This repository contains production-ready implementations of fundamental computer science algorithms, each carefully crafted with detailed documentation, comprehensive testing, and real-world application insights.
Divide and conquer search algorithm with O(log n) complexity
✅ Good Features:
- Logarithmic time complexity for sorted data
- Memory efficient with O(1) space complexity
- Excellent cache locality
- Safe overflow-protected midpoint calculation
❌ Limitations:
- Requires pre-sorted data
- Poor performance on unsorted collections
- Not cache-friendly for very large datasets that don't fit in memory
🏭 Industry Applications:
- Database indexing and query optimization
- IP routing table lookups in networking
- Version control systems (git bisect)
- Rate limiting in distributed systems
- Dictionary implementations and spell checkers
Memory-efficient probabilistic set membership data structure
✅ Good Features:
- Extreme memory efficiency compared to traditional sets
- Constant-time O(k) insertions and lookups
- Zero false negatives guaranteed
- Configurable false positive rate
❌ Limitations:
- Cannot handle deletions (without complex extensions)
- Probabilistic nature means some false positives
- Cannot retrieve actual stored elements
- Performance degrades as filter becomes saturated
🏭 Industry Applications:
- Caching systems for pre-filtering expensive database queries
- Network security for rapid IP/domain blacklist checks
- Data deduplication in storage systems (e.g., VMware VADP)
- Spell checkers for preliminary dictionary lookups
- Content delivery networks for cache optimization
Three classical lossless compression implementations: RLE, LZW, and Huffman
✅ Good Features:
- RLE: Excellent for data with long repeated sequences
- LZW: Adaptive dictionary compression, good for text with patterns
- Huffman: Optimal prefix codes for varying character frequencies
- All algorithms provide perfect reconstruction of original data
❌ Limitations:
- RLE: Poor compression on data without repetition
- LZW: Dictionary overhead can exceed savings for small files
- Huffman: Requires two passes and tree storage overhead
- Not suitable for already compressed or encrypted data
🏭 Industry Applications:
- RLE: Fax transmissions, simple graphics, screen captures
- LZW: GIF images, PDF compression, Unix compress utility
- Huffman: JPEG compression, ZIP files, network protocols
- Data archival and backup systems
- Streaming media optimization
Associative array implementation with multiple hashing strategies
✅ Good Features:
- Average O(1) time complexity for all operations
- Multiple hash function support (std::hash, FNV-1a, Murmur3)
- Dynamic resizing with load factor management
- Strategy pattern for runtime hash function selection
- Comprehensive collision handling via separate chaining
❌ Limitations:
- Worst-case O(n) performance with many collisions
- No guaranteed iteration order
- Memory overhead for pointers and empty buckets
- Hash function quality critical for performance
🏭 Industry Applications:
- Database indexing and caching systems
- Symbol tables in programming language compilers
- De-duplication systems for large datasets
- In-memory key-value stores (Redis-style systems)
- Spell checkers and dictionary implementations
Probabilistic cardinality estimation for massive datasets
✅ Good Features:
- Fixed memory usage regardless of dataset size
- Fast O(1) insertions and cardinality estimates
- Typically within 2% accuracy of actual unique counts
- Excellent for streaming data analysis
❌ Limitations:
- Provides approximations, not exact counts
- Cannot retrieve individual elements
- Accuracy depends on register count configuration
- Not suitable for applications requiring exact counts
🏭 Industry Applications:
- Website unique visitor tracking at scale
- Database query optimization and statistics
- Streaming analytics in big data platforms
- Network traffic analysis and monitoring
- Social media engagement metrics
Dynamic programming solution for sequence comparison
✅ Good Features:
- Optimal solution using dynamic programming
- Foundation for diff algorithms and version control
- Handles sequences of any comparable type
- Provides both LCS length and actual subsequence
❌ Limitations:
- O(n×m) time and space complexity
- Memory intensive for very long sequences
- Not suitable for real-time applications with large inputs
- Poor cache performance for large 2D arrays
🏭 Industry Applications:
- Version control systems (Git diff algorithms)
- DNA sequence alignment in bioinformatics
- File comparison utilities and merge tools
- Plagiarism detection systems
- Data synchronization protocols
Cryptographic hash tree for efficient data verification
✅ Good Features:
- Efficient verification of large datasets
- Tamper detection and data integrity guarantees
- Supports partial verification without full dataset
- Hierarchical structure enables parallel processing
❌ Limitations:
- Requires cryptographically secure hash functions
- Tree reconstruction needed for updates
- Memory overhead for tree structure storage
- Vulnerability to collision attacks with weak hash functions
🏭 Industry Applications:
- Blockchain and cryptocurrency transaction verification
- Peer-to-peer file sharing (BitTorrent verification)
- Distributed version control systems (Git)
- Certificate transparency logs
- Database replication and consistency checking
Efficient in-place divide-and-conquer sorting algorithm
✅ Good Features:
- Average O(n log n) time complexity
- In-place sorting with minimal memory overhead
- Excellent cache performance and locality
- Widely adopted in standard library implementations
❌ Limitations:
- Worst-case O(n²) performance on already sorted data
- Not stable (doesn't preserve relative order of equal elements)
- Recursive implementation can cause stack overflow
- Performance sensitive to pivot selection strategy
🏭 Industry Applications:
- Standard library sorting implementations (C++ std::sort)
- Database query processing and indexing
- Graphics rendering and 3D object sorting
- Scientific computing and numerical analysis
- Real-time systems requiring fast in-place sorting
Each algorithm directory contains:
- Comprehensive source code with detailed comments
- CMake build configuration for easy compilation
- Extensive test suites covering edge cases
- Performance benchmarks and analysis
- Real-world usage examples
To build any algorithm:
cd <algorithm-directory>
mkdir build && cd build
cmake ..
make
./testsThis repository serves as both a learning resource and production reference, featuring:
- Historical context and algorithm evolution
- Complexity analysis and performance characteristics
- Common pitfalls and implementation gotchas
- Industry applications and real-world use cases
- Security considerations where applicable
Contributions are welcome! Please ensure:
- Comprehensive documentation following existing patterns
- Complete test coverage for new features
- Performance analysis for algorithmic changes
- Security review for cryptographic components
All implementations are provided under the MIT License, ensuring free use, modification, and distribution for both academic and commercial purposes.