Using Parks-Mcclellan (or others) for FIR differentiator?

I have never seen the use of Parks-Mcclellan for differentiator - most uses tend to be lowpass filters. Is this because Parks-Mcclellan implementation of FIR differentiator has numerical problems and cannot be successful?

It seems though that Parks-Mcclellan for a differentiator should be more studied and obvious, because in terms of fit of a function, a differentiator has frequency response that is basically a sloped straight line or an identity function.

In general, I am having some difficulty finding established optimal ways to implement FIR differentiators. Are there other known ways that are commonly used for FIR differentiators? What about lowpass differentiators, where frequency response basically is a differentiator up to some frequency and from some frequency to $$\pi$$ it is filtered out, with transition band in the middle?

• AFAIK, the P-M algo can be used, but it's not often used because those filters will be computationally intensive due to them being so precise. So then most people use filters that do coarser approximations. Or you would use P-M but then you'd need to "coarsify" it when you build up a table of filter coefficients, since having too much values to go through would lead to high CPU use. Dec 28, 2018 at 19:09

E.g., the Matlab function firpm, which implements the Parks McClellan method, can design FIR differentiators if you supply differentiator as filter type. The same is true for the least squares filter design function firls.