The document discusses various searching algorithms like linear search and binary search. It provides details on how linear search works by sequentially checking each element of an unsorted array to find a target value. Binary search is described as more efficient for sorted arrays, as it repeatedly divides the search space in half by comparing the target to the middle element. Implementation of both iterative and recursive binary search algorithms is demonstrated through pseudocode.
A one-dimensional array stores elements linearly such that they can be accessed using an index, the document provides an example of finding the address of an element in a 1D array and taking user input to store in an array and display all elements, and abstract data types provide only the interface of a data structure without implementation details.
This document discusses various searching and sorting algorithms. It begins by defining searching as finding an element in a given list. Linear search and binary search are described as two common searching algorithms. Linear search has O(n) time complexity while binary search has O(log n) time complexity but requires a sorted list. The document also discusses different sorting algorithms like bubble sort, insertion sort, and merge sort, and defines key concepts related to sorting like stability, efficiency, and passes.
An array is a collection of similar data types stored in contiguous memory locations. Arrays allow storing multiple values in a single variable rather than declaring separate variables for each value. There are different ways to initialize and access array elements using indexes. Common array operations include traversing, searching, sorting, copying elements between arrays, and performing element-wise operations on multiple arrays. Popular searching techniques include linear search and binary search, while common sorting algorithms are bubble sort and selection sort.
The document discusses linear search and binary search algorithms.
Linear search is the simplest search algorithm that works by sequentially checking each element of a list to see if it matches the search item. It has linear time complexity of O(n) as it may need to traverse the entire list in the worst case.
Binary search works on sorted lists by comparing the search item to the middle element and recursively searching either the left or right half. It has logarithmic time complexity of O(log n) as it cuts the search space in half each step. Pseudocode and examples are provided to illustrate both algorithms.
The document discusses two searching algorithms - linear search and binary search. Linear search sequentially compares the target element to each element in the array, while binary search uses a divide and conquer approach to quickly hone in on the target element in a sorted array. Both algorithms are demonstrated with pseudocode and examples.
The document discusses linear and binary search algorithms.
Linear search is the simplest algorithm that sequentially compares each element of an array to the target element. It has a worst case time complexity of O(N).
Binary search works on a sorted array by comparing the middle element to the target. It eliminates half of the remaining elements with each comparison. It has a worst case time complexity of O(log n), which is faster than linear search for large data sets.
Pseudocode and C programs are provided as examples to implement linear and binary search.
The document discusses various searching algorithms like linear search and binary search. It provides details on how linear search works by sequentially checking each element of an unsorted array to find a target value. Binary search is described as more efficient for sorted arrays, as it repeatedly divides the search space in half by comparing the target to the middle element. Implementation of both iterative and recursive binary search algorithms is demonstrated through pseudocode.
A one-dimensional array stores elements linearly such that they can be accessed using an index, the document provides an example of finding the address of an element in a 1D array and taking user input to store in an array and display all elements, and abstract data types provide only the interface of a data structure without implementation details.
This document discusses various searching and sorting algorithms. It begins by defining searching as finding an element in a given list. Linear search and binary search are described as two common searching algorithms. Linear search has O(n) time complexity while binary search has O(log n) time complexity but requires a sorted list. The document also discusses different sorting algorithms like bubble sort, insertion sort, and merge sort, and defines key concepts related to sorting like stability, efficiency, and passes.
An array is a collection of similar data types stored in contiguous memory locations. Arrays allow storing multiple values in a single variable rather than declaring separate variables for each value. There are different ways to initialize and access array elements using indexes. Common array operations include traversing, searching, sorting, copying elements between arrays, and performing element-wise operations on multiple arrays. Popular searching techniques include linear search and binary search, while common sorting algorithms are bubble sort and selection sort.
The document discusses linear search and binary search algorithms.
Linear search is the simplest search algorithm that works by sequentially checking each element of a list to see if it matches the search item. It has linear time complexity of O(n) as it may need to traverse the entire list in the worst case.
Binary search works on sorted lists by comparing the search item to the middle element and recursively searching either the left or right half. It has logarithmic time complexity of O(log n) as it cuts the search space in half each step. Pseudocode and examples are provided to illustrate both algorithms.
The document discusses two searching algorithms - linear search and binary search. Linear search sequentially compares the target element to each element in the array, while binary search uses a divide and conquer approach to quickly hone in on the target element in a sorted array. Both algorithms are demonstrated with pseudocode and examples.
The document discusses linear and binary search algorithms.
Linear search is the simplest algorithm that sequentially compares each element of an array to the target element. It has a worst case time complexity of O(N).
Binary search works on a sorted array by comparing the middle element to the target. It eliminates half of the remaining elements with each comparison. It has a worst case time complexity of O(log n), which is faster than linear search for large data sets.
Pseudocode and C programs are provided as examples to implement linear and binary search.
The document discusses searching and sorting algorithms in Java programming. It covers sequential search, binary search, selection sort and insertion sort. Sequential search compares each element to find a match, making it inefficient for large lists. Binary search divides the search space in half at each step, requiring few comparisons. Selection sort finds the smallest element and moves it into position, while insertion sort inserts each element into the proper place. Examples are provided to illustrate how to implement these common algorithms in code.
Here are the steps to solve this problem:
1. Declare and initialize a 2D array to store roll numbers and marks of 5 students in 5 subjects.
2. Write a loop to input roll numbers and marks from the user.
3. Calculate the average of each student by summing marks of all subjects and dividing by total subjects.
4. Write if conditions to check averages above 80 and below 40 and print appropriate roll numbers and averages.
5. Calculate overall average by summing averages of all students and dividing by total students.
6. Print the overall average.
This document discusses various sorting and searching algorithms. It begins by listing sorting algorithms like selection sort, insertion sort, bubble sort, merge sort, and radix sort. It then discusses searching algorithms like linear/sequential search and binary search. It provides details on the implementation and time complexity of linear search, binary search, bubble sort, insertion sort, selection sort, and merge sort. Key points covered include the divide and conquer approach of merge sort and how binary search recursively halves the search space.
IRJET- A Survey on Different Searching AlgorithmsIRJET Journal
The document summarizes and compares several common search algorithms:
- Binary search has the best average time complexity of O(log n) but only works on sorted data. Linear search has average time complexity of O(n) and works on any data but is less efficient.
- Hybrid search combines linear and binary search to search unsorted arrays more efficiently than linear search. Interpolation search is an improvement on binary search that may search in different locations based on the search key value.
- Jump search works on sorted data by jumping in blocks of size sqrt(n) and doing a linear search within blocks. It has better average performance than linear search but only works on sorted data.
An array is a data structure that can store a fixed number of items of the same type. It supports basic operations like traversal, insertion, deletion, search, and update. Each item in an array is called an element, which is accessed via its numerical index. The document then provides examples of code implementing these array operations in C.
This document discusses sequential and interval searching techniques in data structures. It provides examples of linear search and binary search algorithms. Linear search sequentially checks each element to find a match, having a worst-case complexity of O(n). Binary search repeatedly targets the midpoint of a sorted array, dividing the search space in half, with a worst-case complexity of O(log n). Selection sort is also discussed as an example of an iterative sorting algorithm, which finds the minimum element and places it in the first position in each pass.
This document discusses various searching and sorting techniques. It begins by describing linear and binary search methods for searching arrays. It then discusses hashing techniques for storing data, including hash tables, hash functions, and methods for resolving collisions like chaining, linear probing, quadratic probing and double hashing. Finally, it covers different sorting algorithms like bubble sort, selection sort, insertion sort, quick sort and merge sort.
1) The document describes writing an MPI program to calculate a quantity called coverage from data files in a distributed manner across a cluster.
2) MPI (Message Passing Interface) is a standard for writing programs that can run in parallel on multiple processors. The program should distribute the computation efficiently across the cluster nodes and yield the same results as a serial code.
3) The MPI program structure involves initialization, processes running concurrently on nodes, communication between processes, and finalization. Communicators define which processes can communicate.
This document provides an introduction and overview of arrays in C++. It defines what an array is, how to declare and initialize arrays, and how to access, insert, search, sort, and merge array elements. Key points covered include:
- An array is a sequenced collection of elements of the same data type. Elements are referenced using a subscript index.
- Arrays can be declared with a fixed or variable length. Elements are initialized sequentially in memory.
- Common array operations like accessing, inserting, searching, sorting and merging are demonstrated using for loops and examples. Searching techniques include sequential and binary search. Sorting techniques include selection, bubble and insertion sort.
- Arrays are passed
1. Linear search sequentially checks each element of an array to find a target item. It adds the item to the end of the array and uses a counter to check each element until it finds a match.
2. Binary search works on a sorted array. It checks the middle element first, then searches either the left or right half depending on if the target is smaller or larger than the middle element.
3. The example demonstrates linear search finding the letter 'G' in an array and binary search locating the number 44 through a series of steps that narrow the search space.
The document discusses linear and binary search algorithms. Linear search is a sequential search where each element of a collection is checked sequentially to find a target value. Binary search improves on this by checking the middle element first and narrowing the search space in half each time based on the comparison. It allows searching sorted data more efficiently in logarithmic time as opposed to linear time for sequential search. The document provides pseudocode to implement binary search and an example to search an integer in a sorted array.
Searching is an extremely fascinating and useful computer science technique. It helps to find the desired object with its location and number of occurrences. The presentation includes the basic principles, algorithms and c-language implementation.
The document discusses two searching algorithms: linear search and binary search. Linear search sequentially compares an element to each element in an unsorted array, making it slower than binary search. Binary search works by dividing the search space in half at each step based on comparing the target element to the middle element. It is faster than linear search but requires the array to be sorted first. Key differences are that linear search works on unsorted data while binary search requires sorted data, and binary search has better time complexity of O(log n) while linear search is O(n).
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.
This document discusses and compares linear and binary search algorithms. Linear search sequentially scans each element of an unordered array to find a target value, with search time proportional to the number of elements. Binary search works on a sorted array, repeatedly dividing the search space in half and comparing the target to the middle element to determine the space to search next, locating the target in logarithmic time. Pseudocode is provided for algorithms of both search methods.
Binary search is an algorithm that searches for a value in a sorted list by repeatedly eliminating half of the list from consideration. It examines the middle element of the search area and eliminates the upper half if it is smaller than the target or the lower half if it is larger. This process repeats with the narrowed search area until the target is found or the entire area is eliminated. Binary search runs in O(log N) time as it cuts the search space in half each step. Java provides Arrays.binarySearch and Collections.binarySearch methods to perform binary searches on arrays and lists.
The document discusses searching and sorting algorithms in Java programming. It covers sequential search, binary search, selection sort and insertion sort. Sequential search compares each element to find a match, making it inefficient for large lists. Binary search divides the search space in half at each step, requiring few comparisons. Selection sort finds the smallest element and moves it into position, while insertion sort inserts each element into the proper place. Examples are provided to illustrate how to implement these common algorithms in code.
Here are the steps to solve this problem:
1. Declare and initialize a 2D array to store roll numbers and marks of 5 students in 5 subjects.
2. Write a loop to input roll numbers and marks from the user.
3. Calculate the average of each student by summing marks of all subjects and dividing by total subjects.
4. Write if conditions to check averages above 80 and below 40 and print appropriate roll numbers and averages.
5. Calculate overall average by summing averages of all students and dividing by total students.
6. Print the overall average.
This document discusses various sorting and searching algorithms. It begins by listing sorting algorithms like selection sort, insertion sort, bubble sort, merge sort, and radix sort. It then discusses searching algorithms like linear/sequential search and binary search. It provides details on the implementation and time complexity of linear search, binary search, bubble sort, insertion sort, selection sort, and merge sort. Key points covered include the divide and conquer approach of merge sort and how binary search recursively halves the search space.
IRJET- A Survey on Different Searching AlgorithmsIRJET Journal
The document summarizes and compares several common search algorithms:
- Binary search has the best average time complexity of O(log n) but only works on sorted data. Linear search has average time complexity of O(n) and works on any data but is less efficient.
- Hybrid search combines linear and binary search to search unsorted arrays more efficiently than linear search. Interpolation search is an improvement on binary search that may search in different locations based on the search key value.
- Jump search works on sorted data by jumping in blocks of size sqrt(n) and doing a linear search within blocks. It has better average performance than linear search but only works on sorted data.
An array is a data structure that can store a fixed number of items of the same type. It supports basic operations like traversal, insertion, deletion, search, and update. Each item in an array is called an element, which is accessed via its numerical index. The document then provides examples of code implementing these array operations in C.
This document discusses sequential and interval searching techniques in data structures. It provides examples of linear search and binary search algorithms. Linear search sequentially checks each element to find a match, having a worst-case complexity of O(n). Binary search repeatedly targets the midpoint of a sorted array, dividing the search space in half, with a worst-case complexity of O(log n). Selection sort is also discussed as an example of an iterative sorting algorithm, which finds the minimum element and places it in the first position in each pass.
This document discusses various searching and sorting techniques. It begins by describing linear and binary search methods for searching arrays. It then discusses hashing techniques for storing data, including hash tables, hash functions, and methods for resolving collisions like chaining, linear probing, quadratic probing and double hashing. Finally, it covers different sorting algorithms like bubble sort, selection sort, insertion sort, quick sort and merge sort.
1) The document describes writing an MPI program to calculate a quantity called coverage from data files in a distributed manner across a cluster.
2) MPI (Message Passing Interface) is a standard for writing programs that can run in parallel on multiple processors. The program should distribute the computation efficiently across the cluster nodes and yield the same results as a serial code.
3) The MPI program structure involves initialization, processes running concurrently on nodes, communication between processes, and finalization. Communicators define which processes can communicate.
This document provides an introduction and overview of arrays in C++. It defines what an array is, how to declare and initialize arrays, and how to access, insert, search, sort, and merge array elements. Key points covered include:
- An array is a sequenced collection of elements of the same data type. Elements are referenced using a subscript index.
- Arrays can be declared with a fixed or variable length. Elements are initialized sequentially in memory.
- Common array operations like accessing, inserting, searching, sorting and merging are demonstrated using for loops and examples. Searching techniques include sequential and binary search. Sorting techniques include selection, bubble and insertion sort.
- Arrays are passed
1. Linear search sequentially checks each element of an array to find a target item. It adds the item to the end of the array and uses a counter to check each element until it finds a match.
2. Binary search works on a sorted array. It checks the middle element first, then searches either the left or right half depending on if the target is smaller or larger than the middle element.
3. The example demonstrates linear search finding the letter 'G' in an array and binary search locating the number 44 through a series of steps that narrow the search space.
The document discusses linear and binary search algorithms. Linear search is a sequential search where each element of a collection is checked sequentially to find a target value. Binary search improves on this by checking the middle element first and narrowing the search space in half each time based on the comparison. It allows searching sorted data more efficiently in logarithmic time as opposed to linear time for sequential search. The document provides pseudocode to implement binary search and an example to search an integer in a sorted array.
Searching is an extremely fascinating and useful computer science technique. It helps to find the desired object with its location and number of occurrences. The presentation includes the basic principles, algorithms and c-language implementation.
The document discusses two searching algorithms: linear search and binary search. Linear search sequentially compares an element to each element in an unsorted array, making it slower than binary search. Binary search works by dividing the search space in half at each step based on comparing the target element to the middle element. It is faster than linear search but requires the array to be sorted first. Key differences are that linear search works on unsorted data while binary search requires sorted data, and binary search has better time complexity of O(log n) while linear search is O(n).
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.
This document discusses and compares linear and binary search algorithms. Linear search sequentially scans each element of an unordered array to find a target value, with search time proportional to the number of elements. Binary search works on a sorted array, repeatedly dividing the search space in half and comparing the target to the middle element to determine the space to search next, locating the target in logarithmic time. Pseudocode is provided for algorithms of both search methods.
Binary search is an algorithm that searches for a value in a sorted list by repeatedly eliminating half of the list from consideration. It examines the middle element of the search area and eliminates the upper half if it is smaller than the target or the lower half if it is larger. This process repeats with the narrowed search area until the target is found or the entire area is eliminated. Binary search runs in O(log N) time as it cuts the search space in half each step. Java provides Arrays.binarySearch and Collections.binarySearch methods to perform binary searches on arrays and lists.
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unit 2 First.pptx Searching - Linear and Binary Search
1. SANJIVANI K. B. P. POLYTECHNIC,
KOPARGAON
With NBA ACCREDIATED programs , Approved by AICTE, New Delhi,
Recognized by Govt. of Maharashtra, Affiliated to Maharashtra State Board of
Technical Education, Mumbai, ISO 9001:2015 Certified Institute
Name of Faculty: Prof. Vaibhav A. Parjane
1
3. Unit Outcome
After going through this unit, the student will be able to:
2a. Explain working of the given search method with an example.
2b. Write an algorithm to search the given key using binary
Search method
2c. Write an Algorithm to sort data using a specified sorting
method.
2d. Explain the working of given sorting method step by-step with
an example and small data set.
4. Topic to be Covered
Searching
Linear Search
4
Sanjivani K. B. P. Polytechnic, Kopargaon Department of Computer Technology Prof. Vaibhav A. Parjane
5. Searching
1. Searching is the process to find the given
element in the list with its position.
2. The search is said to be successful if the given
element is found i.e. the element does exist in
the collection such as an array; other wise it is
unsuccessful.
6. Introduction: (Searching)
• Searching: Searching is a process of finding the
location of a particular element in an array is called
searching.
• There are two types of searching:
Linear Search:
Binary Search:
7. Linear Search
• Linear search or sequential search is a method for
finding a particular value in a list that consists of
checking every one of its elements
• One element at a time and in sequence, until the
desired one is found.
• In Linear Search we start searching – sequentially
starting from the first element and continue until
we find the target [ element ] or we are sure that it
is not in the list.
• It is simplest search algorithm
8. Linear Search
• Consider an array of 20 elements.
• Suppose, element 8 is to be search in the array.
9. Linear Search Algorithm
1. Read the total number of array elements in ‘n’, Set
flag = o.
2. Read the ‘n’ array elements in array ‘a’ as – a [o] …
a [n].
3. Read the target to be searched in variable ‘Key’.
4. For i = o to n.
Compare each element of array with ‘Key’ =>
If (a [i] = = Key )
If true then set flag = 1,
and close the loop.
5. Now, if ( flag = = 1) means target found, so print
the location at which target is found.
6. Stop
10. Program:-
# include <stdio.h>
int main ()
{
int a[20],n,i, key , pos , flag = o;
clrscr ();
printf(“n Enter the number of elements in array : n”);
scanf (“% d”, &n);
printf(“n Enter Array element = “);
For (i = o; i < n; i + +)
{
scanf(“%d”, &a[i]);
}
printf(“n Enter the number to search “);
scanf (“%d”, &key);
11. Program:- .. continued
For (i =o; i <n; i + +)
{
if (a[i] == key)
{
pos = i‘;
flag = 1;
break ;
}
}
if (flag == 1 )
printf (“n Element located as %d “n”, pos);
else
printf “n Element not present “);
getch ();
}
12. Linear Search
• The complexity of any searching algorithm depends
on number of comparisons required to find the
element
• Performance of searching algorithm can be found
by counting the number of comparisons in order to
find the given element.
13. Advantages
1. Simple and easy method.
2. Efficient for only small lists.
3. Better for unsorted data.