A New Lower Bound for Semigroup Orthogonal Range Searching
Peyman Afshani

TL;DR
This paper improves the lower bounds for the space-time trade-off in semigroup orthogonal range searching, especially for low space regimes, and discusses the gap between lower bounds and existing data structures.
Contribution
It introduces a new lower bound for low space scenarios and shows the bound's tightness, highlighting the gap between lower bounds and current data structures.
Findings
Lower bound: m(n) = Omega(n (log n log log n)^{d-1}) for low space
The new lower bound is tight, matching the analysis
The gap suggests the need to study non-idempotent semigroups or specialized data structures
Abstract
We report the first improvement in the space-time trade-off of lower bounds for the orthogonal range searching problem in the semigroup model, since Chazelle's result from 1990. This is one of the very fundamental problems in range searching with a long history. Previously, Andrew Yao's influential result had shown that the problem is already non-trivial in one dimension~\cite{Yao-1Dlb}: using units of space, the query time must be where is the inverse Ackermann's function, a very slowly growing function. In dimensions, Bernard Chazelle~\cite{Chazelle.LB.II} proved that the query time must be where . Chazelle's lower bound is known to be tight for when space consumption is `high' i.e., . We have two main results. The…
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Taxonomy
TopicsData Management and Algorithms · Machine Learning and Algorithms · Optimization and Search Problems
