2026-07-22T11:09:41Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:16950 2024-03-06T10:58:47Z ddc:004 open_access

Computing NP-Hard Repetitiveness Measures via MAX-SAT Bannai, Hideo Goto, Keisuke Ishihata, Masakazu Kanda, Shunsuke Köppl, Dominik Nishimoto, Takaaki repetitiveness measures string attractor bidirectional macro scheme Repetitiveness measures reveal profound characteristics of datasets, and give rise to compressed data structures and algorithms working in compressed space. Alas, the computation of some of these measures is NP-hard, and straight-forward computation is infeasible for datasets of even small sizes. Three such measures are the smallest size of a string attractor, the smallest size of a bidirectional macro scheme, and the smallest size of a straight-line program. While a vast variety of implementations for heuristically computing approximations exist, exact computation of these measures has received little to no attention. In this paper, we present MAX-SAT formulations that provide the first non-trivial implementations for exact computation of smallest string attractors, smallest bidirectional macro schemes, and smallest straight-line programs. Computational experiments show that our implementations work for texts of length up to a few hundred for straight-line programs and bidirectional macro schemes, and texts even over a million for string attractors. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Hideo Bannai and Keisuke Goto and Masakazu Ishihata and Shunsuke Kanda and Dominik Köppl and Takaaki Nishimoto 2022 Is Part Of LIPIcs, Volume 244, 30th Annual European Symposium on Algorithms (ESA 2022) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ESA.2022.12 urn:nbn:de:0030-drops-169505 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2022.12 eng https://creativecommons.org/licenses/by/4.0/legalcode