astro-float VS nalgebra

I am working on https://github.com/stencillogic/astro-float

Since my last post announcing astro-float on Reddit it had several major updates:

I am working on astro-float. My goal is to write a procedural macro which, roughly speaking, given a precision, a rounding mode, and a math expression computes the correctly rounded result of that expression with desired precision.

Experience: I've been learning and writing Rust for more than 3 years. I have several crates published, my most prominent project so far is astro-float.

I'd like to present a library that implements arbitrary precision floating point numbers: https://github.com/stencillogic/astro-float

Ok, so this uses https://rapier.rs/ which is very cool

Rapier, alongside https://nalgebra.org/ (which it uses underneath) has seriously good documentation and some advanced features like cross-platform determinism (something made hard by the way floating point differs between platforms)

> Some GA libraries[1][2] define types for the different kind of objects (grades)

That's nice!

This reminds of me things like, linear algebra libraries that will type-check matrices so that a 2x2 matrix can't be added to a 2x3 matrix (but then you can have a dynamic matrix that will error only in runtime), like https://nalgebra.org/ and others.

Maybe start by looking at https://nalgebra.org/ to see what rust math libraries might look like

And Nalgebra, while better, also seems to have slowed down on commits and responses to issues and PRs. I have a PR there for a relatively simple wrapper type for row vectors which was explicitly requested by a maintainer, which hasn't even gotten a comment since for two weeks.

nalgebra (similar to Eigen in cpp)

nalgebra

nalgebra is for linear algebra.

Take a look into math libraries, like glam, nalgebra, and cgmath. I've only used these through game engines, though, so I can't offer per-basis reviews/advice.

If you do not need GPU then I would recommend looking into Eigen in C++, nalgebra in Rust (with a BLAS in both cases for improved performance) or one of the above options (Julia / Python+JAX).