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Courses/Data Structures and Algorithms/Efficient Sorting Algorithms

Efficient Sorting Algorithms

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Merge Sort

Version 4/8/2025, 9:10:51 AM
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Version 4/8/2025, 9:10:51 AM

We’re doing it with arrays in Java — no fancy lists, no magic. Just clean, recursive, efficient sorting vibes.


🔀 Merge Sort (Array Implementation in Java)

“Why fight the whole battle at once, when you can break it down, conquer each part, then unite like the Avengers?”
— Merge Sort, probably


🧠 What is Merge Sort?

Merge Sort is a Divide and Conquer algorithm that:

  1. Divides the array into halves

  2. Recursively sorts both halves

  3. Merges the sorted halves into a single sorted array

It’s recursive. It’s elegant. And it’s got a consistent time complexity of O(n log n) — even in the worst case. 💪


🔢 Time & Space Complexity

CaseTime
BestO(n log n)
AverageO(n log n)
WorstO(n log n)
SpaceO(n) (for the temporary arrays during merging)

🔧 Merge Sort in Java (Array Version)

Here it is. Fully functional. No imports. Pure Java array goodness.

java
public class MergeSort { // Public method to sort the array public void sort(int[] array) { if (array == null || array.length < 2) return; mergeSort(array, 0, array.length - 1); } // Recursive merge sort private void mergeSort(int[] array, int left, int right) { if (left < right) { int mid = left + (right - left) / 2; // Recursively sort both halves mergeSort(array, left, mid); mergeSort(array, mid + 1, right); // Merge sorted halves merge(array, left, mid, right); } } // Merge two sorted subarrays: [left..mid] and [mid+1..right] private void merge(int[] array, int left, int mid, int right) { // Sizes of the two subarrays int n1 = mid - left + 1; int n2 = right - mid; // Create temporary arrays int[] L = new int[n1]; int[] R = new int[n2]; // Copy data for (int i = 0; i < n1; i++) L[i] = array[left + i]; for (int j = 0; j < n2; j++) R[j] = array[mid + 1 + j]; // Merge the temp arrays back into original int i = 0, j = 0, k = left; while (i < n1 && j < n2) { if (L[i] <= R[j]) { array[k++] = L[i++]; } else { array[k++] = R[j++]; } } // Copy remaining elements, if any while (i < n1) { array[k++] = L[i++]; } while (j < n2) { array[k++] = R[j++]; } } // For demo purposes public static void main(String[] args) { int[] arr = {38, 27, 43, 3, 9, 82, 10}; MergeSort sorter = new MergeSort(); sorter.sort(arr); System.out.println("Sorted array:"); for (int num : arr) { System.out.print(num + " "); } } }

🔍 What’s Happening Here?

  • 🧠 mergeSort() → recursive method that splits the array until it reaches subarrays of size 1

  • 🧪 merge() → merges two sorted arrays into one sorted array

  • 💾 Temporary arrays are created to hold the left and right halves (space complexity tradeoff)


💡 Why Merge Sort Slaps

  • ✅ Stable sort (doesn’t change relative order of equal elements)

  • ✅ Predictable O(n log n) time

  • ✅ Great for large datasets

  • ✅ Used in external sorting (e.g., sorting huge files on disk)


⚠️ Things to Watch Out For

  • Not in-place → needs O(n) space

  • Recursive → watch the stack depth for huge arrays

  • Performance-wise, quick sort may beat it in practice (in-place), but merge sort has guaranteed time


🎓 TL;DR (Too Long, Didn’t Merge)

  • Merge Sort uses Divide and Conquer

  • Splits array → recursively sorts → merges it back

  • Java array implementation needs helper function & temp arrays

  • Worst-case time: O(n log n)

  • Best-case time: O(n log n)

  • Stable, consistent, and super useful for large-scale sorted problems


🧠 Final Thoughts

Merge Sort is the professor-approved, always-reliable choice in your sorting toolkit.
Whether you're prepping for interviews, optimizing regression data, or just trying not to use Arrays.sort() for once —
Merge Sort is your clean, logical, recursive bestie.

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