Optimal hashing-based time-space trade-offs for approximate near neighbors

ABSTRACT

We show tight upper and lower bounds for time-space trade-offs for the c-approximate Near Neighbor Search problem. For the d-dimensional Euclidean space and n-point datasets, we develop a data structure with space n^1+ρ_u+o(1) + O(dn) and query time n^ρ_q+o(1) + dn^o(1) for every ρ_u, ρ_q ≥ 0 with:

[EQUATION]

In particular, for the approximation c = 2 we get:

• Space n^{1.77 ...} and query time n^o(1), significantly improving upon known data structures that support very fast queries [IM98, KOR00];

• Space n^1.14... and query time n^0.14..., matching the optimal data-dependent Locality-Sensitive Hashing (LSH) from [AR15];

• Space n^1+o(1) and query time n^0.43..., making significant progress in the regime of near-linear space, which is arguably of the most interest for practice [LJW⁺07].

This is the first data structure that achieves sublinear query time and near-linear space for every approximation factor c > 1, improving upon [Kap15]. The data structure is a culmination of a long line of work on the problem for all space regimes; it builds on Spherical Locality-Sensitive Filtering [BDGL16] and data-dependent hashing [AINR14, AR15].

Our matching lower bounds are of two types: conditional and unconditional. First, we prove tightness of the whole trade-off (0.1) in a restricted model of computation, which captures all known hashing-based approaches. We then show unconditional cell-probe lower bounds for one and two probes that match (0.1) for ρ_q = 0, improving upon the best known lower bounds from [PTW10]. In particular, this is the first space lower bound (for any static data structure) for two probes which is not polynomially smaller than the one-probe bound. To show the result for two probes, we establish and exploit a connection to locally-decodable codes.

References

References are not available