JOURNAL ARTICLE

Towards a dichotomy theorem for the counting constraint satisfaction problem

Abstract

The Counting Constraint Satisfaction Problem (#CSP) over a finite domain can be expressed as follows: given a first-order formula consisting of a conjunction of predicates, determine the number of satisfying assignments to the formula. #CSP can be parametrized by the set of allowed constraint predicates. In this paper we start a systematic study of subclasses of #CSP restricted in this way. The ultimate goal of this investigation is to distinguish those restricted subclasses of #CSP which are tractable, i.e. solvable in polynomial time, from those which are not. We show that the complexity of any restricted #CSP class on a finite domain can be deduced from the properties of polymorphisms of the allowed constraints, similar to that for the decision CSP. Then we prove that if a subclass of the #CSP is tractable, then constraints allowed by the class satisfy some very restrictive condition: it has to have a Mal'tsev polymorphism, that is a ternary operation m(x, y, z) such that m(x, y, y) = m(y, y, x) = x. This condition uniformly explains all existing complexity results for particular cases of #CSP, and allows us to obtain new results and to conjecture a criterion distinguishing tractable counting CSPs. We also obtain a dichotomy theorem for the complexity of #CSP with a 3-element domain and give new simpler proofs of the dichotomy results for the problem of counting graph homomorphisms.

Keywords:
Constraint satisfaction problem Counting problem Mathematics Homomorphism Discrete mathematics Complexity class Combinatorics Conjecture Complexity of constraint satisfaction Time complexity Mathematical proof Class (philosophy) Domain (mathematical analysis) Constraint satisfaction Computational complexity theory Local consistency Computer science Algorithm

Metrics

21
Cited By
4.70
FWCI (Field Weighted Citation Impact)
49
Refs
0.95
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Graph Theory Research
Physical Sciences →  Computer Science →  Computational Theory and Mathematics
Markov Chains and Monte Carlo Methods
Physical Sciences →  Mathematics →  Statistics and Probability
Complexity and Algorithms in Graphs
Physical Sciences →  Computer Science →  Computational Theory and Mathematics

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