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DOI: 10.4230/LIPIcs.ESA.2023.47

Correlating Theory and Practice in Finding Clubs and Plexes

Authors: Aleksander Figiel, Tomohiro Koana, André Nichterlein, and Niklas Wünsche

Published in: LIPIcs, Volume 274, 31st Annual European Symposium on Algorithms (ESA 2023)

Abstract

For solving NP-hard problems there is often a huge gap between theoretical guarantees and observed running times on real-world instances. As a first step towards tackling this issue, we propose an approach to quantify the correlation between theoretical and observed running times. We use two NP-hard problems related to finding large "cliquish" subgraphs in a given graph as demonstration of this measure. More precisely, we focus on finding maximum s-clubs and s-plexes, i. e., graphs of diameter s and graphs where each vertex is adjacent to all but s vertices. Preprocessing based on Turing kernelization is a standard tool to tackle these problems, especially on sparse graphs. We provide a parameterized analysis for the Turing kernelization and demonstrate their usefulness in practice. Moreover, we demonstrate that our measure indeed captures the correlation between these new theoretical and the observed running times.

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Aleksander Figiel, Tomohiro Koana, André Nichterlein, and Niklas Wünsche. Correlating Theory and Practice in Finding Clubs and Plexes. In 31st Annual European Symposium on Algorithms (ESA 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 274, pp. 47:1-47:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{figiel_et_al:LIPIcs.ESA.2023.47,
  author =	{Figiel, Aleksander and Koana, Tomohiro and Nichterlein, Andr\'{e} and W\"{u}nsche, Niklas},
  title =	{{Correlating Theory and Practice in Finding Clubs and Plexes}},
  booktitle =	{31st Annual European Symposium on Algorithms (ESA 2023)},
  pages =	{47:1--47:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-295-2},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{274},
  editor =	{G{\o}rtz, Inge Li and Farach-Colton, Martin and Puglisi, Simon J. and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2023.47},
  URN =		{urn:nbn:de:0030-drops-187000},
  doi =		{10.4230/LIPIcs.ESA.2023.47},
  annote =	{Keywords: Preprocessing, Turing kernelization, Pearson correlation coefficient}
}

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DOI: 10.4230/LIPIcs.ESA.2022.53

There and Back Again: On Applying Data Reduction Rules by Undoing Others

Authors: Aleksander Figiel, Vincent Froese, André Nichterlein, and Rolf Niedermeier

Published in: LIPIcs, Volume 244, 30th Annual European Symposium on Algorithms (ESA 2022)

Abstract

Data reduction rules are an established method in the algorithmic toolbox for tackling computationally challenging problems. A data reduction rule is a polynomial-time algorithm that, given a problem instance as input, outputs an equivalent, typically smaller instance of the same problem. The application of data reduction rules during the preprocessing of problem instances allows in many cases to considerably shrink their size, or even solve them directly. Commonly, these data reduction rules are applied exhaustively and in some fixed order to obtain irreducible instances. It was often observed that by changing the order of the rules, different irreducible instances can be obtained. We propose to "undo" data reduction rules on irreducible instances, by which they become larger, and then subsequently apply data reduction rules again to shrink them. We show that this somewhat counter-intuitive approach can lead to significantly smaller irreducible instances. The process of undoing data reduction rules is not limited to "rolling back" data reduction rules applied to the instance during preprocessing. Instead, we formulate so-called backward rules, which essentially undo a data reduction rule, but without using any information about which data reduction rules were applied to it previously. In particular, based on the example of Vertex Cover we propose two methods applying backward rules to shrink the instances further. In our experiments we show that this way smaller irreducible instances consisting of real-world graphs from the SNAP and DIMACS datasets can be computed.

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Aleksander Figiel, Vincent Froese, André Nichterlein, and Rolf Niedermeier. There and Back Again: On Applying Data Reduction Rules by Undoing Others. In 30th Annual European Symposium on Algorithms (ESA 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 244, pp. 53:1-53:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{figiel_et_al:LIPIcs.ESA.2022.53,
  author =	{Figiel, Aleksander and Froese, Vincent and Nichterlein, Andr\'{e} and Niedermeier, Rolf},
  title =	{{There and Back Again: On Applying Data Reduction Rules by Undoing Others}},
  booktitle =	{30th Annual European Symposium on Algorithms (ESA 2022)},
  pages =	{53:1--53:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-247-1},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{244},
  editor =	{Chechik, Shiri and Navarro, Gonzalo and Rotenberg, Eva and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2022.53},
  URN =		{urn:nbn:de:0030-drops-169914},
  doi =		{10.4230/LIPIcs.ESA.2022.53},
  annote =	{Keywords: Kernelization, Preprocessing, Vertex Cover}
}

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Documents authored by Figiel, Aleksander

Correlating Theory and Practice in Finding Clubs and Plexes

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There and Back Again: On Applying Data Reduction Rules by Undoing Others

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