Quick Sort as Divide and Conquer in DSA Java - Time & Space Complexity

Time Complexity: Quick Sort as Divide and Conquer

O(n log n)

Understanding Time Complexity

We want to understand how the time to sort a list using Quick Sort changes as the list grows.

How does Quick Sort's work increase when the input size gets bigger?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

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

private 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

Identify the loops, recursion, array traversals that repeat.

• Primary operation: The partition loop that compares and swaps elements around the pivot.
• How many times: This loop runs for each recursive call, covering parts of the array until fully sorted.
• Recursion: The array is split into two parts and quickSort is called on each part.

How Execution Grows With Input

When the array size doubles, the partition work roughly doubles, but the recursive calls split the problem.

Input Size (n)	Approx. Operations
10	About 50 to 70 comparisons and swaps
100	About 700 to 900 comparisons and swaps
1000	About 10,000 to 13,000 comparisons and swaps

Pattern observation: The work grows a bit faster than the input size but much slower than the square of input size.

Final Time Complexity

Time Complexity: O(n log n)

This means the time to sort grows a little more than linearly, multiplying by the input size and the number of splits.

Common Mistake

[X] Wrong: "Quick Sort always sorts in linear time because it splits the array."

[OK] Correct: Sometimes the splits are uneven, causing more work and slower sorting, especially if the pivot is poor.

Interview Connect

Understanding Quick Sort's time complexity helps you explain how divide and conquer algorithms work efficiently in real problems.

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 as a divide and conquer method?

easy

A. Divide the array into smaller parts, sort them, and combine the results

B. Sort the array by repeatedly swapping adjacent elements

C. Use extra space to merge two sorted arrays

D. Find the maximum element and place it at the end repeatedly

Solution

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

   Quick Sort divides the array into parts based on a pivot, sorts each part separately.

2. Step 2: Recognize the combining step

   After sorting parts, the array is combined naturally as parts are sorted in place.

3. Final Answer:

   Divide the array into smaller parts, sort them, and combine the results -> Option A

4. Quick Check:

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

Hint: Quick Sort splits and sorts parts separately [OK]

Common Mistakes:

• Confusing Quick Sort with Bubble Sort
• Thinking Quick Sort uses extra space like Merge Sort
• Believing Quick Sort only swaps adjacent elements

2. Which of the following is the correct way to choose a pivot in Quick Sort in Java?

easy

A. int pivot = arr[length / 2];

B. int pivot = arr[arr.length / 2];

C. int pivot = arr[arr.length];

D. int pivot = arr[-1];

Solution

1. Step 1: Identify valid pivot index

   Array indices go from 0 to length-1, so middle index is length/2.

2. Step 2: Check each option

   A uses length (undefined, invalid), B uses arr.length (out of bounds), C uses middle element (valid), D uses negative index (invalid in Java).

3. Final Answer:

   int pivot = arr[arr.length / 2]; -> Option B

4. Quick Check:

   Pivot index must be within 0 to length-1 [OK]

Hint: Pivot index must be valid array index [OK]

Common Mistakes:

• Using arr.length as index (out of bounds)
• Using negative indices (not allowed in Java)
• Always choosing first element without reason

3. What will be the output array after the first partition step of Quick Sort on int[] arr = {4, 7, 2, 9, 1} if pivot is chosen as the first element?

medium

A. [2, 1, 4, 7, 9]

B. [1, 2, 4, 9, 7]

C. [4, 7, 2, 9, 1]

D. [9, 7, 4, 2, 1]

Solution

1. Step 1: Choose pivot and partition

   Pivot = 4. Elements less than 4 go left, greater go right.

2. Step 2: Rearrange array after partition

   Left side: 2,1; pivot:4; right side:7,9. So array becomes [2, 1, 4, 7, 9].

3. Final Answer:

   [2, 1, 4, 7, 9] -> Option A

4. Quick Check:

   Partition arr around pivot 4 = [2,1,4,7,9] [OK]

Hint: Partition places pivot between smaller and larger elements [OK]

Common Mistakes:

• Not moving elements smaller than pivot to left
• Leaving array unchanged after partition
• Placing pivot at wrong position

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

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;
    }
  }
  return i;
}

medium

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

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

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

D. Swap logic is incorrect

Solution

1. Step 1: Analyze loop boundary

   The loop runs j from low to high inclusive, but arr[high] is pivot and should not be compared inside loop.

2. Step 2: Correct loop condition

   Loop should run j from low to high-1 to avoid swapping pivot with itself.

3. Final Answer:

   Loop should run until j < high, not j <= high -> Option C

4. Quick Check:

   Partition loop excludes pivot index [OK]

Hint: Loop excludes pivot index during partition [OK]

Common Mistakes:

• Including pivot in partition loop
• Starting i at wrong index
• Changing pivot selection unnecessarily

5. Given the array {10, 80, 30, 90, 40, 50, 70}, if Quick Sort always chooses the last element as pivot, what is the sequence of pivot values chosen during the entire sorting process?

hard

A. [70, 50, 40, 30, 10, 90, 80]

B. [70, 90, 80, 50, 40, 10, 30]

C. [70, 90, 80, 50, 40, 30, 10]

D. [70, 50, 40, 30, 10, 80, 90]

Solution

1. Step 1: Identify pivots chosen at each recursive call

   First pivot: last element 70. After partition, left subarray ends at 50, right subarray ends at 80.

2. Step 2: Continue choosing last element as pivot in subarrays

   Next pivots: 50 (left), then 40, then 30, then 10; right subarray pivots: 80, then 90.

3. Final Answer:

   [70, 50, 40, 30, 10, 80, 90] -> Option D

4. Quick Check:

   Last element pivot sequence matches [OK]

Hint: Pivot is always last element in current subarray [OK]

Common Mistakes:

• Mixing pivot order with sorted array order
• Choosing first element as pivot instead
• Ignoring recursive calls on subarrays