Lower Bounds on Non-Adaptive Data Structures Maintaining Sets of Numbers, from Sunflowers
We prove new cell-probe lower bounds for dynamic data structures that maintain a subset of {1,2,...,n}, and compute various statistics of the set. The data structure is said to handle insertions non-adaptively if the locations of memory accessed depend only on the element being inserted, and not on the contents of the memory. For any such data structure that can compute the median of the set, we prove that: t_{med} >= Omega(n^{1/(t_{ins}+1)}/(w^2 * t_{ins}^2)), where t_{ins} is the number of memory locations accessed during insertions, t_{med} is the number of memory locations accessed to compute the median, and w is the number of bits stored in each memory location. When the data structure is able to perform deletions non-adaptively and compute the minimum non-adaptively, we prove t_{min} + t_{del} >= Omega(log n /(log w + log log n)), where t_{min} is the number of locations accessed to compute the minimum, and t_{del} is the number of locations accessed to perform deletions. For the predecessor search problem, where the data structure is required to compute the predecessor of any element in the set, we prove that if computing the predecessors can be done non-adaptively, then either t_{pred} >= Omega(log n/(log log n + log w)), or t_{ins} >= Omega(n^{1/(2(t_{pred}+1))}), where t_{pred} is the number of locations accessed to compute predecessors.
These bounds are nearly matched by Binary Search Trees in some range of parameters. Our results follow from using the Sunflower Lemma of Erdös and Rado [Paul Erdös and Richard Rado, 1960] together with several kinds of encoding arguments.
Non-adaptive data structures
Sunflower lemma
Theory of computation~Cell probe models and lower bounds
27:1-27:16
Regular Paper
Sivaramakrishnan
Natarajan Ramamoorthy
Sivaramakrishnan Natarajan Ramamoorthy
Paul G. Allen School for Computer Science & Engineering, University of Washington, Seattle, USA
Supported by the National Science Foundation under agreement CCF-1420268 and CCF-1524251
Anup
Rao
Anup Rao
Paul G. Allen School for Computer Science & Engineering, University of Washington, Seattle, USA
Supported by the National Science Foundation under agreement CCF-1420268 and CCF-1524251
10.4230/LIPIcs.CCC.2018.27
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Sivaramakrishnan Natarajan Ramamoorthy and Anup Rao
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