Iterative Belief Propagation for Sparse Combinatorial Optimization
Optimization and Control
2024-11-04 v1
Abstract
In this note we study an iterative belief propagation (IBP) algorithm and demonstrate it's ability to solve sparse combinatorial optimization problems. Similar to simulated annealing (SA), our IBP algorithm attempts to sample from the Boltzmann distribution of the objective function but also uses belief propagation (BP) to improve convergence.
Keywords
Cite
@article{arxiv.2411.00135,
title = {Iterative Belief Propagation for Sparse Combinatorial Optimization},
author = {Sam Reifenstein and Timothée Leleu},
journal= {arXiv preprint arXiv:2411.00135},
year = {2024}
}