Merge Sort Algorithm in DSA C# - Time & Space Complexity

Time Complexity: Merge Sort Algorithm

O(n log n)

Understanding Time Complexity

We want to understand how the time taken by merge sort changes as the list size grows.

How does the number of steps grow when sorting bigger lists?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

public void MergeSort(int[] array, int left, int right) {
    if (left >= right) return;
    int mid = (left + right) / 2;
    MergeSort(array, left, mid);
    MergeSort(array, mid + 1, right);
    Merge(array, left, mid, right);
}

void Merge(int[] array, int left, int mid, int right) {
    int[] temp = new int[right - left + 1];
    int i = left, j = mid + 1, k = 0;
    while (i <= mid && j <= right) {
        temp[k++] = (array[i] <= array[j]) ? array[i++] : array[j++];
    }
    while (i <= mid) temp[k++] = array[i++];
    while (j <= right) temp[k++] = array[j++];
    for (int m = 0; m < temp.Length; m++) {
        array[left + m] = temp[m];
    }
}
    

This code sorts an array by dividing it into halves, sorting each half, and then merging them back together.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Recursive splitting and merging of subarrays.
• How many times: The array is split into halves recursively until single elements remain, then merged back up.
• The merge step loops through elements to combine two sorted halves.

How Execution Grows With Input

Each split divides the list in half, creating layers of work. At each layer, merging all parts takes about the same total steps as the size of the list.

Input Size (n)	Approx. Operations
10	About 40 steps
100	About 700 steps
1000	About 10,000 steps

Pattern observation: The steps grow a bit faster than just doubling; they grow roughly like n times log n.

Final Time Complexity

Time Complexity: O(n log n)

This means if the list size doubles, the work grows a little more than double, but much less than square.

Common Mistake

[X] Wrong: "Merge sort is as slow as bubble sort because it compares elements repeatedly."

[OK] Correct: Merge sort smartly splits the list and merges sorted parts, so it does fewer total comparisons than bubble sort, which compares every pair repeatedly.

Interview Connect

Understanding merge sort's time complexity shows you can analyze recursive divide-and-conquer algorithms, a key skill in many coding challenges.

Self-Check

"What if we changed the merge step to use extra space only once for the whole array instead of for each merge? How would the time complexity change?"

Practice

1. What is the main idea behind the Merge Sort algorithm?

easy

A. Pick the smallest element and place it at the beginning repeatedly

B. Divide the list into halves, sort each half, then merge them

C. Compare each element with every other element and swap if needed

D. Build a sorted list by inserting elements one by one

Solution

1. Step 1: Understand the divide and conquer approach

   Merge Sort splits the list into two halves recursively until each sublist has one element.

2. Step 2: Merge sorted halves

   Then it merges these small sorted lists back together in order to form a sorted list.

3. Final Answer:

   Divide the list into halves, sort each half, then merge them -> Option B

4. Quick Check:

   Merge Sort = Divide and conquer [OK]

Hint: Remember: split, sort halves, then merge [OK]

Common Mistakes:

• Confusing Merge Sort with Bubble Sort
• Thinking it sorts by swapping adjacent elements
• Believing it sorts by inserting elements one by one

2. Which of the following is the correct way to declare the merge function header in C# for Merge Sort?

easy

A. void Merge(int[] arr, int left, int mid, int right)

B. int Merge(int arr[], int left, int mid, int right)

C. void merge(int arr[], int start, int end)

D. int merge(int[] arr, int left, int right)

Solution

1. Step 1: Identify correct parameter types and names

   The merge function typically takes the array and three indices: left, mid, and right to merge two sorted halves.

2. Step 2: Check C# syntax for method declaration

   In C#, arrays are declared as int[] arr and the function returns void because it modifies the array in place.

3. Final Answer:

   void Merge(int[] arr, int left, int mid, int right) -> Option A

4. Quick Check:

   Correct signature = void Merge(int[] arr, int left, int mid, int right) [OK]

Hint: Look for void return and correct parameters [OK]

Common Mistakes:

• Using incorrect return type (int instead of void)
• Wrong parameter types or order
• Using lowercase function names in C#

3. What will be the output of the following C# code snippet using Merge Sort on array [4, 1, 3, 2]?

int[] arr = {4, 1, 3, 2};
MergeSort(arr, 0, arr.Length - 1);
Console.WriteLine(string.Join(", ", arr));

medium

A. 1, 3, 2, 4

B. 4, 3, 2, 1

C. 1, 2, 3, 4

D. 2, 1, 3, 4

Solution

1. Step 1: Apply Merge Sort to the array [4, 1, 3, 2]

   Split into [4,1] and [3,2], sort each half to [1,4] and [2,3].

2. Step 2: Merge the two sorted halves

   Merging [1,4] and [2,3] results in [1, 2, 3, 4].

3. Final Answer:

   1, 2, 3, 4 -> Option C

4. Quick Check:

   Sorted array = 1, 2, 3, 4 [OK]

Hint: Merge halves stepwise to get sorted array [OK]

Common Mistakes:

• Confusing merge order and producing unsorted output
• Not splitting array correctly
• Printing before sorting completes

4. Identify the bug in this Merge Sort merge function snippet:

void Merge(int[] arr, int left, int mid, int right) {
  int n1 = mid - left + 1;
  int n2 = right - mid;
  int[] L = new int[n1];
  int[] R = new int[n2];
  for (int i = 0; i < n1; i++) L[i] = arr[left + i];
  for (int j = 0; j < n2; j++) R[j] = arr[mid + 1 + j];
  // merging logic follows
}

medium

A. Incorrect array sizes for L and R

B. Loop conditions should use < instead of <= to avoid out of bounds

C. Wrong indices used for copying elements

D. Missing return statement in void function

Solution

1. Step 1: Check array size calculations

   The size of the left array should be mid - left + 1 to include the mid element; the right array size is correct as right - mid.

2. Step 2: Analyze loop conditions for copying elements

   Loops should use < to avoid out of bounds errors.

3. Final Answer:

   Incorrect array sizes for L and R -> Option A

4. Quick Check:

   Left array size must be mid - left + 1 [OK]

Hint: Array size for left = mid - left + 1 [OK]

Common Mistakes:

• Using <= causes index out of range errors
• Miscalculating array sizes
• Confusing mid index offsets

5. Given a large dataset with many duplicate values, how can you modify the Merge Sort algorithm to maintain stability (keep duplicates in original order)?

hard

A. Change Merge Sort to Quick Sort for better stability

B. Sort duplicates separately after Merge Sort completes

C. Use a hash set to remove duplicates before sorting

D. When merging, if elements are equal, take element from left subarray first

Solution

1. Step 1: Understand stability in sorting

   A stable sort keeps equal elements in the same order as original.

2. Step 2: Modify merge step to preserve order

   When merging, if elements are equal, always pick from the left subarray first to keep original order.

3. Final Answer:

   When merging, if elements are equal, take element from left subarray first -> Option D

4. Quick Check:

   Stable merge = pick left element first on ties [OK]

Hint: Pick left element first when equal during merge [OK]

Common Mistakes:

• Removing duplicates before sorting changes data
• Assuming Quick Sort is stable (it is not)
• Sorting duplicates separately after merge