Fast counting with tensor networks
Statistical Mechanics
2019-11-14 v2 Data Structures and Algorithms
Computational Physics
Abstract
We introduce tensor network contraction algorithms for counting satisfying assignments of constraint satisfaction problems (#CSPs). We represent each arbitrary #CSP formula as a tensor network, whose full contraction yields the number of satisfying assignments of that formula, and use graph theoretical methods to determine favorable orders of contraction. We employ our heuristics for the solution of #P-hard counting boolean satisfiability (#SAT) problems, namely monotone #1-in-3SAT and #Cubic-Vertex-Cover, and find that they outperform state-of-the-art solvers by a significant margin.
Keywords
Cite
@article{arxiv.1805.00475,
title = {Fast counting with tensor networks},
author = {Stefanos Kourtis and Claudio Chamon and Eduardo R. Mucciolo and Andrei E. Ruckenstein},
journal= {arXiv preprint arXiv:1805.00475},
year = {2019}
}
Comments
v2: added results for monotone #1-in-3SAT; published version