Nearly Optimal List Labeling

Fuente: arXiv
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Main Authors: Bender, Michael A., Conway, Alex, Farach-Colton, Martín, Komlós, Hanna, Koucký, Michal, Kuszmaul, William, Saks, Michael
Format: Preprint
Published: 2024
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author Bender, Michael A.
Conway, Alex
Farach-Colton, Martín
Komlós, Hanna
Koucký, Michal
Kuszmaul, William
Saks, Michael
author_facet Bender, Michael A.
Conway, Alex
Farach-Colton, Martín
Komlós, Hanna
Koucký, Michal
Kuszmaul, William
Saks, Michael
contents 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 $m = (1 + Θ(1))n$. 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 $Ω(\log n)$, 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 $O(\log n \operatorname{polyloglog} n)$ 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.
format Preprint
id arxiv_https___arxiv_org_abs_2405_00807
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Nearly Optimal List Labeling
Bender, Michael A.
Conway, Alex
Farach-Colton, Martín
Komlós, Hanna
Koucký, Michal
Kuszmaul, William
Saks, Michael
Data Structures and Algorithms
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 $m = (1 + Θ(1))n$. 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 $Ω(\log n)$, 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 $O(\log n \operatorname{polyloglog} n)$ 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.
title Nearly Optimal List Labeling
topic Data Structures and Algorithms
url https://arxiv.org/abs/2405.00807