Lower bounds for oblivious near-neighbor search

Kasper Green Larsen, Tal Malkin, Omri Weinstein, Kevin Yeo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations


We prove an Ω(dlg n/(lg lg n)2) lower bound on the dynamic cell-probe complexity of statistically oblivious approximate-near-neighbor search (ANN) over the ddimensional Hamming cube. For the natural setting of d = Θ(lg n), our result implies an Ω(lg 2 n) lower bound, which is a quadratic improvement over the highest (non-oblivious) cell-probe lower bound for ANN. This is the first super-logarithmic unconditional lower bound for ANN against general (non black-box) data structures. We also show that any oblivious static data structure for decomposable search problems (like ANN) can be obliviously dynamized with O(lg n) overhead in update and query time, strengthening a classic result of Bentley and Saxe (Algorithmica, 1980).

Original languageAmerican English
Title of host publication31st Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2020
EditorsShuchi Chawla
PublisherAssociation for Computing Machinery
Number of pages19
ISBN (Electronic)9781611975994
StatePublished - 2020
Externally publishedYes
Event31st Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2020 - Salt Lake City, United States
Duration: 5 Jan 20208 Jan 2020

Publication series

NameProceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms


Conference31st Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2020
Country/TerritoryUnited States
CitySalt Lake City

Bibliographical note

Publisher Copyright:
Copyright © 2020 by SIAM


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