Algorithm Complexity Calculator
Online algorithm complexity calculator with time and space estimation, growth curve visualization, and optimization suggestions for code review and performance pre-checks
Algorithm Code
Algorithm Type
Complexity Visualization
Complexity Analysis
Time ComplexityO(n)
Space ComplexityO(1)
Performance Suggestions
0 suggestionsReady. Enter code to analyze complexity in real time.
Guide
Algorithm Complexity Calculator estimates time and space complexity from code patterns and algorithm category, then generates actionable optimization hints for study, review, and performance pre-check workflows.
Key Features
- Supports multiple algorithm categories for quick complexity estimation.
- Analyzes code in real time and shows operation-growth examples.
- Visualizes complexity curves to compare growth trends by input size.
- Outputs prioritized optimization suggestions with rewrite direction.
How To Use
- Select an algorithm category and paste code or load example code.
- Run analysis or read real-time complexity updates.
- Address high-priority suggestions first and iterate improvements.
- Validate with benchmark tests on expected production input sizes.
Complexity Reference
| Complexity | Description | n=10³ | n=10⁶ |
|---|---|---|---|
| O(1) | Constant | 1 | 1 |
| O(log n) | Logarithmic | 10 | 20 |
| O(n) | Linear | 1K | 1M |
| O(n log n) | Linearithmic | 10K | 20M |
| O(n²) | Quadratic | 1M | 1T |
| O(2ⁿ) | Exponential | ∞ | ∞ |
Optimization Strategies
Time Complexity
- Prefer data structures and algorithms with better asymptotic behavior.
- Reduce unnecessary nested traversals and duplicated scans.
- Convert repeated subproblems into cached or DP transitions.
Space Complexity
- Reduce intermediate copies and reuse temporary buffers.
- Allocate memory on demand instead of oversizing containers.
- Watch recursion depth and object allocation hotspots.
Optimization Strategies
- Balance time and space instead of optimizing one metric blindly.
- Validate assumptions with realistic benchmark datasets.
- Ensure correctness first, then optimize measurable bottlenecks.
FAQ
- Is the result exact: no, it is rule-based estimation and should be reviewed with implementation details.
- Why similar code gives different results: loop depth, recursion branches, and copy behavior affect outcomes.
- What does the chart show: growth trend of estimated operation counts over input scale.
- Will code be uploaded: no, analysis and visualization run locally in your browser.
Privacy
This tool never uploads your algorithm code. Complexity analysis, suggestions, and chart rendering are processed locally in your browser.