Computing String Covers in Sublinear Time
September 22, 2024 Β· Declared Dead Β· π Theory of Computing Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Jakub Radoszewski, Wiktor Zuba
arXiv ID
2409.14559
Category
cs.DS: Data Structures & Algorithms
Citations
4
Venue
Theory of Computing Systems
Last Checked
4 months ago
Abstract
Let $T$ be a string of length $n$ over an integer alphabet of size $Ο$. In the word RAM model, $T$ can be represented in $O(n /\log_Οn)$ space. We show that a representation of all covers of $T$ can be computed in the optimal $O(n/\log_Οn)$ time; in particular, the shortest cover can be computed within this time. We also design an $O(n(\logΟ+ \log \log n)/\log n)$-sized data structure that computes in $O(1)$ time any element of the so-called (shortest) cover array of $T$, that is, the length of the shortest cover of any given prefix of $T$. As a by-product, we describe the structure of cover arrays of Fibonacci strings. On the negative side, we show that the shortest cover of a length-$n$ string cannot be computed using $o(n/\log n)$ operations in the PILLAR model of Charalampopoulos, Kociumaka, and Wellnitz (FOCS 2020).
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Data Structures & Algorithms
π
π
The Cartographer
R.I.P.
π»
Ghosted
Route Planning in Transportation Networks
R.I.P.
π»
Ghosted
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
R.I.P.
π»
Ghosted
Hierarchical Clustering: Objective Functions and Algorithms
R.I.P.
π»
Ghosted
Graph Isomorphism in Quasipolynomial Time
π
π
The Cartographer
Simulation optimization: A review of algorithms and applications
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted