mlsanityData to 7 Oct 2026

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

arXiv 2603.10599 ↗First posted 11 Mar 2026cs.MS cs.LGcited by 2

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Abstract (arXiv)

We present a JAX implementation of the Self-Scaled Broyden family of quasi-Newton methods, fully compatible with JAX and building on the Optimistix~citeraderoptimistix2024 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/ssbroydenoptimistix.git.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 2 of its 7 references that are in the map. First posted on arXiv 11 Mar 2026.

Builds on

2 of 7 references.

References · 2

  1. 2025Optimizing the Optimizer for Physics-Informed Neural Networks and Kolmogorov-Arnold Networkscited by 41
  2. 2024Optimistix: modular optimisation in JAX and Equinoxcited by 17

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Tackling Failure Modes of PINNs and PIKANs Using Conflict-Free Gradientscited by 2
  2. 2026Adjoint Method versus Physics-Informed Neural Networks in PDE-Constrained Inverse Problemscited by 0