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Sorting is the process of organizing elements in a structured manner. QuickSort is one of the most popular sorting algorithms that uses nlogn comparisons to sort an array of n elements in a typical situation. QuickSort is based on the divide-and-conquer strategy.

QuickSort is a sorting algorithm that uses a divide-and-conquer strategy to sort an array. It does so by selecting a pivot element and then sorting values larger than it on one side and smaller to the other side, and then it repeats those steps until the array is sorted. It is useful for sorting big data sets.

Well take a look at the quicksort algorithm in this tutorial and see how it works.

QuickSort is a fast sorting algorithm that works by splitting a large array of data into smaller sub-arrays. This implies that each iteration splits the input into two components, sorts them and then recombines them. The technique is highly efficient for big data sets because its average and best-case complexity is O(n*logn).

QuickSort was created by Tony Hoare in 1961 and remains one of the most effective general-purpose sorting algorithms available today. It works by recursively sorting the sub-lists to either side of a given pivot and dynamically shifting elements inside the list around that pivot.

As a result, the quicksort method can be summarized in three steps:

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Lets take a look at an example to get a better understanding of the quicksort algorithm. In this example, the array below contains unsorted values, which we will sort using quicksort.

The process starts by selecting one element known as the pivot from the list. This can be any element. A pivot can be:

For this example, well use the last element, 4, as our pivot.

Now, the goal here is to rearrange the list such that all the elements less than the pivot are to its left, and all the elements greater than the pivot are to the right of it. Remember:

Lets simplify the above example,

As elements 2, 1, and 3 are less than 4, they are on the pivots left side. Elements can be in any order: 1,2,3, or 3,1,2, or 2,3,1. The only requirement is that all of the elements must be less than the pivot. Similarly, on the right side, regardless of their sequence, all components should be greater than the pivot.

In other words, the algorithm searches for every value that is smaller than the pivot. Values smaller than pivot will be placed on the left, while values larger than pivot will be placed on the right. Once the values are rearranged, it will set the pivot in its sorted position.

Once we have partitioned the array, we can break this problem into two sub-problems. First, sort the segment of the array to the left of the pivot, and then sort the segment of the array to the right of the pivot.

Lets cover a few key advantages of using quicksort:

Despite being the fastest algorithm, quicksort has a few drawbacks. Lets look at some of the drawbacks of quicksort.

The subarrays are rearranged in a certain order using the partition method. You will find various ways to partition. Here, we will see one of the most used methods.

Lets look at quicksort programs written in JavaScript and Python programming languages. Wellstart by creating a function that allows you to swap two components.

Now, lets add a function that uses the final element (last value) as the pivot, moves all smaller items to the left of pivot, and all larger elements to the right of the pivot, and places the pivot in the appropriate location in the sorted array.

Next, lets add the main function that will partition and sort the elements.

Lets finish off by adding a function to print the array.

Here is the full code of quicksort implementation for JavaScript:

Now, lets look at quicksort programs written in Python.Well start by creating a function which is responsible for sorting the first and last elements of an array.

Next, well add the main function that implements quick_sort.

Lets finish off by adding a function to print the array.

Here is the full code of quicksort implementation in Python.

Lets look at the space and time complexity of quicksort in the best, average and worst case scenarios. In general, the time consumed by quicksort can be written as follows:

Here, T(k) and T(n-k-1)refer to two recursive calls, while the last term O(n) refers to the partitioning process. The number of items less than pivot is denoted by k.

When the partitioning algorithm always chooses the middle element or near the middle element as the pivot, the best case scenario happens. Quicksorts best-case time complexity is O (n*logn). The following is the best-case recurrence.

This occurs when the array elements are in a disordered sequence that isnt increasing or decreasing properly. QuickSorts average case time complexity is O(n*logn). The following is the average-case recurrence.

The worst-case situation is when the partitioning algorithm picks the largest or smallest element as the pivot element every time. The worst-case time complexity of quicksort is O (n2). The following is the worst-case recurrence.

The space complexity for quicksort is O(log n).

The sorting algorithm is used to find information, and since quicksort is the fastest, it is frequently used as a more efficient search approach.

QuickSort may have a few drawbacks, but it is the fastest and most efficient sorting algorithm available. QuickSort has an O (logn) space complexity, making it an excellent choice for situations where space is restricted.

Although the worst-case running time is always the same, quicksort is often faster than HeapSort (nlogn). QuickSort takes up less space than heap sort due to the fact that a heap is nearly a full binary tree with pointers overhead. So, when it comes to sorting arrays, quicksort is preferred.

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There might be a few drawbacks to quicksort, but it is the fastest sorting algorithm out there. QuickSort is an efficient algorithm that performs well in practice.

In this article, we learned what quicksort is, its benefits and drawbacks and how to implement it.

See more here:

QuickSort Algorithm: An Overview - Built In