I was recently made aware of the paper “Efficient and Modular Implicit Differentiation”:
which is implemented in Jax here:
It is about automatically generating differentiation rules for optimization problem solutions given a function defining the optimality conditions.
I wanted to bring this up to see if it is on anyone’s radar, it sounds like it would be a nice general feature to add to the Julia autodiff ecosystem. We would have many use cases in our application area of tensor networks and quantum computing.
I have a feeling that this functionality may be available in some form in the extensive Julia optimization and differential equation ecosystem, but I’m not so familiar with that part of Julia so if it is available it would be nice to hear about it!