PropFlow Documentation

PropFlow is a Python toolkit for building and experimenting with belief propagation and other distributed constraint optimization (DCOP) algorithms on factor graphs.

PyPI version Python versions License

Quick Start

Install PropFlow from PyPI:

pip install propflow

Basic Example:

from propflow import BPEngine, FGBuilder
from propflow.configs import create_random_int_table

# Create a simple factor graph
fg = FGBuilder.build_cycle_graph(
    num_vars=5,
    domain_size=2,
    ct_factory=create_random_int_table,
    ct_params={'low': 1, 'high': 10}
)

# Run belief propagation
engine = BPEngine(fg)
engine.run(max_iter=20)

print(f"Solution: {engine.assignments}")

Learning Path

PropFlow’s documentation follows the same top-down pipeline implemented in the runtime:

  1. Quick Start Guide gives you runnable snippets using the recommended flow (FGBuilder → engine → simulator).

  2. PropFlow User Guide dives into each layer in detail—from agents up through analyzer tooling—highlighting when to rely on helpers versus manual wiring.

  3. PropFlow Handbook covers operational practices, deployments, and deeper troubleshooting once you’re running larger studies.

Contents:

Key Features

  • Belief Propagation Variants: Min-Sum, Max-Sum, Sum-Product algorithms

  • Search-Based DCOP Solvers: DSA, MGM, K-Opt MGM algorithms

  • Extensible Policy Framework: Damping, splitting, cost reduction, and more

  • Dynamic Graph Construction: Cycle graphs, random graphs, custom topologies

  • Simulation and Analysis: Parallel execution and result comparison

  • Visualization Tools: Factor graph plotting and analysis visualization

Indices and tables