Paper Info
Title
Computing NP-Hard Repetitiveness Measures via MAX-SAT
Abstract
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.
Year
DOI
Venue
2022
10.4230/LIPICS.ESA.2022.12
European Symposium on Algorithms (ESA)
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Hideo Bannai162079.87
Keisuke Goto29813.93
Masakazu Ishihata301.01
Shunsuke Kanda401.69
Dominik Köppl53010.31
Takaaki Nishimoto601.69