Belief Propagation Min-Sum Algorithm for Generalized Min-Cost Network Flow
Machine Learning
2021-09-15 v2 Data Structures and Algorithms
Abstract
Belief Propagation algorithms are instruments used broadly to solve graphical model optimization and statistical inference problems. In the general case of a loopy Graphical Model, Belief Propagation is a heuristic which is quite successful in practice, even though its empirical success, typically, lacks theoretical guarantees. This paper extends the short list of special cases where correctness and/or convergence of a Belief Propagation algorithm is proven. We generalize formulation of Min-Sum Network Flow problem by relaxing the flow conservation (balance) constraints and then proving that the Belief Propagation algorithm converges to the exact result.
Cite
@article{arxiv.1710.07600,
title = {Belief Propagation Min-Sum Algorithm for Generalized Min-Cost Network Flow},
author = {Andrii Riazanov and Yury Maximov and Michael Chertkov},
journal= {arXiv preprint arXiv:1710.07600},
year = {2021}
}