Issue #1658 - Add SSBroyden and SSBFGS optimizers to optax.contrib #1659
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armbrusl wants to merge 3 commits intogoogle-deepmind:mainfrom
Open
Issue #1658 - Add SSBroyden and SSBFGS optimizers to optax.contrib #1659armbrusl wants to merge 3 commits intogoogle-deepmind:mainfrom
armbrusl wants to merge 3 commits intogoogle-deepmind:mainfrom
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Port the Self-Scaled Broyden and Self-Scaled BFGS quasi-Newton optimizers from PyTorch (SciMBA) to JAX/optax. These maintain a dense inverse Hessian approximation with self-scaling updates, complementing the existing L-BFGS implementation for small to medium scale problems. New public API: - optax.contrib.ssbroyden() — SS-Broyden variant - optax.contrib.ssbfgs() — SS-BFGS variant - optax.contrib.scale_by_ss_quasi_newton() — shared core transform Both integrate with scale_by_zoom_linesearch (same pattern as optax.lbfgs) and include tests on quadratic and Rosenbrock functions. Reference: Urbán et al. (2025), Journal of Computational Physics, 523, 113656.
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Port the Self-Scaled Broyden and Self-Scaled BFGS quasi-Newton optimizers from PyTorch (SciMBA) to JAX/optax. These maintain a dense inverse Hessian approximation with self-scaling updates, complementing the existing L-BFGS implementation for small to medium scale problems.
New public API:
Both integrate with scale_by_zoom_linesearch (same pattern as optax.lbfgs) and include tests on quadratic and Rosenbrock functions.
Reference: Urbán et al. (2025), Journal of Computational Physics, 523, 113656.