Heap Sort Algorithm in DSA C++ - Time & Space Complexity

Time Complexity: Heap Sort Algorithm

O(n log n)

Understanding Time Complexity

We want to understand how the time taken by heap sort changes as the input size grows.

Specifically, how the number of steps increases when sorting more items.

Scenario Under Consideration

Analyze the time complexity of the following heap sort code snippet.

void heapify(int arr[], int n, int i) {
    int largest = i;
    int left = 2 * i + 1;
    int right = 2 * i + 2;

    if (left < n && arr[left] > arr[largest])
        largest = left;
    if (right < n && arr[right] > arr[largest])
        largest = right;

    if (largest != i) {
        std::swap(arr[i], arr[largest]);
        heapify(arr, n, largest);
    }
}

void heapSort(int arr[], int n) {
    for (int i = n / 2 - 1; i >= 0; i--)
        heapify(arr, n, i);

    for (int i = n - 1; i > 0; i--) {
        std::swap(arr[0], arr[i]);
        heapify(arr, i, 0);
    }
}
    

This code builds a max heap from the array, then repeatedly extracts the largest element to sort the array.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: The heapify function called multiple times to maintain the heap property.
• How many times: First, heapify is called for each non-leaf node once (about n/2 times). Then, it is called once per element during sorting (n times), each possibly recursing down the tree.

How Execution Grows With Input

As the input size grows, the number of operations grows roughly proportional to n times the height of the heap.

Input Size (n)	Approx. Operations
10	About 30 to 40 heapify steps
100	About 700 to 800 heapify steps
1000	About 10,000 to 12,000 heapify steps

Pattern observation: Operations grow a bit faster than linearly but much slower than quadratically, roughly like n times log n.

Final Time Complexity

Time Complexity: O(n log n)

This means the time to sort grows a little more than linearly, multiplying the number of items by the height of the heap.

Common Mistake

[X] Wrong: "Heap sort is just like bubble sort and takes O(n²) time because of nested loops."

[OK] Correct: Heap sort uses a tree structure and heapify steps that take logarithmic time, so it is much faster than simple nested loops.

Interview Connect

Understanding heap sort's time complexity shows you can analyze algorithms that combine loops and recursion, a key skill in many coding challenges.

Self-Check

"What if we changed the heap from a max heap to a min heap? How would the time complexity change?"

Practice

1. What is the main purpose of the heapify operation in the Heap Sort Algorithm?

easy

A. To reverse the array elements

B. To sort the array directly without building a heap

C. To swap the first and last elements of the array

D. To build a max heap or min heap from an unordered array

Solution

1. Step 1: Understand heapify's role

   Heapify rearranges elements to satisfy the heap property, making the array a max heap or min heap.

2. Step 2: Connect heapify to heap sort

   Heap sort uses heapify to build the heap before sorting by repeatedly extracting the root.

3. Final Answer:

   To build a max heap or min heap from an unordered array -> Option D

4. Quick Check:

   Heapify builds heap = A [OK]

Hint: Heapify creates heap structure before sorting [OK]

Common Mistakes:

• Thinking heapify sorts the array directly
• Confusing heapify with swapping elements
• Assuming heapify reverses the array

2. Which of the following is the correct syntax to swap two elements arr[i] and arr[j] in C++ during heap sort?

easy

A. arr.swap(i, j);

B. arr[i] = arr[j]; arr[j] = arr[i];

C. swap(arr[i], arr[j]);

D. swap arr[i], arr[j];

Solution

1. Step 1: Recall C++ swap syntax

   The standard way to swap two elements is using the swap() function with parentheses and commas.

2. Step 2: Check each option

   swap(arr[i], arr[j]); uses correct syntax. arr[i] = arr[j]; arr[j] = arr[i]; overwrites values incorrectly. swap arr[i], arr[j]; misses parentheses. arr.swap(i, j); is invalid method call.

3. Final Answer:

   swap(arr[i], arr[j]); -> Option C

4. Quick Check:

   Correct swap syntax = D [OK]

Hint: Use swap() with parentheses and commas in C++ [OK]

Common Mistakes:

• Assigning values instead of swapping
• Missing parentheses in swap function
• Using non-existent swap methods

3. What will be the output array after applying heap sort on arr = [4, 10, 3, 5, 1]? Print the sorted array.

medium

A. [1, 3, 4, 5, 10]

B. [10, 5, 4, 3, 1]

C. [1, 4, 3, 5, 10]

D. [5, 10, 4, 3, 1]

Solution

1. Step 1: Build max heap from array

   Max heap of [4,10,3,5,1] rearranges to [10,5,3,4,1].

2. Step 2: Extract max and heapify repeatedly

   Extract 10, heapify remaining; then 5, then 4, then 3, then 1 results in sorted ascending array [1,3,4,5,10].

3. Final Answer:

   [1, 3, 4, 5, 10] -> Option A

4. Quick Check:

   Heap sort ascending = [1,3,4,5,10] [OK]

Hint: Heap sort outputs ascending sorted array [OK]

Common Mistakes:

• Confusing max heap with sorted array
• Reading heap array as sorted directly
• Outputting descending order instead

4. Identify the error in this heap sort snippet and choose the correct fix:

void heapify(int arr[], int n, int i) {
  int largest = i;
  int left = 2*i;
  int right = 2*i + 1;
  if (left < n && arr[left] > arr[largest]) largest = left;
  if (right < n && arr[right] > arr[largest]) largest = right;
  if (largest != i) {
    swap(arr[i], arr[largest]);
    heapify(arr, n, largest);
  }
}

medium

A. Remove recursive call to heapify

B. Change left and right to 2*i + 1 and 2*i + 2 respectively

C. Change largest initialization to 0

D. Change swap to arr[i] = arr[largest]

Solution

1. Step 1: Check child index calculation

   In 0-based arrays, left child = 2*i + 1, right child = 2*i + 2. Current code uses 2*i and 2*i + 1 which is incorrect.

2. Step 2: Fix child indices

   Changing left and right to 2*i + 1 and 2*i + 2 fixes indexing and prevents out-of-bound errors.

3. Final Answer:

   Change left and right to 2*i + 1 and 2*i + 2 respectively -> Option B

4. Quick Check:

   Child indices = 2*i+1, 2*i+2 [OK]

Hint: Use 0-based indexing: left=2*i+1, right=2*i+2 [OK]

Common Mistakes:

• Using 1-based indexing formulas in 0-based arrays
• Removing recursion breaks heapify
• Incorrect swap assignment instead of swap function

5. Given an array arr = [12, 11, 13, 5, 6, 7], how many times will the heapify function be called during the build heap phase of heap sort?

hard

A. 4 times

B. 3 times

C. 2 times

D. 5 times

Solution

1. Step 1: Understand build heap calls

   Build heap calls heapify from last non-leaf node down to root. For array size 6, last non-leaf node is at index 2 (0-based).

2. Step 2: Count heapify calls

   Heapify called at indices 2, 1, 0 during build heap phase, totaling 3 calls. However, heapify may call itself recursively inside these calls. Counting only initial calls, it's 3.

3. Step 3: Clarify question intent

   If question means initial calls only, answer is 3. If counting recursive calls inside heapify, index 1 and 0 calls may cause extra heapify calls. For this array, heapify at index 1 calls heapify at index 4 (leaf, no call), index 0 calls heapify at index 1 again, so total calls are 4.

4. Final Answer:

   4 times -> Option A

5. Quick Check:

   Build heap calls heapify 3 + 1 recursive = 4 [OK]

Hint: Count initial + recursive heapify calls during build heap [OK]

Common Mistakes:

• Counting only initial calls, ignoring recursion
• Confusing build heap with sorting phase calls
• Misidentifying last non-leaf node index