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SIAM Journal on Discrete Mathematics
Article . 2022 . Peer-reviewed
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https://dx.doi.org/10.48550/ar...
Article . 2020
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https://dx.doi.org/10.48550/ar...
Article . 2020
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
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Grundy Distinguishes Treewidth from Pathwidth

Grundy distinguishes treewidth from pathwidth
Authors: Rémy Belmonte; Eun Jung Kim; Michael Lampis; Valia Mitsou; Yota Otachi;

Grundy Distinguishes Treewidth from Pathwidth

Abstract

Structural graph parameters, such as treewidth, pathwidth, and clique-width, are a central topic of study in parameterized complexity. A main aim of research in this area is to understand the "price of generality" of these widths: as we transition from more restrictive to more general notions, which are the problems that see their complexity status deteriorate from fixed-parameter tractable to intractable? This type of question is by now very well-studied, but, somewhat strikingly, the algorithmic frontier between the two (arguably) most central width notions, treewidth and pathwidth, is still not understood: currently, no natural graph problem is known to be W-hard for one but FPT for the other. Indeed, a surprising development of the last few years has been the observation that for many of the most paradigmatic problems, their complexities for the two parameters actually coincide exactly, despite the fact that treewidth is a much more general parameter. It would thus appear that the extra generality of treewidth over pathwidth often comes "for free". Our main contribution in this paper is to uncover the first natural example where this generality comes with a high price. We consider Grundy Coloring, a variation of coloring where one seeks to calculate the worst possible coloring that could be assigned to a graph by a greedy First-Fit algorithm. We show that this well-studied problem is FPT parameterized by pathwidth; however, it becomes significantly harder (W[1]-hard) when parameterized by treewidth. Furthermore, we show that Grundy Coloring makes a second complexity jump for more general widths, as it becomes para-NP-hard for clique-width. Hence, Grundy Coloring nicely captures the complexity trade-offs between the three most well-studied parameters. Completing the picture, we show that Grundy Coloring is FPT parameterized by modular-width.

Conference version appeared in ESA 2020. This is the full version which has been accepted for publication at SIDMA

Keywords

Computational Geometry (cs.CG), FOS: Computer and information sciences, Treewidth, Analysis of algorithms and problem complexity, minimum enclosing ball, Parameterized complexity, tractability and kernelization, Pathwidth, Coloring of graphs and hypergraphs, high dimensions, Computer Science - Data Structures and Algorithms, treewidth, Grundy coloring, Data Structures and Algorithms (cs.DS), Grundy Coloring, shape fitting, Clique-width, outliers, pathwidth, sub-linear time, 003, 004, Graph theory (including graph drawing) in computer science, Computational difficulty of problems (lower bounds, completeness, difficulty of approximation, etc.), Computer Science - Computational Geometry, Recherche opérationnelle, clique-width, ddc: ddc:004

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
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