Tighter relaxations for MAP-MRF optimization via Singleton Arc Consistency
数据结构与算法
2026-05-14 v1
摘要
We consider the MAP-MRF inference task, that is, minimizing a function of discrete variables represented as a sum of unary and pairwise terms. A prominent approach for tackling this NP-hard problem in practice is to solve its natural LP relaxation and then iteratively tighten the relaxation by adding clusters. Based on some theoretical observations, we propose a new technique for identifying such clusters. It works by running the Singleton Arc Consistency algorithm in a certain CSP instance. Experimental results indicate that the new tightening technique outperforms the previous approach by [Sontag et al. UAI 2012] that searches for frustrated cycles. Our code will be made available at https://github.com/vnk-ist/MAP-MRF/.
引用
@article{arxiv.2605.13392,
title = {Tighter relaxations for MAP-MRF optimization via Singleton Arc Consistency},
author = {Asaf Lev-Ran and Pavel Arkhipov and Vladimir Kolmogorov},
journal= {arXiv preprint arXiv:2605.13392},
year = {2026}
}