# algorithm > Browse 21 items tagged with algorithm. ## Items - [FLANN (Fast Library for Approximate Nearest Neighbors)](https://vectordb.ever.works/uk/items/flann-fast-library-for-approximate-nearest-neighbors) — A C++ library for performing fast approximate nearest neighbor searches in high dimensional spaces. Contains multiple ANN algorithms and automatic algorithm selection based on dataset characteristics. - [ACORN Algorithm for Filtered Vector Search](https://vectordb.ever.works/uk/items/acorn-algorithm-for-filtered-vector-search) — Advanced algorithm designed to make hybrid searches combining metadata filters and vector similarity more efficient, implemented in Apache Solr and other vector search systems. - [Early Termination Strategy for HNSW](https://vectordb.ever.works/uk/items/early-termination-strategy-for-hnsw) — Optimization technique that allows HNSW vector searches to exit early when the candidate queue remains saturated, reducing latency and resource usage with minimal recall impact. - [LIRE Protocol](https://vectordb.ever.works/uk/items/lire-protocol) — Lightweight incremental rebalancing protocol used in SPFresh for billion-scale vector updates with only 1% DRAM and <10% cores compared to global rebuild approaches. - [Approximate Nearest Neighbors (ANN)](https://vectordb.ever.works/uk/items/approximate-nearest-neighbors-ann) — Algorithms and techniques for finding nearest neighbors in high-dimensional vector spaces with speed-accuracy trade-offs. ANN methods like HNSW, IVF, and DiskANN enable billion-scale vector search by sacrificing small amounts of recall for massive performance gains over exact search. - [k-NN Search](https://vectordb.ever.works/uk/items/k-nn-search) — k-Nearest Neighbors search finds the k closest vectors to a query vector in high-dimensional space. A fundamental operation in vector databases and machine learning, k-NN can be exact (brute force) or approximate (ANN) depending on performance requirements and dataset size. - [Anisotropic Vector Quantization](https://vectordb.ever.works/uk/items/anisotropic-vector-quantization) — An advanced quantization technique introduced by Google's ScaNN that prioritizes preserving parallel components between vectors rather than minimizing overall distance. Optimized for Maximum Inner Product Search (MIPS) and significantly improves retrieval accuracy. - [ANN Algorithm Complexity Analysis](https://vectordb.ever.works/uk/items/ann-algorithm-complexity-analysis) — Computational complexity comparison of approximate nearest neighbor algorithms including build time, query time, and space complexity. Essential for understanding performance characteristics and choosing appropriate algorithms for different scales. - [Maximum Inner Product Search (MIPS)](https://vectordb.ever.works/uk/items/maximum-inner-product-search-mips) — A search problem focused on finding vectors that maximize the inner product with a query vector. Common in recommendation systems and neural search where magnitude carries semantic meaning, requiring specialized algorithms like those in ScaNN. - [Navigable Small World (NSW)](https://vectordb.ever.works/uk/items/navigable-small-world-nsw) — A graph-based approximate nearest neighbor search algorithm that uses both long-range and short-range links to achieve poly-logarithmic search complexity. Foundation for the more advanced HNSW algorithm. - [SOAR (Spilling with Orthogonality-Amplified Residuals)](https://vectordb.ever.works/uk/items/soar-spilling-with-orthogonality-amplified-residuals) — A major algorithmic advancement to Google's ScaNN that introduces controlled redundancy to the vector index, leading to improved search efficiency. Enables even faster vector search while maintaining or improving accuracy. - [HNSW (Hierarchical Navigable Small World)](https://vectordb.ever.works/uk/items/hnsw-hierarchical-navigable-small-world) — Graph-based algorithm for approximate nearest neighbor search that maintains multi-layer graph structures for efficient vector similarity search with logarithmic complexity, widely used in modern vector databases. - [Locality Sensitive Hashing (LSH)](https://vectordb.ever.works/uk/items/locality-sensitive-hashing-lsh) — Algorithmic technique for approximate nearest neighbor search in high-dimensional spaces using hash functions to map similar items to the same buckets with high probability. - [IVF](https://vectordb.ever.works/uk/items/ivf) — Inverted File Index vector search algorithm that partitions high-dimensional vectors into clusters using k-means, enabling efficient nearest neighbor search by restricting searches to relevant clusters and dramatically reducing search space. - [IVF (Inverted File Index)](https://vectordb.ever.works/uk/items/ivf-inverted-file-index) — Clustering-based approximate nearest neighbor algorithm that partitions vector space into Voronoi cells. Fast search through coarse-to-fine strategy, often combined with Product Quantization (IVF-PQ). - [Dot Product Similarity](https://vectordb.ever.works/uk/items/dot-product-similarity) — Vector similarity metric combining both angle and magnitude information for comprehensive similarity measurement, equivalent to cosine similarity when vectors are normalized. - [Euclidean Distance](https://vectordb.ever.works/uk/items/euclidean-distance) — Straight-line distance metric between vectors in multidimensional space, sensitive to both magnitude and direction, ideal when embedding magnitude carries important information. - [IVF-FLAT Index](https://vectordb.ever.works/uk/items/ivf-flat-index) — Inverted File Index with flat vectors using K-means clustering to partition high-dimensional space into regions, enhancing search efficiency by narrowing search area through neighbor partitions. - [LoRANN](https://vectordb.ever.works/uk/items/lorann) — Low-Rank Matrix Factorization algorithm for Approximate Nearest Neighbor Search, offering competitive performance with faster query times than leading libraries at various recall levels. - [NSW (Navigable Small World)](https://vectordb.ever.works/uk/items/nsw-navigable-small-world) — Graph-based algorithm for approximate nearest neighbor search where vertices represent vectors and edges are constructed heuristically. Foundation for HNSW with (poly/)logarithmic search complexity using greedy routing. - [Vamana](https://vectordb.ever.works/uk/items/vamana) — Graph-based indexing algorithm powering Microsoft's DiskANN. Uses flat graph structure with minimized search diameter for efficient disk-based nearest neighbor search with 40x GPU speedup available via NVIDIA cuVS. --- _Canonical page: https://vectordb.ever.works/uk/tags/algorithm_