ALGORHYTHM

Experience the beauty of computer science. Visualize sorting algorithms in real-time 3D orchestrated with procedural audio.

Understanding Sorting Algorithms

Sorting is one of the most fundamental problems in Computer Science. Different algorithms tackle this problem with varying strategies, each having its own Trade-offs in terms of Time Complexity (Speed) and Space Complexity (Memory).

Bubble Sort

O(n²) - Slow

The simplest sorting algorithm. It repeatedly swaps adjacent elements if they are in the wrong order. Great for learning, terrible for large datasets.

Merge Sort

O(n log n) - Fast

A "Divide and Conquer" algorithm. It recursively splits the list into halves and merges them back together. Very efficient and stable.

Quick Sort

O(n log n) - Very Fast

Uses a "Pivot" to partition the array. Elements smaller than pivot go left, larger go right. Often faster than Merge Sort in practice but can be worst-case O(n²).

Heap Sort

O(n log n) - Fast

Visualize the data as a binary heap tree. It's efficient and uses constant space, unlike Merge Sort which needs extra memory.