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Algorithms such as Pathfinder (paper, blackjax-devs/blackjax#157) need as input all the optimization path, and not only the result, of a L-BFGS optimization run.
Differently from R (and consistently with scipy) jax's jax._src.scipy.optimize._lbfgs._minimize_lbfgs does not expose the path, but just the result. A generalization of such fuction that expose the optimization path would look like this and would allow to implement Pathfinder without rewriting a jax internal function and keeping it in sync with jax main branch over time.
Happy to submit a proper pull request if such feature would be considered for merging.
The text was updated successfully, but these errors were encountered:
We are looking to deprecate jax.scipy.minimize and recommend JAXopt instead, so I'd bring @mblondel into this thread (and perhaps eventually we should move the discussion to the jaxopt repository).
Algorithms such as Pathfinder (paper, blackjax-devs/blackjax#157) need as input all the optimization path, and not only the result, of a L-BFGS optimization run.
Differently from R (and consistently with scipy) jax's
jax._src.scipy.optimize._lbfgs._minimize_lbfgs
does not expose the path, but just the result. A generalization of such fuction that expose the optimization path would look like this and would allow to implement Pathfinder without rewriting a jax internal function and keeping it in sync with jax main branch over time.Happy to submit a proper pull request if such feature would be considered for merging.
The text was updated successfully, but these errors were encountered: