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# Fastest Sorting Algorithm

Last Updated on September 22, 2023 by Mayank Dham The quest for the fastest sorting algorithm has been a cornerstone of computer science for decades. Sorting algorithms are essential tools used to organize data into a specific order efficiently. However, with various sorting algorithms available, each boasting its strengths and weaknesses, the question of which one is the fastest remains a subject of interest and debate. In this article, we delve into the world of sorting algorithms, explore some of the fastest contenders, and analyze the factors that influence their performance. By understanding the trade-offs between speed, memory usage, and input characteristics, we can make informed decisions about selecting the most suitable sorting algorithm for specific scenarios. Let’s see which is the fastest sorting algorithm?

## Which is the Fastest Sorting Algorithm?

Quick sort is the fastest sorting algorithm. The quick sort algorithm works in O(n*logn) time for the best and average cases. Now, we will understand the quick sort(fastest sorting algorithm) in detail.

## What is Selection Sort?

In the selection sort algorithm, first, a random element is picked up from an array which is called the pivot. The pivot divides the array into two parts and this process continues until the array becomes sorted.

There are various ways to select the pivot element from the array.

• Select the first element as the pivot element
• Select the last element as the pivot element
• Select the random element as the pivot element
• Select the median element as the pivot element

After selecting a pivot, we need to partition around that pivot. The partition() function is very important in quick sorting. If we selected pivot p after that, we need to put all the smaller elements than p to the left side of p and all the greater elements to the right side of the p. Let’s see how the partition() function works using an example. In the above example, first, we have chosen the last element (5) as a pivot element and we can see that we have divided the array into two subarrays around 5. For the left subarray, we have again chosen the last element (4) as a pivot and we did partition around 4. For the right subarray, we have again chosen the last element (8) as a pivot and we did partition around 8. We will repeat this process until the partition size is less than or equal to 1.

## Pseudo code for the Quick Sort (fastest sorting algorithm):

Pseudo code for the Quick Sort:

``````quick_sort(array[], low, high){
if(low < high){
// find the right position for the pivot element
p=partition(array, low, high)

// perform quick_sort on the left subarray
quick_sort(array[], low, p-1)

// perform quick_sort on the right subarray
quick_sort(array[], p+1, high)
}
}``````

Pseudo code for the partition():

``````partition(array[], low, high){
// select the last element as a pivot
pivot = array[high];

i = (low – 1)  // Index of the smaller element and indicates the
// right position of pivot found so far

for (j = low; j <= high- 1; j++){

// If the current element is smaller than the pivot
if (array[j]  pivot (80 > 30) so we will not do anything and move ahead.``````

## Quick Sort (Fastest Sorting Algorithm) with an Example Dry-Run:

Let’s take an example to understand how partition works in the quick sort algorithm. First, we will choose the pivot element from the above array.
pivot = last element = 30

We will also initialize two variables,
i=0
j=0

Now, let’s see how the partition() function works.

Run a loop for j = low to j = high-1

First pass (i=0, j=0):
We will compare arr[j] with pivot, in this case, arr > pivot (80 > 30) so we will not do anything and move ahead. Second pass (i=0, j=1):
We will compare arr[j] with pivot, in this case, arr > pivot (70 > 30) so we will not do anything and move ahead. Third pass (i=0, j=2):
We will compare arr[j] with pivot, in this case, arr > pivot (60 > 30) so we will not do anything and move ahead. Fourth pass (i=0, j=3):
We will compare arr[j] with pivot, in this case, arr 30) so we will swap arr[i] (80) and arr[j] (20) and increment the value of i. After performing the swap, the array will look like the one below. Fifth pass (i=0, j=4):
We will compare arr[j] with pivot, in this case, arr 30) so we will swap arr[i] (70) and arr[j] (10) and increment the value of i. After performing the swap, the array will look like the one below. Sixth pass (i=0, j=5):
We will compare arr[j] with pivot, in this case, arr > pivot (900 > 30) so we will not do anything and move ahead. After completion of all the passes, in the end, we will swap arr[i] (60) and pivot (30). After performing the swap, the array will look like the one below. In the above image, we can see that the pivot element (30) is in its right position. All the elements left to the pivot are smaller than the pivot and all the elements right to the pivot are greater than the pivot.

## Quick Sort Program:

We know how quick sort algorithm work. Now, let’s see how to write the program for quick sorting (the fastest sorting algorithm).

```# Python3 implementation of QuickSort

def partition(array, low, high):
pivot = array[high]

i = low - 1
for j in range(low, high):
if array[j] <= pivot:
i = i + 1
# Swapping element at i with element at j
array[i], array[j] = array[j], array[i]

# Swap the pivot element with element at index i
array[i + 1], array[high] = array[high], array[i + 1]

# Return the position from where partition is done
return i + 1

def quick_sort(array, low, high):
if low < high:

# Find position of pivot element
p = partition(array, low, high)

# perform quick sort on left part
quick_sort(array, low, p - 1)

# perfrom quick sort on right part
quick_sort(array, p + 1, high)

array = [10, 7, 8, 9, 1, 5]

print(f'Array before performing quick sort: {array}')

quick_sort(array, 0, len(array) - 1)

print(f'Array after performing quick sort: {array}')

```

Output:

``````Array before performing quick sort: [10, 7, 8, 9, 1, 5]
Array after performing quick sort: [1, 5, 7, 8, 9, 10]``````

Conclusion
Determining the fastest sorting algorithm is not a straightforward task, as it heavily depends on the specific context in which the sorting is performed. Algorithms like Quick Sort, Merge Sort, and Heap Sort have shown remarkable speed under certain conditions. However, the efficiency of a sorting algorithm is influenced by various factors such as input size, data distribution, memory usage, and implementation details. What is considered the fastest can change based on the requirements of the problem at hand. Choosing the right sorting algorithm involves understanding these factors and selecting an algorithm that aligns with the specific characteristics of the data and the desired performance trade-offs.

## FAQs Related to Quick Sort Algorithm

1) The Quick Sort algorithm is in place?
Yes, the Quick Sort algorithm is in place. We are using extra space only to store recursive function calls.

2) The Quick Sort algorithm is stable?
This default Quick Sort algorithm is not stable. Though, we can make the Quick Sort algorithm stable by considering the index for comparisons.

3) Why Quick Sort is preferred over Merge Sort?
In the Quick Sort algorithm, we do not require any extra space. While, in Merge Sort, we require O(n) extra space where n is the size of the array. This extra space of Merge Sort used for allocation and de-allocating takes much time and that is why Quick Sort is preferred over Merge Sort.

4) What are other ways to implement the Quick Sort algorithm?
We can also implement quick sorting using the iterative method. The Quick Sort can also be implemented using a singly linked list and a doubly linked list.