Why is Quick Sort Preferred For Arrays And Merge Sort For Linked Lists?

Working on different data structures is a part of a programmer’s life!

However, you must understand that every data structure is not the same. The same goes for the algorithms and methods applied to them. Whether you have to merge two sorted linked lists or sort an array, different algorithms are used.

Though, the confusion with so many different sorting algorithms is obvious. One sorting algorithm which works perfectly for one type of data may not work for another. Therefore, the sorting algorithm you have to use is completely dependent on the data structure you are using.

Generally, Quick sort is preferred for sorting elements of an array and merge sort is preferred for linked lists.

Considering that both these sorting algorithms use a divide and conquer approach, it becomes important to understand why quick sort and merge sort are preferred for arrays and linked lists respectively.

Here, let’s discuss some crucial points which justify this point of preference.

What is Quick Sort?

Quick sort follows a divide and conquer approach to sort the elements of an array. The algorithm consists of two major processes. One is to swap the items and the other is to divide and make partitions of the given array.

The algorithm will first choose a pivot element and then it will partition the remaining elements present around it. The elements smaller than the pivot are shifted to its left and the greater elements are shifted to the right. If any element is equal to the pivot, it will also be shifted to the right.

Now, the sorting of elements is done separately for the right and left side elements. A pivot point is chosen in both the subarrays and the same procedure is followed. When all the subarrays are sorted, they are combined together to form a single sorted array.

Now that you are aware of how Quick sort works, let’s understand why it is preferred to sort an array.

Reasons To Choose Quick Sort To Sort An Array

Quick sort is the best sorting algorithm for an array because of the following reasons:

Being an in-place sorting algorithm, quick sort is mostly preferred for sorting arrays. This means that the quick sort requires no additional space to sort an array. Whereas, when it comes to merge sort, it needs O(N) space to complete the algorithm. Here, N represents the size of the array. Continuously, assigning and removing additional space considerably increases the time required to complete the sorting.
When it comes to the average complexity of the sorting algorithms, it is observed that quick sort and merge sort both have the same O(NlogN) complexity. However, this constant sometimes differs. While working on arrays, the merge sort requires O(N) additional storage space and therefore, it is not preferred.
Quick sort also employs a great reference locality and therefore, it is a more cache-friendly sorting algorithm.
With quick sort, the tail call optimization process is also performed because of its tail recursive nature.
Other than this, the randomized version of the quick sort is mostly used in practical life as it works well. This version of the algorithm has O(nlogn) time complexity. However, there are chances that worst-case complexities can occur while working with a randomized version but this does not happen with a specific pattern.

What Is Merge Sort?

Merge sort is another sorting algorithm based on comparison which uses a divide and conquer approach. In this algorithm, the given problem will be divided into different subproblems which are then solved separately. After each subproblem is sorted, the problems are then combined together to form a sorted data structure.

In the merge sort, we first divide the given linked list into two sublists. The sizes of these sublists should be equal or can have a size difference of 1 based on the link length’s parity. After this, the process of division is performed on sublists again till the list of 1 element is not formed.

When done, these elements are sorted and then it merges two sorted linked lists to form a single sorted linked list.

Merge sorts are generally preferred to sort linked lists in practical applications. Let’s understand why.

Reasons To Choose Merge Sort To Sort Linked List?

Merge sort is the best sorting algorithm for a linked list because of the following reasons:

The memory allocation is quite different for linked lists than arrays. Whereas In the linked list, the data nodes are not adjacent to each other. Therefore, operations on linked lists are performed differently than arrays.
In linked lists, you can add items even in the extra space’s middle in the O(1) number of time in case you are provided a reference or a pointer to another node. Therefore, the merging process of the merge sort is performed without any additional space.
Because all the elements of an array are available continuously in the memory, you can access any variable randomly. This means that if 4 elements are there in an array, you can access 3rd variable without accessing others.
Such access is required in the Quick sort. However, when you have to access an ith element of a linked list, you will have to go through all the nodes from the beginning. This is because of discontinuous memory allocation in the linked list. Therefore, there are high chances that overhead increases while using quick sort. When it comes to merge sort, the data is accessed in a sequence and the chances to access randomly are negligible.

How to remove a loop in a linked list?

To remove loop in linked list, a function can be defined which will detect if there is a loop in the given list. If there is any loop, the function will remove the loop and then return true. However, it is important to detect the loop before you remove it.

Bonus Topic

Finding 2 Elements With Difference K In A Sorted Array

Programming as a concept is not only vast but also broadly categorized in terms of storing information and data.

From arrays to linked lists you will find complex data structures that initiate storing of data in a systematic format. Did you know that you could create multidimensional arrays using regular and curly brackets? Since arrays as a form of data structures are used to store data in an efficient manner, you can have multidimensional sorted arrays that are arranged in a definite sequence of alphabetical and numerical order.

This blog will focus on discussing problem statements where you will have to merge k sorted arrays using different methods and approaches. Quite similar to the arrays, we have strings which are also created by eliminating a few components without tampering with the data structure.

This concept is referred to as the subsequence of a string about which we have discussed a few pointers.

What is a sorted array?

Before we discuss the different approaches that you can employ in order to find the difference between k elements in a sorted array, let us clarify the definition for a sorted array. A sorted array can be defined as the structural form of combining data elements that have been arranged alphabetically or numerically.

The elements in a sorted array are placed in a finite form of structure with equal distances between the addresses and the memory of the elements. Learning the different forms of data structure used in the context of storing information is the basic pillar of programming.

Now that you have an idea of what exactly is a sorted array, let us discuss the approaches you can employ for finding 2 elements with k difference in a sorted array.

Conclusion

Different factors play an important role in deciding which sorting algorithm should be used on which data structure. Arrays and linked lists are way too different types of data structures especially when it comes to memory allocation.

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