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ACM Transactions on Computation Theory
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Sparsification Lower Bounds for List H -Coloring

Authors: Hubie Chen; Bart M. P. Jansen; Karolina Okrasa; Astrid Pieterse; Paweł Rzążewski;

Sparsification Lower Bounds for List H -Coloring

Abstract

We investigate the List H -Coloring problem, the generalization of graph coloring that asks whether an input graph G admits a homomorphism to the undirected graph H (possibly with loops), such that each vertex v ∈ V ( G ) is mapped to a vertex on its list L ( v ) ⊆ V ( H ). An important result by Feder, Hell, and Huang [JGT 2003] states that List H -Coloring is polynomial-time solvable if H is a so-called bi-arc graph , and NP-complete otherwise. We investigate the NP-complete cases of the problem from the perspective of polynomial-time sparsification: can an n -vertex instance be efficiently reduced to an equivalent instance of bitsize \(\mathcal {O} (n^{2-\varepsilon })\) ( n 2-ɛ ) for some ɛ > 0? We prove that if H is not a bi-arc graph, then List H -Coloring does not admit such a sparsification algorithm unless \(\mathsf {NP \subseteq coNP/poly}\) . Our proofs combine techniques from kernelization lower bounds with a study of the structure of graphs H which are not bi-graphs.

Keywords

FOS: Computer and information sciences, sparsification, Sparsification, Theory of computation → Problems, reductions and completeness, Computational Complexity (cs.CC), List H-coloring, 004, Computer Science - Computational Complexity, Theory of computation → Graph algorithms analysis, List H-Coloring, Mathematics of computing → Graph coloring, Theory of computation → Parameterized complexity and exact algorithms, Constraint Satisfaction Problem, constraint satisfaction problem, 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!
0
Average
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Average
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