Merge Sort Algorithm in DSA Java - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main idea behind the Merge Sort algorithm?

Merge Sort works by dividing the list into smaller parts, sorting each part, and then merging them back together in order.

intermediate

What is the time complexity of Merge Sort in the average and worst cases?

The time complexity of Merge Sort is O(n log n) for both average and worst cases, where n is the number of elements to sort.

intermediate

Why does Merge Sort require extra space compared to some other sorting algorithms?

Merge Sort needs extra space because it creates temporary arrays to hold the divided parts while merging them back in sorted order.

beginner

Show the result of merging two sorted arrays: [1, 4, 7] and [2, 3, 6].

The merged sorted array is [1, 2, 3, 4, 6, 7].

beginner

What are the two main steps in the Merge Sort algorithm?

1. Divide the array into halves until each part has one element.
2. Merge the parts back together in sorted order.

What technique does Merge Sort use to sort the array?

A Greedy

B Brute force

C Dynamic programming

D Divide and conquer

Merge Sort divides the array into smaller parts, sorts them, and merges them back, which is the divide and conquer approach.

What is the space complexity of Merge Sort?

A O(n)

B O(n log n)

C O(log n)

D O(1)

Merge Sort requires O(n) extra space for temporary arrays during the merge process.

Which of these is NOT a step in Merge Sort?

A Splitting the array into halves

B Merging sorted halves

C Swapping adjacent elements repeatedly

D Recursively sorting subarrays

Swapping adjacent elements repeatedly is a step in Bubble Sort, not Merge Sort.

If you have an array of 8 elements, how many times will it be split in Merge Sort before merging?

A 7 times

B 4 times

C 3 times

D 8 times

An array of 8 elements is split into single elements by splitting 7 times (n-1 splits).

What is the best case time complexity of Merge Sort?

A O(n)

B O(n log n)

C O(log n)

D O(n^2)

Merge Sort always runs in O(n log n) time regardless of input order.

Explain how Merge Sort divides and merges an array to sort it.

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

Describe the advantages and disadvantages of using Merge Sort compared to simpler sorting methods.

Consider speed, memory use, and stability.

Practice

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

easy

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

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

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

D. Build a heap and extract elements in sorted order

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

   It then merges these sorted halves to produce a fully sorted array.

3. Final Answer:

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

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 in place without merging
• Believing it picks smallest elements one by one

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

easy

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

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

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

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

Solution

1. Step 1: Identify parameters needed for merging

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

2. Step 2: Check return type and parameter order

   It returns nothing (void) and parameters match the signature in void merge(int[] arr, int left, int mid, int right).

3. Final Answer:

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

4. Quick Check:

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

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

Common Mistakes:

• Using wrong parameter order
• Returning int instead of void
• Missing mid index parameter

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

int[] arr = {4, 1, 3, 2};
mergeSort(arr, 0, arr.length - 1);
System.out.println(Arrays.toString(arr));

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: Understand mergeSort on {4,1,3,2}

   It splits into {4,1} and {3,2}, sorts each half to {1,4} and {2,3}.

2. Step 2: Merge sorted halves

   Merging {1,4} and {2,3} results in fully sorted array {1,2,3,4}.

3. Final Answer:

   [1, 2, 3, 4] -> Option A

4. Quick Check:

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

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

Common Mistakes:

• Confusing merge order
• Not sorting halves before merging
• Printing original unsorted array

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

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 + j];
  // merging logic follows...
}

medium

A. Array L should be of size n1 + 1

B. Lengths n1 and n2 should be mid - left + 1 and right - mid

C. Copying elements from arr to R should start from mid + 1

D. No bug, code is correct

Solution

1. Step 1: Check array lengths and copying ranges

   Lengths n1 = mid - left + 1 and n2 = right - mid are correct for the left half (left to mid) and right half (mid+1 to right).

2. Step 2: Verify copying elements to R array

   Copying to R starts at arr[mid], but it should start at arr[mid + 1] to avoid overlap.

3. Final Answer:

   Copying elements from arr to R should start from mid + 1 -> Option C

4. Quick Check:

   Right half copy starts at mid+1 [OK]

Hint: Right half starts copying from mid+1, not mid [OK]

Common Mistakes:

• Starting right half copy at mid instead of mid+1
• Incorrect array length calculation
• Assuming no overlap in copying

5. Given an array with many duplicate elements, how can you modify Merge Sort to maintain the original order of equal elements (stable sort)?

hard

A. When merging, always pick from the right subarray first

B. Merge Sort cannot be stable with duplicates

C. Use a hash map to track duplicates and reorder after sorting

D. When merging, if elements are equal, pick from the left subarray first

Solution

1. Step 1: Understand stability in sorting

   A stable sort keeps equal elements in their original order.

2. Step 2: Modify merge step for stability

   When merging, if elements are equal, always choose the element from the left subarray first to preserve order.

3. Final Answer:

   When merging, if elements are equal, pick from the left subarray first -> Option D

4. Quick Check:

   Stable merge picks left element first on ties [OK]

Hint: Pick left element first when equal to keep order [OK]

Common Mistakes:

• Picking right element first breaks stability
• Thinking Merge Sort is unstable by default
• Using extra data structures unnecessarily