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.
linear search and binary search, Class lecture of Data Structure and Algorithms and Python.
Stack, Queue, Tree, Python, Python Code, Computer Science, Data, Data Analysis, Machine Learning, Artificial Intellegence, Deep Learning, Programming, Information Technology, Psuedocide, Tree, pseudocode, Binary Tree, Binary Search Tree, implementation, Binary search, linear search, Binary search operation, real-life example of binary search, linear search operation, real-life example of linear search, example bubble sort, sorting, insertion sort example, stack implementation, queue implementation, binary tree implementation, priority queue, binary heap, binary heap implementation, object-oriented programming, def, in BST, Binary search tree, Red-Black tree, Splay Tree, Problem-solving using Binary tree, problem-solving using BST, inorder, preorder, postorder
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.
In this article, different types of sorting algorithms like the bubble sort, selection sort, etc are discussed. The working method, implementation using C language, and time complexity of different algorithms are also discussed.
linear search and binary search, Class lecture of Data Structure and Algorithms and Python.
Stack, Queue, Tree, Python, Python Code, Computer Science, Data, Data Analysis, Machine Learning, Artificial Intellegence, Deep Learning, Programming, Information Technology, Psuedocide, Tree, pseudocode, Binary Tree, Binary Search Tree, implementation, Binary search, linear search, Binary search operation, real-life example of binary search, linear search operation, real-life example of linear search, example bubble sort, sorting, insertion sort example, stack implementation, queue implementation, binary tree implementation, priority queue, binary heap, binary heap implementation, object-oriented programming, def, in BST, Binary search tree, Red-Black tree, Splay Tree, Problem-solving using Binary tree, problem-solving using BST, inorder, preorder, postorder
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.
In this article, different types of sorting algorithms like the bubble sort, selection sort, etc are discussed. The working method, implementation using C language, and time complexity of different algorithms are also discussed.
In the binary search, if the array being searched has 32 elements in.pdfarpitaeron555
In the binary search, if the array being searched has 32 elements in it, how many elements of the
array must be examined to be certain that the array does not contain the key? What about 1024
elements? Note: the answer is the same regardless of whether the algorithm is recursive or
iterative.
Solution
Binary Search Algorithm- Fundamentals, Implementation and Analysis
Hitesh Garg | May 15, 2015 | algorithms | 5 Comments
Binary Search Algorithm and its Implementation
In our previous tutorial we discussed about Linear search algorithm which is the most basic
algorithm of searching which has some disadvantages in terms of time complexity,so to
overcome them to a level an algorithm based on dichotomic (i.e. selection between two distinct
alternatives) divide and conquer technique is used i.e. Binarysearch algorithm and it is used to
find an element in a sorted array (yes, it is a prerequisite for this algorithm and a limitation too).
In this algorithm we use the sorted array so as to reduce the time complexity to O(log n). In this,
size of the elements reduce to half after each iteration and this is achieved by comparing the
middle element with the key and if they are unequal then we choose the first or second half,
whichever is expected to hold the key (if available) based on the comparison i.e. if array is sorted
in an increasing manner and the key is smaller than middle element than definitely if key exists,
it will be in the first half, we chose it and repeat same operation again and again until key is
found or no more elements are left in the array.
Recursive Pseudocode:
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// initially called with low = 0, high = N – 1
BinarySearch_Right(A[0..N-1], value, low, high) {
// invariants: value >= A[i] for all i < low
value < A[i] for all i > high
if (high < low)
return low
mid = low +((high – low) / 2) // THIS IS AN IMPORTANT STEP TO AVOID BUGS
if (A[mid] > value)
return BinarySearch_Right(A, value, low, mid-1)
else
return BinarySearch_Right(A, value, mid+1, high)
}
Iterative Pseudocode:
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BinarySearch_Right(A[0..N-1], value) {
low = 0
high = N - 1
while (low <= high) {
// invariants: value >= A[i] for all i < low
value < A[i] for all i > high
mid = low +((high – low) / 2) // THIS IS AN IMPORTANT STEP TO AVOID BUGS
if (A[mid] > value)
high = mid - 1
else
low = mid + 1
}
return low
}
Asymptotic Analysis
Since this algorithm halves the no of elements to be checked after every iteration it will take
logarithmic time to find any element i.e. O(log n) (where n is number of elements in the list) and
its expected cost is also proportional to log n provided that searching and comparing cost of all
the elements is same
Data structure used -> Array
Worst case performance -> O(log n)
Best case performance -> O(1)
Average case performance -> O(log n)
Worst case space complexity -> O(1)
So the idea is-
RECURSIVE Implementation of Binary search in C programming language
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Array is a container which can hold a fix number of items and these items should be of the same type. Most of the data structures make use of arrays to implement their algorithms. Following are the important terms to understand the concept of array.
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Searching
1. By: Er. Anupam Sharma
Assistant Professor in CSE
Searching
2. Linear search
Linear search is the simplest searching algorithm
which is sometimes known as sequential search.
In this algorithm each element of array is
compared with the targeted element sequentially.
Worst Case Time Complexity -O(N)
Best Case Time Complexity: O(1)
3. Linear Search Algorithm
Linear Search ( Array A, Value x)
Step 1: Set i to 1
Step 2: if i > n then go to step 7
Step 3: if A[i] = x then go to step 6
Step 4: Set i to i + 1
Step 5: Go to Step 2
Step 6: Print Element x Found at index i and go to step 8
Step 7: Print element not found
Step 8: Exit
4. Pseudocode for Linear Search
procedure linear_search (list, value)
for each item in the list
if match item == value
return the item's location
end if
end for
end procedure
6. Program for Linear Search in C
#include<stdio.h>
int main()
{
int a[20],i,x,n;
printf("How many elements?");
scanf("%d",&n);
printf("Enter array elements:n");
for(i=0;i<n;++i)
scanf("%d",&a[i]);
printf("nEnter element to search:");
scanf("%d",&x);
for(i=0;i<n;++i)
if(a[i]==x)
break;
if(i<n)
printf("Element found at index %d",i);
else
printf("Element not found");
return 0;
}
Output
How many elements?4
Enter array elements:
6 8 9 1
Enter element to
search:9
Element found at index
2
7. Binary Search
Binary search algorithm can be applied on a sorted array
to search an element. Search begins with comparing
middle element of array to target element. If both are
equal then position of element is returned. If target
element is less than middle element of array then upper
half of array is discarded and again search continued by
dividing the lower half. If target element is greater than
middle element then lower half is discarded and search is
continued in upper half.
Worst Case Time Complexity: O(log n)
Best Case Time Complexity: O(1)
8. Binary Search Algorithm
Following are the steps of implementation that we will
be following:
Start with the middle element:
If the target value is equal to the middle element of the
array, then return the index of the middle element.
If not, then compare the middle element with the target
value,
If the target value is greater than the number in the middle
index, then pick the elements to the right of the middle index,
and start with Step 1.
If the target value is less than the number in the middle index,
then pick the elements to the left of the middle index, and start
with Step 1.
When a match is found, return the index of the
element matched.
9. Pseudo code for Binary Search
Procedure binary_search
A ← sorted array
n ← size of array
x ← value to be searched
Set lowerBound = 1
Set upperBound = n
while x not found
if upperBound < lowerBound
EXIT: x does not exists.
set midPoint = lowerBound + ( upperBound - lowerBound ) / 2
if A[midPoint] < x set lowerBound = midPoint + 1
if A[midPoint] > x set upperBound = midPoint - 1
if A[midPoint] = x EXIT: x found at location midPoint
end while
end procedure
11. #include <stdio.h>
int main()
{ int c, first, last, middle, n, search, array[100];
printf("Enter number of elementsn");
scanf("%d",&n);
printf("Enter %d integersn", n);
for (c = 0; c < n; c++)
scanf("%d",&array[c]);
printf("Enter value to findn");
scanf("%d", &search);
first = 0; last = n - 1;
middle = (first+last)/2;
while (first <= last) {
if (array[middle] < search)
first = middle + 1;
else if (array[middle] == search) {
printf("%d found at location %d.n", search, middle+1);
break; }
else
last = middle - 1;
middle = (first + last)/2; }
if (first > last)
printf("Not found! %d isn't present in the list.n", search);
return 0; }
Program for Binary Search in
C