Unverified50% confidenceFactExact time
Approximate nearest neighbor search (ANN) reduces retrieval complexity from O(N) for exact nearest neighbor to approximately O(log N) at the cost of a small amount of recall, making millisecond-level retrieval possible under registries of tens of thousands of entries.
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50%
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Long-term
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10/15/2026
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Unverified精确的 K 近邻搜索（KNN）时间复杂度为 O(n×d)，在数百万级别记忆条目下延迟不可接受，ANN 算法通过牺牲极小精度换取数量级速度提升78% similarUnverifiedHNSW算法的时间复杂度约为O(log N)，近似最近邻算法在召回率95-99%下将检索时间从线性降至对数级别73% similarVerifiedHNSW reduces search complexity to approximately O(log n) by constructing a multi-layer graph index, while IVF partitions the vector space via K-means clustering to achieve orders-of-magnitude speed improvements at the cost of approximately 5–10% recall.72% similarUnverifiedThe d·log p sample complexity is the advantageous order possessed by previous sharp methods that relied on subset search, and HOST achieves this level without sacrificing polynomial runtime.71% similarUnverified局部敏感哈希LSH由Indyk和Motwani于1998年提出，可将billion级向量的最近邻搜索时间复杂度从O(n)降至近似O(1)70% similar
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