HAREN++: Hybrid Associative Retrieval Engine with Nullified Worst-Case
Project description
HAREN++: Hybrid Associative Retrieval Engine with Nullified Worst-Case
HAREN++ is a high-performance hybrid search algorithm that redefines practical search efficiency across both sorted and unsorted arrays. It combines direct memory indexing, adaptive hashing, predictive segment regression, and sub-logarithmic fallback strategies to achieve constant-time average search performance and compressed worst-case complexity.
Unlike traditional search methods such as linear search (O(N)) and binary search (O(log N)), HAREN++ delivers near-constant search performance even in datasets containing millions of elements. It is designed for real-time applications where ultra-fast retrieval is essential.
Key Features
- O(1) Average-Case Lookup: Memory-accelerated direct indexing.
- O(log log N) Worst-Case Search: Fast fallback using anchor tree search.
- Sorted and Unsorted Arrays: Supports mixed datasets without performance loss.
- Predictive Indexing: Uses lightweight learned segment models to guide fast searches.
- Benchmark Proven: Up to 25,000× faster than linear search and up to 20× faster than binary search on million-element datasets.
- Layered Search Strategy: Combines direct map, adaptive hashing, predictive regression, and a fallback anchor tree.
Installation
pip install harenpp
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