An algorithm for sorting that uses the divide-and-conquer strategy is called merge sort. It works by recursively breaking the input array into smaller subarrays, sorting those subarrays, and then merging them back together to form the sorted array.
Merge Sort refers to the Divide and Conquer method. This strategy divides an issue into subproblems. The solution is found by solving each sub-problem individually. Finally, the results of the subproblems are integrated to create the final answer.
Merge sort is a common sorting algorithm identified for its speed and stability. To sort a given array of elements, it uses the divide-and-conquer algorithm.
This occurs when no sorting is necessary, implying that the array is already sorted. The best-case time complexity for merging sort is O(n*logn).
This arises when the array elements are jumbled and do not appropriately climb or descend. The average case time complexity for merge sort is O(n*logn).
This occurs when array members must be sorted in reverse order. That is, assume you need to sort the array elements in ascending order but the elements are in descending order. The worst-case time complexity of merge sort is O(n * logn).
We need an additional sorted array to store the final sorted array, hence the space-time complexity is O(n).