Merge sort is an efficient, general-purpose, comparison-based sorting algorithm. It is a divide and conquer algorithmic paradigm which was developed by John von Neumann in 1945. Merge sort works by recursively breaking down a list into multiple sub-lists until each sub-list consists of only one element and merging those sub-lists in a natural order to produce the sorted list.
Merge sort’s run time complexity is considered optimal, meaning that it cannot be done faster than the O(n log n) time complexity. This makes it a popular choice for sorting large datasets. Merge sort is a stable sorting algorithm, meaning that it preserves the ordering of duplicate elements in the sorted list.
Merge sort is commonly implemented using an iterative, top-down, or bottom-up technique. In the iterative implementation, an auxiliary array is allocated, which is used to store the intermediate results while the list is being sorted. The top-down approach, on the other hand, uses recursion to break down the list into smaller collections of elements, while the bottom-up approach uses a series of merging operations that traverse the data in an upward direction.
The algorithm has many variations, such as the four-way merge sort, the natural merge sort, and the polyphase merge sort. Although merge sort is slower than quicksort, which is another sorting algorithm, its advantage is that its worst-case running time is still O(n log n). This makes it suitable for applications where efficiency is a concern, such as embedded systems, large data sets, and sorting of lists with overhead linked list operations.
Merge sort is used widely in computer science, particularly in the fields of sorting, databases, and algorithms. It is often used in operating systems to order data like files and folders in a meaningful order, and it is also used in software development to simplify the development process.
Merge sort is relatively simple to understand and code, and is a useful sorting algorithm for beginners to learn as an introduction to algorithms.
