Adam depends on the gradient distribution during training, which, as far as I know, we don't understand well?
Here, adapted from the Adam paper, v_t is the var estimate, G_t is the gradient r.v. and X_t is an error r.v. for distribution shift.
⭐ Lineax is now on arXiv! ⭐
If you’re doing linear solves or linear least-squares in JAX, give it a shot today!
Lineax
is fast ⚡️,
has new solvers (eg. QR, tridiagonal),
supports general linear Operators.
github: github.com/google/lineax
arXiv: arxiv.org/abs/2311.17283
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Tikhnov regularised trust-region methods (*cough* Levenberg-Marquardt) oddly use two different approximations to the objective function at each step.
One regularised, one not.
What if we just regularised both?
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Hurrah! 🎉🥳
Very excited this is for this release, after many months of work!
Lots of really cool things in Optimistix that I've wanted in optimisation software for a long time. I hope other researchers find as exciting as I do 😃
Announcing a new #JAX and #Equinox nonlinear optimisation library:
⭐️ Optimistix ⭐️
(GitHub: github.com/patrick-kidger…)
- Minimisation
- Nonlinear least-squares
- Root-finding
- Fixed-points
With blazing fast compile times and fast run times 🔥
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