Iterative Belief Propagation for Sparse Combinatorial Optimization

TL;DR

This paper introduces an iterative belief propagation algorithm designed to efficiently solve sparse combinatorial optimization problems by sampling from the Boltzmann distribution and enhancing convergence through belief propagation.

Contribution

The paper presents a novel IBP algorithm that combines belief propagation with sampling techniques to improve solutions for sparse combinatorial optimization.

Findings

IBP effectively samples from the Boltzmann distribution.

IBP improves convergence in sparse combinatorial problems.

The method outperforms traditional approaches in specific benchmarks.

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.