Metadata-Version: 2.4
Name: optdunc-hazy
Version: 0.0.1
Summary: Optimization benchmark functions and pytest-oriented evaluation helpers.
Author-email: hazy <hazy@hazysite.com>
Project-URL: Repository, https://cnb.cool/uestc/optimization-test
Keywords: optimization,benchmark,pytest,pytorch
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Framework :: Pytest
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.12
Description-Content-Type: text/markdown
Requires-Dist: numpy>=2.4.4
Requires-Dist: pytest>=9.0.3
Requires-Dist: pytest-xdist>=3.8.0
Provides-Extra: torch-cpu
Requires-Dist: torch>=2.11.0; extra == "torch-cpu"
Provides-Extra: torch-gpu
Requires-Dist: torch>=2.11.0; (sys_platform == "linux" or sys_platform == "win32") and extra == "torch-gpu"

# optfunc

`optfunc` provides differentiable benchmark functions for unconstrained
optimization experiments, along with pytest-oriented checks you can reuse when
validating an optimizer against known global minima.

## Install

```bash
pip install "optdunc-hazy[torch-cpu]"
```

Then import it with:

```python
import optfunc
```

## Local development

```bash
uv build --no-sources
uv run pytest
```

## Release flow

1. Update the package version in `pyproject.toml`, or run `uv version <version> --frozen`.
2. Create and push a Git tag named `v<version>`.
3. CNB will publish that tag to PyPI through the `tag_push` pipeline.

Benchmark definitions are adapted from [SFU's optimization benchmark
collection](https://www.sfu.ca/~ssurjano/optimization.html).
