Nearly Optimal List Labeling

The list-labeling problem captures the basic task of storing a dynamically changing set of up to$n$elements in sorted order in an array of size$m=(1+\Theta(1))n$• The goal is to support insertions and deletions while moving around elements within the array as little as possible. Until recently, the best known upper bound stood at$O(\log^{2}n)$amortized cost. This bound, which was first established in 1981, was finally improved two years ago, when a randomized$O(\log^{3/2}n)$expected-cost algorithm was discovered. The best randomized lower bound for this problem remains$\Omega(\log n)$, and closing this gap is considered to be a major open problem in data structures. In this paper, we present the See-Saw Algorithm, a randomized list-labeling solution that achieves a nearly optimal bound of$O(\log n \text{polyloglog}\ n)$amortized expected cost. This bound is achieved despite at least three lower bounds showing that this type of result is impossible for large classes of solutions.