Graph Neural Preconditioners for Iterative Solutions of Sparse Linear Systems
Paper • 2406.00809 • Published
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MatrixPFN Notebooks
Executable Colab notebooks for MatrixPFN — Graph Neural Networks as learned preconditioners for sparse linear systems.
Notebooks
| Notebook | Description |
|---|---|
07_MatrixPFN_EndToEnd.ipynb |
Full pipeline: train ContextResGCN, benchmark against Jacobi, solve with FGMRES |
colab_benchmark.ipynb |
SuiteSparse benchmark: 838/867 matrices × 6 classical preconditioners (ILU, AMG, Jacobi, Block Jacobi, GMRES-Inner, None) |
ablation_edge_features_v3.ipynb |
Definitive GCN vs MPNN ablation: 5 seeds, 1000 epochs, 3 domains |
Usage
Open any notebook in Google Colab:
https://colab.research.google.com/github/...
Or download via the HuggingFace Hub:
from huggingface_hub import hf_hub_download
path = hf_hub_download(
repo_id="Csed-dev/matrixpfn-notebooks",
repo_type="dataset",
filename="colab_benchmark.ipynb",
)
Related
- Package: matrixpfn on PyPI
- Evaluation dataset: Csed-dev/matrixpfn-suitesparse (867 SuiteSparse matrices)
- Paper reference: GNP (arXiv 2406.00809v3)
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