Less strong trait bounds for algorithms

Hello, I would like to use nalgebra for my project, however one thing that prevents me from that (and which also confuses me) is that all the e.g. matrix algorithms (QR, SVD, ...) require the base type to implement `ComplexField`.

But this trait is _huge_, requiring to implement sin, exp, acosh and what not. It also has a lot of supertraits. I guess those linear algebra algorithms never use those trigonometric methods, also also don't need all the supertraits, so why are the requirements so strong? Couldn't there be a trait with less strong requirements?

I want to use nalgebra together with arbitrary-precision floats, like the ones from [rug](https://docs.rs/rug/latest/rug/). I would argue this is a quite common/important use case, but the huge trait `ComplexField` which is difficult to implement poses a significant barrier for that.

Other people had the same problem: https://stackoverflow.com/questions/73871027/use-nalgebra-with-float-types-from-rug