Comments (3)
I think I might have found the reason - the newest version of lineax require jax 0.4.26 which is quite new. Is there any particular reason for this?
from optimistix.
Yup. JAX version 0.4.26 fixed a correctness issue when using complex numbers, that we were hitting in Lineax.
If you really need to use an earlier version, and you're not using complex numbers, then you can probably get away with it though :)
from optimistix.
Ahh, I see. Thanks for the super fast response!
from optimistix.
Related Issues (20)
- 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
- 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
- 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 16
- Damped Newton solve(?) / Scipy and Optimistix HOT 5
- Support complex-to-real optimization HOT 11
- AssertionError: assert _is_global_function(fn_primal) HOT 3
- FutureWarning (unhashable type) thrown with LM solver HOT 1
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from optimistix.