Time Complexity Estimator
Analyze your code snippets and algorithms to estimate their time complexity and performance characteristics
Code Analysis
Complexity Analysis Results
Analyze your code to see the detailed complexity explanation.
Complexity Breakdown
Your detailed complexity analysis will appear here.
📊 Common Time Complexities
| Complexity | Notation | Example | Operations at n=1,000 |
|---|---|---|---|
| Constant | O(1) | Array access | 1 |
| Logarithmic | O(log n) | Binary search | ~10 |
| Linear | O(n) | Simple loop | 1,000 |
| Linearithmic | O(n log n) | Efficient sorts | ~10,000 |
| Quadratic | O(n²) | Nested loops | 1,000,000 |
| Cubic | O(n³) | Triple nested loops | 1,000,000,000 |
| Exponential | O(2ⁿ) | Subset generation | 1.07e+301 |
| Factorial | O(n!) | Permutations | 4.02e+2567 |
Note: Actual performance depends on hardware, implementation details, and constant factors.
🚀 Optimization Tips
Identify Bottlenecks
Use profiling tools to find the slowest parts of your code before optimizing.
Choose Better Algorithms
Often the biggest gains come from switching to a more efficient algorithm.
Use Efficient Data Structures
Hash tables for O(1) lookups, heaps for priority queues, etc.
Memoization
Cache results of expensive function calls to avoid recomputation.
Early Termination
Break loops early when possible to save unnecessary iterations.
Divide and Conquer
Split problems into smaller subproblems (like merge sort does).
Note: This estimator provides an approximation of time complexity based on code structure analysis. Actual performance depends on hardware, implementation details, and constant factors not captured in Big O notation.