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
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