Fast recognition of pushdown automaton and context-free languages
Information and Control
Matrix multiplication via arithmetic progressions
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
A practical algorithm for Boolean matrix multiplication
Information Processing Letters
Rectangular matrix multiplication revisited
Journal of Complexity
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Fast rectangular matrix multiplication and applications
Journal of Complexity
C2P: Clustering based on Closest Pairs
Proceedings of the 27th International Conference on Very Large Data Bases
The bit vector intersection problem
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
On Diameter Verification and Boolean Matrix Multiplication.
On Diameter Verification and Boolean Matrix Multiplication.
Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
L1 pattern matching lower bound
Information Processing Letters
Multi-index hashing for information retrieval
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
Randomized fast design of short DNA words
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Bounds for Binary Codes With Narrow Distance Distributions
IEEE Transactions on Information Theory
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Finding the closest pair among a given set of points under Hamming Metric is a fundamental problem with many applications. Let n be the number of points and D the dimensionality of all points. We show that for 0 D ≤ n 0.294, the problem, with the binary alphabet set, can be solved within time complexity $O\left(n^{2+o(1)}\right)$, whereas for n 0.294 D ≤ n , it can be solved within time complexity $O\left(n^{1.843} D^{0.533}\right)$. We also provide an alternative approach not involving algebraic matrix multiplication, which has the time complexity $O\left(n^2D/\log^2 D\right)$ with small constant, and is effective for practical use. Moreover, for arbitrary large alphabet set, an algorithm with the time complexity $O\left(n^2\sqrt{D}\right)$ is obtained for 0 D ≤ n 0.294, whereas the time complexity is $O\left(n^{1.921} D^{0.767}\right)$ for n 0.294 D ≤ n . In addition, the algorithms propose in this paper provides a solution to the open problem stated by Kao et al.