Self-Scaled Broyden Family of Quasi-Newton Methods in JAX
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908878986280960 |
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| author | Bioli, Ivan Abarrategi, Mikel Mendibe |
| author_facet | Bioli, Ivan Abarrategi, Mikel Mendibe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_10599 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Self-Scaled Broyden Family of Quasi-Newton Methods in JAX Bioli, Ivan Abarrategi, Mikel Mendibe Mathematical Software Machine Learning 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. |
| title | Self-Scaled Broyden Family of Quasi-Newton Methods in JAX |
| topic | Mathematical Software Machine Learning |
| url | https://arxiv.org/abs/2603.10599 |