Comments (4)
For something simple like box constraints, then it should be fairly easy to add that in. We'd be happy to take a PR adding that. (Note that box constraints are already implemented for the root-finders, so you could follow a similar pattern to there.)
from optimistix.
So right now we do support hypercube constraints in our root-finding methods (e.g. Newton
or Bisection
). And moreover it is sort-of-possible to cast a minimisation problem as a root-finding problem, by seeking a root of the gradient of the function. (You have to be careful as this may also converge to any other external point.) So I think the particular example you've given could be tackled today, although I'm not sure how efficient it would be as compared to a dedicated solver.
Other than that we don't currently support any kind of constraints in our solves. We may do at some point, but as always developer time is the limiting factor!
from optimistix.
Hello! Was also curious on this front. In particular, I am wondering if there are plans to support box constraints in BFGS, or if this can already be done in the current framework. jaxopt
seems to have an implementation of this, but Iām not sure how well maintained it is.
from optimistix.
If box constraints are supported it shouldn't be too much work to add an augmented Lagrangian type method for general constraints. Could maybe be like a "meta solver" where you can wrap another solver to manage the unconstrained sub problems? I can hopefully take a look at it once I finish up a few other projects.
from optimistix.
Related Issues (20)
- Error in "optimistix/docs/examples /optimise_diffeq.ipynb" HOT 1
- 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
- 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
- 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
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from optimistix.