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JAX gets new quasi-Newton optimization methods

By PulseAugur Editorial · [1 sources] ·

A new implementation of the Self-Scaled Broyden family of quasi-Newton methods has been released for JAX, a numerical computation library. This implementation, built on the Optimistix library, includes various BFGS, DFP, and Broyden variants, along with L-BFGS and a Zoom line search. The developers state this is a technical note to facilitate adoption within the JAX community, with the code available on PyPI. AI

IMPACT This implementation could improve the efficiency of numerical computations within JAX, potentially benefiting AI research that relies on such methods.

RANK_REASON The item is a technical note documenting an implementation of existing optimization methods for a specific software library, not a novel research contribution. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

JAX gets new quasi-Newton optimization methods

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The item is a technical note documenting an implementation of existing optimization methods for a specific software library, not a novel research contribution. [lever_c_demoted from research: ic=1 …
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ivan Bioli, Mikel Mendibe Abarrategi ·

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

    arXiv:2603.10599v2 Announce Type: replace-cross 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 incl…

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