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Autore principale: Dinklage, Patrick
Natura: Preprint
Pubblicazione: 2024
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Accesso online:https://arxiv.org/abs/2409.07840
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author Dinklage, Patrick
author_facet Dinklage, Patrick
contents The LZ-End parsing [Kreft & Navarro, 2011] of an input string yields compression competitive with the popular Lempel-Ziv 77 scheme, but also allows for efficient random access. Kempa and Kosolobov showed that the parsing can be computed in time and space linear in the input length [Kempa & Kosolobov, 2017], however, the corresponding algorithm is hardly practical. We put the spotlight on their suboptimal algorithm that computes the parsing in time $\mathcal{O}(n \lg\lg n)$. It requires a comparatively small toolset and is therefore easy to implement, but at the same time very efficient in practice. We give a detailed and simplified description with a full listing that incorporates undocumented tricks from the original implementation, but also uses lazy evaluation to reduce the workload in practice and requires less working memory by removing a level of indirection. We legitimize our algorithm in a brief benchmark, obtaining the parsing faster than the state of the art.
format Preprint
id arxiv_https___arxiv_org_abs_2409_07840
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Computing the LZ-End parsing: Easy to implement and practically efficient
Dinklage, Patrick
Data Structures and Algorithms
The LZ-End parsing [Kreft & Navarro, 2011] of an input string yields compression competitive with the popular Lempel-Ziv 77 scheme, but also allows for efficient random access. Kempa and Kosolobov showed that the parsing can be computed in time and space linear in the input length [Kempa & Kosolobov, 2017], however, the corresponding algorithm is hardly practical. We put the spotlight on their suboptimal algorithm that computes the parsing in time $\mathcal{O}(n \lg\lg n)$. It requires a comparatively small toolset and is therefore easy to implement, but at the same time very efficient in practice. We give a detailed and simplified description with a full listing that incorporates undocumented tricks from the original implementation, but also uses lazy evaluation to reduce the workload in practice and requires less working memory by removing a level of indirection. We legitimize our algorithm in a brief benchmark, obtaining the parsing faster than the state of the art.
title Computing the LZ-End parsing: Easy to implement and practically efficient
topic Data Structures and Algorithms
url https://arxiv.org/abs/2409.07840