Which sorting algorithm is the most efficient for large datasets and uses a divide-and-conquer approach?

Merge Sort is a divide-and-conquer algorithm that divides the dataset into smaller subarrays, sorts each recursively, and merges them back to form a sorted array. Its time complexity is O(n log n) in all cases, making it highly efficient for large datasets.Advantages of Merge Sort include:

• Stability: It maintains the order of equal elements.
• Predictable performance: Consistent time complexity across best, worst, and average cases.
• Handles large datasets effectively, especially when external memory (e.g., disk storage) is involved.

Why Other Options Are Incorrect: ·         Option A (Bubble Sort): Bubble Sort has a time complexity of O(n²) and is inefficient for large datasets. It repeatedly swaps adjacent elements, making it suitable only for small or nearly sorted datasets. ·         Option B (Selection Sort): Although Selection Sort also has a time complexity of O(n²) , it is not as efficient as Merge Sort for large datasets due to its lack of adaptability and unnecessary comparisons. ·         Option D (Insertion Sort): Insertion Sort is efficient for small or nearly sorted datasets with a time complexity of O(n²) in the worst case, but it cannot compete with the efficiency of Merge Sort for larger datasets. ·         Option E (Radix Sort): Radix Sort is suitable for sorting integers or strings but relies on digit-based operations. It is efficient for specific data types but less versatile than Merge Sort.