License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.08261.4
URN: urn:nbn:de:0030-drops-16937
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2008/1693/
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Sakamoto, Hiroshi

A Space-Saving Approximation Algorithm for Grammar-Based Compression

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08261.SakamotoHiroshi.Paper.1693.pdf (0.4 MB)

Abstract

A space-efficient approximation algorithm for the grammar-based compression
problem, which requests for a given string to find a smallest
context-free grammar deriving the string, is presented. For the input
length n and an optimum CFG size g, the algorithm consumes only
O(g log g) space and O(n log^n) time to achieve O((log^n) log n) approximation
ratio to the optimum compression, where log^n is the maximum
number of logarithms satisfying log log · · · logn > 1. This ratio is thus
regarded to almost O(log n), which is the currently best approximation
ratio. While g depends on the string, it is known that g =(log n) and
g=O(n/log_k n) for strings from a k-letter alphabet [12].

BibTeX - Entry

@InProceedings{sakamoto:DagSemProc.08261.4,
  author =	{Sakamoto, Hiroshi},
  title =	{{A Space-Saving Approximation Algorithm for Grammar-Based Compression}},
  booktitle =	{Structure-Based Compression of Complex Massive Data},
  pages =	{1--14},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8261},
  editor =	{Stefan B\"{o}ttcher and Markus Lohrey and Sebastian Maneth and Wojcieh Rytter},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2008/1693},
  URN =		{urn:nbn:de:0030-drops-16937},
  doi =		{10.4230/DagSemProc.08261.4},
  annote =	{Keywords: Grammar based compression, space efficient approximation}
}

Keywords: Grammar based compression, space efficient approximation
Collection: 08261 - Structure-Based Compression of Complex Massive Data
Issue Date: 2008
Date of publication: 20.11.2008

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