Randomized Data Structures for the Dynamic Closest-Pair Problem

We describe a new randomized data structure, the {\em sparse partition}, for solving the dynamic closest-pair problem. Using this data structure the closest pair of a set of $n$ points in $k$-dimensional space, for any fixed $k$, can be found in constant time. If the points are chosen from a finite universe, and if the floor function is available at unit-cost, then the data structure supports insertions into and deletions from the set in expected $O(\log n)$ time and requires expected $O(n)$ space. Here, it is assumed that the updates are chosen by an adversary who does not know the random choices made by the data structure. The data structure can be modified to run in $O(\log^2 n)$ expected time per update in the algebraic decision tree model of computation. Even this version is more efficient than the currently best known deterministic algorithms for solving the problem for $k>1$