Comments (2)
Such compile times are an unfortunate facet of JAX when describing very large computation graphs. (Like this one.)
Probably the simplest thing to do would be to call params, unflatten = jax.flatten_util.ravel_pytree(...)
to squash all your parameters together into one array prior to calling least_squares
, and then unflatten
them again inside your target function.
from optimistix.
Perfect, thank you!
from optimistix.
Related Issues (20)
- 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
- Would an exhaustive grid search have a place in `optimistix`? HOT 2
- 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
- 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
- Optimization across multidimensional array HOT 4
- Accessing the (success/not) result of the solver HOT 1
- `UnexpectedTracerError` when enabling `EQX_ON_ERROR=breakpoint` HOT 1
- Difficulties getting good results HOT 3
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from optimistix.