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), 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 https://github.com/IvanBioli/ssbroyden_optimistix.git.

翻译：本文提出了自缩放Broyden族拟牛顿方法在JAX中的实现方案，该方案完全兼容JAX框架，并构建于Optimistix优化库之上。该实现包含BFGS、DFP、Broyden及其自缩放变体（SSBFGS、SSDFP、SSBroyden），同时配备了满足强Wolfe条件的Zoom线搜索算法。本文作为技术说明而非研究论文，不主张任何创新性贡献，其主要目的在于记录实现细节并促进JAX社区对这些优化器的采用。相关代码已发布于https://github.com/IvanBioli/ssbroyden_optimistix.git。