Algebraic and algorithmic synergies between promise and infinite-domain CSPs

We establish a framework that allows us to transfer results between some constraint satisfaction problems with infinite templates and promise constraint satisfaction problems. On the one hand, we obtain new algebraic results for infinite-domain CSPs giving new criteria for NP-hardness. On the other hand, we show the existence of promise CSPs with finite templates that reduce naturally to tractable infinite-domain CSPs in the scope of the Bodirsky-Pinsker conjecture, but that are not finitely tractable, thereby showing a non-trivial connection between those two fields of research. In an important part of our proof, we also obtain uniform polynomial-time algorithms solving temporal constraint satisfaction problems.

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Publication Details

Published
2025-06-23
DOI
https://doi.org/10.1109/lics65433.2025.00034
Citations
1
Primary Topic
Constraint Satisfaction and Optimization
Type
article
Field-Weighted Citation Impact
1.49
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article

Algebraic and algorithmic synergies between promise and infinite-domain CSPs

Antoine Mottet
1 citations
Constraint Satisfaction and Optimization
1.49
article

Algebraic and algorithmic synergies between promise and infinite-domain CSPs

Antoine Mottet
article en
1 citations

Abstract

We establish a framework that allows us to transfer results between some constraint satisfaction problems with infinite templates and promise constraint satisfaction problems. On the one hand, we obtain new algebraic results for infinite-domain CSPs giving new criteria for NP-hardness. On the other hand, we show the existence of promise CSPs with finite templates that reduce naturally to tractable infinite-domain CSPs in the scope of the Bodirsky-Pinsker conjecture, but that are not finitely tractable, thereby showing a non-trivial connection between those two fields of research. In an important part of our proof, we also obtain uniform polynomial-time algorithms solving temporal constraint satisfaction problems.

Universität Hamburg (DE), Hamburg University of Technology (DE)
Openalex Percentile: Top 12%
Constraint Satisfaction and Optimization
1.49
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