Metadata-Version: 2.4
Name: sfit_minimizer
Version: 1.0.3
Summary: A. Gould's sfit gradient-type minimization algorithm
License-Expression: BSD-3-Clause
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: matplotlib
Provides-Extra: mulens
Requires-Dist: MulensModel; extra == "mulens"
Provides-Extra: test
Requires-Dist: pytest; extra == "test"
Requires-Dist: MulensModel; extra == "test"
Dynamic: license-file

# sfit_minimizer

<dl> sfit_minimizer is a gradient-type minimization algorithm known to work 
particularly well for point lens microlensing light curves. This algorithm 
generalizes Simpson's idea that a 1-D function is well
described by it first derivative (which can easily be solved
exactly) to several dimensions, i.e., the function is well
described by a tangent plane.  </dl>

[**Detailed documentation: https://jenniferyee.github.io/sfit_minimizer/**](
https://jenniferyee.github.io/sfit_minimizer/)

[Latest release: 1.0.3](https://github.com/jenniferyee/sfit_minimizer/releases/latest)

## Acknowledgements

If you are using sfit_minimizer for scientific research: please cite 
[Yee & Gould 2025](https://arxiv.org/abs/2502.04486)

## Examples

Please see the "examples" folder.

## How to install?

Clone this repository.

## License

[BSD 3-Clause](LICENSE)
