This release is a slightly bigger deal than the previous ones because it contains the algorithms from the (just published!)
> Gradients of functions of large matrices. Nicholas Krämer, Pablo Moreno-Munoz, Hrittik Roy, Søren Hauberg. Preprint on arXiv:2405.17277. 2024.
[Here is a link to the preprint](https://arxiv.org/abs/2405.17277).
To be precise, with release v0.0.17, Matfree will provide efficient gradients of Lanczos and Arnoldi iterations, which makes much of matrix-free linear algebra reverse-mode differentiable! To access this code, continue using `matfree.tridiag_sym` and `matfree.hessenberg`, and ensure that `custom_vjp` is set to `True`.
Other changes include a new Hessenberg-factorisation via the Arnoldi iteration, some information regarding citations and Matfree's API policy, removing matfree.pinv, and moving `matfree.decomp.svd_approx` to `matfree.eig.svd_partial`. The full changelog is below.
What's Changed
* Implement decomp.hessenberg() via the Arnoldi decomposition by pnkraemer in https://github.com/pnkraemer/matfree/pull/192
* Implement differentiable Lanczos and Arnoldi iterations by pnkraemer in https://github.com/pnkraemer/matfree/pull/193
* Add citation information to matfree's Arnoldi and Lanczos iterations by pnkraemer in https://github.com/pnkraemer/matfree/pull/197
* Remove matfree.pinv and implement matfree.eig by pnkraemer in https://github.com/pnkraemer/matfree/pull/198
* Explain Matfree's API policy by pnkraemer in https://github.com/pnkraemer/matfree/pull/199
**Full Changelog**: https://github.com/pnkraemer/matfree/compare/v0.0.16...v0.0.17