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The Price of Being Partial: Complexity of Partial Generalized Dominating Set on Bounded-Treewidth Graphs

Jakob Greilhuber, Dániel Marx

Abstract

For fixed sets $σ, ρ$ of non-negative integers, the $(σ, ρ)$-domination framework introduced by Telle [Nord. J. Comput. 1994] captures many classical graph problems. For a graph $G$, a $(σ,ρ)$-set is a set $S$ of vertices such that for every $v\in V(G)$, we have (1) if $v \in S$, then $|N(v) \cap S| \in σ$, and (2) if $v \notin S$, then $|N(v) \cap S| \in ρ$. We initiate the study of a natural partial variant $(σ,ρ)$-MinParDomSet of the problem, in which the constraints given by $σ, ρ$ need not be fulfilled for all vertices, but we want to find a set of size at most $k$ that maximizes the number of vertices that are satisfied in the sense that they satisfy (1) or (2) above. Our goal is to understand whether $(σ,ρ)$-MinParDomSet can be solved in the same running time as the nonpartial version, or whether it is strictly harder. Formally, we consider nonempty finite or simple cofinite sets $σ$ and $ρ$ (simple cofinite sets are of the form $\mathbb{Z}_{\geq c}$), and we try to determine the smallest constant $c_{σ,ρ}$ such that there is a $c_{σ,ρ}^{tw}\cdot n^{O(1)}$ time algorithm for the problem if a tree decomposition of width $tw$ is given. We obtain matching upper and lower bounds on $c_{σ,ρ}$ for every such fixed $σ$ and $ρ$ under the Primal Pathwidth Strong Exponential Time Hypothesis, and establish whether the partial problem is harder than the nonpartial variant. For some sets $σ$ and $ρ$, the more general $(σ,ρ)$-MinParDomSet has the same complexity as the nonpartial special case (e.g., for Dominating Set), while for other choices, the partial version is significantly harder (e.g., for Perfect Code).

The Price of Being Partial: Complexity of Partial Generalized Dominating Set on Bounded-Treewidth Graphs

Abstract

For fixed sets of non-negative integers, the -domination framework introduced by Telle [Nord. J. Comput. 1994] captures many classical graph problems. For a graph , a -set is a set of vertices such that for every , we have (1) if , then , and (2) if , then . We initiate the study of a natural partial variant -MinParDomSet of the problem, in which the constraints given by need not be fulfilled for all vertices, but we want to find a set of size at most that maximizes the number of vertices that are satisfied in the sense that they satisfy (1) or (2) above. Our goal is to understand whether -MinParDomSet can be solved in the same running time as the nonpartial version, or whether it is strictly harder. Formally, we consider nonempty finite or simple cofinite sets and (simple cofinite sets are of the form ), and we try to determine the smallest constant such that there is a time algorithm for the problem if a tree decomposition of width is given. We obtain matching upper and lower bounds on for every such fixed and under the Primal Pathwidth Strong Exponential Time Hypothesis, and establish whether the partial problem is harder than the nonpartial variant. For some sets and , the more general -MinParDomSet has the same complexity as the nonpartial special case (e.g., for Dominating Set), while for other choices, the partial version is significantly harder (e.g., for Perfect Code).

Paper Structure

This paper contains 10 sections, 8 theorems, 2 equations, 1 table.

Key Result

Theorem 3

Let $\sigma, \rho$ be nonempty finite or simple cofinite sets. When a tree decomposition of width ${\textnormal{tw}}$ is provided together with the input, the problem $(\sigma,\rho)$-$\textnormal{MinDomSet}$ can be solved in time

Theorems & Definitions (17)

  • Definition 2: Constants $s_\sigma$ and $s_\rho$
  • Theorem 3: Due to results of fockeTightComplexityBoundsUpperBoundvanrooijGenericConvolutionAlgorithm2021
  • Definition 4
  • Definition 5: Constants $s_\sigma^\textup{p}$ and $s_\rho^{\textup{p}}$
  • Definition 5: State Sets for the Partial Problem
  • Lemma 5
  • Lemma 5
  • Lemma 5
  • Lemma 5
  • Definition 5: Good Instances
  • ...and 7 more