Welcome, aspiring programmer! Today's topic is Merge Sort. Merge Sort is a sorting technique similar to arranging a deck of shuffled cards in order. However, for data on an Internet scale, Merge Sort outperforms regular techniques. Today, we'll explore Merge Sort, code it in C++, and analyze its speed. Ready? Let's get started!

In computer science, Merge Sort is a popular method for sorting elements. Merge Sort uses the same 'divide-and-conquer' strategy for sorting as the familiar Quick Sort algorithm. In the three steps of Merge Sort:

1. Split the array into halves.
2. Sort each half separately.
3. Merge the sorted halves back together.

We will start by building code for merging two sorted parts. The merge process makes two halves play sort and seek. It compares elements from two halves and merges them so that the resulting list is sorted as well.

Let's code a merge() function in C++ that will do just that. Note that the final variant of the Merge Sort function will perform every operation "in place," meaning there will not be actual two arrays; we will operate on parts of one array. Bearing this in mind, let's implement the merge function to take just one array and treat its parts like separate arrays.

C++
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1void merge(int arr[], int left, int mid, int right)
2{
3    int n1 = mid - left + 1; // Find number of elements in the left array
4    int n2 = right - mid; // Find the number of elements in the right array
5
6    int* Left = new int[n1]; // Define left array
7    int* Right = new int[n2]; // Define right array
8
9    // Let's fill in these arrays
10    for (int i = 0; i < n1; i++)
11        Left[i] = arr[left + i];
12    for (int j = 0; j < n2; j++)
13        Right[j] = arr[mid + 1 + j];
14}

So far, we've divided our original list into two halves, Left and Right.

Now, we'll sort and merge these halves:

C++
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1    int i = 0, j = 0;
2    int k = left;
3    while (i < n1 && j < n2)
4    {
5        if (Left[i] <= Right[j])
6        {
7            arr[k++] = Left[i++];
8        }
9        else
10        {
11            arr[k++] = Right[j++];
12        }
13    }
14}

Seemingly tricky, the code is very straightforward:

• We place two pointers, i and j, at the beginning of the Left and Right arrays.

• We choose the smaller element, put it in the final array arr, and move the corresponding pointer further.

• We keep doing this until one of the pointers reaches the end of its array.

We stop the process when one of the pointers reaches the end of its array, but some elements could be left in the other array.

To handle this, let's copy the remaining elements of both arrays (if any) to the end of the resulting arr array.

C++
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1    // Copy remaining elements of Left[] if any
2    while (i < n1)
3    {
4        arr[k++] = Left[i++];
5    }
6
7    // Copy remaining elements of Right[] if any
8    while (j < n2)
9    {
10        arr[k++] = Right[j++];
11    }
12
13    delete[] Left; // Clean up dynamically allocated memory
14    delete[] Right; // Clean up dynamically allocated memory

The merge section is completed. It successfully merges two halves back together in sorted order.

Now, let's implement the method to divide the array into two halves. Coding-wise, we'll need to define a sort() method to split the array and manage the merge process. We will split the array and its halves recursively until we end up with small arrays of just one element, which are naturally sorted! Next, we will merge these arrays back together into one big sorted array.

Let's start building the sort() method. Initially, we'll handle the splitting part:

C++
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1void sort(int arr[], int left, int right)
2{
3    if (left < right)
4    {
5        int mid = left + (right - left) / 2; // Finding the midpoint
6    }
7}

Now, we can split our list into two halves, but they still need to be sorted.

Next, we need to sort these halves and merge them together:

C++
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1        sort(arr, left, mid); // Sorting the left half
2        sort(arr, mid + 1, right); // Sorting the right half
3        merge(arr, left, mid, right); // Merging the sorted halves
4    }
5}

Phew! We've now implemented our Merge Sort algorithm in C++.

Here is the full code for the Merge Sort algorithm implemented in C++:

C++
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1#include <iostream>
2
3using namespace std;
4
5// Method to merge two halves
6void merge(int arr[], int left, int mid, int right)
7{
8    int n1 = mid - left + 1;
9    int n2 = right - mid;
10
11    int* Left = new int[n1];
12    int* Right = new int[n2];
13
14    // Fill the left and right arrays
15    for (int i = 0; i < n1; i++)
16        Left[i] = arr[left + i];
17    for (int j = 0; j < n2; j++)
18        Right[j] = arr[mid + 1 + j];
19
20    int i = 0, j = 0;
21    int k = left;
22    while (i < n1 && j < n2)
23    {
24        if (Left[i] <= Right[j])
25        {
26            arr[k++] = Left[i++];
27        }
28        else
29        {
30            arr[k++] = Right[j++];
31        }
32    }
33
34    // Copy remaining elements of Left[] if any
35    while (i < n1)
36    {
37        arr[k++] = Left[i++];
38    }
39
40    // Copy remaining elements of Right[] if any
41    while (j < n2)
42    {
43        arr[k++] = Right[j++];
44    }
45
46    delete[] Left;
47    delete[] Right;
48}
49
50// Method to divide and sort the array
51void sort(int arr[], int left, int right)
52{
53    if (left < right)
54    {
55        int mid = left + (right - left) / 2;
56
57        // Recursively sorting the first and second halves
58        sort(arr, left, mid);
59        sort(arr, mid + 1, right);
60
61        // Merging the sorted halves
62        merge(arr, left, mid, right);
63    }
64}
65
66int main() 
67{
68    int arr[] = {38, 27, 43, 3, 9, 82, 10};
69    int arr_size = sizeof(arr)/sizeof(arr[0]);
70
71    cout << "Given array is \n";
72    for(int i = 0; i < arr_size; i++)
73        cout << arr[i] << " ";
74    cout << endl;
75
76    sort(arr, 0, arr_size - 1);
77
78    cout << "\nSorted array is \n";
79    for(int i = 0; i < arr_size; i++)
80        cout << arr[i] << " ";
81    cout << endl;
82
83    return 0;
84}

In the computing world, performance matters. The less time it takes for a sorting algorithm to run, the better. Merge Sort shows good performance with a time complexity of O(n log n), similar to sorting a huge deck of cards quickly. Thus, it excels when dealing with massive data sets.

Merge Sort is consistent. It's like a reliable late-night tutor that offers predictable performance, regardless of the initial order of the data input. However, it tends to use extra memory, creating new arrays during the merge process.

Great job! We've broken down Merge Sort and coded it in C++. Next up, we have some exciting hands-on exercises for you. Ready to put what you've learned into practice? Let's dive into the fun part! Let's get coding!