When to use each Sorting Algorithm
A sorting algorithm is an algorithm that makes the input data set arranged in a certain order. The fundamental task is to put the items in the desired order so that the records are re-arranged for making searching easier. Below is one by one description of when to use which sorting algorithm for better performance:
1. Selection Sort
This sorting algorithm sorts an array by repeatedly finding the minimum element (considering ascending order) from the unsorted part and putting it at the beginning. The algorithm maintains two subarrays in a given array, the subarray which is already sorted, and the remaining subarray which is unsorted. In every iteration of the selection sort, the minimum element (considering ascending order) from the unsorted subarray is picked and moved to the sorted subarray.
We can use Selection Sort as per the below constraints:
- When the list is small. As the time complexity of the selection sort is O(N2) which makes it inefficient for a large list.
- When memory space is limited because it makes the minimum possible number of swaps during sorting.
2. Bubble Sort
This sorting algorithm is the simplest sorting algorithm that works by repeatedly swapping the adjacent elements if they are in the wrong order. If we have total N elements, then we need to repeat the above process for N-1 times.
We can use Bubble Sort as per the below constraints:
- It works well with large datasets where the items are almost sorted because it takes only one iteration to detect whether the list is sorted or not. But if the list is unsorted to a large extend then this algorithm holds good for small datasets or lists.
- This algorithm is fastest on an extremely small or nearly sorted set of data.
3. Insertion Sort
This sorting algorithm is a simple sorting algorithm that works the way we sort playing cards in our hands. It places an unsorted element at its suitable place in each iteration.
We can use Insertion Sort as per the below constraints:
- If the data is nearly sorted or when the list is small as it has a complexity of O(N2) and if the list is sorted a minimum number of elements will slide over to insert the element at its correct location.
- This algorithm is stable and it has fast running case when the list is nearly sorted.
- The usage of memory is a constraint as it has space complexity of O(1).
4. Merge Sort
This sorting algorithm is based on the Divide and Conquer algorithm. It divides the input array into two halves, calls itself for the two halves, and then merges the two sorted halves. The merge() function is used for merging two halves. The merge(arr, l, m, r) is a key process that assumes that arr[l . . . m] and arr[m+1 . . . r] are sorted and merges the two sorted sub-arrays into one.
We can use Merge Sort as per the below constraints:
- Merge sort is used when the data structure doesn’t support random access since it works with pure sequential access that is forward iterators, rather than random access iterators.
- It is widely used for external sorting, where random access can be very, very expensive compared to sequential access.
- It is used where it is known that the data is similar data.
- Merge sort is fast in the case of a linked list.
- It is used in the case of a linked list as in a linked list for accessing any data at some index we need to traverse from the head to that index and merge sort accesses data sequentially and the need of random access is low.
- The main advantage of the merge sort is its stability, the elements compared equally retain their original order.
5. Quick Sort
This sorting algorithm is also based on the Divide and Conquer algorithm. It picks an element as a pivot and partitions the given list around the picked pivot. After partitioning the list on the basis of the pivot element, the Quick is again applied recursively to two sublists i.e., the sublist to the left of the pivot element and the sublist to the right of the pivot element.
We can use Quick Sort as per the below constraints:
- Quick sort is the fastest, but it is not always O(N*log N), as there are worst cases where it becomes O(N2).
- Quicksort is probably more effective for datasets that fit in memory. For larger data sets it proves to be inefficient so algorithms like merge sort are preferred in that case.
- Quick Sort is an in-place sort (i.e. it doesn’t require any extra storage) so it is appropriate to use it for arrays.
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