String Attractors: Verification and Optimization

Authors Dominik Kempa , Alberto Policriti , Nicola Prezza , Eva Rotenberg



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Author Details

Dominik Kempa
  • Department of Computer Science, University of Helsinki, Finland
Alberto Policriti
  • Department of Computer Science, University of Udine, Italy
Nicola Prezza
  • Department of Computer Science, University of Pisa, Italy
Eva Rotenberg
  • DTU Compute, Technical University of Denmark, Denmark

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Dominik Kempa, Alberto Policriti, Nicola Prezza, and Eva Rotenberg. String Attractors: Verification and Optimization. In 26th Annual European Symposium on Algorithms (ESA 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 112, pp. 52:1-52:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.ESA.2018.52

Abstract

String attractors [STOC 2018] are combinatorial objects recently introduced to unify all known dictionary compression techniques in a single theory. A set Gamma subseteq [1..n] is a k-attractor for a string S in Sigma^n if and only if every distinct substring of S of length at most k has an occurrence crossing at least one of the positions in Gamma. Finding the smallest k-attractor is NP-hard for k >= 3, but polylogarithmic approximations can be found using reductions from dictionary compressors. It is easy to reduce the k-attractor problem to a set-cover instance where the string's positions are interpreted as sets of substrings. The main result of this paper is a much more powerful reduction based on the truncated suffix tree. Our new characterization of the problem leads to more efficient algorithms for string attractors: we show how to check the validity and minimality of a k-attractor in near-optimal time and how to quickly compute exact solutions. For example, we prove that a minimum 3-attractor can be found in O(n) time when |Sigma| in O(sqrt[3+epsilon]{log n}) for some constant epsilon > 0, despite the problem being NP-hard for large Sigma.

Subject Classification

ACM Subject Classification
  • Theory of computation → Data compression
Keywords
  • Dictionary compression
  • String attractors
  • Set cover

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