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
1 maintainer
Metadata
1 maintainer
Release files for mewtax 0.0.1
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| mewtax-0.0.1.tar.gz | 6.7 kB | Without provenance | Details for mewtax-0.0.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| mewtax-0.0.1-py3-none-any.whl | Python 3 | none | any | Details for mewtax-0.0.1-py3-none-any.whl |
Total release size: 12.2 kB
Release files / mewtax-0.0.1.tar.gz
| Download URL | mewtax-0.0.1.tar.gz |
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| Size | 6.7 kB |
| Tags | Source |
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Release files / mewtax-0.0.1-py3-none-any.whl
| Download URL | mewtax-0.0.1-py3-none-any.whl |
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| Size | 5.5 kB |
| Tags | Python 3 |
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Security for mewtax 0.0.1
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