Differentiable minimization in jax using Newton's method.
Project description
Differentiable minimization in jax using Newton's method
v0.0.1
This project essentially repackages code from the implicit layers tutorial 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
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file mewtax-0.0.1.tar.gz.
File metadata
- Download URL: mewtax-0.0.1.tar.gz
- Upload date:
- Size: 6.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.0 CPython/3.12.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
601d9acb23b979403ce1a954cdff20fa485dc99434f8e0d2560eac5de8cff396
|
|
| MD5 |
92ddd43669d2d1b34fb0b973fef576fb
|
|
| BLAKE2b-256 |
08497d5883333d201caed32d898ef58821f8a2c51c9bbb492ca2b37be855d926
|
File details
Details for the file mewtax-0.0.1-py3-none-any.whl.
File metadata
- Download URL: mewtax-0.0.1-py3-none-any.whl
- Upload date:
- Size: 5.5 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.0 CPython/3.12.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
2c42d16d257137d06c4f5a02dd3b144c18cd53f2cc5592fa5c2b2e069a3004fe
|
|
| MD5 |
7ad4de87043586981a1a608b2873decc
|
|
| BLAKE2b-256 |
8ae4d745634168cd43df9bdea7269c91d7edae67cd0752dda8d2c4281981a144
|
