Algorithms For Similarity Search And Pseudorandomness

We study the problem of approximate near neighbor (ANN) search and show the following results:

An improved framework for solving the ANN problem using locality-sensitive hashing, reducing the number of evaluations of locality-sensitive hash functions and the word-RAM complexity compared to the standard framework.
A framework for solving the ANN problem with space-time tradeoffs as well as tight upper and lower bounds for the space-time tradeoff of framework solutions to the ANN problem under cosine similarity.
A novel approach to solving the ANN problem on sets along with a matching lower bound, improving the state of the art.
A self-tuning version of the algorithm is shown through experiments to outperform existing similarity join algorithms.
Tight lower bounds for asymmetric locality-sensitive hashing which has applications to the approximate furthest neighbor problem, orthogonal vector search, and annulus queries.
A proof of the optimality of a well-known Boolean locality-sensitive hashing scheme. We study the problem of efficient algorithms for producing high-quality pseudorandom numbers and obtain the following results:
A deterministic algorithm for generating pseudorandom numbers of arbitrarily high quality in constant time using near-optimal space.
A randomized construction of a family of hash functions that outputs pseudorandom numbers of arbitrarily high quality with space usage and running time nearly matching known cell-probe lower bounds.

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