Datasets:
Tasks:
Tabular Regression
Formats:
parquet
Size:
< 1K
ArXiv:
Tags:
sparse-matrices
linear-systems
preconditioners
numerical-linear-algebra
suitesparse
scientific-computing
License:
Upload README.md with huggingface_hub
Browse files
README.md
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| 1 |
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---
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| 2 |
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license: cc-by-4.0
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task_categories:
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- tabular-regression
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tags:
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- sparse-matrices
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| 7 |
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- linear-systems
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- preconditioners
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- numerical-linear-algebra
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- suitesparse
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- scientific-computing
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- benchmark
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size_categories:
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- n<1K
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configs:
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- config_name: manifest
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data_files: manifest.parquet
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---
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| 19 |
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# MatrixPFN SuiteSparse Evaluation Set
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A curated subset of the [SuiteSparse Matrix Collection](https://sparse.tamu.edu/) for benchmarking learned preconditioners on sparse linear systems, matching the evaluation criteria from the GNP paper ([arXiv 2406.00809v3](https://arxiv.org/abs/2406.00809v3)).
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## Dataset Description
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| 25 |
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- **Source:** [SuiteSparse Matrix Collection](https://sparse.tamu.edu/)
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| 27 |
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- **License:** CC-BY 4.0
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| 28 |
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- **Total matrices:** 867
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| 29 |
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- **Format:** Matrix Market (.mtx)
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| 30 |
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- **Size on disk:** ~5.7 GB
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| 31 |
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| Property | Value |
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| 33 |
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|---|---|
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| Rows/Cols | 1,000 - 100,000 |
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| Nonzeros | 1,314 - 1,990,919 |
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| 36 |
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| Shape | Square |
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| 37 |
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| Type | Real, non-SPD |
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| 38 |
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| Problem domains | 50 categories |
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| 39 |
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### Domain Distribution (top 10)
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| 41 |
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| 42 |
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| Count | Domain |
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| 43 |
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|---|---|
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| 44 |
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| 123 | Circuit simulation |
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| 45 |
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| 75 | Computational fluid dynamics |
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| 46 |
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| 69 | Optimization |
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| 47 |
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| 67 | Optimal control |
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| 48 |
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| 59 | Economic |
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| 49 |
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| 49 | Chemical process simulation |
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| 50 |
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| 47 | Undirected weighted graph |
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| 51 |
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| 32 | Circuit simulation (frequency-domain) |
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| 52 |
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| 31 | 2D/3D problem |
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| 53 |
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| 30 | Eigenvalue/model reduction |
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| 54 |
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## Dataset Structure
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```
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.
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├── README.md
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├── manifest.parquet # Matrix metadata (id, group, name, rows, cols, nnz, kind)
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| 61 |
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├── suitesparse/
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| 62 |
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│ ├── 2D_27628_bjtcai/
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| 63 |
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│ │ └── 2D_27628_bjtcai.mtx
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| 64 |
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│ ├── ACTIVSg2000/
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│ │ └── ACTIVSg2000.mtx
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| 66 |
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│ └── ... # 867 matrix directories
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| 67 |
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└── benchmark_results/
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| 68 |
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└── benchmark_gnp_paper.jsonl # FGMRES benchmark with 6 preconditioners
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| 69 |
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```
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### manifest.parquet
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Browsable in the HuggingFace Dataset Viewer. Fields:
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| 74 |
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| Column | Type | Description |
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| 76 |
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|---|---|---|
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| 77 |
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| `id` | int | SuiteSparse matrix ID |
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| 78 |
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| `group` | str | Collection group (e.g. "HB", "SNAP") |
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| 79 |
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| `name` | str | Matrix name |
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| 80 |
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| `rows` | int | Number of rows |
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| 81 |
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| `cols` | int | Number of columns |
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| 82 |
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| `nnz` | int | Number of nonzeros |
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| 83 |
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| `kind` | str | Problem domain |
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| 84 |
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| 85 |
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### benchmark_results/
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| 86 |
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| 87 |
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JSONL file with FGMRES solver results per matrix for 6 preconditioners: None, Jacobi, Block Jacobi, ILU(0), AMG (AIR), GMRES-Inner. Includes convergence status, iteration count, residual history, and AUC metrics.
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## Dataset Creation
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| 90 |
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### Selection Criteria
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| 92 |
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Matches the GNP paper evaluation set:
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- Square matrices only (`rows == cols`)
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| 95 |
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- Real-valued (not complex)
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| 96 |
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- Non-SPD (not symmetric positive definite)
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| 97 |
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- 1,000 to 100,000 rows
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| 98 |
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- Less than 2,000,000 nonzeros
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| 99 |
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### Source Data
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| 101 |
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All matrices originate from the [SuiteSparse Matrix Collection](https://sparse.tamu.edu/), downloaded unmodified in Matrix Market format via [ssgetpy](https://github.com/drdarshan/ssgetpy).
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## Usage
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| 105 |
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```python
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from scipy.io import mmread
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| 108 |
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from huggingface_hub import hf_hub_download
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path = hf_hub_download(
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repo_id="Csed-dev/matrixpfn-base",
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filename="suitesparse/ACTIVSg2000/ACTIVSg2000.mtx",
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repo_type="dataset",
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)
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matrix = mmread(path)
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print(f"Shape: {matrix.shape}, NNZ: {matrix.nnz}")
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| 117 |
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```
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```python
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from datasets import load_dataset
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| 121 |
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| 122 |
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manifest = load_dataset("Csed-dev/matrixpfn-base", "manifest")
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| 123 |
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print(manifest["train"].to_pandas().head())
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| 124 |
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```
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## Considerations
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| 127 |
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| 128 |
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- Matrix Market (.mtx) files are not natively streamable via `datasets`. Use `hf_hub_download` or `snapshot_download` for the sparse matrices.
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| 129 |
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- Some matrices may have near-singular or zero diagonals, causing preconditioner construction failures (documented in benchmark results).
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| 130 |
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- The benchmark results use spectral-radius scaling (dividing by the infinity norm) before solving.
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| 131 |
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| 132 |
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## Citation
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| 133 |
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| 134 |
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This dataset is a redistribution of matrices from the SuiteSparse Matrix Collection under the CC-BY 4.0 license.
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**Required citations:**
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| 137 |
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| 138 |
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```bibtex
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| 139 |
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@article{Davis2011,
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| 140 |
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author = {Timothy A. Davis and Yifan Hu},
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| 141 |
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title = {The University of Florida Sparse Matrix Collection},
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| 142 |
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journal = {ACM Transactions on Mathematical Software},
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| 143 |
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volume = {38},
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| 144 |
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number = {1},
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| 145 |
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year = {2011},
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| 146 |
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doi = {10.1145/2049662.2049663}
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| 147 |
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}
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| 148 |
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| 149 |
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@article{Kolodziej2019,
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| 150 |
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author = {Scott P. Kolodziej and Mohsen Aznaveh and Matthew Bullock and Jarrett David and Timothy A. Davis and Matthew Henderson and Yifan Hu and Read Sandstrom},
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| 151 |
+
title = {The SuiteSparse Matrix Collection Website Interface},
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| 152 |
+
journal = {Journal of Open Source Software},
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| 153 |
+
volume = {4},
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| 154 |
+
number = {35},
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| 155 |
+
pages = {1244},
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| 156 |
+
year = {2019},
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| 157 |
+
doi = {10.21105/joss.01244}
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| 158 |
+
}
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| 159 |
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```
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| 160 |
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| 161 |
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Individual matrices may have additional citations. See https://sparse.tamu.edu/ for per-matrix metadata.
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| 162 |
+
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| 163 |
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## License
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| 164 |
+
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| 165 |
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CC-BY 4.0 (inherited from SuiteSparse Matrix Collection)
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