| Name | sksparse-minimal JSON |
| Version |
0.2
JSON |
| download |
| home_page | |
| Summary | A drop in replacement for sksparse for sparse Cholseky factorization |
| upload_time | 2024-01-02 19:32:31 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
| license | |
| keywords |
cholesky
sksparse
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# sksparse_minimal
This project is forked from https://github.com/rgl-epfl/cholespy
Changes made:
- Add support for all solving modes (CHOLMOD_A, CHOLMOD_L, CHOLMOD_Lt, CHOLMOD_P, etc)
- Remove support for GPU solving (because I didn't want to bother implementing it for all modes and I didn't need it)
- Emulate sksparse API for ease of use
- Change CHOLMOD configuration to match sksparse
- Update build to match recommendations from https://nanobind.readthedocs.io/en/latest/building.html
# Installing
## With PyPI (recommended)
```bash
pip install sksparse_minimal
```
## From source
```bash
git clone --recursive https://github.com/tansey-lab/sksparse_minimal.git
pip install .
```
# Example usage
```python
import numpy as np
from sksparse_minimal import SparseCholesky
from scipy.sparse import csc_matrix
M = np.array([[4, 12, -16],
[12, 37, -43],
[-16, -43, 98]], dtype=np.float64)
M = csc_matrix(M)
sparse_cholesky = SparseCholesky(M)
b = np.array([1, 2, 3], dtype=np.float64)
sparse_cholesky.solve_A(b)
```
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