| | --- |
| | 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)) |
| |
|
