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paiml/aprender-tsp-poc

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Model Card

aprender-tsp POC Models

Pre-trained TSP (Traveling Salesman Problem) optimization models using Ant Colony Optimization, built with aprender-tsp.

Models Included

Model	Instance	Cities	Optimal	Achieved	Gap	Tier
berlin52-aco.apr	berlin52	52	7,542	7,687	1.92%	Good
att48-aco.apr	att48	48	10,628	11,085	4.30%	Acceptable
eil51-aco.apr	eil51	51	426	443	4.07%	Acceptable

All models achieve < 5% gap from TSPLIB optimal solutions.

Quick Start

bash

# Install aprender-tsp
cargo install aprender-tsp

# Download a model
huggingface-cli download paiml/aprender-tsp-poc berlin52-aco.apr

# Solve a new instance using the model
aprender-tsp solve -m berlin52-aco.apr your-instance.tsp

# View model info
aprender-tsp info berlin52-aco.apr

# Benchmark against known optimal
aprender-tsp benchmark berlin52-aco.apr --instances berlin52.tsp

Training Parameters

All models trained with identical ACO parameters for reproducibility:

Parameter	Value	Description
Algorithm	ACO (Ant Colony Optimization)	Uses core aprender::AntColony
Iterations	2000	Number of optimization iterations
Ants	20	Number of artificial ants
Alpha (α)	1.0	Pheromone importance
Beta (β)	2.5	Heuristic importance
Rho (ρ)	0.1	Evaporation rate
Seed	42	Random seed for reproducibility

Instance Sources

Models are trained on standard TSPLIB benchmark instances:

—berlin52: 52 locations in Berlin, Germany (Groetschel)
—att48: 48 state capitals of the contiguous USA (Padberg/Rinaldi)
—eil51: 51-city problem (Christofides/Eilon)

Reference: TSPLIB

File Format

Models use the .apr binary format:

—Magic bytes: APR\0
—Version: 1
—CRC32 checksum for integrity
—Compact size: ~77 bytes per model

Solution Quality Tiers

Tier	Gap from Optimal
Optimal	< 0.1%
Excellent	< 1%
Good	< 2%
Acceptable	< 5%
Poor	>= 5%

Train Your Own

bash

# Train on your instance
aprender-tsp train your-instance.tsp -o your-model.apr --algorithm aco --iterations 2000 --seed 42

# Or use other algorithms
aprender-tsp train your-instance.tsp -o model.apr --algorithm tabu   # Tabu Search (2-opt)
aprender-tsp train your-instance.tsp -o model.apr --algorithm ga     # Genetic Algorithm
aprender-tsp train your-instance.tsp -o model.apr --algorithm hybrid # GA + Tabu + ACO

Citation

bibtex

@software{aprender,
  title = {Aprender: Machine Learning in Pure Rust},
  author = {PAIML},
  url = {https://github.com/paiml/aprender},
  year = {2025}
}

License

MIT License - see LICENSE file.