Big O Complexity Visualizer
Overview
The Big O Complexity Visualizer provides an interactive way to understand how different algorithm time complexities scale with input size. Compare O(1), O(log n), O(n), O(n log n), O(n²), O(n³), and O(2ⁿ) complexities through animated visualizations and concrete operation counts. This tool helps build intuition for why algorithm efficiency matters and how performance degrades with different complexity classes.
Tips
- Start with small input sizes (n=10) to see all complexities, then gradually increase to observe exponential growth
- Notice how O(1) remains constant regardless of input size - this is the ideal complexity
- O(log n) and O(n log n) are highly efficient even for large inputs - common in well-designed algorithms
- O(n²) and O(n³) become impractical quickly - avoid these for large datasets when possible
- O(2ⁿ) grows so fast it becomes unusable even for small inputs (n>30) - typically seen in brute force solutions
- Use the speed slider to see relative differences between complexity classes more clearly
- The operations table shows concrete counts - notice how quickly some complexities become astronomical
- Real-world algorithm selection depends heavily on expected input size and these growth rates