Dominating Set with Quotas: Balancing Coverage and Constraints

TL;DR

This paper introduces the Dominating Set with Quotas (DSQ), a generalized problem balancing coverage and constraints, and explores its computational complexity and algorithmic solutions across various graph classes.

Contribution

It proves DSQ is W[1]-hard on sparse graphs but fixed-parameter tractable on graphs with bounded treewidth and nowhere dense classes, and provides a subexponential algorithm for apex-minor-free graphs.

Findings

DSQ is W[1]-hard on graphs with degeneracy 2 and excluding K_{3,3}

DSQ is FPT when parameterized by solution size and treewidth

A subexponential algorithm for DSQ on apex-minor-free graphs

Abstract

We study a natural generalization of the classical \textsc{Dominating Set} problem, called \textsc{Dominating Set with Quotas} (DSQ). In this problem, we are given a graph \( G \), an integer \( k \), and for each vertex \( v \in V(G) \), a lower quota \( \mathrm{lo}_v \) and an upper quota \( \mathrm{up}_v \). The goal is to determine whether there exists a set \( S \subseteq V(G) \) of size at most \( k \) such that for every vertex \( v \in V(G) \), the number of vertices in its closed neighborhood that belong to \( S \), i.e., \( |N[v] \cap S| \), lies within the range \( [\mathrm{lo}_v, \mathrm{up}_v] \). This richer model captures a variety of practical settings where both under- and over-coverage must be avoided -- such as in fault-tolerant infrastructure, load-balanced facility placement, or constrained communication networks. While DS is already known to be computationally hard, we show that the added expressiveness of per-vertex quotas in DSQ introduces additional algorithmic challenges. In particular, we prove that DSQ becomes \W[1]-hard even on structurally sparse graphs -- such as those with degeneracy 2, or excluding \( K_{3,3} \) as a subgraph -- despite these classes admitting FPT algorithms for DS. On the positive side, we show that DSQ is fixed-parameter tractable when parameterized by solution size and treewidth, and more generally, on nowhere dense graph classes. Furthermore, we design a subexponential-time algorithm for DSQ on apex-minor-free graphs using the bidimensionality framework. These results collectively offer a refined view of the algorithmic landscape of DSQ, revealing a sharp contrast with the classical DS problem and identifying the key structural properties that govern tractability.