Metadata-Version: 2.1
Name: mewtax
Version: 0.0.1
Summary: Differentiable minimization in jax using Newton's method.
Author-email: Martin Schubert <mfschubert@gmail.com>
Maintainer-email: Martin Schubert <mfschubert@gmail.com>
License: MIT License
        
        Copyright (c) 2024 Martin F. Schubert
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Keywords: jax,differentiable optimization,metalearning
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: jax
Requires-Dist: jaxlib
Provides-Extra: tests
Requires-Dist: numpy; extra == "tests"
Requires-Dist: parameterized; extra == "tests"
Requires-Dist: pytest; extra == "tests"
Requires-Dist: pytest-cov; extra == "tests"
Provides-Extra: dev
Requires-Dist: bump-my-version; extra == "dev"
Requires-Dist: darglint; extra == "dev"
Requires-Dist: mypy; extra == "dev"
Requires-Dist: pre-commit; extra == "dev"

# Differentiable minimization in jax using Newton's method
`v0.0.1`

This project essentially repackages code from the [implicit layers tutorial](https://implicit-layers-tutorial.org/implicit_functions/) to provide a `minimize_newton` function.

Given a function `fn(params, z)`, it finds the `z_star` which minimizes `fn` for given `params`. Further, the gradient of the solution with respect to `params` can be computed; this is done using a custom vjp rule, as shown in the tutorial.

## Installation

mewtax can be installed via pip:
```
pip install mewtax
```
