Mastering the Merge Sort Algorithm: A Comprehensive Guide

Within computer science, sorting algorithms play a crucial role in structuring data. The larger the data, the more you can relay in the merge sort algorithm than in basic algorithms. This blog discusses the mechanics and practical application of merge sort, shedding light on its complexities, advantages, and disadvantages. 

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What is the Merge Sort Algorithm?

Merge sort is a popular sorting algorithm that follows the divide-and-conquer rule to efficiently sort a list or array of elements. The algorithm works by recursively dividing the unsorted list into smaller sublists until each sublist contains only one element. Then, it systematically merges these sublists in a sorted manner until the entire list is sorted. The key step in the merge process involves comparing elements from the two sorted sublists and merging them into a new sorted sublist.

Merge sort is known for its stability, consistent performance characteristics (O(n log n) time complexity in the worst, average, and best cases), and suitability for sorting large datasets. It requires additional memory for the merging process, making it less space-efficient compared to some other algorithms. Its reliable and efficient performance makes it a popular choice in various applications where sorting is essential.

How Does the Merge Sort Algorithm Work?

Merge sort operates on the principle of divide and conquer, breaking down the sorting process into smaller, more manageable tasks. Here’s a step-by-step explanation of how it works:

1. Divide: The unsorted array is recursively divided into halves until individual elements remain, creating a set of subproblems.
2. Conquer: Each subproblem involves sorting the individual elements and establishing a foundation for the merging phase.
3. Merge: The sorted subarrays are systematically merged to reconstruct a fully sorted array. During this process, elements are compared and rearranged to ensure the correct order.

Steps 1 to 3 are repeated recursively until the entire array is sorted.

Merge sort is effective because it divides a difficult sorting process into smaller, more manageable steps, guaranteeing a dependable and consistent sorting result.

Let’s study the working of the merge sort algorithm:

Consider the input array to be: [9, 6, 4, 7, 1, 3] 

Every time while dividing the array, we take the starting and ending index of the array and divide it by 2.

We will repeat this step until we obtain all the individual elements of the given array.

While conquering the individual elements of the array, each time, we create a new array and copy the sorted elements into it. We will repeat this process until we identify two distinct sorted arrays.

In the final step, we perform a merge operation on the two distinct sorted arrays, resulting in a unified array that serves as our final sorted array.

Implementation of Merge Sort

Here’s a practical implementation of bubble sort in C programming:

#include <stdio.h>
void merge(int arr[], int l, int m, int r) {
    int i, j, k;
    int n1 = m - l + 1;
    int n2 = r - m;
    int L[n1], R[n2];
    for (i = 0; i < n1; i++)
        L[i] = arr[l + i];
    for (j = 0; j < n2; j++)
        R[j] = arr[m + 1 + j];
    i = 0;
    j = 0;
    k = l;
    while (i < n1 && j < n2) {
        arr[k++] = (L[i] <= R[j]) ? L[i++] : R[j++];
    }
    while (i < n1)
        arr[k++] = L[i++];
    while (j < n2)
        arr[k++] = R[j++];
}
void mergeSort(int arr[], int l, int r) {
    if (l < r) {
        int m = l + (r - l) / 2;
        mergeSort(arr, l, m);
        mergeSort(arr, m + 1, r);
        merge(arr, l, m, r);
    }
}
void printArray(int arr[], int size) {
    for (int i = 0; i < size; i++)
        printf("%d ", arr[i]);
    printf("\n");
}
int main() {
    int arr[] = {12, 11, 13, 5, 6, 7};
    int arr_size = sizeof(arr) / sizeof(arr[0]);
    printf("Given array is \n");
    printArray(arr, arr_size);
    mergeSort(arr, 0, arr_size - 1);
    printf("\nSorted array is \n");
    printArray(arr, arr_size);
    return 0;
}

Output: 

Given array is 

12 11 13 5 6 7 

Sorted array is 

5 6 7 11 12 13

Complexities of Merge Sort

Programming complexity is the study of an algorithm’s time and space efficiency, which reveals how the algorithm’s performance changes as the size of the input increases. Merge sort has the following time and space complexities:

Time Complexity

Time complexity is a measure of the computational efficiency of an algorithm, representing the amount of time it takes to run as a function of the size of the input.

• Worst Case: O(n log n) – This occurs when the array is repeatedly divided into halves until individual elements are reached, and then the merging process takes place.
• Average Case: O(n log n) – Similar to the worst case, the algorithm consistently divides the array into halves and then merges them.
• Best Case: O(n log n) – Even when the array is partially sorted, merge sort divides it into halves and merges them, resulting in the same time complexity as the worst and average cases.

Space Complexity

Space complexity refers to the amount of memory an algorithm requires in relation to the size of its input, describing the maximum storage space needed during the execution of the algorithm.

• Total Space: O(n) – Merge sort requires additional space for the temporary arrays used in the merging process. The space complexity is linear with respect to the size of the input array.
• Auxiliary Space: O(n) – The auxiliary space complexity is also linear, as the additional space is used for the temporary arrays during merging.

Advantages and Disadvantages of Merge Sort

Merge sort, an efficient divide-and-conquer sorting algorithm, has its own set of advantages and disadvantages:

Advantages

• Consistent O(n log n) time complexity, making it efficient for large datasets
• Stable sorting algorithm, preserving the relative order of equal elements
• Well-suited for linked lists and external sorting due to its sequential access patterns

Disadvantages

• Requires additional memory space for the temporary arrays during the merging process, leading to higher space complexity
• Slower for small datasets compared to simpler algorithms like insertion sort
• More complex to implement compared to some simpler algorithms

Merge sort’s consistent O(n log n) time complexity and its stability (maintains the relative order of equal elements) make it a reliable choice for sorting large datasets, despite its use of additional space.

Wrap Up

Merge sort is a fundamental sorting algorithm that serves as a foundation for comprehending advanced sorting techniques. Known for its consistent and efficient performance, merge sort is valuable for educational purposes, offering a deeper understanding of divide-and-conquer strategies. As a reliable and stable sorting method with O(n log n) time complexity, merge sort outshines simpler algorithms like bubble sort when dealing with larger datasets. When making decisions for real-world applications, embracing advanced sorting algorithms such as quicksort or merge sort becomes crucial, given their superior efficiency and scalability. It’s essential to assess the advantages of merge sort while considering the trade-offs to ensure optimal algorithm selection for specific business requirements.

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