Space-Efficient Parameterized Algorithms on Graphs of Low Shrubdepth

Authors Benjamin Bergougnoux, Vera Chekan , Robert Ganian , Mamadou Moustapha Kanté , Matthias Mnich , Sang-il Oum , Michał Pilipczuk, Erik Jan van Leeuwen

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Benjamin Bergougnoux
  • Institute of Informatics, University of Warsaw, Poland
Vera Chekan
  • Humboldt-Universität zu Berlin, Germany
Robert Ganian
  • Algorithms and Complexity Group, TU Wien, Austria
Mamadou Moustapha Kanté
  • Université Clermont Auvergne, Clermont Auvergne INP, LIMOS, CNRS, Clermont-Ferrand, France
Matthias Mnich
  • Hamburg University of Technology, Institute for Algorithms and Complexity, Germany
Sang-il Oum
  • Discrete Mathematics Group, Institute for Basic Science (IBS), Daejeon, Korea
  • Department of Mathematical Sciences, KAIST, Daejeon, Korea
Michał Pilipczuk
  • Institute of Informatics, University of Warsaw, Poland
Erik Jan van Leeuwen
  • Dept. Information and Computing Sciences, Utrecht University, The Netherlands


This work was initiated at the Graph Decompositions: Small Width, Big Challenges workshop held at the Lorentz Center in Leiden, The Netherlands, in 2022.

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Benjamin Bergougnoux, Vera Chekan, Robert Ganian, Mamadou Moustapha Kanté, Matthias Mnich, Sang-il Oum, Michał Pilipczuk, and Erik Jan van Leeuwen. Space-Efficient Parameterized Algorithms on Graphs of Low Shrubdepth. In 31st Annual European Symposium on Algorithms (ESA 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 274, pp. 18:1-18:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


Dynamic programming on various graph decompositions is one of the most fundamental techniques used in parameterized complexity. Unfortunately, even if we consider concepts as simple as path or tree decompositions, such dynamic programming uses space that is exponential in the decomposition’s width, and there are good reasons to believe that this is necessary. However, it has been shown that in graphs of low treedepth it is possible to design algorithms which achieve polynomial space complexity without requiring worse time complexity than their counterparts working on tree decompositions of bounded width. Here, treedepth is a graph parameter that, intuitively speaking, takes into account both the depth and the width of a tree decomposition of the graph, rather than the width alone. Motivated by the above, we consider graphs that admit clique expressions with bounded depth and label count, or equivalently, graphs of low shrubdepth. Here, shrubdepth is a bounded-depth analogue of cliquewidth, in the same way as treedepth is a bounded-depth analogue of treewidth. We show that also in this setting, bounding the depth of the decomposition is a deciding factor for improving the space complexity. More precisely, we prove that on n-vertex graphs equipped with a tree-model (a decomposition notion underlying shrubdepth) of depth d and using k labels, - Independent Set can be solved in time 2^𝒪(dk) ⋅ n^𝒪(1) using 𝒪(dk²log n) space; - Max Cut can be solved in time n^𝒪(dk) using 𝒪(dk log n) space; and - Dominating Set can be solved in time 2^𝒪(dk) ⋅ n^𝒪(1) using n^𝒪(1) space via a randomized algorithm. We also establish a lower bound, conditional on a certain assumption about the complexity of Longest Common Subsequence, which shows that at least in the case of Independent Set the exponent of the parametric factor in the time complexity has to grow with d if one wishes to keep the space complexity polynomial.

Subject Classification

ACM Subject Classification
  • Theory of computation → Parameterized complexity and exact algorithms
  • Parameterized complexity
  • shrubdepth
  • space complexity
  • algebraic methods


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