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: tmed >=Omega (n1/tins+1 w2 center dot t2 ins) where tins is the number of memory locations accessed during insertions, tmed 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 tmin + tdel Omega (log n log w + log log n), where tmin is the number of locations accessed to compute the minimum, and tdel 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 tpred Omega (log n/log log n + log w), or tins Omega (n 1 2(tpred+1), where tpred 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 Erdos and Rado [11] together with several kinds of encoding arguments.