Merge Sort as Divide and Conquer in DSA C++ - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main idea behind the 'Divide and Conquer' approach in Merge Sort?

Divide the array into two halves, sort each half recursively, then merge the two sorted halves into one sorted array.

beginner

In Merge Sort, what is the role of the 'merge' step?

The merge step combines two sorted subarrays into a single sorted array by comparing elements one by one.

intermediate

Why is Merge Sort considered efficient for large datasets?

Because it consistently divides the problem into smaller parts and merges them in O(n log n) time, making it faster than simple sorts like bubble sort for large data.

beginner

What is the base case in the recursive Merge Sort algorithm?

When the array has one or zero elements, it is already sorted, so recursion stops.

intermediate

How does Merge Sort handle the original array during sorting?

It does not sort in place; it creates temporary arrays during the merge step to hold sorted elements before copying them back.

What is the time complexity of Merge Sort in the average case?

A O(n)

B O(n^2)

C O(log n)

D O(n log n)

Merge Sort divides the array and merges in logarithmic steps, resulting in O(n log n) time.

Which step in Merge Sort is responsible for combining sorted arrays?

A Divide

B Conquer

C Merge

D Sort

The merge step combines two sorted arrays into one sorted array.

What happens when the array size reaches 1 in Merge Sort?

A The recursion stops because the array is sorted

B The array is split further

C The array is merged immediately

D The array is reversed

An array of size 1 is already sorted, so recursion stops.

Merge Sort is an example of which algorithm design technique?

A Divide and Conquer

B Greedy

C Dynamic Programming

D Backtracking

Merge Sort uses divide and conquer by splitting and merging.

Which of the following is a disadvantage of Merge Sort?

A It is unstable

B It uses extra memory for merging

C It has worst-case O(n^2) time

D It cannot be implemented recursively

Merge Sort requires extra space for temporary arrays during merging.

Explain how Merge Sort uses the divide and conquer approach to sort an array.

Think about splitting, sorting, and merging steps.

Describe the merge step in Merge Sort and why it is important.

Focus on how two sorted parts become one sorted whole.

Practice

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

easy

A. Select a pivot and partition the array around it

B. Sort the array by repeatedly swapping adjacent elements

C. Divide the array into halves, sort each half, then merge them

D. Insert elements one by one into a sorted list

Solution

1. Step 1: Understand the divide and conquer approach

   Merge Sort splits the array into two halves recursively until single elements remain.

2. Step 2: Merge sorted halves

   After splitting, it merges the halves back in sorted order to form the sorted array.

3. Final Answer:

   Divide the array into halves, sort each half, then merge them -> Option C

4. Quick Check:

   Merge Sort = Divide, sort halves, merge [OK]

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

Common Mistakes:

• Confusing Merge Sort with Bubble Sort
• Thinking Merge Sort uses a pivot like Quick Sort
• Believing Merge Sort sorts in place without merging

2. Which of the following is the correct base case condition in a typical Merge Sort implementation in C++?

easy

A. if (start > end) return;

B. if (start < end) return;

C. if (start == end + 1) return;

D. if (start >= end) return;

Solution

1. Step 1: Identify when to stop recursion

   The base case is when the subarray has one or zero elements, meaning start index is greater or equal to end index.

2. Step 2: Match condition to code

   The condition if (start >= end) return; correctly stops recursion when the subarray cannot be divided further.

3. Final Answer:

   if (start >= end) return; -> Option D

4. Quick Check:

   Base case = start >= end [OK]

Hint: Stop recursion when start is not less than end [OK]

Common Mistakes:

• Using start > end misses single element subarray
• Using start == end + 1 is incorrect indexing
• Using start < end returns too early

3. What will be the output of the following C++ code snippet using Merge Sort on array {4, 1, 3, 2}?

void merge(int arr[], int l, int m, int r) {
    int n1 = m - l + 1, n2 = r - m;
    int L[n1], R[n2];
    for (int i = 0; i < n1; i++) L[i] = arr[l + i];
    for (int j = 0; j < n2; j++) R[j] = arr[m + 1 + j];
    int i = 0, j = 0, k = l;
    while (i < n1 && j < n2) {
        if (L[i] <= R[j]) arr[k++] = L[i++];
        else arr[k++] = 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) return;
    int m = l + (r - l) / 2;
    mergeSort(arr, l, m);
    mergeSort(arr, m + 1, r);
    merge(arr, l, m, r);
}

int main() {
    int arr[] = {4, 1, 3, 2};
    int n = 4;
    mergeSort(arr, 0, n - 1);
    for (int i = 0; i < n; i++) std::cout << arr[i] << " ";
    return 0;
}

medium

A. 1 2 3 4

B. 4 3 2 1

C. 1 3 2 4

D. 2 1 3 4

Solution

1. Step 1: Trace recursive splitting and merging

   The array splits into [4,1] and [3,2], then further into single elements. Each pair is merged sorted: [1,4] and [2,3].

2. Step 2: Final merge

   Merging [1,4] and [2,3] results in [1,2,3,4]. The output prints this sorted array.

3. Final Answer:

   1 2 3 4 -> Option A

4. Quick Check:

   Sorted array = 1 2 3 4 [OK]

Hint: Merge Sort always outputs sorted array [OK]

Common Mistakes:

• Confusing merge order causing wrong output
• Forgetting to merge after recursive calls
• Printing array before sorting completes

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

void merge(int arr[], int l, int m, int r) {
    int n1 = m - l + 1, n2 = r - m;
    int L[n1], R[n2];
    for (int i = 0; i <= n1; i++) L[i] = arr[l + i];
    for (int j = 0; j < n2; j++) R[j] = arr[m + 1 + j];
    int i = 0, j = 0, k = l;
    while (i < n1 && j < n2) {
        if (L[i] <= R[j]) arr[k++] = L[i++];
        else arr[k++] = R[j++];
    }
    while (i < n1) arr[k++] = L[i++];
    while (j < n2) arr[k++] = R[j++];
}

medium

A. The loop 'for (int i = 0; i <= n1; i++)' causes out-of-bounds access

B. The merge condition should be 'L[i] < R[j]' instead of '<='

C. The arrays L and R should be dynamically allocated

D. The indices i and j should start from 1, not 0

Solution

1. Step 1: Check loop bounds for copying left array

   The loop uses 'i <= n1', which runs one extra iteration causing out-of-bounds access since valid indices are 0 to n1-1.

2. Step 2: Correct loop condition

   The loop should be 'i < n1' to copy exactly n1 elements without overflow.

3. Final Answer:

   The loop 'for (int i = 0; i <= n1; i++)' causes out-of-bounds access -> Option A

4. Quick Check:

   Loop bounds must be less than array size [OK]

Hint: Check array loops use < not <= for indexing [OK]

Common Mistakes:

• Using <= instead of < in loops
• Starting indices at 1 instead of 0
• Ignoring dynamic allocation but it's not mandatory here

5. Given an array {5, 3, 8, 6, 2, 7, 4, 1}, how many total merge operations will be performed during a full Merge Sort?

hard

A. 15

B. 7

C. 8

D. 4

Solution

1. Step 1: Understand merge operations in Merge Sort

   Merge Sort splits the array until single elements, then merges pairs stepwise. For n=8 elements, merges happen at each level combining subarrays.

2. Step 2: Calculate total merges

   Number of merges = n - 1 = 8 - 1 = 7 merges in total to combine all elements into one sorted array.

3. Final Answer:

   7 -> Option B

4. Quick Check:

   Total merges = n - 1 = 7 [OK]

Hint: Total merges in Merge Sort = array size minus one [OK]

Common Mistakes:

• Counting merges as number of elements
• Confusing merges with recursive calls
• Assuming merges double at each level