CarGDev/divide-and-conquer-analysis
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Add comprehensive performance analysis and comparison

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- Created ANALYSIS.md with detailed performance metrics
- Analyzed execution time, memory usage, and operation counts
- Discussed discrepancies between theoretical and practical performance
- Explained Python-specific performance characteristics
- Updated README with link to analysis document
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Carlos Gutierrez
2025-10-30 21:33:45 -04:00
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README.md
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@@ -361,6 +361,22 @@ black src/ tests/
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## Performance Analysis
See **[ANALYSIS.md](ANALYSIS.md)** for a comprehensive comparison and analysis of the algorithms, including:
- Detailed performance metrics across sorted, reverse sorted, and random datasets
- Execution time and memory usage comparisons
- Operation counts (comparisons and swaps)
- Discussion of discrepancies between theoretical analysis and practical performance
- Explanations for observed performance characteristics
The analysis document includes:
- Performance tables for all dataset types
- Theoretical vs practical performance analysis
- Scalability analysis
- Recommendations for algorithm selection
## Acknowledgments
- Algorithms based on standard divide-and-conquer implementations