Suppose we are to sort a set of 'n' integers using an algorithm with time complexity O (n) that takes 1 ms (milliseconds) in the worst case when n = 50,000. How large an element can the algorithm then handle at least 0.2 ms?

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A: Ans: Suppose arrays A and B are both sorted in increasing order and both contain n elements, the…

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A: Dear Student, The order of an algorithm is based on the highest time taking function.

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A: In the worst-case analysis, we calculate the upper bound on the running time of an algorithm.

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A: Given:

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A:

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Counting sort

The counting sort algorithm sorts an array’s contents by counting the repetition of each element that occurs in the array. A counting sort algorithm is a stable algorithm that works the best when the range of elements in the array is small, say for example from 0 to 9. The count which is the number of repetitions of an element in the array is mapped to an index of an auxiliary array, which is then saved in an auxiliary array. The counting sort algorithm has a stable time complexity of the order of O(size of array + size of auxiliary array). Let’s discuss the algorithm in detail as follows.