Quick Sort Partition Lomuto and Hoare in DSA Javascript - Time & Space Complexity

Time Complexity: Quick Sort Partition Lomuto and Hoare

O(n)

Understanding Time Complexity

We want to understand how the time taken by Quick Sort's partition steps changes as the input size grows.

Specifically, we look at Lomuto and Hoare partition methods to see how their operations scale.

Scenario Under Consideration

Analyze the time complexity of these two partition methods used in Quick Sort.

// Lomuto Partition
function lomutoPartition(arr, low, high) {
  let pivot = arr[high];
  let i = low - 1;
  for (let j = low; j < high; j++) {
    if (arr[j] <= pivot) {
      i++;
      [arr[i], arr[j]] = [arr[j], arr[i]];
    }
  }
  [arr[i + 1], arr[high]] = [arr[high], arr[i + 1]];
  return i + 1;
}

// Hoare Partition
function hoarePartition(arr, low, high) {
  let pivot = arr[low];
  let i = low - 1;
  let j = high + 1;
  while (true) {
    do { i++; } while (arr[i] < pivot);
    do { j--; } while (arr[j] > pivot);
    if (i >= j) return j;
    [arr[i], arr[j]] = [arr[j], arr[i]];
  }
}
    

These functions rearrange elements around a pivot to help Quick Sort work efficiently.

Identify Repeating Operations

Both methods use loops to compare and swap elements around the pivot.

• Primary operation: Comparing elements to the pivot and swapping if needed.
• How many times: Each element between low and high is checked once in Lomuto; in Hoare, pointers move inward, each element visited at most once.

How Execution Grows With Input

As the number of elements (n) increases, the number of comparisons and swaps grows roughly in proportion to n.

Input Size (n)	Approx. Operations
10	About 10 comparisons and some swaps
100	About 100 comparisons and swaps
1000	About 1000 comparisons and swaps

Pattern observation: The operations increase linearly as input size grows.

Final Time Complexity

Time Complexity: O(n)

This means the partition step takes time proportional to the number of elements it processes.

Common Mistake

[X] Wrong: "Partitioning is a constant time operation because it just picks a pivot."

[OK] Correct: Partitioning compares and swaps many elements, so its time grows with input size, not fixed.

Interview Connect

Understanding partition time helps you explain Quick Sort's efficiency clearly, a key skill in interviews.

Self-Check

"What if the pivot is always the smallest element? How would that affect the partition's time complexity?"

Practice

1. What is the main difference between Lomuto and Hoare partition schemes in Quick Sort?

easy

A. Lomuto uses one pointer, Hoare uses two pointers moving inward

B. Lomuto sorts in descending order, Hoare sorts in ascending order

C. Lomuto is used only for arrays of even length, Hoare for odd length

D. Lomuto swaps elements only once, Hoare swaps multiple times

Solution

1. Step 1: Understand Lomuto partition

   Lomuto partition uses a single pointer to track the boundary of smaller elements and swaps with the pivot at the end.

2. Step 2: Understand Hoare partition

   Hoare partition uses two pointers starting from both ends moving inward to swap elements out of place.

3. Final Answer:

   Lomuto uses one pointer, Hoare uses two pointers moving inward -> Option A

4. Quick Check:

   Pointer count difference = Lomuto(1), Hoare(2) [OK]

Hint: Remember: Lomuto one pointer, Hoare two pointers inward [OK]

Common Mistakes:

• Confusing the number of pointers used
• Thinking Lomuto sorts differently by order
• Assuming partition depends on array length

2. Which of the following is the correct syntax to swap two elements arr[i] and arr[j] in JavaScript during partition?

easy

A. temp = arr[i]; arr[i] = arr[j]; arr[j] = temp;

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

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

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

Solution

1. Step 1: Identify correct swap syntax in JavaScript

   JavaScript does not support tuple unpacking; swapping requires a temporary variable.

2. Step 2: Check each option

   temp = arr[i]; arr[i] = arr[j]; arr[j] = temp; uses a temp variable correctly; B is Python syntax; C is undefined function; D overwrites values incorrectly.

3. Final Answer:

   temp = arr[i]; arr[i] = arr[j]; arr[j] = temp; -> Option A

4. Quick Check:

   Use temp variable for swap in JS [OK]

Hint: Use temp variable to swap in JS, no tuple unpacking [OK]

Common Mistakes:

• Using Python-style tuple swap in JavaScript
• Calling undefined swap function
• Overwriting values without temp variable

3. What is the output array after applying Lomuto partition on arr = [8, 3, 7, 6, 2] with pivot as last element?

medium

A. [8, 3, 7, 6, 2]

B. [3, 2, 7, 6, 8]

C. [2, 3, 7, 6, 8]

D. [2, 3, 6, 7, 8]

Solution

1. Step 1: Identify pivot and initialize pointers

   Pivot = 2 (last element). i starts at -1.

2. Step 2: Iterate and swap elements smaller than pivot

   Check elements: 8 > 2 no swap; 3 > 2 no swap; 7 > 2 no swap; 6 > 2 no swap. Finally swap pivot with arr[i+1] = arr[0].

3. Step 3: Final array after swap

   Swap arr[0] and arr[4]: [2, 3, 7, 6, 8]

4. Final Answer:

   [2, 3, 7, 6, 8] -> Option C

5. Quick Check:

   Lomuto partition places pivot correctly [OK]

Hint: Pivot is last element; swap smaller elements left [OK]

Common Mistakes:

• Not swapping pivot at the end
• Swapping elements incorrectly
• Confusing pivot position

4. Given this Hoare partition code snippet, what is the bug?

function hoarePartition(arr, low, high) {
  let pivot = arr[low];
  let i = low - 1;
  let j = high + 1;
  while (true) {
    do { i++; } while (arr[i] < pivot);
    do { j--; } while (arr[j] > pivot);
    if (i >= j) return j;
    [arr[i], arr[j]] = [arr[j], arr[i]];
  }
}

medium

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

B. The loop condition should be while(i < j), not while(true)

C. Swap syntax is invalid in JavaScript

D. Initialization of i and j is incorrect; should be i=low, j=high

Solution

1. Step 1: Check initialization of pointers

   Hoare partition requires i = low and j = high to start inside the array.

2. Step 2: Analyze effect of incorrect initialization

   Standard Hoare partition initializes i = low, j = high, starting the pointers at the array bounds and moving inward.

3. Final Answer:

   Initialization of i and j is incorrect; should be i=low, j=high -> Option D

4. Quick Check:

   Correct pointer start = i=low, j=high [OK]

Hint: Start pointers inside array for Hoare partition [OK]

Common Mistakes:

• Setting i and j outside array bounds
• Confusing pivot position
• Misusing swap syntax

5. You want to sort an array using Quick Sort with Hoare partition. Which of these is the correct recursive call pattern after partitioning at index p?

hard

A. quickSort(arr, low, p - 1); quickSort(arr, p, high);

B. quickSort(arr, low, p); quickSort(arr, p + 1, high);

C. quickSort(arr, low, p + 1); quickSort(arr, p, high);

D. quickSort(arr, low, p - 1); quickSort(arr, p + 1, high);

Solution

1. Step 1: Understand Hoare partition return value

   Hoare partition returns an index p such that elements arr[low..p] ≤ pivot and arr[p+1..high] ≥ pivot. The pivot is not guaranteed to be at position p.

2. Step 2: Determine correct recursive calls

   Recurse on the left subarray from low to p (inclusive) and right subarray from p+1 to high: quickSort(arr, low, p); quickSort(arr, p + 1, high).

3. Final Answer:

   quickSort(arr, low, p); quickSort(arr, p + 1, high); -> Option B

4. Quick Check:

   Hoare recursion: low to p, p+1 to high [OK]

Hint: Hoare: quickSort(low, p); quickSort(p+1, high) [OK]

Common Mistakes:

• Using p+1 in left recursive call
• Ignoring infinite recursion risk
• Confusing Lomuto and Hoare recursion