Self-Scaled Broyden Family of Quasi-Newton Methods in JAX

Abstract:We present a JAX implementation of the Self-Scaled Broyden family of quasi-Newton methods, fully compatible with JAX and building on the Optimistix~\cite{rader_optimistix_2024} optimisation library. The implementation includes BFGS, DFP, Broyden and their Self-Scaled variants (SSBFGS, SSDFP, SSBroyden), as well as L-BFGS, together with a Zoom line search satisfying the strong Wolfe conditions. This is a short technical note, not a research paper, as it does not claim any novel contribution; its purpose is to document the implementation and ease the adoption of these optimisers within the JAX community. The code is available at this https URL and on PyPI (pip install ssbroyden-optimistix).

Mathematical Software (cs.MS); Machine Learning (cs.LG)

arXiv:2603.10599 [cs.MS]

(or arXiv:2603.10599v2 [cs.MS] for this version)

https://doi.org/10.48550/arXiv.2603.10599 Focus to learn more arXiv-issued DOI via DataCite

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