FloydNet_TSP_exp

FloydNet (Non-Metric TSP / Explicit TSP)

Model Summary

FloydNet is a graph reasoning architecture designed to mimic the execution of algorithms via a learned, global Dynamic Programming operator. This checkpoint (_exp) is trained to solve the Non-Metric (Explicit) Traveling Salesman Problem, where edge weights are generic integers and do not necessarily obey the triangle inequality.

Unlike standard GNNs that rely on local message passing, FloydNet maintains and refines a global all-pairs relationship tensor, achieving 3-WL (2-FWL) expressive power.

Model Details

• Model ID: ocxlabs/FloydNet_TSP_exp
• Architecture: FloydNet (Deep relational layers with Pivotal Attention)
• Task: General Traveling Salesman Problem (Non-Metric)
• Paper: FloydNet: A Learning Paradigm for Global Relational Reasoning
• Demo Dataset: ocxlabs/FloydNet_TSP_demo

Performance

On General TSP instances (N=100-200), FloydNet demonstrates capabilities significantly exceeding strong heuristics:

• Optimality: Achieves an optimality rate of 99.8% (with 10 samples) on held-out graphs, compared to 38.8% by the Linkern heuristic.
• Exact Solutions: On single-solution instances, it finds the exact optimal tour in 92.6% of cases.

Usage: Inference & Evaluation

Reproducing TSP results at full scale is computationally heavy. For convenience, we provide a small demo dataset and pre-trained checkpoints.

1. Preparation

Download the demo dataset from Hugging Face. Unzip it and place the extracted folder under example/data/.

2. Inference

Run inference in --test_mode using torchrun. The command below assumes a single-node setup with 8 GPUs. Ensure --subset is set to exp.

source .venv/bin/activate
cd example

torchrun \
  --nproc_per_node=8 \
  -m TSP.run \
  --subset exp \
  --output_dir ./outputs/TSP_exp \
  --load_checkpoint path/to/TSP_exp/epoch_01000.pt \
  --test_mode \
  --split_factor 1 \
  --sample_count_per_case 10