| --- |
| license: cc-by-4.0 |
| tags: |
| - sparse-matrices |
| - linear-systems |
| - preconditioners |
| - numerical-linear-algebra |
| - graph-neural-networks |
| - scientific-computing |
| --- |
| |
| # MatrixPFN Notebooks |
|
|
| Executable Colab notebooks for [MatrixPFN](https://pypi.org/project/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: |
|
|
| ```python |
| 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](https://pypi.org/project/matrixpfn/) |
| - **Evaluation dataset**: [Csed-dev/matrixpfn-suitesparse](https://huggingface.co/datasets/Csed-dev/matrixpfn-suitesparse) (867 SuiteSparse matrices) |
| - **Paper reference**: GNP ([arXiv 2406.00809v3](https://arxiv.org/abs/2406.00809v3)) |
|
|
