## GCSE Computer Science: Merge Sort

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### Frequently Asked Questions

#### What is the time complexity of the merge sort algorithm?

Merge sort has a time complexity of O(n log n) in the best, average, and worst-case scenarios. This is because the algorithm divides the dataset into smaller subsets logarithmically, and then merges the sorted subsets linearly.

#### Is merge sort a stable sorting algorithm?

Yes, merge sort is a stable sorting algorithm. This means that the relative order of equal elements is preserved after the sorting process. Stability is a desirable property in some applications, as it allows for the preservation of additional information associated with the sorted data.

#### How does merge sort compare to other sorting algorithms, such as quick sort?

Merge sort is a highly efficient and stable sorting algorithm, with a time complexity of O(n log n) in all cases. Quick sort, on the other hand, has a best and average-case time complexity of O(n log n), but a worst-case time complexity of O(n^2). While quick sort is often faster in practice, merge sort guarantees a consistent performance, making it more suitable for large datasets and scenarios where stability is important.

#### Does merge sort work well with linked lists?

Yes, merge sort works well with linked lists. In fact, it is one of the most efficient sorting algorithms for linked lists. The algorithm can take advantage of the linked list's inherent properties, such as constant-time insertions and deletions, to merge the sorted subsets without the need for additional memory allocation.

#### Is merge sort an in-place sorting algorithm?

Merge sort is not an in-place sorting algorithm when implemented with arrays, as it requires additional memory for the merging process. However, merge sort can be implemented as an in-place algorithm when working with linked lists, as the merging step can be performed without the need for additional memory allocation.