Merge Sort as Divide and Conquer in DSA Java - 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 does the 'merge' step do?

It combines two sorted arrays into one sorted array by repeatedly picking the smallest element from the front of each array.

intermediate

Why is Merge Sort considered efficient for large data sets?

Because it consistently divides the problem into smaller parts and merges them in O(n log n) time, which is faster than simple sorting methods for big arrays.

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 no further division is needed.

intermediate

How does Merge Sort handle the original array during sorting?

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

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

A O(log n)

B O(n log n)

C O(n^2)

D O(n)

Merge Sort divides the array and merges in O(n log n) time consistently.

Which step is NOT part of the Merge Sort algorithm?

A Divide the array into halves

B Merge the sorted halves

C Swap adjacent elements repeatedly

D Sort each half recursively

Swapping adjacent elements repeatedly is typical of Bubble Sort, not Merge Sort.

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

A The array is considered sorted

B The array is merged immediately

C The array is split further

D The algorithm stops completely

An array with one element is already sorted, so recursion stops dividing further.

Which data structure is primarily used during the merge step?

A Linked lists

B Stacks

C Queues

D Temporary arrays

Temporary arrays hold elements while merging two sorted halves.

Merge Sort is an example of which algorithm design technique?

A Divide and Conquer

B Dynamic Programming

C Greedy Algorithm

D Backtracking

Merge Sort divides the problem into smaller parts and conquers each recursively.

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

Think about breaking the problem into smaller parts and then combining results.

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 in the context of divide and conquer?

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. Build a heap and extract elements in sorted order

Solution

1. Step 1: Understand divide and conquer in Merge Sort

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

2. Step 2: Merge sorted halves

   It then merges these sorted halves back together to form a sorted array.

3. Final Answer:

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

4. Quick Check:

   Divide and conquer = split, sort, merge [OK]

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

Common Mistakes:

• Confusing Merge Sort with Bubble Sort
• Thinking Merge Sort uses pivot like Quick Sort
• Ignoring the merge step after dividing

2. Which of the following is the correct way to declare the merge function header in Java for merging two sorted subarrays in Merge Sort?

easy

A. int merge(int arr[], int start, int end)

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

C. void merge(int arr[], int low, int high)

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

Solution

1. Step 1: Identify parameters needed for merging

   The merge function needs the array and indices: left, mid, right to merge two halves.

2. Step 2: Check method signature correctness

   It returns void and takes int[] arr, int left, int mid, int right as parameters.

3. Final Answer:

   void merge(int[] arr, int left, int mid, int right) -> Option B

4. Quick Check:

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

Hint: Merge needs array and three indices: left, mid, right [OK]

Common Mistakes:

• Using wrong parameter names or missing mid index
• Returning int instead of void
• Confusing start/end with left/right without mid

3. What will be the output of the following Java code snippet using Merge Sort on the array [4, 1, 3, 9, 7]?

int[] arr = {4, 1, 3, 9, 7};
mergeSort(arr, 0, arr.length - 1);
System.out.println(Arrays.toString(arr));

medium

A. [1, 4, 3, 7, 9]

B. [4, 1, 3, 7, 9]

C. [9, 7, 4, 3, 1]

D. [1, 3, 4, 7, 9]

Solution

1. Step 1: Apply Merge Sort to the array

   Merge Sort divides and merges to sort: [4,1,3,9,7] -> [1,3,4,7,9]

2. Step 2: Verify final sorted array

   The sorted array is ascending: [1, 3, 4, 7, 9]

3. Final Answer:

   [1, 3, 4, 7, 9] -> Option D

4. Quick Check:

   Sorted array = ascending order [OK]

Hint: Merge Sort always outputs sorted ascending array [OK]

Common Mistakes:

• Confusing merge step output with partial arrays
• Assuming original order remains
• Mixing up ascending and descending order

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

void merge(int[] arr, int left, int mid, int right) {
  int n1 = mid - left;
  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 calculation of n1 size

B. Wrong copying of elements into R array

C. Missing return statement in void method

D. Using < instead of <= in for loops

Solution

1. Step 1: Check size calculations for subarrays

   n1 should be mid - left + 1 and n2 should be right - mid to cover all elements.

2. Step 2: Identify impact of wrong sizes

   Incorrect sizes cause array index errors or missing elements during merge.

3. Final Answer:

   Incorrect calculation of n1 size -> Option A

4. Quick Check:

   Sizes must include mid element [OK]

Hint: Remember: n1 = mid-left+1, n2 = right-mid [OK]

Common Mistakes:

• Forgetting +1 in n1 size calculation
• Copying elements starting at wrong index
• Confusing loop bounds with <= instead of <

5. Given an array with many duplicate values, how should the merge step in Merge Sort be adapted to maintain stability (preserve original order of equal elements)?

hard

A. When elements are equal, copy from the left subarray first

B. When elements are equal, copy from the right subarray first

C. Skip duplicates during merge to reduce array size

D. Sort duplicates separately after merge

Solution

1. Step 1: Understand stability in sorting

   Stable sort keeps equal elements in original order.

2. Step 2: Apply stability rule in merge step

   When equal, copy element from left subarray first to preserve order.

3. Final Answer:

   When elements are equal, copy from the left subarray first -> Option A

4. Quick Check:

   Left first on equals = stable merge [OK]

Hint: Copy left element first on equal values to keep stability [OK]

Common Mistakes:

• Copying right element first breaks stability
• Removing duplicates during merge
• Sorting duplicates separately after merge