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The 2-Attractor Problem Is NP-Complete

Authors Janosch Fuchs , Philip Whittington

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Janosch Fuchs
  • RWTH Aachen University, Germany
Philip Whittington
  • ETH Zürich, Switzerland

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Janosch Fuchs and Philip Whittington. The 2-Attractor Problem Is NP-Complete. In 41st International Symposium on Theoretical Aspects of Computer Science (STACS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 289, pp. 35:1-35:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.STACS.2024.35

Abstract

A k-attractor is a combinatorial object unifying dictionary-based compression. It allows to compare the repetitiveness measures of different dictionary compressors such as Lempel-Ziv 77, the Burrows-Wheeler transform, straight line programs and macro schemes. For a string T ∈ Σⁿ, the k-attractor is defined as a set of positions Γ ⊆ [1,n], such that every distinct substring of length at most k is covered by at least one of the selected positions. Thus, if a substring occurs multiple times in T, one position suffices to cover it. A 1-attractor is easily computed in linear time, while Kempa and Prezza [STOC 2018] have shown that for k ≥ 3, it is NP-complete to compute the smallest k-attractor by a reduction from k-set cover. The main result of this paper answers the open question for the complexity of the 2-attractor problem, showing that the problem remains NP-complete. Kempa and Prezza’s proof for k ≥ 3 also reduces the 2-attractor problem to the 2-set cover problem, which is equivalent to edge cover, but that does not fully capture the complexity of the 2-attractor problem. For this reason, we extend edge cover by a color function on the edges, yielding the colorful edge cover problem. Any edge cover must then satisfy the additional constraint that each color is represented. This extension raises the complexity such that colorful edge cover becomes NP-complete while also more precisely modeling the 2-attractor problem. We obtain a reduction showing k-attractor to be NP-complete and APX-hard for any k ≥ 2.

Subject Classification

ACM Subject Classification
  • Theory of computation → Problems, reductions and completeness
  • Theory of computation → Approximation algorithms analysis
  • Theory of computation → Data compression
Keywords
  • String attractors
  • dictionary compression
  • computational complexity

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