Quick Sort Algorithm in DSA C - Time & Space Complexity

Time Complexity: Quick Sort Algorithm

O(n log n)

Understanding Time Complexity

We want to understand how the time taken by Quick 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 Quick Sort code snippet.

void quickSort(int arr[], int low, int high) {
    if (low < high) {
        int pi = partition(arr, low, high);
        quickSort(arr, low, pi - 1);
        quickSort(arr, pi + 1, high);
    }
}

int partition(int arr[], int low, int high) {
    int pivot = arr[high];
    int i = low - 1;
    for (int j = low; j < high; j++) {
        if (arr[j] <= pivot) {
            i++;
            int temp = arr[i]; arr[i] = arr[j]; arr[j] = temp;
        }
    }
    int temp = arr[i + 1]; arr[i + 1] = arr[high]; arr[high] = temp;
    return i + 1;
}
    

This code sorts an array by picking a pivot, dividing the array around it, and sorting the parts recursively.

Identify Repeating Operations

Look at the parts that repeat as the input grows.

• Primary operation: The for-loop inside partition that compares and swaps elements.
• How many times: This loop runs for each element in the current sub-array during each recursive call.
• Recursion: The function calls itself twice on smaller parts after partitioning.
• Dominant operation: The repeated partitioning and recursive calls dominate the time.

How Execution Grows With Input

Intuitively, Quick Sort splits the list and sorts parts recursively.

Input Size (n)	Approx. Operations
10	About 30 to 50 comparisons and swaps
100	About 700 to 1000 operations
1000	About 10,000 to 15,000 operations

Pattern observation: The operations grow a bit faster than linear but much slower than quadratic, roughly like n times log n.

Final Time Complexity

Time Complexity: O(n log n)

This means the time grows a little more than linearly as the list gets bigger, making Quick Sort efficient for large lists.

Common Mistake

[X] Wrong: "Quick Sort always runs in linear time because it divides the list quickly."

[OK] Correct: Sometimes the pivot divides the list unevenly, causing more work and making the time closer to quadratic.

Interview Connect

Understanding Quick Sort's time complexity helps you explain why it is fast and when it might slow down, a key skill in coding interviews.

Self-Check

"What if we always picked the middle element as pivot instead of the last? How would the time complexity change?"

Practice

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

easy

A. Divide the array into smaller parts using a pivot and sort them recursively

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

C. Insert elements one by one into a sorted list

D. Merge two sorted arrays into one sorted array

Solution

1. Step 1: Understand Quick Sort's approach

   Quick Sort picks a pivot element and divides the array into parts smaller and larger than the pivot.

2. Step 2: Recursive sorting of parts

   It then sorts these parts recursively, combining them to get the sorted array.

3. Final Answer:

   Divide the array into smaller parts using a pivot and sort them recursively -> Option A

4. Quick Check:

   Quick Sort = Divide and conquer [OK]

Hint: Remember: Quick Sort uses a pivot to split and sort recursively [OK]

Common Mistakes:

• Confusing Quick Sort with Bubble Sort
• Thinking Quick Sort merges arrays like Merge Sort
• Believing Quick Sort inserts elements one by one

2. Which of the following is the correct way to declare the partition function header in C for Quick Sort?

easy

A. int partition(int arr, int low, int high);

B. int partition(int arr[], int low, int high);

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

D. int partition(int arr[], int start, int end, int pivot);

Solution

1. Step 1: Check parameter types for partition

   The partition function takes an array and two indices low and high to work on a subarray.

2. Step 2: Confirm return type

   It returns an integer index of the pivot after partitioning.

3. Final Answer:

   int partition(int arr[], int low, int high); -> Option B

4. Quick Check:

   Partition returns pivot index [OK]

Hint: Partition returns pivot index; use array and low, high indices [OK]

Common Mistakes:

• Using void return type for partition
• Passing array as int instead of int[]
• Adding extra parameters like pivot

3. What will be the output array after applying Quick Sort on arr = {3, 6, 8, 10, 1, 2, 1}?

medium

A. [3, 6, 8, 10, 1, 2, 1]

B. [10, 8, 6, 3, 2, 1, 1]

C. [1, 1, 2, 3, 6, 8, 10]

D. [1, 2, 1, 3, 6, 8, 10]

Solution

1. Step 1: Apply Quick Sort logic on the array

   Quick Sort rearranges elements around pivots recursively, sorting the array in ascending order.

2. Step 2: Sort the given array

   Sorting {3,6,8,10,1,2,1} results in {1,1,2,3,6,8,10}.

3. Final Answer:

   [1, 1, 2, 3, 6, 8, 10] -> Option C

4. Quick Check:

   Sorted array ascending = [1,1,2,3,6,8,10] [OK]

Hint: Quick Sort outputs sorted array ascending by default [OK]

Common Mistakes:

• Assuming Quick Sort keeps original order
• Confusing ascending with descending order
• Not sorting duplicates correctly

4. Identify the error in this Quick Sort partition function snippet:

int partition(int arr[], int low, int high) {
  int pivot = arr[high];
  int i = low;
  for (int j = low; j < high; j++) {
    if (arr[j] < pivot) {
      i++;
      swap(&arr[i], &arr[j]);
    }
  }
  swap(&arr[i + 1], &arr[high]);
  return i + 1;
}

medium

A. Variable i should start at low - 1, not low

B. Pivot should be arr[low], not arr[high]

C. Loop should run till j <= high, not j < high

D. Swap function is called with wrong arguments

Solution

1. Step 1: Check initialization of i

   In partition, i should start at low - 1 to correctly track smaller elements.

2. Step 2: Analyze loop and swaps

   Starting i at low causes skipping the first element comparison and wrong swaps.

3. Final Answer:

   Variable i should start at low - 1, not low -> Option A

4. Quick Check:

   i = low - 1 fixes partition indexing [OK]

Hint: Initialize i to low - 1 before partition loop [OK]

Common Mistakes:

• Starting i at low causes incorrect partitioning
• Changing pivot position unnecessarily
• Extending loop beyond high index

5. Given an array with many duplicate elements, which modification to Quick Sort helps improve performance?

hard

A. Use Bubble Sort instead for duplicates

B. Always choose the first element as pivot

C. Skip sorting duplicates by checking equality before partition

D. Use 3-way partitioning to separate less than, equal to, and greater than pivot

Solution

1. Step 1: Understand problem with duplicates in Quick Sort

   Standard Quick Sort can degrade with many duplicates due to repeated swaps and partitions.

2. Step 2: Apply 3-way partitioning

   3-way partitioning divides array into three parts: less than, equal to, and greater than pivot, reducing unnecessary work.

3. Final Answer:

   Use 3-way partitioning to separate less than, equal to, and greater than pivot -> Option D

4. Quick Check:

   3-way partitioning optimizes duplicates [OK]

Hint: 3-way partitioning handles duplicates efficiently [OK]

Common Mistakes:

• Choosing fixed pivot without randomization
• Skipping duplicates without partitioning
• Switching to slower sorts unnecessarily