Searching in Data Structure -Linear Search and Binary search. Detail PPT File completing all basics with time complexity and with advantage and disadvantage

1. Data Structures
Raghav Birla
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2. Searching Methods
Two popular methods for searching
Linear search
Binary search

3. Linear search
In linear search, we compare every elements of the array one by one in a sequential
manner until a match is found

4. Linear search algorithm
Linear_search(A[], n, e)
Step 1) Set i = 0 and loc = 0.
Step 2) repeat steps 3, 4 while(loc=0 && i<n)
Step 3) if A[i] = e, then set loc = i;
Step 4) Set i = i+1
Step 5) If loc = 0 , then, element ‘e’ is not in array
Step 6) else return i (location of e).
Here, A= name of array
n= size of array A
i= used for index of array
e= element to be searched
loc= this will store the location of searched element
Complexity = O(n)

5. Advantages & Disadvantages
 Advantages:
 We can apply for both sorted and unsorted data.
 Best case running time complexity is O(1)
Disadvantages:
Worst case Running Time complexity is O(n)
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6. Binary search
 Here, we compare search elements ‘e’ with the middle element of array A,
that is A[mid], where mid = n/2. and n = size of array.
 In binary search array elements must be sorted (either in ascending or
descending order)
 If e = A[mid]  we have found element
 If e < A[mid]  restrict search to A[0] to A[m-1]
 If e > A[mid]  restrict search to A[m+1] to A[n-1]

7. Algorithm of binary search
Binary_search(A[], n, e)
Step 1) set low = 0, high = n-1
Step 2) repeat steps 3 and 4 while(low<=high)
Step 3) mid = (low + high)/2
Step 4) if (e == A[mid])
Return mid
Else if (e< A[mid])
High = mid – 1
Else low = mid + 1
Step 5) return -1
Here, low = lower bound of array A of size n
high = upper bound of array A
e= element to be searched
::Complexity = O(log n)

8. Advantages & Disadvantages
 Advantages
 Worst case running time complexity is O(logn).
 Perform better than linear search
 Disadvatages
 We can use only for the sorted data.
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