02

Merge Sort Visualizer

Chapter 2 • Intermediate

90 min

Merge Sort Visualizer

Algorithm

Divide & Conquer Strategy:

  1. Divide: Split array into two halves
  2. Conquer: Recursively sort both halves
  3. Combine: Merge sorted halves

Implementation

python.js
def merge_sort(arr):
    """
    Merge sort algorithm.
    Time: O(n log n), Space: O(n)
    """
    if len(arr) <= 1:
        return arr
    
    # Divide
    mid = len(arr) // 2
    left = merge_sort(arr[:mid])
    right = merge_sort(arr[mid:])
    
    # Conquer & Combine
    return merge(left, right)

def merge(left, right):
    """Merge two sorted arrays"""
    result = []
    i = j = 0
    
    while i < len(left) and j < len(right):
        if left[i] <= right[j]:
            result.append(left[i])
            i += 1
        else:
            result.append(right[j])
            j += 1
    
    # Add remaining elements
    result.extend(left[i:])
    result.extend(right[j:])
    
    return result

In-Place Merge Sort

python.js
def merge_sort_inplace(arr, left=0, right=None):
    """In-place merge sort"""
    if right is None:
        right = len(arr) - 1
    
    if left < right:
        mid = (left + right) // 2
        merge_sort_inplace(arr, left, mid)
        merge_sort_inplace(arr, mid + 1, right)
        merge_inplace(arr, left, mid, right)

def merge_inplace(arr, left, mid, right):
    """Merge two sorted subarrays in-place"""
    # Create temporary arrays
    left_arr = arr[left:mid + 1]
    right_arr = arr[mid + 1:right + 1]
    
    i = j = 0
    k = left
    
    while i < len(left_arr) and j < len(right_arr):
        if left_arr[i] <= right_arr[j]:
            arr[k] = left_arr[i]
            i += 1
        else:
            arr[k] = right_arr[j]
            j += 1
        k += 1
    
    while i < len(left_arr):
        arr[k] = left_arr[i]
        i += 1
        k += 1
    
    while j < len(right_arr):
        arr[k] = right_arr[j]
        j += 1
        k += 1

Complexity Analysis

  • Time: O(n log n) - all cases
  • Space: O(n) - for temporary arrays
  • Stable: Yes
  • In-place: No (standard version)

Recurrence Relation

T(n) = 2T(n/2) + O(n)

Using Master Theorem:

  • a = 2, b = 2, f(n) = n
  • log_b a = 1
  • f(n) = Θ(n¹) = Θ(n^(log_b a))
  • Case 2: T(n) = Θ(n log n)

Visual Trace

python.js
def merge_sort_trace(arr, depth=0):
    """Merge sort with trace output"""
    indent = "  " * depth
    print(f"{indent}Sorting: {arr}")
    
    if len(arr) <= 1:
        return arr
    
    mid = len(arr) // 2
    left = merge_sort_trace(arr[:mid], depth + 1)
    right = merge_sort_trace(arr[mid:], depth + 1)
    
    merged = merge(left, right)
    print(f"{indent}Merged: {merged}")
    return merged

Practice Problems

  1. Implement merge sort
  2. Count inversions using merge sort
  3. Merge K sorted arrays
  4. External merge sort for large files
  5. Optimize merge sort for nearly sorted arrays