds of direct connections to the d...Fwdays

What can go wrong if you allow each service to access the database directly? In a startup, this seems like a quick and easy solution, but as the system scales, problems appear that no one could have guessed. In my talk, I'll share Solidgate's experience in transforming its architecture: from the chaos of direct connections to a service-based data access model. I will talk about the transition stages, bottlenecks, and how isolation affected infrastructure support. I will honestly show what worked and what didn't. In short, we will analyze the controversy of this talk.

FIDO Seminar: New Data: Passkey Adoption in the Workforce.pptxFIDO Alliance

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.

MuleSoft for AgentForce : Topic Center and API Catalogshyamraj55

This presentation dives into how MuleSoft empowers AgentForce with organized API discovery and streamlined integration using Topic Center and the API Catalog. Learn how these tools help structure APIs around business needs, improve reusability, and simplify collaboration across teams. Ideal for developers, architects, and business stakeholders looking to build a connected and scalable API ecosystem within AgentForce.

Creating Inclusive Digital Learning with AI: A Smarter, Fairer FutureImpelsys Inc.

Have you ever struggled to read a tiny label on a medicine box or tried to navigate a confusing website? Now imagine if every learning experience felt that way—every single day. For millions of people living with disabilities, poorly designed content isn’t just frustrating. It’s a barrier to growth. Inclusive learning is about fixing that. And today, AI is helping us build digital learning that’s smarter, kinder, and accessible to everyone. Accessible learning increases engagement, retention, performance, and inclusivity for everyone. Inclusive design is simply better design.

FIDO Seminar: Targeting Trust: The Future of Identity in the Workforce.pptxFIDO Alliance

FIDO Alliance Seminar State of Passkeys.pptxFIDO Alliance

The Future of Technology: 2025-2125 by Saikat Basu.pdfSaikat Basu

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.

"How to survive Black Friday: preparing e-commerce for a peak season", Yurii ...Fwdays

From Manual to Auto Searching- FME in the Driver's SeatSafe Software

Finding a specific car online can be a time-consuming task, especially when checking multiple dealer websites. A few years ago, I faced this exact problem while searching for a particular vehicle in New Zealand. The local classified platform, Trade Me (similar to eBay), wasn’t yielding any results, so I expanded my search to second-hand dealer sites—only to realise that periodically checking each one was going to be tedious. That’s when I noticed something interesting: many of these websites used the same platform to manage their inventories. Recognising this, I reverse-engineered the platform’s structure and built an FME workspace that automated the search process for me. By integrating API calls and setting up periodic checks, I received real-time email alerts when matching cars were listed. In this presentation, I’ll walk through how I used FME to save hours of manual searching by creating a custom car-finding automation system. While FME can’t buy a car for you—yet—it can certainly help you find the one you’re after!

2025_06_18 - OpenMetadata Community Meeting.pdfOpenMetadata

The community meetup was held Wednesday June 18, 2025 @ 9:00 AM PST. Catch the next OpenMetadata Community Meetup @ https://www.meetup.com/openmetadata-meetup-group/ In this month's OpenMetadata Community Meetup, "Enforcing Quality & SLAs with OpenMetadata Data Contracts," we covered data contracts, why they matter, and how to implement them in OpenMetadata to increase the quality of your data assets! Agenda Highlights: 👋 Introducing Data Contracts: An agreement between data producers and consumers 📝 Data Contracts key components: Understanding a contract and its purpose 🧑‍🎨 Writing your first contract: How to create your own contracts in OpenMetadata 🦾 An OpenMetadata MCP Server update! ➕ And More!

FIDO Seminar: Evolving Landscape of Post-Quantum Cryptography.pptxFIDO Alliance

AI vs Human Writing: Can You Tell the Difference?Shashi Sathyanarayana, Ph.D

OWASP Barcelona 2025 Threat Model LibraryPetraVukmirovic

9-1-1 Addressing: End-to-End Automation Using FMESafe Software

Securing AI - There Is No Try, Only Do!.pdfPriyanka Aash

Techniques for Automatic Device Identification and Network Assignment.pdfPriyanka Aash

OpenPOWER Foundation & Open-Source Core InnovationsIBM

You are not excused! How to avoid security blind spots on the way to productionMichele Leroux Bustamante

"Database isolation: how we deal with hundreds of direct connections to the d...Fwdays

FIDO Seminar: New Data: Passkey Adoption in the Workforce.pptxFIDO Alliance

Connecting Data and Intelligence: The Role of FME in Machine LearningSafe Software

MuleSoft for AgentForce : Topic Center and API Catalogshyamraj55

Creating Inclusive Digital Learning with AI: A Smarter, Fairer FutureImpelsys Inc.

FIDO Seminar: Targeting Trust: The Future of Identity in the Workforce.pptxFIDO Alliance

FIDO Alliance Seminar State of Passkeys.pptxFIDO Alliance

The Future of Technology: 2025-2125 by Saikat Basu.pdfSaikat Basu

OpenACC and Open Hackathons Monthly Highlights June 2025OpenACC

"How to survive Black Friday: preparing e-commerce for a peak season", Yurii ...Fwdays

From Manual to Auto Searching- FME in the Driver's SeatSafe Software

2025_06_18 - OpenMetadata Community Meeting.pdfOpenMetadata

FIDO Seminar: Evolving Landscape of Post-Quantum Cryptography.pptxFIDO Alliance

Array linear data_structure_2 (1)

1. Array(ADT) Linear Data Structure www.eshikshak.co.in

2. What are Arrays ? ● An Aay is collection of elements stored in adjacent memory locations. ● By ‘finite’ – specific number of elements in an Aay ● By ‘similar’ – all the elements in an Aay are of the same data type www.eshikshak.co.in

3. What are Arrays ? (cont.) ● An Aay containing number of element is reference using an index values 0, 1, …n-1 ○ Lower bound–Lowest index value ○ Upper bound–Highest index value ● An Aay is set of pairs of an index and a value, for each index there is value associated with it. ● Various categories of Aay ○ 1D, 2D and Multi-D www.eshikshak.co.in

4. What are Arrays ? (Cont.) ● The number of elements in the Aay is called its range. ● No matter how big an Aay is, its elements are always stored in contiguous memory locations. www.eshikshak.co.in

5. Array Operations Operation Description Traversal Processing each element in the Aay Search Finding the location of an element with a given value Insertion Adding new element to an Aay Deletion Removing an element from an Aay Sorting Organizing the elements in some order Merging Combining two Aays into a single Aay Reversing Reversing the elements of an Aay www.eshikshak.co.in

6. Row-Major and Column-Major Arrangement ● All the elements of Aay are stored in adjacent memory. ● This leads to two possible Aangements of elements in memory ○ Row Major ○ ColumnMajor ● Base address , no. of rows ,& no. of columns helps to know any element in an Aay www.eshikshak.co.in

7. Algorithm for Array Traversal ● Let A be a linear Aay with Lower Bound LB and Upper Bound UB. The following algorithm traverses A applying an operations PROCESS to each element of A Step 1. Initialize Counter Set Counter = LB Step 2. Repeat steps 3 and 4 while counter <= UB Else GoTo Step 5 Step 3. Visit element Apply PROCESS to A[counter] Step 4. Increase Counter Set counter = counter + 1 GoTo 2 Step 5. Exit www.eshikshak.co.in

8. Algorithm for Insertion Let A be a Linear Array, N is number of elements, k is the positive integer such that k<=N, VAL to insert element at kth Position in an Array A Step 1. Start Step 2. Initialize Counter Set J = N Step 3. Repeat Steps 3 and 4 while J>=k otherwise GoTo Step Step 4. Move Jth element downward Set A[J+1] = A[J] Step 5. Decrease Counter Set J = J + 1 End of step 2 loop Step 6. Insert element Set A[k] = ITEM Step 7. Reset N Set N = N + 1 Step 8. Exit www.eshikshak.co.in

9. Algorithm for Deletion DELETE(A, N, K, VAL) Let A be an linear Aay. N is the number of elements, k is the positive integer such that k<=N. The algorithm deletes kth element from the Aay. Step 1. Start Step 2. Set VAL = A[k] Step 3. Repeat for J = k to N-1 [Move J+1 element Upward] Set A[J] = A[J+1) End of Loop Step 4. Reset the number N of elements in A Set N = N– 1 Step 5. Exit www.eshikshak.co.in

10. Algorithm for Linear Search Suppose A is linear Array with N elements, and VAL is the given item of information. This algorithm finds the location LOC of item in A or sets LOC=0 if search is unsuccessful Step 1. Start Step 2. [Insert VAL at the end of A] Set A[N+1] = VAL Step 3. [Initialize counter] SET LOC = 1 Step 4. [Search for VAL] Repeat while A[LOC] != VAL Set LOC = LOC + 1 [End of loop] Step 5. [Successful ?] if LOC = N+1 then set LOC = 0 Step 6. Exit www.eshikshak.co.in

11. Algorithm for sorting Let A be an Aay of N elements. The following algorithm sorts the elements of A. Step 1. Start Step 2. Repeat Steps 2 and 3 for k=1 to N-1 Step 3. Set PTR = 1 [Initialize pass pointer PTR] Step 4. Repeat while PTR<=N-K [Execute Pass] a. If A[PTR] > A[PTR+1], then Interchange A[PTR] and A[PTR+1] [End of if structure] b. Set PTR = PTR + 1 [End of inner loop] [End of step1 outer loop] Step 5. Exit www.eshikshak.co.in

Array linear data_structure_2 (1)

Recommended

More Related Content

What's hot (20)

Viewers also liked (13)

Similar to Array linear data_structure_2 (1) (20)

More from eShikshak (20)

Recently uploaded (20)

Array linear data_structure_2 (1)