On data structures and asymmetric communication complexity
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
Communication complexity
Lectures on Discrete Geometry
Approximation of Diameters: Randomization Doesn't Help
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Tight Lower Bounds for the Distinct Elements Problem
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Optimal space lower bounds for all frequency moments
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Lower bounds in communication complexity based on factorization norms
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
The pattern matrix method for lower bounds on quantum communication
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Disjointness Is Hard in the Multi-party Number-on-the-Forehead Model
CCC '08 Proceedings of the 2008 IEEE 23rd Annual Conference on Computational Complexity
A Multi-Round Communication Lower Bound for Gap Hamming and Some Consequences
CCC '09 Proceedings of the 2009 24th Annual IEEE Conference on Computational Complexity
An optimal lower bound on the communication complexity of gap-hamming-distance
Proceedings of the forty-third annual ACM symposium on Theory of computing
Tight bounds for distributed functional monitoring
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
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Gap Hamming Distance is a well-studied problem in communication complexity, in which Alice and Bob have to decide whether the Hamming distance between their respective n-bit inputs is less than n/2 -√n or greater than n/2 + √n. We show that every k-round bounded-error communication protocol for this problem sends a message of at least Ω(n/(k2 log k)) bits. This lower bound has an exponentially better dependence on the number of rounds than the previous best bound, due to Brody and Chakrabarti. Our communication lower bound implies strong space lower bounds on algorithms for a number of data stream computations, such as approximating the number of distinct elements in a stream.