Comments (2)
Yup! optx.minimise(..., throw=False)
.
from optimistix.
Thank you. Just tested. This is exactly what I needed. Shame on me for not seeing it in the function signature / FAQ. ;-)
from optimistix.
Related Issues (20)
- 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
- Slow compile of least_squares with large dict parameters HOT 2
- Can't vmap across input using Gauss Newton fwd HOT 11
- 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
- First step of `GradientDescent` optimizer is a no-op HOT 1
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from optimistix.