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Iterative Belief Propagation for Sparse Combinatorial Optimization

Year: 2024

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2411.00135

Authors: Reifenstein, Sam, Leleu, Timothée

Institutions: NTT (United States), Stanford University

Language: English

Topic: Constraint Satisfaction and Optimization

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4404344664

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