Comments (2)
patrick-kidger
commented on July 19, 2024
I think probably not, to be honest. The problem with optimisation is how many kinds of it they are... ! So for example Bayesian optimisation wouldn't be in scope here either.
Thank you for the offer nonetheless! (I am starting to think we should find a place to collect "extra" JAX scientific libraries, if for example you were to implement such a thing yourself.)
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mjo22
commented on July 19, 2024
This completely makes sense! I didn’t think it would fit to be honest, but wanted to ask to be sure. Regardless, I will probably use optimistix-like ideas when writing an API!
And would be very happy to learn more what you’re thinking along these lines, this would be very helpful.
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Related Issues (20)
- Issue with vmap `optx.least_squares`. HOT 2
- grad of vmap of function which wraps an optax solver occasionally fails HOT 2
- `BestSoFar...` wanted behavior ? HOT 1
- Classical newton methods HOT 6
- Non-finite values in the root function are not handled well HOT 2
- Will constrained optimization be supported? HOT 4
- Behavior of BFGS HOT 2
- pytree output structure mismatch error in backprop during vmap HOT 9
- Incompatibility of least_squares and custom_vjp HOT 2
- Extracting intermediate function values/ losses from the solve HOT 4
- Zero implicit gradients when using `ImplicitAdjoint` with CG solver HOT 4
- Using `optimistix` with an `equinox` model HOT 2
- Incompatibility with jax 0.4.27 HOT 1
- Possibly of interest HOT 1
- Unexpected behaviour with JAX version HOT 3
- Slow compile of least_squares with large dict parameters HOT 2
- Can't vmap across input using Gauss Newton fwd HOT 11
- Question: errorhandling, BFGS minimization, vmap, and best practices HOT 2
- Parallel multi start HOT 10
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