The algebraic structure of the densification and the sparsification tasks for CSPs

TL;DR

This paper explores the algebraic structure of densification and sparsification tasks in constraint satisfaction problems (CSPs), linking their complexity to implicational systems and bounded width classes, with implications for efficient computation.

Contribution

It introduces the densification operator as a closure operator related to implicational systems, characterizes classes where these systems are polynomial-sized, and analyzes their computational complexity.

Findings

Polynomial-sized implicational systems exist iff the language has bounded width.

Such systems can be computed in log-space or logarithmic time with parallel processing.

Densification and sparsification tasks correspond to closure and minimal key computations.

Abstract

The tractability of certain CSPs for dense or sparse instances is known from the 90s. Recently, the densification and the sparsification of CSPs were formulated as computational tasks and the systematical study of their computational complexity was initiated. We approach this problem by introducing the densification operator, i.e. the closure operator that, given an instance of a CSP, outputs all constraints that are satisfied by all of its solutions. According to the Galois theory of closure operators, any such operator is related to a certain implicational system (or, a functional dependency) $\Sigma$. We are specifically interested in those classes of fixed-template CSPs, parameterized by constraint languages $\Gamma$, for which there is an implicational system $\Sigma$ whose size is a polynomial in the number of variables $n$. We show that in the Boolean case, such implicational systems exist if and only if $\Gamma$ is of bounded width. For such languages, $\Sigma$ can be computed in log-space or in a logarithmic time with a polynomial number of processors. Given an implicational system $\Sigma$, the densification task is equivalent to the computation of the closure of input constraints. The sparsification task is equivalent to the computation of the minimal key.