patrick-kidger / patrick-kidger/optimistix
New solvers
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feature
- Dominant language
- Python
- Stars
- 623
- Forks
- 54
- PR merge metrics
- No merged PRs in 30d
Description
- Anderson acceleration
- LBFGS
- Affine
Powell's (unconstrained) derivative free optimisers:
- UOBYQA
- NEWUOA
On an affine solvers: such systems can be handled with a single linear solve. JAX can detect affine functions via
import jax
import jax.interpreters.partial_eval as pe
def is_affine(f, *args, **kwargs):
jaxpr = jax.make_jaxpr(jax.jacfwd(f))(*args, **kwargs)
_, used_inputs = pe.dce_jaxpr(jaxpr.jaxpr, [True] * len(jaxpr.out_avals))
return all(not x for x in used_inputs)
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the existing LBFGS solver and the solver architecture in the repository, then compare the unchecked Anderson, affine, UOBYQA, and NEWUOA items with the JAX affine-detection sketch in the issue. Done means the selected new solvers are implemented and covered by the project's existing validation or test approach.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100