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The Element Extraction Problem and the Cost of Determinism and Limited Adaptivity in Linear Queries

by   Amit Chakrabarti, et al.
Dartmouth College

Two widely-used computational paradigms for sublinear algorithms are using linear measurements to perform computations on a high dimensional input and using structured queries to access a massive input. Typically, algorithms in the former paradigm are non-adaptive whereas those in the latter are highly adaptive. This work studies the fundamental search problem of element-extraction in a query model that combines both: linear measurements with bounded adaptivity. In the element-extraction problem, one is given a nonzero vector ๐ณ = (z_1,โ€ฆ,z_n) โˆˆ{0,1}^n and must report an index i where z_i = 1. The input can be accessed using arbitrary linear functions of it with coefficients in some ring. This problem admits an efficient nonadaptive randomized solution (through the well known technique of โ„“_0-sampling) and an efficient fully adaptive deterministic solution (through binary search). We prove that when confined to only k rounds of adaptivity, a deterministic element-extraction algorithm must spend ฮฉ(k (n^1/k -1)) queries, when working in the ring of integers modulo some fixed q. This matches the corresponding upper bound. For queries using integer arithmetic, we prove a 2-round ฮฉ(โˆš(n)) lower bound, also tight up to polylogarithmic factors. Our proofs reduce to classic problems in combinatorics, and take advantage of established results on the zero-sum problem as well as recent improvements to the sunflower lemma.


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