Linear search(recursive) algorithm used in _____________
Question
Linear search(recursive) algorithm used in _____________
Solution
The Linear search (recursive) algorithm is used in computer programming. Here are the steps to implement it:
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Start from the leftmost element of the array and one by one compare the element to be searched with each element of the array.
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If the element to be searched is found, return the index of the element.
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If the element is not found in the array, return -1 or a message stating that the element is not in the array.
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If the array is not empty, repeat the process by calling the function recursively, but this time for a smaller array (excluding the element that has just been checked).
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The base case for the recursion would be when the array is empty, i.e., when the element is not found in the array.
This algorithm is simple and easy to implement, but it is not efficient for large datasets as its time complexity is O(n), where n is the number of elements in the array.
Similar Questions
Linear search(recursive) algorithm used in _____________ans.Never usedWhen the size of the dataset is largeWhen the dataset is unorderedWhen the size of the dataset is low Previous Marked for Review Next
Program to implement linear search.
Which of the following code snippet performs linear search recursively? for(i=0;i<n;i++) { if(a[i]==key) printf("element found"); } LinearSearch(int[] a, n,key) { if(n<1) return False if(a[n]==key) return True else LinearSearch(a,n-1,key) } LinearSearch(int[] a, n,key) { if(n<1) return True if(a[n]==key) return False else LinearSearch(a,n-1,key) } LinearSearch(int[] a, n,key) { if(n<1) return False if(a[n]==key) return True else LinearSearch(a,n+1,key) }
In a linear search, how does the search process proceed?Divides the array into halves and searches each half recursivelyCompares each element with the target value sequentiallyUses a hash table to find the target valueSorts the array and performs a binary search
The complexity of linear search algorithm isQuestion 6Answera.O(log n)b.Oc.O(n log n)d.O(n2)
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